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Transplantation of human oligodendrocyte progenitor cells in an animal model of diffuse traumatic axonal injury: survival and differentiation

Leyan Xu1*†, Jiwon Ryu1†, Hakim Hiel2, Adarsh Menon1, Ayushi Aggarwal1, Elizabeth Rha1, Vasiliki Mahairaki1, Brian J Cummings3 and Vassilis E Koliatsos145

  • Corresponding author: Leyan Xu

† Equal contributors
Author Affiliations
1 Division of Neuropathology, Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore 21205, MD, USA

2 Department of Otolaryngology-Head and Neck Surgery, The Johns Hopkins University School of Medicine, Baltimore 21205, MD, USA

3 Departments of Physical and Medical Rehabilitation, Neurological Surgery, and Anatomy and Neurobiology, Sue and Bill Gross Stem Cell Research Center, Institute for Memory Impairments and Neurological Disorders, University of California at Irvine, Irvine 92697, CA, USA

4 Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore 21205, MD, USA

5 Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore 21205, MD, USA

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Stem Cell Research & Therapy 2015, 6:93 doi:10.1186/s13287-015-0087-0

Leyan Xu and Jiwon Ryu contributed equally to this work.

The electronic version of this article is the complete one and can be found online at:

Received: 19 December 2014
Revisions received: 13 March 2015
Accepted: 1 May 2015
Published: 14 May 2015
© 2015 Xu et al.; licensee BioMed Central.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Diffuse axonal injury is an extremely common type of traumatic brain injury encountered in motor vehicle crashes, sports injuries, and in combat. Although many cases of diffuse axonal injury result in chronic disability, there are no current treatments for this condition. Its basic lesion, traumatic axonal injury, has been aggressively modeled in primate and rodent animal models. The inexorable axonal and perikaryal degeneration and dysmyelination often encountered in traumatic axonal injury calls for regenerative therapies, including therapies based on stem cells and precursors. Here we explore the proof of concept that treatments based on transplants of human oligodendrocyte progenitor cells can replace or remodel myelin and, eventually, contribute to axonal regeneration in traumatic axonal injury.

We derived human oligodendrocyte progenitor cells from the human embryonic stem cell line H9, purified and characterized them. We then transplanted these human oligodendrocyte progenitor cells into the deep sensorimotor cortex next to the corpus callosum of nude rats subjected to traumatic axonal injury based on the impact acceleration model of Marmarou. We explored the time course and spatial distribution of differentiation and structural integration of these cells in rat forebrain.

At the time of transplantation, over 90 % of human oligodendrocyte progenitor cells expressed A2B5, PDGFR, NG2, O4, Olig2 and Sox10, a profile consistent with their progenitor or early oligodendrocyte status. After transplantation, these cells survived well and migrated massively via the corpus callosum in both injured and uninjured brains. Human oligodendrocyte progenitor cells displayed a striking preference for white matter tracts and were contained almost exclusively in the corpus callosum and external capsule, the striatopallidal striae, and cortical layer 6. Over 3 months, human oligodendrocyte progenitor cells progressively matured into myelin basic protein(+) and adenomatous polyposis coli protein(+) oligodendrocytes. The injured environment in the corpus callosum of impact acceleration subjects tended to favor maturation of human oligodendrocyte progenitor cells. Electron microscopy revealed that mature transplant-derived oligodendrocytes ensheathed host axons with spiral wraps intimately associated with myelin sheaths.

Our findings suggest that, instead of differentiating locally, human oligodendrocyte progenitor cells migrate massively along white matter tracts and differentiate extensively into ensheathing oligodendrocytes. These features make them appealing candidates for cellular therapies of diffuse axonal injury aiming at myelin remodeling and axonal protection or regeneration.

Axonal injury is the defining feature of diffuse axonal injury (DAI), but is also present in blast injuries [1], chronic traumatic encephalopathy [2], and even mild head injuries [3]. Axonal damage in models of DAI is referred to as traumatic axonal injury (TAI), a term often used interchangeably with DAI [4], [5]. In the case of DAI, axonal injury causes disconnection of neural circuits at multiple central nervous system (CNS) sites [6]–[8] and can lead to a number of neurological impairments, including long-term memory problems, emotional disturbances, unconsciousness, and/or a persistent vegetative state. These neurological impairments have no satisfactory treatment besides symptomatic alleviation of various subsyndromes with physical, occupational, speech and language therapy and various categories of CNS-acting drugs including antispasmodics, antidepressants, and mood stabilizers. Although some retraining of circuits is anticipated over time and syndromic pharmacotherapies have some effectiveness, most patients with DAI still remain severely symptomatic years and decades later.

Stem cell therapy presents a promising treatment approach for traumatic brain injury (TBI). Some early success in models of ischemic brain injury [9] has encouraged the use of stem cell or neural precursor (NP) transplantation, primarily in models of focal TBI [10]. Much less is known about the role of stem cell therapies in DAI/TAI. Axonal repair as a target of treatment separate from nerve cell regeneration is not as well established in TBI as in spinal cord injury, and this is especially true with the problem of myelin repair/remyelination [11]. However, demyelination appears to contribute to degeneration of axons in TAI [12], [13] and TAI is associated with active and ongoing attempts at axonal repair [14]. Therefore, adding exogenous oligodendrocyte progenitor cells (OPCs) may furnish competent oligodendrocytes that can assist in remyelination/myelin remodeling and prevent axonal degeneration or help myelinate regenerating axons in TAI.

Animal models are invaluable tools in establishing proof of concept that remyelination by exogenously provided oligodendrocytes is possible in TAI settings. Models of inertial acceleration and impact acceleration (IA) are frequently used for experimental studies of DAI/TAI [5], [15]. In the present study we use the IA model of DAI/TAI [16] and transplant human embryonic stem cell (ESC)-derived OPCs (hOPCs) into the deep sensorimotor cortex next to the corpus callosum. Our findings indicate that exogenous hOPCs differentiate into mature oligodendrocytes, migrate extensively along white matter tracts, and begin to myelinate host axons. Our data are consistent with the view that stem cell grafts may serve as effective myelin remodeling tools in TBI scenarios featured by DAI/TAI.

Materials and methods
Human embryonic stem cell culture and differentiation to human oligodendrocyte progenitor cells
The human ESC line H9 from WiCell (Madison, WI, USA) was maintained according to standard stem cell culture protocols. H9 cells (WA-09; passages 30 to 41) were grown on mitotically inactivated mouse embryonic fibroblasts essentially as described in [17]. hOPCs were generated through extensive passaging as neurospheres based on the method of Hu and colleagues [18], [19] with minor modifications (Additional file 1: Fig. S1). The ventralizing factor sonic hedgehog (SHH; 100 ng/mL) along with the caudalizing factor retinoic acid (0.1 μM) were used to initially pattern neuroepithelial cells; glial differentiation medium (GDM; Dulbecco’s modified Eagle’s medium (DMEM)/F12, B27 without vitamin A, N1, MEM-NEAA, cAMP, biotin, 60 ng/mL triiodothyronine, 10 ng/mL platelet-derived growth factor (PDGF)-AA, insulin-like growth factor (IGF)1 and neurotrophin (NT)3) was used for further differentiation. Cells were trypsinized with TrypLE (Life Technologies, Grand Island, NY, USA) at day 84 after induction of differentiation, counted, and plated on p-L-ornithine- and laminin-coated plates. Cells were grown in GDM supplemented with PDGF-AA, IGF1 and NT3 for 12 days, then trypsinized, counted, and resuspended at high concentration (2.0 × 108 per mL), and finally transplanted on day 98 after induction of differentiation.

Characterization of human oligodendrocyte progenitor cells used for transplantation with immunocytochemistry
Two weeks before transplantation (on day 84, a time point chosen to correspond to the remaining time in differentiation of hOPCs destined for transplantation), hOPC neurospheres were trypsinized with TrypLE and counted. Twenty thousand cells were plated on polyornithine- and laminin-coated coverslips or Matrigel-coated four-well slide chambers and cultured in GDM supplemented with PDGF, IGF and NT3 for 2 weeks. Cultures were then fixed with 4 % paraformaldehyde in phosphate-buffered saline for 20 minutes and then subjected to immunocytochemistry with the oligodendrocytic markers A2B5, platelet-derived growth factor receptor (PDGFR)α, NG2, Sox10, and O4; the neuronal marker type III-tubulin epitope J1 (TUJ1); astrocyte marker glial fibrillary acidic protein (GFAP); and the mesodermal marker bone morphogenetic protein 4 (Table 1).

Table 1. Primary antibodies used for immunocytochemistry, immunohistochemistry and ultrastructural immunohistochemistry
Animals and surgical procedures
Ten-week old male nude rats (Crl:NIH-Foxn1rnu; Charles River, Wilmington, MA, USA) were used for hOPC transplantation. Nude rats were chosen because immunodeficient animals yield greater engraftment and survival of human cells than immunocompetent animals treated with immunosuppressants [20]. All surgical procedures were carried out according to protocols approved by the Animal Care and Use Committee of the Johns Hopkins Medical Institutions using gas anesthesia (isoflurane:oxygen:nitrous oxide = 1:33:66) and aseptic methods. In order to explore the fate of transplanted hOPCs and compare differentiation between injured and uninjured scenarios, animals were separated into IA and sham groups. In the IA group, animals were subjected to injury with full artificial ventilation as described by Marmarou and colleagues [16]. In the present experiments, we employed a severe TBI regimen using a 450 g weight that was freely dropped onto the steel disc through a Plexiglass tube from a height of 2 meters. In the sham group, animals received all aspects of the regimen except the injury itself (weight on the steel disc). One week after injury, a time point that appears to optimize survival and differentiation [21], [22], 200,000 live hOPCs were transplanted into two sites 1 mm apart in the right deep motor cortex next to the corpus callosum (1 mm and 0 mm anterior to bregma, 2 mm lateral to midline and 3 mm ventral to pia) of either injured (n = 10) or sham (n = 5) animals using procedures that have been detailed in our published work [21], [23], [24]. To explore the progress of differentiation of transplanted hOPCs in the TAI environment, animals in the TAI group were allowed to survive for 6 weeks and 3 months. Sham animals with transplanted hOPCs were euthanized at 3 months.

Histology, immunohistochemistry and microscopy
Brain tissues were prepared from animals perfused transcardially with 4 % phosphate-buffered paraformaldehyde. The axonal injury, survival, location and phenotypic fate of hOPC grafts were assessed with ABC peroxidase immunohistochemistry (IHC) and dual-label fluorescent IHC in serial coronal or sagittal sections (40 μm) through the brain as described previously [22], [24], [25]. Axonal injury was studied with well-established TAI markers, including an antibody against the amyloid precursor protein (APP), the monoclonal antibody RMO14 binding to the rod domain of neurofilaments H and M, and a monoclonal antibody against the 68-kDa light chain neurofilament protein. hOPC survival was studied with human-specific nuclei (HNu) or human-specific cytoplasm (SC121) antibody using immunoperoxidase or immunofluorescence labeling. Differentiation was studied with dual-label immunofluorescence combining HNu or SC121 with other oligodendrocyte markers – that is, the progenitor and early marker PDGFRα, the early markers O4 and GalC and late markers myelin basic protein (MBP) and adenomatous polyposis coli protein (APC). Appositions between axons and transplant-derived oligodendrocytes were visualized with the combination of antibodies against the heavy neurofilament subunit (NF-H) and the SC121 epitope as generic axonal and transplant-derived cell markers, respectively. The nuclear mitotic marker Ki67, the early neuronal marker TUJ1 and the astroglial cell marker GFAP were also used in separate co-localization experiments with HNu or SC121 as described elsewhere [22], [24], [26], [27]. All antibody information is listed in Table 1. The Gallyas silver staining method [28] was used to evaluate injured and/or degenerating axons and terminals. For this purpose, sections were processed with a commercially available kit (NeuroSilver kit II; FD Neurotechnologies, Ellicott City, MD, USA) as described previously [29].

Stained sections were studied on a Zeiss Axiophot microscope equipped for epifluorescence (Diagnostic Instruments Inc., Sterling Heights, MI, USA) or a Zeiss LSM 510 inverted confocal microscope (Carl Zeiss Inc., Oberkochen, Germany). Confocal microscopic images were captured with pinhole set at 0.8 μm to ensure co-localization of multiple labels at the same resolution level as semithin sections. Three-dimensional reconstruction by Z-stack scanning through regions of interest was acquired with LSM software (Carl Zeiss Inc., Oberkochen, Germany). Adobe Photoshop 7.0 software (Adobe Systems, San Jose, CA, USA) was used for montaging and image processing. All staining, image collection, and quantification were done in a fashion blind to group assignment.

Ultrastructural immunohistochemistry
Myelin formation by transplanted hOPCs was assessed ultrastructurally with electron microscopy using standard pre-embedding immunoperoxidase-3,3′-diaminobenzidine IHC for the human cytoplasmic antigen SC121 as described previously [23]–[25], [30]. Briefly, brain sections prepared as in the previous section were treated with a solution containing 4 % paraformaldehyde and 0.2 % glutaraldehyde for 24 hours. Sections were then rinsed in 0.1 M phosphate buffer (pH 7.3) for 3 to 10 minutes, immersed in 1 % osmium tetroxide for 15 minutes, dehydrated in graded concentrations of ethanol, embedded in Poly/Bed 812 (Polysciences Inc., Warrington, PA, USA), polymerized at 60 °C for 72 hours, and then finally embedded in BEEM® capsules (Electron Microscopy Sciences, Hatfield, PA, USA). Half the sections were stained en block in uranyl acetate prior to embedding. Serial ultrathin sections were collected on Formvar-coated slotted grids and viewed with a Hitachi H7600 transmission electron microscope equipped with a 2 k×2 k bottom mount AMT XR-100 CCD camera (Hitachi High-Technologies Corporation, Tokyo, Japan). Only sections that were not stained with uranyl acetate were used for studying ensheathment profiles originating in hOPC transplants.

Stereological quantification of human oligodendrocyte progenitor cell survival and differentiation
Numbers of surviving hOPCs were counted in serial, systematically and randomly sampled coronal sections based on the optical fractionator concept with the aid of a motorized stage Axioplan microscope (Carl Zeiss Inc.) equipped with Stereo Investigator (MicroBrightField Bioscience, lliston, VT, USA) as described previously [22]. To evaluate the migration and possible final residing location of differentiating hOPCs, only the contralateral side of transplantation was examined. hOPCs in corpus callosum and cortex were also counted separately for this purpose. Every twelfth serial coronal section through the transplant/injury site was selected for stereological analysis. The counting frame was set at 50×50 μm and the sampling grid and counting depth were 200×200 μm and 10 μm, respectively. Cells around the transplantation site were not counted because of difficulties in discerning individual cells in the densely packed center of the transplant.

Differentiation of survived hOPCs was estimated in a non-stereological fashion as described previously [27]. Briefly, we counted the total number of SC121(+) cells, as well as cells dually labeled with SC121 and the mature oligodendrocyte marker MBP from our immunofluorescent preparations, on randomly selected fields of cortex and corpus callosum using 40× magnification and avoiding the transplantation site. At least three fields in each of four serial sections were used from each animal. Numbers of SC121(+) and double-labeled profiles were pooled from each case and grouped per experimental protocol. Average numbers of single- and double-labeled cells were generated for two TBI groups and one Sham group (n = 5 per group). Differentiation rate was expressed as percentage of SC121 and MBP double-labeled cells in the population of SC121(+) cells.

Migration mapping of oligodendrocyte lineage cells derived from human oligodendrocyte progenitor cell grafts
The positions of all SC121(+) cells were mapped on every twelfth coronal section through brain levels containing the grafted cells and their lineage using Neurolucida software (MicroBrightField Bioscience). Representative cells differentiated from hOPCs and their processes were also traced with Neurolucida software.

Statistical methods
Variance between and across samples of numbers of oligodendrocyte-lineage cells classified by experimental history (IA versus sham), migratory destination in brain (corpus callosum versus neocortex), and time point after transplantation (6 weeks or 3 months) was analyzed with two-way analysis of variance (ANOVA) or t test. In the case of ANOVA, significant differences were further analyzed with post hoc tests to reveal important main effects or interactions. Statistical analyses were performed with STATISTICA 8.0 (StatSoft Inc., Tulsa, OK, USA).

Axonal injury in nude rats using the impact acceleration model
Immunocompromised nude rats were used here to avoid immune rejection of human cell xenografts into rodent brain [20]. Because the original IA model was developed in Sprague-Dawley [16] and Wistar [31] rats, we first explored whether the same IA settings as the ones used in those strains can cause TAI in nude rats. Induction of TAI was studied with IHC strategies routinely used in TBI studies – that is, antibodies against APP, the monoclonal antibody RMO14 binding to the rod domain of neurofilament heavy and medium chains that are exposed after lesion-induced sidearm proteolysis, and a monoclonal antibody against the 68-kDa light chain neurofilament protein (NF68). IHC was used in brain sections from nude rats exposed to a standard severe IA injury (450 g weight drop from a height of 2 meters) [16]. Tissues were also processed with a modification of the Gallyas silver method. Twenty-four hours post-injury, APP, RMO14 and NF68 IHC consistently labeled axonal pathologies such as undulated axons, axonal swellings and bulbs in the corpus callosum and the corticospinal tract as described in several published studies [32]–[35] (data not shown). Argyrophilic axonal degeneration based on Gallyas silver staining became evident 1 week post-injury in the corpus callosum (Fig. 1a), the optic tract and the corticospinal tract (Fig. 1b). Axonal degeneration labeled with Gallyas silver was still present in the corpus callosum (Fig. 1c), corticospinal tract (Fig. 1d) and other white matter tracts 3 months post-injury. These data suggest that the pattern of TAI in nude rates exposed to IA injury is qualitatively similar to the one described for Sprague-Dawley and Wistar rats and, therefore, the nude rat model is suitable for research into hOPC transplantation outcomes in a diffuse TBI background.

thumbnailFig. 1. Traumatic axonal injury in impact acceleration-injured nude rats as demonstrated by Gallyas silver staining. One week after exposure of nude rats to severe impact acceleration injury, argyrophilic axonal degeneration is pronounced in (a) the corpus callosum and (b) the corticospinal tract (arrows). c,d At 3 months after injury, degenerating axons are still evident in these regions (arrows). Axonal bulbs are also present in (d) (arrowhead). Scale bars = 50 μm
Differentiation of human embryonic stem cells to human oligodendrocyte progenitor cells in vitro
As per Hu and colleague’s original description [18], [19], columnar epithelial cells began to appear and organize into rosettes 10 days after induction of differentiation of embryoid bodies with human ESC medium without fibroblast growth factor (FGF)2 (DMEM/F12, Knockout serum replacer, MEM-nonessential amino acid, 0.1 mM β-mercaptoethanol, 4 ng/mL FGF2) for 3 days and then with neural differentiation medium (NDM; DMEM/F12, N2, MEM-nonessential amino acid, 2 ug/mL heparin) for 6 days. Neuroepithelial cells were initially cultured in the presence of 0.1 μM retinoic acid on laminin-coated plates for 4 days as described by Hu and colleagues [18]. The resulting rosette-rich colonies were manually detached and grown into spheres and then continued to be patterned with retinoic acid and SHH for 10 more days. To generate pre-oligodendrocyte progenitors, spheres were passaged by Accutase and cultured in NDM supplemented with B27, SHH (100 ng/mL) and FGF2 (10 ng/mL) for 10 days. For further differentiation into hOPCs, spheres were cultured in GDM (DMEM/F12, N1, B27, MEM-nonessential amino acid, 60 ng/mL T3, 1 μM cAMP, 0.1 μg/mL biotin) supplemented with SHH, PDGF-AA, IGF1 and NT3 for 2 weeks and then dissociated by Accutase and continued to be feed with the same GDM without SHH (day 49). Every 2 or 3 weeks, the spheres were passaged by the same dissociation method using Accutase and cultured in the same GDM with PDGF-AA, IGF1 and NT3. On day 84 after induction of differentiation, spheres were trypsinized into dissociated hOPCs, plated and cultured further as in Materials and methods. On day 98, cells were detached from the plate and resuspended at a high concentration (2.0 × 108 per mL) for transplantation.

As described in Materials and methods, the cell composition of hOPC transplants was analyzed in a representative sample of cells destined for transplantation with immunocytochemistry for protein markers of various neural cell lineages [36]–[39] (Fig. 2). Results show that only a small percentage of hOPCs (less than 10 %) expressed the neuronal marker TUJ1, an even smaller percentage (less than 1 %) were positive for astrocytic markers (GFAP), and no bone morphogenetic protein(+) mesodermal-lineage cells were detected (Fig. 2a-c). In contrast, these hOPC samples were enriched for cells expressing oligodendrocyte-lineage markers including A2B5, PDGFRα, O4, NG2, Sox10 and MBP (Fig. 2d-i).

thumbnailFig. 2. Characterization of human oligodendrocyte progenitor cells used for transplantation 99 days after induction of differentiation of human embryonic stem cells. (a) No mesodermal lineage cells were detected (bone morphogenetic protein; BMP) and very little (b) neural (type III-tubulin epitope J1; TUJ1) and (c) astrocyte (glial fibrillary acidic protein; GFAP) markers were expressed in the oligodendrocyte progenitor cells (OPCs). Most cells (90-95 %) were positive for early and late OPC and pre-oligodendrocyte markers ((d) A2B5, (e) platelet-derived growth factor receptor (PDGFR)α and (f) O4). Fifty percent of cells were (g) NG2 positive and (h) most cells were positive for the transcriptional factor Sox10. (i) About 50 % of cells showed varied expression of the oligodendrocyte marker myelin basic protein (MBP). Insets in (b) and (c) show typical rare neuronal and astrocytic profiles in these human OPC cultures. Inset in (b) is taken from a culture that was double immunostained for NG2 (green) and TUJ1 (red). Insets in (d-i) are magnifications of cells or groups of cells labeled with asterisks to showcase typical cytologies and immunoreactivities, including nuclear (h) and non-nuclear (d,e,f,g,i) localizations. Other than the nuclear localization of the transcription factor Sox10 (H), most immunoreactivities are especially prominent in processes (e,g) or have punctate peripheral localization (f,i). Scale bars = 20 μm
Survival and migration of transplanted human oligodendrocyte progenitor cells in the rat brain
Human OPCs were transplanted into the deep sensorimotor cortex of IA- and sham-injured rats. Transplanted hOPCs survived very well in the brains of injured and uninjured animals and migrated extensively away from the transplantation site in both groups. We mapped and counted migratory profiles at 6 weeks and 3 months post-transplantation. For the time point of 6 weeks we have data only on IA-injured animals. In the case of 3 months, we have data from both IA-injured and sham animals.

At 6 weeks, there was some migration of transplant-derived SC121(+) human cells into the ipsilateral corpus callosum and external capsule and into the contralateral corpus callosum adjacent to cingulate gyrus (Additional file 2: Fig. S2). At 3 months, transplanted cells had migrated much further (Figs. 3 and 4). hOPCs had densely populated the ipsilateral corpus callosum and the entire length of the external capsule and reached further into the corpus callosum and external capsule on the contralateral side; some cells had entered the contralateral neocortex (Fig. 4). In many cases, these cells had migrated 5 mm or more in antero-posterior distance from the transplantation site (Fig. 4). There was little migration into the gray matter, and cortical invasion of hOPCs was limited to layers adjacent to corpus callosum (lower layer 6). In the case of migration into the neostriatum (Fig. 4), hOPCs were localized strictly inside the white matter striae.

thumbnailFig. 3. Extensive migration of transplant-derived cells 3 months post-transplantation. This representative section from an impact acceleration-injured rat shows that SC121(+) cells (brown) migrate extensively from the transplantation site (arrowhead) along the corpus callosum on both sides. Methylene green was used as counterstain. Arrows denote human oligodendrocyte progenitor cell distribution. Insets are enlarged from the frames to convey information on cytology
thumbnailFig. 4. Migration map of human oligodendrocyte progenitor cell transplant-derived cells from a representative case of an impact acceleration-injured rat. Images were acquired and processed from serial coronal sections (40 μm; every 24th) from a case of an impact acceleration-injured rat 3 months post-transplantation using Neurolucida software. Distance between neighboring sections is 0.96 mm. Distance between migrating cells in section 9 and edge of transplantation site (section 4) is 4.8 mm. The maximal migration at 3 months was greater than 5 mm based on the fact that section 9 was not the furthest section containing human cells (other sections are not shown)
Stereological counts of transplant-derived cells contralateral to the injection side provide a good measure of the migratory potential of transplanted hOPCs. Cell counts in IA-injured animals at 6 weeks and 3 months show massive and progressive migration into the corpus callosum and adjacent cortical layer 6 (Fig. 5). For example, the number of oligodendrocyte-lineage cells in the contralateral corpus callosum is 30 times higher at 3 months compared to 6 weeks (Fig. 5a). Two-way ANOVA examining interaction between time and location shows that time tends to advance the position of cells from the corpus callosum into deep cortical layers (Fig. 5a). Experimental history (IA versus sham) shows no effect on numbers of oligodendrocyte-lineage cells in corpus callosum or cortex at 3 months. There are about three to four times as many oligodendrocyte-lineage cells in corpus callosum compared to cortical layer 6 in both IA-injured and sham animals (Fig. 5b). Two-way ANOVA addressing the interaction between experimental history and location shows no significant effect (that is, lesion does not promote more advanced migration into deep cortical layers).

thumbnailFig. 5. Stereological counts of transplanted human oligodendrocyte progenitor cells migrating into the contralateral hemisphere. a Migratory patterns at two time points (6 weeks and 3 months) after transplantation in two brain regions (corpus callosum and deep cortex) in impact acceleration (IA) animals. b Migratory tendencies in the same two locations based on experimental history (IA versus sham) at 3 months post-transplantation. In (A), the difference in cell numbers between 6 weeks and 3 months is significant by t test (*P < 0.05) in both corpus callosum and deep cortex (layer 6); two-way analysis of variance (ANOVA) shows that there is interaction between time and location (that is, time tends to favor deep cortical over callosal location; P < 0.05). In (B), there are no differences in cell numbers between sham and IA-injured subjects in corpus callosum or deep cortex at 3 months post-transplantation. In both groups of subjects, there are more cells in corpus callosum than deep cortex by t test (P < 0.05). Two-way ANOVA shows that there is no interaction between experimental history and location (that is, injury does not seem to influence the location of cells in one site over the other). OPC, oligodendrocyte progenitor cell
At 3 months post-transplantation, a majority of oligodendrocyte-lineage cells around the transplantation site (the triangular region of Fig. 3) had round perikaryal profiles and multiple radial processes consistent with type I morphology (Fig. 6a-c) [40]. On the other hand, the majority of transplant-derived cells in the corpus callosum were spindle-shaped with parallel processes consistent with type II morphology (Fig. 6d-f) [40]. In the gray matter away from the injection site (ipsilateral or contralateral), cytology was mixed.

thumbnailFig. 6. Some cytological features of human oligodendrocyte progenitor transplant-derived cells 3 months post-transplantation. a-c Around the transplantation site, a large number of transplant-derived SC121(+) cells (brown) have round features with extensive and radially arrayed processes. d-f In the corpus callosum (cc), the majority of SC121(+) cells are spindle shaped with long parallel processes. (b,e) Enlargements of bracketed areas in (a) and (d), respectively. (c,f) Neurolucida tracings of representative cells from (b) and (e) indicated with asterisks. Scale bars: (a,d) = 50 μm; (b,e) = 20 μm
Very few (less than 1 %) hOPCs at the transplantation site or within the main migratory domains (corpus callosum and deep neocortex) were positive for the mitotic marker-Ki67 at 6 weeks or 3 months, in injured or sham animals. This pattern suggests that, at the time points studied here, surviving cells are not proliferative at the original transplant site or in their migratory paths and destinations (Fig. 7).

thumbnailFig. 7. Proliferative activity of human oligodendrocyte progenitor cells at 6 weeks and 3 months post-transplantation. Only rare HNu(+) transplant-derived cells (red) are Ki67(+) cycling cells (green) (arrow, the color turns into yellow because of overlapping with red color from HNu) at the transplantation site at 6 weeks (a) or, after migration, in the corpus callosum (cc) at 3 months (b). Images are from a representative impact acceleration-injured rat. Scale bars = 50 μm
Differentiation of transplanted human oligodendrocyte progenitor cells in the rat brain
At 6 weeks and 3 months post-transplantation, under either IA or sham conditions, no neurons and very few astrocytes were derived from transplanted hOPCs (Fig. 8a,b). The majority of transplanted cells were identified as PDGFRα(+) (Fig. 8i-k) or MBP(+) (Fig. 8c-h) profiles in both the transplantation site and migratory pathways/destinations. MBP immunoreactivity was expressed in both round and spindle-shaped oligodendrocyte profiles derived from the transplant (Fig. 8c-h). At 3 months, most transplant-derived cells around the transplantation site, in corpus callosum, and deep cortical layers were also APC(+) (Fig. 8l-n).

thumbnailFig. 8. Differentiation of transplanted human oligodendrocyte progenitor cells at 3 months post-transplantation. All images are from representative impact acceleration-injured animals. a At 3 months, we found no SC121(+) cells expressing type III-tubulin epitope J1 (TUJ1(+)) neuronal phenotypes, and only rare SC121(+) human oligodendrocyte progenitor cells (hOPCs; red) had differentiated into glial fibrillary acidic protein (GFAP(+)) astrocytes (green) at the transplantation site (arrow). Inset in (a) is a magnification of the astrocytic profile indicated by the arrow in the main panel. b In the corpus callosum (cc), no hOPCs (red) are immunoreactive for GFAP (green). c-h Confocal images to show that various types of cells derived from the hOPC transplant (round in (c) or spindle-shaped in (f), red) become mature myelin basic protein (MBP) (+) oligodendrocytes (green in (d) and (g)); both cell bodies (arrows) and processes (arrowheads) of transplant-derived cells are immunoreactive for MBP. i-n Platelet-derived growth factor receptor (PDGFR)α(+) (i-k) and adenomatous polyposis coli protein (APC) (+) (l-n) cell bodies of transplant-derived cells in the corpus callosum. (e,h,k,n) Merged images of panels (c,d), (f,g), (i,j) and (l,m), respectively. Scale bars: (a,b) = 50 μm; (c-n) = 20 μm
In the area surrounding the transplantation site of IA-injured animals, cell counts of PDGFRα(+) profiles show that 74.6 ± 9 % of graft-derived cells are PDGFRα(+) at 6 weeks post-transplantation; this number is significantly reduced to 49.4 ± 11 % at 3 months. Conversely, the percentage of MBP(+) oligodendrocytes derived from hOPCs is significantly higher at 3 months (67.8 ± 12 %) compared to 6 weeks (37.1 ± 9 %) (Fig. 9a, left). A similar pattern is seen in the corpus callosum (Fig. 9a, right), but trends in this case do not reach statistical significance. In the area surrounding the transplantation site, there are no significant differences in PDGFRα(+) or MBP(+) cell rates between sham and injured animals (Fig. 9b, left). In the corpus callosum, the percentage of MBP(+) cells is significantly higher in injured animals compared to shams (Fig. 9b, right).

thumbnailFig. 9. Population sizes of platelet-derived growth factor receptor α- and myelin basic protein-immunoreactive cells derived from human oligodendrocyte progenitor cell transplant. a Differentiation patterns at 6 weeks and 3 months after transplantation in two brain regions (corpus callosum versus transplantation area in cortex) in impact acceleration (IA) animals. b Differentiation patterns in the same two locations based on experimental history (IA versus sham) at 3 months post-transplantation. In (a), IA rats at 3 months have more myelin basic protein (MBP) (+) and fewer platelet-derived growth factor receptor (PDGFR)α(+) transplant-derived oligodendrocytes than at 6 weeks post-transplantation; counts were performed at the transplantation site. In (b), IA rats at 3 months have more transplant-derived MBP(+) oligodendrocytes compared to sham in the corpus callosum (cc). *P < 0.05
Confocal microscopy with three-dimensional image reconstruction for SC121- and NF-H-immunoreactive structures was used to visualize appositions between SC121(+) processes belonging to transplant-derived cells and NF-H(+) SC121(−) host axons. Reconstructed confocal images demonstrated many ensheathing appositions between processes of transplant-derived oligodendrocytes and host axons (Fig. 10).

thumbnailFig. 10. Ensheathment of axons by transplant-derived oligodendrocytes. This three-dimensional reconstructed confocal micrograph depicts SC121(+) process (red) from transplant-derived oligodendrocytes ensheathing neurofilament H(+) axons (NFH, green) in a representative impact acceleration-injured animal at 3 months post-transplantation (arrows on z plane). Ensheathment is confirmed on x and y planes at the corresponding cross-sectional locations (arrow heads). Scale bar = 10 μm
Ultrastructural IHC for the human cytosolic epitope SC121 was used to disclose the involvement of transplant-derived oligodendrocyte processes in the ensheathment of host-derived axons or the formation of myelin. In a pattern similar to the one revealed with confocal microscopy, semi-thin preparations accompanying thin sections showed SC121(+) processes co-localizing with toluidine blue-stained myelin sheaths (Fig. 11a). Using thin sections, we found numerous SC121(+) cytoplasmic projections juxtaposed to or ensheathing unlabeled (host) axons. Ensheathment was featured by complex configurations, including the presence of outer and inner cytoplasmic tongues and close juxtapositions with compact myelin (Fig. 11b,c). It was not possible to ascertain whether compact myelin belonging to the same host axons, as SC121(+) sheaths were continuous with the latter in our preparations because the cytoplasmic human epitope SC121 would not be expected to be present within dense myelin.

thumbnailFig. 11. Ensheathing profiles issued by transplant-derived oligodendrocytes as shown by ultrastructural immunohistochemistry. Preparations are from an injured animal 3 month post-transplantation. a Companion toluidine blue-stained semi-thin section through the corpus callosum shows the co-localization of SC121(+) (brown) processes with blue myelin sheaths in transverse (arrow) axonal profiles. Co-localization profiles are dark brown. Asterisk shows a group of SC121(−) axons. b An SC121(+) process (arrowhead) is shown to ensheath an unlabeled axon. This profile is adjacent to one SC121(+) cell (1) and also one unlabeled cell (2). Cells 1 and 2 have the appearance of oligodendrocytes. c A magnification of the framed area in (b) shows detailed ultrastructural features of ensheathment by transplant-derived oligodendrocytes. SC121(+) tongue processes (arrowheads) are wrapped around a myelinated axon. Myelin sheath on the inside appears to be unlabeled. Scale bars: (a) = 5 μm; (b) = 1 μm; (c) = 500 nm
Our findings indicate that the IA model of Marmarou can be effectively replicated in the nude rat background. Using the nude rat IA model, hOPC transplants survive well in the deep sensorimotor cortex and behave in a fashion very different from NPs; that is, they migrate massively and show almost exclusive affinity for white matter tracts, especially the corpus callosum and adjacent white matter in deep cortical layers. The progressive migration of transplanted hOPCs is accompanied by progressive maturation into MBP(+) and APC(+) oligodendrocytes that ensheath host axons. Our findings provide further support to the notion that human ESCs and neural stem cells can be coaxed to specific fates that continue to progress to fully differentiated progenies after transplantation into the adult CNS. These progenies behave in a fashion that is strikingly similar to indigenous differentiated neural cells. Given the very low level of proliferation of transplanted cells as early as 6 weeks post-transplantation and their prompt differentiation into mature oligodendrocytes, the possibility of overgrowth and, hence, tumorigenic risk is very low. We postulate that the high numbers of cells present in the contralateral hemisphere by 3 months had started as pre-existing hOPCs in the dense center of the transplant and did not derive from ongoing cell divisions.

The use of human ESCs such as line H9 was based on a number of considerations including: thorough characterization and inexhaustible supply of the parent line [41]–[44]; great versatility to differentiate to any neural cell type in sufficient quantity for transplantation [18], [43]–[50]; and well-established methods for in vitro manipulation to fate determination prior to transplantation. The choice of human ESCs is based on availability and access considerations, the greater translational value of such cells, and a long experience in our laboratory using human cells as transplants in rodent hosts [21]–[24], [27], [50]–[54].

In adulthood, the sources of usable stem cells or neural progenitors in the CNS are limited to a few forebrain niches, and the yield or repair potential of such niches is low. For example, in mouse models of multiple sclerosis, the limited recruitment of endogenous hOPCs into demyelination sites does not suffice for effective remyelination [55]. Therefore, supplementation of such limited stem cell pools with exogenous progenitors is a reasonable first step for a cellular therapy. Besides providing sources of fully differentiated nerve cells competent to replenish lost cells, transplanted progenitors also release neuroprotective molecules [27], [56] and, importantly, may induce endogenous stem cells/progenitor cells to proliferate and differentiate as auxiliary niches, thereby improving the efficacy of self-repair mechanisms [52].

Generation of human oligodendrocyte progenitor cells from human embryonic stem cells
In the body of in vivo studies reported here, we used hOPCs that were prepared from human ESC line H9 following the methods described by Hu and colleagues with minor modifications [19], [45], [57]. Our experience with culturing and differentiating H9 cells and then characterizing the derived hOPCs in vitro is very similar to the original description of Hu and colleagues; hOPCs derived in this manner express PDGFRα, NG2, O4, and Sox10, a pattern consistent with a classical hOPC identity [36]. Methodological issues concerning hOPC derivation are very important, because the use of highly concentrated hOPCs for grafting is key for achieving the desired outcomes (that is, myelination of host axons) within a limited time period.

Differentiation of human ESCs into hOPCs is a longer and more arduous process than the one leading to neuronal progenitors. In work reported here we initially used two methods [18], [58] for deriving and characterizing hOPCs prior to transplantation. In our hands, the method of Hu and colleagues appeared to be more successful in generating viable transplants, although this observation was not confirmed in a systematic fashion. Major differences between the two methods are: trophic factors used; extracellular matrix used; enzyme used to passage the cells; length of time in three-dimensional culture; and timing of hOPC harvest. In previous published work, the method of Nistor and colleagues generated hOPCs with good viability after transplantation [58]–[62], but the transplantation site of these authors (spinal cord) was different from ours (neocortex). Interestingly, the same team of investigators reported that their hOPCs did not survive past 2 weeks after transplantation in animal models of multiple sclerosis, but these mice were immunocompetent C57B6 mice [63]. Our in vivo outcomes using hOPCs prepared as per Hu and colleagues are consistent with the ones reported by that team on shiverer mice [19].

Issues related to in vivo differentiation of human oligodendrocyte progenitor cell transplants
Two important trends in hOPC maturation were the progressive phenotypic differentiation in the transplantation area and the higher rate of maturation of hOPCs in the corpus callosum of injured subjects. With respect to the former, we have observations only from IA-injured animals, but we speculate that there is a similar differentiation trend in control (sham) animals. We also postulate that differentiation trends in the corpus callosum are not too dissimilar to those in the cortex around the transplantation site and that differences in significance may be caused by the fact that hOPC maturation may be earlier in the white matter [64] compared to grey matter. Regarding the latter, it would appear that injury may contribute to hOPC differentiation; the difference between corpus callosum and cortex around the transplantation site may be due to the fact that cortex is not a primary site of injury in the IA model that preferentially affects white matter tracts. We have previously reported that the injured spinal cord niche influences both the proliferation and differentiation of human CNS stem cells propagated as neurospheres [65].

The role of injured or otherwise pathological environment as a niche for differentiation deserves further commentary. Environments associated with acquired neurological injury such as stroke and spinal cord injury have been shown to promote endogenous stem cell differentiation [9], [66], [67] and this effect may be mediated, in part, by trophic and cytokine signals such as stem cell factor [68], stromal cell-derived factor 1α [69], FGF-2 [70], vascular endothelial growth factor [71], ciliary neurotrophic factor, and CXC chemokine receptor 4 [72]. Some of these factors are known to act as tropic cues for migration or to specify the definitive phenotype of endogenous or exogenous stem cells [68], [73]–[79].

The invasion of migrated hOPCs and their differentiated progenies into deep cortical layers matches native cortical myelination patterns; these patterns show an overall denser myelination in lower cortical layers and variable myelination in superficial layers. It is also interesting that, at least up to 3 months post-transplantation, the differentiated progeny of transplanted hOPCs does not advance to more superficial layers. This distribution is identical to that of native mature oligodendrocytes; in contrast to hOPCs that are radially spread across cortical layers, oligodendrocytes favor deep layers and follow the deep-to-superficial-layer myelin gradient [80].

The presumed intent of OPC transplants as cell therapies is the replenishment of damaged or destroyed myelin sheaths. Although molecular myelin markers such as MBP may be telling of the myelin-forming potential of OPCs and their progenies, they do not directly show that MBP(+) cells are forming myelin. The three-dimensional reconstruction of confocal images indicates a close, ensheathment-like, apposition of host axons with processes of transplant-derived oligodendrocytes, but the presence of structurally mature myelin can only be ascertained ultrastructurally. Using ultrastructural IHC or electron microscopy combined with histochemistry, previous studies have demonstrated the ability of specific progenies of neural stem cells or OPCs to ensheath [21] or increase the thickness of abnormal myelin sheaths in hosts [19], [26], [59], [61], [81], [82], but labeling of exogenously derived myelin is technically difficult. In the present study, ultrastructural IHC divulges that transplant-derived cells ensheath host axons in intimate proximity to myelin sheaths, but the host-versus-transplant identity of the myelin itself is difficult to ascertain. The human cell marker SC121 is a cytosolic marker and, therefore, it is found in oligodendrocyte cytoplasmic projections and ensheathing tongues but not in the membranous myelin sheath itself. Unstained preparations that were used here have not been particularly useful, primarily because of this reason. Furthermore, existing myelin antibodies cannot resolve between human and rodent myelin and this problem limits their usefulness in confocal microscopy or ultrastructural IHC.

Ultimately, the proof of concept that remyelination or myelin remodeling by OPC transplants can be beneficial in TAI/DAI will depend on the demonstration that such exogenous OPCs afford functional benefits. In the case of IA-injured rodents, such benefits can be sought out in a number of behavioral domains. One approach is assessment of motor control, which depends on the intactness of the corticospinal tract. Experiments addressing functional repair with exogenous OPCs have been successfully performed in models of spinal cord injury [10], [11] and demyelination [82]–[85]. In TBI models, motor recovery may occur within the first month after injury in the absence of any therapy, making the assessment of the efficacy of cell therapies more challenging. In such models, the assessment of efficacy or human neural cell therapies may be more straightforward using tasks of cognition or anxious/emotional disposition, faculties that become chronically impaired in rodent TBI. Chronicity of deficits is important because these human cells may take months to proliferate, migrate and terminally differentiate [15].

Stem cell transplantation as experimental therapy for traumatic brain injury
Some success in models of ischemic brain injury [9] has encouraged the use of stem cell/NP transplantation in models of focal TBI [10], [86]. However, because of the complexity of TBI and its animal models, there is a need to identify specific repair targets based on key pathological mechanisms. Such repair tasks include replacing dead neurons, supporting injured neurons, and protecting axons or assisting with axonal repair/regeneration. The problem of neuronal injury/death is encountered both in focal injury [87], [88] and in the course of TAI [89], [90]. Neuronal cell death in focal TBI is acute and has necrotic components, whereas in TAI/DAI it is slow with apoptotic features and may be associated with retrograde and trans-synaptic effects [8], [89], [91]. Although axonal repair/remyelination as a therapeutic target separate from neuronal regeneration is best established in spinal cord injury [11], there is evidence that demyelination may contribute to degeneration of axons in TAI [12], [13]. Therefore, contributing exogenous hOPCs in the case of TAI may assist in remyelination and prevent axonal degeneration and disconnection within brain circuits.

There is very little published work on stem cell-based therapies for models of TAI/DAI. However, the field of TBI and more specifically TAI/DAI can borrow from spinal cord injury that invariably involves trauma in long tracts [59], [60], [92]–[96]. Experimental cell therapies in animal models of spinal cord injury have utilized various stem cell preparations including neurospheres and OPCs [59], [60], [96], [97], and there are several ongoing clinical trials using neural stem cells [98], [99]. In one report, OPCs were found to remyelinate and restore locomotion after contusional spinal cord injury in rodents [59], but functional recovery reported in this study occurred within 12 days of transplant, a time point that is too early with respect to migration and terminal differentiation of OPCs. In contrast to spinal cord injury, where long tracts course in relatively circumscribed areas, DAI involves disparate white matter tracts [100], [101] and it would be difficult to transplant cells into all these sites. Therefore, transplantation route (systemic, ventricular, and parenchymal) and location of transplant (in the case of parenchymal delivery) are critical. The choice of transplantation site may be based on factors such as concentration of axonal pathology or sites of injury responsible for critical symptoms. The choice of transplantation into deep sensorimotor cortex in the present study was based on the expectation that this site would provide oligodendrocytes for both the corpus callosum and the corticospinal tract, which are affected in the IA injury [16], [35]. Our findings indicate that there was little, if any, invasion of cells into the internal capsule by 3 months, but the extensive migration of OPCs via the corpus callosum and descending tracts and their remarkable differentiation into mature oligodendrocytes predicts a broader remyelination potential with longer survival times. Of course, transplantation sites can also be optimized to the desired functional outcome or involve multiple locations as we have shown in models of motor neuron disease [23], [52], [53].

In conclusion, the findings in this study support the idea that hOPCs can serve as a competent source of mature oligodendrocytes that ensheath CNS axons after TAI, and provide proof of concept that regenerative strategies targeting myelin remodeling can be further considered in TBI models in the future. In addition, we demonstrate that the nude rat is a suitable animal model for studying human cell transplants in neurotrauma. In view of the fact that stem cell therapies are being progressively introduced in clinical trials of neurodegenerative and traumatic diseases of the CNS [11], [98], [102]–[108], these timely results should encourage further translational work targeting the problem of axonal degeneration in the context of DAI and, specifically, interventions designed to regenerate or remodel the myelin sheath.

ANOVA: analysis of variance

APC: adenomatous polyposis coli protein

APP: amyloid precursor protein

CNS: central nervous system

DAI: diffuse axonal injury

DMEM: Dulbecco’s modified Eagle’s medium

ESC: embryonic stem cell

FGF: fibroblast growth factor

GDM: glial differentiation medium

GFAP: glial fibrillary acidic protein

hOPC: human oligodendrocyte progenitor cell

IA: impact acceleration

IGF: insulin-like growth factor

IHC: immunohistochemistry

MBP: myelin basic protein

NDM: neural differentiation medium

NP: neural precursor

NY: neurotrophin

OPC: oligodendrocyte progenitor cell

PDGF: platelet-derived growth factor

PDGFR: platelet-derived growth factor receptor

SHH: sonic hedgehog

TAI: traumatic axonal injury

TBI: traumatic brain injury

TUJ1: type III-tubulin epitope J1

Competing interests
The authors declare that they have no competing interests.

Authors’ contributions
LX, configured the details of experimental plan, performed all animal surgeries and IHC staining, analyzed in vivo data and prepared the first draft of manuscript. JR, established and maintained human OPC cultures and characterized them prior to transplantation. HH, performed ultrastructural IHC. AM, performed stereological counts of PDGFRα and MBD(+) cells derived from the OPC transplant. AA, helped with stereology. ER, performed mapping and stereological counting of migrated OPC-lineage cells. VM, participated in the design of the project and assisted in IHC. BJC, participated in the design of the project and edited the manuscript. VEK, principal investigator, designed the project, troubleshot the experiments and prepared the final manuscript. All authors read and approved the manuscript.

Authors’ information
LX (MD PhD) is an expert on stem cell therapies for animal models of neurological disease and animal models of traumatic brain injury. JR (PhD) is an expert in embryonic and neural stem cell culture and manipulation techniques. HH (PhD) specializes in electron microscope techniques for the nervous system. AM, AA and ER are undergraduate students at Johns Hopkins University. VM (PhD) is an expert in embryonic and neural stem cell culture and manipulation techniques. BJC (PhD) is an expert in stem cell biology and animal models of neurodegeneration and neurotrauma. VEK (MD) is an expert in neural injury and repair, neurodegenerative disease, and traumatic brain injury.

Additional files
Additional file 1: Fig. S1.. A schematic illustration (A) of the method used to prepare hOPCs for transplantation and representative cultures and cells derived (B). Method sketched in A was based on Hu and colleagues [18] with minor modifications in the final stage before transplantation (day 84–99). Panel B shows representative morphologies of hOPCs on days 3, 7, 12, 17, 23, 27, 41, 90 and 97 that roughly correspond to milestones in A. Human ESC H9 colonies were detached by dispase on day 1 to prepare embryoid bodies (EBs) that were subsequently cultured for 3 days in hES medium without FGF (B, Day 3) and then for 3 days in NDM. Embryoid bodies (B, Day 7) were then plated on laminin-coated plates and cultured with NDM for 3 days and NDM with RA for another 5 days (B, Day 12). On day 15, colonies were manually detached and cultured as spheres (B, Day 17) for 10 days in NDM with RA, B27 and SHH. On day 24, big spheres (B, Day 23) were dissociated by Accutase (ACT) into small spheres (B, Day 27) and cultured with NDM containing B27, SHH and FGF for 10 days. On day 35, medium was switched to GDM with SHH, PDGF, IGF and NT3 and cultured for 2 weeks (B, Day 41). On days 49 and 70, cells were passaged with Accutase treatment and treated with GDM with PDGF-AA, IGF1 and NT3 for the remaining of the protocol. On day 84, spheres were trypsinized and plated on p-L-ornithine and laminin-coated plates and coverslips and cultured for 2 weeks in the same medium for transplantation or immunocytochemistry, respectively. On day 99, cells were trypsinized with TrypLE, counted, resuspended in high concentration and used for transplantation (* in A). Scale bars in B: Day 12, 500 μm, Day 3 and 17; 200 μm, all others; 100 μm.
Format: TIFF Size: 6.7MB Download fileOpen Data
Additional file 2: Fig. S2.. Migration of transplant-derived cells at 6 weeks and 3 months post-transplantation. Photographs are taken through the corpus callosum on the side contralateral to transplantation from representative animals. Panel A shows a few scattered SC121(+) profiles at 6 weeks. At 3 months post-transplantation (B), there are numerous SC121(+) cells that had migrated from the transplantation site. Insets are enlargements of framed areas in main panels. Scale bars: 100 μm.
Format: TIFF Size: 18.1MB Download fileOpen Data
This work was supported by a Maryland Technology Development Corporation (TEDCO) grant to VEK funded as companion to CIRM grant TR2-01767 to BJC. We would like to thank Ms Devon Hitt who offered great technical help with immunostaining and collection of some quantitative data.

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Neuron anatomy structure reconstruction based on a sliding filter

Gongning Luo1, Dong Sui1, Kuanquan Wang1* and Jinseok Chae2*

Author Affiliations

1Research Center of Perception and Computing, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

2Department of Computer Science and Engineering, Incheon National University, Incheon, Korea

For all author emails, please log on.

BMC Bioinformatics 2015, 16:342  doi:10.1186/s12859-015-0780-0

The electronic version of this article is the complete one and can be found online at:

Received: 14 May 2015
Accepted: 16 October 2015
Published: 24 October 2015

© 2015 Luo et al.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.



Reconstruction of neuron anatomy structure is a challenging and important task in neuroscience. However, few algorithms can automatically reconstruct the full structure well without manual assistance, making it essential to develop new methods for this task.


This paper introduces a new pipeline for reconstructing neuron anatomy structure from 3-D microscopy image stacks. This pipeline is initialized with a set of seeds that were detected by our proposed Sliding Volume Filter (SVF), given a non-circular cross-section of a neuron cell. Then, an improved open curve snake model combined with a SVF external force is applied to trace the full skeleton of the neuron cell. A radius estimation method based on a 2D sliding band filter is developed to fit the real edge of the cross-section of the neuron cell. Finally, a surface reconstruction method based on non-parallel curve networks is used to generate the neuron cell surface to finish this pipeline.


The proposed pipeline has been evaluated using publicly available datasets. The results show that the proposed method achieves promising results in some datasets from the DIgital reconstruction of Axonal and DEndritic Morphology (DIADEM) challenge and new BigNeuron project.


The new pipeline works well in neuron tracing and reconstruction. It can achieve higher efficiency, stability and robustness in neuron skeleton tracing. Furthermore, the proposed radius estimation method and applied surface reconstruction method can obtain more accurate neuron anatomy structures.


Neuron anatomy structure reconstruction; Radius estimation; Sliding filter; Open curve snake model


Neuron morphology and structure information is critical for neuroscience research. Hence, reconstructing the entire anatomy structure of a neuron is an essential task in the field of neuron informatics [1], [2]. However, reconstructing the anatomy structure of a neuron artificially is labor intensive. Efficient, advanced methods for anatomy structure reconstruction of neurons are greatly demanded in this field. Specifically, with the rapid development of microscopic imaging technology, a wide range of scales of bio-images can be obtained, which is helpful for us to develop new methods and algorithms to meet the needs in neuroscience research [3], [4]. The reconstructed digital neuron structure, including axons and dendrites as well as thickness information, can be used in conjunction with electrophysiological simulations to determine the complex mechanisms of the nervous system [5], [6].

The computer-aided manual neuron reconstruction method was first proposed in 1965 and was achieved by a biologist using a microscope [7]. Following this milestone, numerous algorithms and open softwares were introduced to reduce manual labor consisting of the boring task of tracing and analysis [8]–[11], but most of them were still limited to semi-automation and required manual validation by experts to achieve accurate reconstruction of whole neurons. Hence, the lack of powerful and effective computational tools for automatically reconstructing neuron cells has emerged as a major technical bottleneck in neuroscience research. This problem motivated the DIgital reconstruction of Axonal and DEndritic Morphology (DIADEM) challenge [12] and BigNeuron project [13], [14], which began in 2010 and 2015 respectively. They provided an open-source platform for researchers from all over the world and aimed to promote the development of computer algorithms for reconstructing the full anatomy structure of neurons. The data sets from DIADEM are most widely used in the domain of neuron reconstruction to date. However, the BigNeuron proposed some new challenges for the further research in the field of neuron reconstruction.

Generally speaking, before the DIADEM project, the neuron tracing methods were categorized into several types: shortest path methods [15], [16], minimum spanning tree methods [17], [18], sequential tracing methods [19], [20], skeletonization methods [21], [22], neuromuscular projection fiber tracing methods [23]–[25] and active contour-based tracing methods [26]–[28]. Based on these methods, some new improved methods were proposed [29]. The DIADEM final listed five well-performed algorithms: the model-based method [30], geometry-based method[31], probabilistic approach-based method [32], open snake-based method [33] and cost minimization trees-based approach [34]. In the model-based method, Myers’s team employed the idea of shortest paths to refine local tracing, which is based on the model of Al-Kofahi [19] and a formal tube model. This pipeline can reconstruct the neuron from raw or preprocessed images[30]. In the geometry-based method, Erdogmus’s team introduced a principal curve to represent the skeleton of axons, and they then extracted the topology information using a recursive principal curve tracing method [31]. In the probabilistic approach-based method, Gonzalez’s team built a set of candidate trees to choose the best one by a global objective function, which combined geometric priors from image evidence [32]. In the open snake-based method, Roysam’s team proposed a three dimensional open curve snake model that was initiated automatically by a set of skeletons from binary images generated by the 2-D graph cut pre-segmentation method, and the snake curve could be stretched bi-directionally along the centerline to trace the neuron cell structure [33]. Stepanyants’s team proposed trees-based method, which can merge individual branches into trees based on a cost minimization strategy [34]. After the DIADEM final, Liu’s group proposed a 3D neuronal morphology reconstruction method based on the augmented ray burst sampling method [35]. This method consisted of a single step to achieve the tracing and reconstruction, in which the centerline extraction or the extra radius estimation was unnecessary but the first seed must be set artificially. Peng’s team proposed series of efficient methods for neuron reconstruction, such as an anisotropic path searching method [36], an all-path pruning method [37], a hierarchical-path pruning method based on a gray-weighted image distance-tree[38], an automatic distance-field neuron tracing method based on global threshold foreground extraction [39], a smart tracing method based on machine learning [40] and a method based on reverse mapping and assembling of 2D projections [41]. These methods can work well with the neuron center lines tracing under the complex and noisy background. Kakadiaris’s team proposed a learning 3D tubular models-based method, which can use a morphology-guided deformable model to extract the dendritic centerline and use minimum shape-cost tree to represent the neuron morphology [42]. In addition, to achieve more accurate neuron tracing results, some open source softwares have been developed, such as flNeuronTool [35], FarSight [33], V3D [10], and Vaa3D[43], [44]. Along with all the existing algorithms, these open source softwares also promote the development of neuron reconstruction.

Despite the large number of proposed neuron tracing algorithms mentioned above, few methods can automatically reconstruct the complete and detailed neuron morphology, including complex dendritic and axonal arbors and variable thickness information. Moreover, because of the limited computer power, the automatic and accurate reconstruction of neuron anatomy structure is still a significant challenge.

In this paper, we propose a new 3D seed detection method based on Sliding Volume Filter (SVF) to initialize our framework, and we designed an open curve snake model combined with a SVF external force for centerline extraction and tracing. This open curve snake model has higher efficiency in the convergence of endpoints and detection of branch collision. In addition, radius estimation is another critical problem in neuron reconstruction, and accurate radius estimation can benefit simulation and functional research. Hence, this paper also proposes a new radius estimation method based on a 2D sliding band to estimate the radius of a neuron. The proposed radius estimation method can fit the real edges of neuron non-circular cross-sections better than previous methods. Finally, a surface reconstruction method based on contour lines is adopted to reconstruct detailed neuron morphology.


As shown in Fig. 1, some critical steps, such as seeding, tracing, radius estimating and surface reconstruction, are included in the pipeline of our protocol. The details of every critical step will be explained.

thumbnailFig. 1. Pipeline of neuron anatomy structure reconstruction

Seed detection

Seed detection is a critical procedure in the open snake-based tracing protocol, and an ideal seed list can ensure tracing accuracy. The proposed seeding method includes the following two stages:

(1) We used the proposed SVF based method to select coarse seeding points in the interior of neuron cells.

(2) The ridge criterion was used to achieve the further filter to obtain better seeding points, which are always near the center of the neuron cell.

A. SVF-Based seeding

In the field of computer vision and image processing, the convex region is defined as follows:

a) A rounded convex region is a region with higher intensity in the center than the edge, and the gradient vectors of this region point to its center.

b) A tube-like convex region is a region with higher intensity along its centerline than the edge, and the gradient vectors point to the centerline from the edge.

Quelhas’s group proposed a 2D Sliding Band Filter (SBF) for cell nucleus detection based on the characteristic of a rounded convex region [45]. In the data sets of microscopic imaging, a 3D neuron cell has not only a tube-like convex region but also a non-circular cross-section. Given these two characteristics, we extended the SBF into 3D space and designed a Sliding Volume Filter (SVF) to enhance the tube-like convex region for seed detection of neuron volume data.

To explain the calculation of SVF, we first explained the Voxel Convergence Index (VCI). As shown in Fig. 2a, O is an interested voxel in 3D volume datasets with its coordinate located at (x, y, z). A sphere support region R is located around the center O, and P are the voxels in the support regionR except at O, whose coordinate is (i, j, k). ϕ(i, j, k) is the angle between PO and the gradient vector direction. The VCI of P is defined as follows:

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>


thumbnailFig. 2. Scheme of Spatial Convergence Index. a The model of 3D spatial convergence index. b The model of 3D sliding volume filter in y-z plate section. c the discretization calculation of SVF using the polar coordinates

Figure 2b and c show the calculation scheme of the sliding volume filter in a support region Rwhose radius is rad. To finish the discretization computation efficiently, the polar coordinate is introduced into this scheme, and the SVF is calculated as

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>



<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>


where M is the number of support region lines radiating from the center pixel O(x, y, z), ρ denotes the radial coordinate, a and b stand for the angular coordinates, d is the thickness of sliding volume, r is the center position of the sliding volume in the support region line ranging from R minto R max , Q is the points between [r−d/2,r + d/2], and φ(qx ρ , qy ρ , qz ρ ) is the angle between the gradient vector at Q and the direction of QO. Additionally, the angles a ∈ [0, 2π] and b ∈ [0, π] are divided into 2 L parts and L parts, respectively. Thus, M = 2 L 2 . Specially, the number of parts of L determines the accuracy and efficiency of computation.

After the SVF was applied to the neuron volume data for seed detection voxel by voxel, we selected the voxels as the raw seeds whose SVF response values are higher than the threshold T. Notably, there are more gradient vectors that point to the center of a tube-like structure in the marginal regions than in the other regions [45]. Hence, the sliding volumes of support regions of interior points are more likely to converge in the marginal regions. As shown in Fig. 3a, the voxelA in the interior of the nerve is more likely to be selected as a raw seed than the external voxel B. Because A has a higher SVF response value than voxel B, the orientations of gradient vectors in the sliding volumes of support region of A are more likely to point to A. However, the orientations of gradient vectors in the sliding volumes of the support region of B are not consistent and sometimes point away from B. Moreover, a nerve cell is not a uniform tube-like structure but instead has variable thickness. Therefore, SVF is the proper filter for raw seed selection.

thumbnailFig. 3. Scheme of computation of Sliding Volume Filter, as well as the selection of seed points. a The procedure of seed points filtrate, after the SVF and ridge criterion, proper seed points are chosen. b The seeding points selection after step1 and step2

B. Ridge criterion

The Aylward’s ridge criterion method was applied to the raw seeds for the final seed choice [20]. As shown in Fig. 3a, I is the volume data set, ∇I(p) is the gradient vector at voxel p with its coordinate (x, y, z) in I, and ev1 , ev2 and ev3 are the eigenvectors computed from Hessian matrix of I. ev1 (p) is the principle direction along the center lines of the tube-like structure, and ev2 (p) and ev3 (p) are the other two orthogonal eigenvectors. The seeds near the center of the tube-like structcure meet the condition of Eq. 4.

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>


The raw seeding points were further chosen according to the ridge criterion Eq. 4. As shown in Fig. 3b, after the steps of SVF and ridge criterion, the proper seeds near the center line of the nerve are chosen and included in the seed list, in which the seed points are sorted by the response values. Simultaneously, response values from SVF are used to enhance the intensity of voxels in the original data, which benefits the deformation of the open curve model in the following step. The SVF volume enhancement method is denoted as

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>


where I SVF (p) is the intensity of point p after SVF enhancement, I(p) is the intensity of point pbefore SVF enhancement, and SVF(p) is the SVF value of point p.

SEF-OCS Neuron tracing

Tracing the full neuron skeleton is still a challenging task in neuron science, although many methods have been proposed. In this section, a new tracing model is proposed called an SVF external force open curve snake (SEF-OCS, SEF-Open Curve Snake). The open curve snake model was initially applied to automated actin filament segmentation and tracking [33], [46]. Extended the application of the open curve snake model to neuron tracing. However, the computation was tedious in the tracing framework of [33], especially in the step of branch detection. The proposed SEF-OCS includes three parts: open curve deformation, curve extension, and collision detection.

A. Open curve deformation

This model is a parametric open curve model, and the total snake energy can be defined as

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>


E Total is the total image energy combined with internal energy and external energy. This model is a traditional deformable model, which resembles previous work in [16]. The open snake model is a parametric curve, c(s) = (x(s), y(s), z(s)), s∈[0, 1], and the snake internal and external energy are defined as follows:

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>


<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>



<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>

In Eq. 7, α and β are the “elasticity coefficient” and “stiffness coefficient”, respectively, in internal energy, and they can control the regularity of the curve in the process of evolution. In Eq. 8, the external energy term is used to make the snake deform near the center line of the neuron and stretch the endpoints to the tail of the neuron. ∇E im is the negative normalized Gradient Vector Flow (GVF) of the volume data enhanced by SVF, p signifies point (x(s), y(s), z(s)) on the open curve, and I SVF is the volume after SVF enhancement in this paper. Instead of the original 3D image GVF, we calculated the GVF of I SVF . The SVF can enhance the tube-like convex region to smooth the GVF. As shown in Fig. 4a, the blue arrows show examples of gradient vectors from the volume enhanced by SVF. The vectors point toward the centers of neurons, which can make the seed points (the yellow points in Fig. 4a) move to the center position (the position of the red points in Fig. 4a). Specifically, the stretching force ∇E str (c(s)) is only implemented to the final endpointsc (0) and c (1). The cs (s)/||cs (s)|| denotes the direction of the stretching force. The value <a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a> is used to measure the tube-like level around the end point. When a curve reaches the end of a neuron, the end points will lose the tube-like characteristic. Hence, ∇Estr (c(s)) approaches zero, and the open active curve converges. According to a large number of experiments, this strategy is not only efficient and reliable but also can avoid the leakage of the neuron boundary. To minimize the energy function E Total , the points on the snake curves are updated as:

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>


thumbnailFig. 4. Scheme of SEF open curve snake model. a The open curve is driven to the center of neuron by external force in the volume after SVF.b The procedure of open snake curve extension and collision detection in the branching region

where the parameters t and γ control the iteration numbers and size of the step at each iteration, respectively. The iterations are stopped when t reaches the threshold of the max iteration number.

B. Curve extension

The initial open snake curve is formed by three points (fewer than three points will not be traced as a branch of neuron). The first point p has the best response value in the seed list, and the other two points are generated by extending along the first principal direction to ev1 (p) and −ev1 (p). As shown in Fig. 4b, along with the open snake curve moving to the center of neuron, it also extends toward the two inverse tangential directions, cs (p0 ) and −cs (p1 ), in which the p0 and p1 are the two temporary endpoints. During the procedure of extension, the seed points belonging to one curve were labelled with new values (the default value is zero) in accord with the ID of the curve. For example, in Fig. 4b the yellow points and green points belong to different curves.

C. Collision detection

Neurons have many branches, especially in the dendrite region. Hence, detecting branching points and handling collision are essential. In the proposed scheme, two types of collision exist in the collision region and are shown in Fig. 4b. The first collision is branching point collision, which occurs when the open snakes reach a seed point whose value is not zero, and this point is recorded as the branching point (pink point in Fig. 4b). This branching point detection strategy is based on labelling seeds and is highly efficient. It also can handle the second type of collision, contour lines collision. The contour lines coming from the following step of radius estimation are the foundation of neuroanatomy reconstruction. However, due to the ambiguity of radius estimation in the collision region, the contour lines from two curves easily intersect. In Fig. 4b, this situation is illustrated in the imaginary pink circle and the embedded image, which is an experimental result in the branching region. This collision will influence the accuracy of the following reconstruction algorithm. Hence, a backoff strategy is proposed to avoid contour line collision. First, radius estimation in the branching points will not be executed. Second, if an extending curve reaches the branching point, it will be cut back the length of D, which is usually set as double the average estimated radius of the current curve.

In other words, the imaginary pink circle is not necessary in radius estimation because the following reconstruction algorithm would interpolate the information using triangular meshes automatically. Finally, the tracing algorithm ends when all the seed points are traversed.

The entire tracing algorithm procedure is shown as follows:

In summary, compared to the open snake method in [33], we improved this model in the following three aspects. First, the volume after SVF enhancement has more straightforward gradient vectors, which point to the center line of the neuron and can be used in driving the initial lines to the center of the neuron. Second, the proposed method can cut down the computation of the stretching force of end nodes. Third, in the step of collision detection, compared to the method based on labelling neighbor voxels, the method based on labelling seeds has higher detection efficiency and benefits the following reconstruction procedure.

Radius estimation

Radius estimation is another critical task in neuron anatomy reconstruction, and it can provide more quantitative information for neuroscience research. Peng, Aylward, and Wang had proposed some radius estimation methods [16], [20], [33], but most of them are based on the assumption that the neuron have a uniform tube-like structure, whose cross-sections are regular circles. However, the real cross-sections are not regular circles, as shown in the embedded image of Fig. 5. To reconstruct the neuron morphological structure more accurately, fitting the real edge of the neuron cell is achieved by a new proposed radius estimation method based on a 2D Sliding Band Filter (SBF) [45]. The SBF can converge on the real edge of a neuron cross-section that has the rounded convex region in [45].

thumbnailFig. 5. Illustration of radius estimation of the neuron cross-section. The left embedded image shows the real cross-section of neuron, and the estimation result with different parameters. And in the right image v 1 is the tangential direction in S i , v 2 and v 3 are the orthogonal vectors which define the cross-section

Figure 5 shows the scheme of the radius estimation method based on a 2D sliding band filter. We could obtain the cross-section according to the normal vector v 1 , which points to the tangential direction of the open curve. Additionally, the v 2 and v 3 are the orthogonals in the cross-section. To obtain an accurate estimation of neuron cross-section, n radiuses radiating from the center point S on the snake curve are estimated as different lengths. The radius lengths are equal to r in the Eq. 10 with the maximum SBF response value.

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>



<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>

where B is the boundary points on the cross-section, which are at the centers of sliding bands and will be used to fit the real edge. (x rn , y rn , z rn ) are the spatial coordinates of S. The computation method of SCI in point P, which is in the range of [r−d/2, r + d/2], has been introduced in Eq. 4. dis the width of the sliding band, r is the distance between B and the center point S, and it can slide in the range of [R min , R max ] to obtain the optimal position of B with the maximum SBF response value. The boundary points B can be connected clockwise to fit the edge of the neuron cell.

In the proposed method, the parameter n is related to the accuracy of radius estimation. As shown in the embedded image of Fig. 5, the larger n is, the more accurate the cross-section fitting will be. However, considering the efficiency and accuracy in the actual application, the parameter n should be adjusted flexibly.

Neuron surface reconstruction

Most of the traditional neuron reconstruction methods were based on the fast marching method and some supplemental processes for connecting different fragments [33], [47]. However, in this paper, Liu’s non-parallel contour lines surface reconstruction method is employed for surface reconstruction [48], considering the non-parallel characteristic of circles generated from previous steps. On the premise of an accurate description of the entire neuron anatomy structure, this method is efficient. Although this method had been widely used in other biological models, it has rarely been used in neuron model reconstruction. The generated mesh model of the neuron can benefit the future finite element mesh subdivision and simulation.

Figure 6 shows the scheme of Liu’s method. First, it constructs medial axes (MA) between adjacent contour lines (Fig. 6a). Second, the points and lines from different contours are projected on the MA (Fig. 6b). Third, triangular meshes are used to connect the curve networks to their projection points on the MA (Fig. 6c). Finally, the surface meshes, which are connected with different contour lines, are formed as the boundaries between neighboring compartments [48]. To obtain a smoother neuron surface, we use a surface diffusion smoothing algorithm to minimize the curvature of the model surface to obtain a smooth 3D model [49]. As shown in Fig. 6, the initial neuron surface (Fig. 6e) is formed by contour lines (Fig. 6d) which are obtained by radius estimation, and the final smooth neuron surface is shown in Fig. 6f. In addition, the most outstanding advantage of Liu’s method is that it can automatically handle branch reconstruction, especially of circles without intersections in the branching region (the intersection problem was resolved through removing the collisions of circles in the SEF-OCS neuron tracing step). As shown in Fig. 7, in the branching region, two branches could be automatically reconstructed with different label colors.

thumbnailFig. 6. Process of contour reconstruction. a Construction of projective plate MA [48]. b Projection of points and lines on MA [48]. c Triangulation of adjacent contour lines [48]. d The contour lines of neuron cell. e The initial surface from triangulation of adjacent contour lines. f The final surface model after smoothing

thumbnailFig. 7. Process of neuron reconstruction in branching region. a The input branching data from [48]. b The reconstruction result of input data of (a).c The input data of neuron contour lines. d The reconstruction result of (c), in which different branches are labelled by different colors

Results and discussion


We validated and evaluated the steps of seeding, tracing, radius estimation and neuron reconstruction in the proposed method using synthetic data and real data from the DIADEM challenge [12] and parts of datasets from BigNeuron project [13], [14]. All of the experiments were performed on an ordinary computer (Intel Core i5 3.2 CPU, NVIDIA GeForce GTX 960, 8 GB RAM, Windows 7). The proposed algorithm was developed using C++ language. In addition, to compare the other methods equally, we did not adopt any manual interactive operations shown in the Fig. 1, such as preprocessing, picking and expending seeds, checking and validating data, tracing editing, branch refining, and rooting setting, although these operation can improve the result of neuron tracing.

According to the image data sets (DIADEM challenge and BigNeuron project) chosen in this paper, Table 1 shows the parameters selected for the following experiments. Some experimental parameters such as d, T, L, γ, ɑ, and β remain constant for the following experiments. Other parameters such as rad, R max , R min , n and t could be adjusted for optimal results.

Table 1. Parameter selection


Generally speaking, an ideal seed point is located in the neuron body of the foreground, and its position is near the center of the neuron cell’s cross-section. To quantify the performance of SVF seed detection, we evaluated the seeding method using an artificial helix dataset and the real dataset of the DIADEM challenge [12], according to following point deviation measurement principle:

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>


where P g denotes point sets in the gold standard, P s denotes point sets generated by tested methods and N is the number of points in P s . d min is the distance between a seeding point and its nearest point in the gold standard.

We compared the proposed seeding methods with the two most widely used seeding point detection methods, the global threshold method [18] and the LoG threshold method [50]. We set the parameters rad = 20, R max= 16, and R min= 5 in this experiments.

In the proposed SVF filter method, compared with two other methods, most of the seed points are in the interior of the neuron body. Table 2 shows the seed deviation results of the artificial helix body and OP_1, in which the SVF seeding method can achieve the lowest deviation.

Table 2. Comparisons of different seeding methods on test datasets

As shown in Fig. 8, some seeds fall outside of the artificial helix body, which are detected by traditional threshold methods and highlighted with arrows. Figure 9 shows the seeding results in drosophila olfactory axonal datasets (OP_1 of DIADEM challenge), which are generated by the three mentioned methods. These results also suggest that our method is better than the two other seed detection methods.

thumbnailFig. 8. Comparison of seeding method on test dataset; a The seeds detection result of global threshold method. b The seeds detection result of LoG threshold method. c The seeds detection result of SVF method

thumbnailFig. 9. Comparison of seeding method on drosophila olfactory axonal data sets (OP_1). a The seeds detection result of global threshold method. b The seeds detection result of LoG threshold method. c The seeds detection result of SVF method and the enlargement image in intensive region of dendrites

We also compared the enhancement results from the three methods, and we chose the same cross-section of the neuron volume image to demonstrate that the SVF seeding method can enhance the region around the center line and simultaneously save the contrast information of the tube-like volume. The results are shown in Fig. 10, in which the red part has a higher response value than the blue part. This result suggests that the SVF method can extract the center region better than the other two methods and can enhance the original volume data properly. Furthermore, the better results in both seed detection and SVF volume enhancement can benefit neuron tracing.

thumbnailFig. 10. Comparisons among seeding methods for image enhancement. The results of global threshold method lose the contrast information between center and edge; The results of LoG threshold method extend the center region exorbitantly; The results of SVF seeding method can enhance the original volume properly

Skeleton tracing

A. Tracing accuracy

We adopted the drosophila olfactory axonal datasets (OP_1 to OP_9 from DIADEM challenge) and neocortical layer 1 axons subset 1 datasets (NC_1 to NC_6 from DIADEM challenge) to evaluate the performance of the proposed neuron skeleton tracing method in the term of accuracy. Meanwhile, we compared the SEF-OCS tracing method with some start-of-the-art algorithms, such as the Open Curve Snake tracing method (OCS) [33], Neural Circuit Tracer method (NCT) [34], all-path pruning method (APP) [37], improved all-path pruning method (APP2) [38], distance-field based method (DF) [39], and 3D tubular models based method (TM) [42].

To compare with these methods fairly, we conducted the experiments without any manual interactions and corrections, using the widely used accuracy principle to measure the test results. Similarly, we set the parameters rad = 20, R max= 16, R min= 5, and t = 10 in the proposed method, and we chose the better parameters for other six methods according to the features of different datasets. The measured principle is defined as:

<a onClick="popup('','MathML',630,470);return false;" target="_blank" href="">View MathML</a>


where Precision is measured as the proportion of the length of correct traces to the total length of the traces generated by the tested methods, and Recall is the proportion of the length of correct traces to the length of the gold standard of adopted datasets.

Figure 11a and b show the reconstruction results of OP_1, and Fig. 11c and d show the results of OP_4. All of these results were generated by our proposed SEF-Open curve snake method. To illustrate the higher performance of our proposed method, we choose different colors to indicate the differences; the blue lines are the gold standard, and the green lines are the skeleton reconstructed by our method. Additionally, more tracing results of the other datasets are shown in the Additional file 1.

thumbnailFig. 11. The tracing results of OP_1 and OP_4. a The tracing result of OP_1 from multi-view. b The magnified result of branch parts of OP_1. cThe tracing result of OP_4 from multi-view; d The magnified result of branch parts of OP_4

Table 3 summarizes the comparisons between the OCS and other six methods in terms of precision and recall. We can see that SEF-OCS is far better than other six methods in most datasets in terms of accuracy and recall. In addition, the SEF-OCS outperforms other six methods in average accuracy and recall. We also conducted the DIADEM score test [51] to evaluate the proposed method in the precision of reconstructed neuron topology and compare with the other methods. To the best of our knowledge, due to the various features of different datasets, no methods can get higher DIADEM score in all the datasets automatically. Hence a box plot is adopted to show the DIADEM score distribution of different methods tested in the different datasets. As we can see from Fig. 12, our method can achieve higher DIADEM score in most of the tested datasets and outperforms other six methods in average value (0.87 ± 0.001), median (0.86) and minimum (0.81). This results also proved that the proposed method has higher stability. In order to evaluate the automaticity of the proposed method, we used fixed parameters to test our method in this paper. Actually, The changed parameters can also be tried to get more meaningful tracing results. For instance, when the neuron data includes a big cell body, the bigger radparameter is needed. Additionally, some other methods also can be tried to trace neuron according to the features of different neuron cells. For example, the APP, APP2 and DF methods can achieve better results sometimes.

Table 3. Comparisons among different methods with different image datasets in tracing accuracy

thumbnailFig. 12. The box plot of DIADEM scores of the different methods for different datasets (OP_1 to OP_9 and NC_1 to NC_6 datasets). The median is the middle pink bar. The box indicates the lower quartile (splits 25 % of lowest data) and the upper quartile (splits 75 % of highest data). The red bar and blue bar are the maximum and minimum values. The blue diamond denotes the mean value of the scores

B. Tracing robustness

To verify the robustness of our method, we designed three kinds of experiments. Firstly, the datasets with different levels of signal attenuation were tested. Secondly, the datasets with deferent levels of Gaussian noise were tested. Thirdly, the datasets (checked6_frog_scrippts, checked6_human_culturedcell_Cambridge_in_vitro_confocal_GFP,checked6_human_allen_confocal and checked6_fruitfly_larvae_gmu) from BigNeuron project were also tested using the proposed method.

Firstly, we compared the length of the traced skeleton with OCS in handling image signal reduction. Compared with the traditional robustness test method, which always added Gaussian noise to the original volume, this paper’s test method has a special meaning. Unbalanced light will lead to different levels of signal attenuation in the process of capturing images from a microscope. The OP_1 data set is chosen as an example, and we reduced the image signal from 10 % to 40 %. The content of the neuron images with different degrees of reduction is shown in Fig. 13. In Fig. 14, we list the lengths of the neuron skeleton traced by the two methods for comparison. With the image information reducing from 10 % to 40 %, our method can trace more information than the open curve snake in skeleton length. This result conveys that our method has higher robustness upon image signal reduction. All these results suggest that the proposed method performs better than the OCS method.

thumbnailFig. 13. Comparison in signal removed image datasets. To achieve more clear comparison, we follow the same experiment design in [16]

thumbnailFig. 14. Changes of skeleton length with signal reduction (Unit: Volex)

Secondly, we tested the robustness of our method using the datasets with different levels of Gaussian noise (The mean is 0 and the variances are 0.01, 0.02, 0.03 and 0.04 respectively.). As we can see from Fig. 15, the blue lines represent the tracing results of the proposed method. The tracing results are tolerable even when the variance is 0.04 and the major branches of neurons are not missed.

thumbnailFig. 15. The tracing results of NC_2 dataset with different levels of Gaussian noise. a The dataset with a variance of 0.01. b The dataset with a variance of 0.02. c The dataset with a variance of 0.03. d The dataset with a variance of 0.04. To prove the robustness of our method clearly, we follow the similar design of experiment in [39]

Thirdly, the datasets from the BigNeuron project were also tested. To my best knowledge, the BigNeuron will be a new trend in this field and most of datasets are challenging. The Fig. 16 shows some tracing results of the datasets of the BigNeuron project. Similarly, the blue lines represent the tracing results of our method. The results are tolerable even these datasets are complex and sometimes include a big cell body (In the Fig. 16, the big cell body is highlighted using the red rectangle). However, rad parameter must be set bigger (we set the parameters rad = 35, Rmax = 31, Rmin = 5, and t = 10 in the proposed method) to get better results when the datasets contain a big cell body. Additionally, the APP2 from Vaa3D [43], [44] can also achieve good results automatically when a big cell body exists in the datasets.

thumbnailFig. 16. The tracing results of the datasets from the BigNeuron project. aThe tracing result of the done_err_Recon112012no2-2 data of checked6_frog_scrippts. b The tracing result of image 7 data of checked6_human_culturedcell_Cambridge_in_vitro_confocal_GFP. c The tracing result of in_house1 data of checked6_human_allen_confocal. dThe tracing result of done_1_CL-I_X_OREGON_R_ddaD_membrane-GFP data of checked6_fruitfly_larvae_gmu

Radius estimation and surface reconstruction

Adaptive radius estimation and surface reconstruction methods can improve the neuron model, which has branches of varying widths. In radius estimation, the proposed method can fit the edge of the cross-section of the neuron cell. Furthermore, the credibility of radius estimation can be adjusted by the parameter n. As we can see from Fig. 5, the higher parameter n, the greater the credibility of radius estimation and the higher the computation intensity. Though a higher credibility of radius estimation must be achieved by higher computation intensity, the proposed method has solved the non-circular radius estimation problem mentioned in [33]. Generally, the parameter n = 16 is sufficient for most applications. Hence, we set n = 16 in the efficiency comparison experiments. Figure 17b shows the radius estimation results of the entire neuron using the proposed method based on the skeleton of the original volume, which is shown in Fig. 17a.

thumbnailFig. 17. Radius estimation and anatomical reconstruction of OP_1. a The original result by volume rendering method. b The result of radius estimation by 2D sliding band method, in which the blue contour lines are used to fit the edges of neuron cross-sections. c The anatomical reconstruction result based on the contour lines, in which the different branches are labelled with different colors

The adopted reconstruction method can interpolate meshes based on contour lines from radius estimation and can also handle branching reconstruction problems efficiently. To illustrate the performance of the proposed framework, the reconstruction result of the most complex data (OP_1) in the OP series data sets is shown in Fig. 17. The morphology of a reconstructed neuron cell of OP_1 was obtained, and the different branches were labelled with different colors during reconstruction.

Computational efficiency

In the term of automatic computation efficiency, we tested every step of the proposed pipeline and compared with other methods to evaluate the 3D neuron reconstruction efficiency. In addition, we set the parameters rad = 20, Rmax = 16, Rmin = 5, and t = 10 in the proposed method.

As we can see from Table 4, the proposed method is more efficient than the OCS framework, especially in the seeding step because seeding in the OCS framework is based on a complex procedure including graph-cut segmentation and skeleton and seeding point selection. In contrast, our seeding method is more concise and efficient. The higher tracing efficiency also demonstrated the improvements in stretching force in the open snake model and the collision point detection strategy. Additionally, Table 4 also shows that the radius estimation and reconstruction are more efficient in the SEF-OCS framework than in the OCS framework. These results prove that the proposed radius estimation and surface reconstruction methods outperform the corresponding methods in the OCS framework.

Table 4. Comparisons with OCS method in the efficiency of the proposed pipeline

To test the ability of parallel computation, we also developed our CUDA implementation for the four main steps. The computation time is shown in the Table 4. We can see that the proposed method is faster than OCS method in the same parallel environment. In addition, the average speedup ratio of SEF-OCS can achieve 63.94, which is higher than OCS’s average speedup ratio 48.8. This also demonstrates that the proposed method has higher parallel computation ability.

Table 5 shows the efficiency comparison results with some other methods from Table 3. Actually, most of neuron reconstruction methods don’t contain the surface reconstruction procedure. Hence, we conducted the comparison experiments in another way. We cut down the computational cost of the surface reconstruction step in order to carry out the comparison among different methods fairly. What’s more, the experiments are conducted three times for every method corresponding to every data set to avoid the errors from the operation system environment. The average results of three times are shown in Table 5. The comparison results show that the proposed method achieve the lowest average computational cost. We also can see that the computational cost of our method mainly depend on the size of the data sets and will realize higher efficiency with the development of computation parallel capacity.

Table 5. Comparisons in the efficiency of neuron tracing


Neuron cell anatomy structure reconstruction plays a very important role in the field of neurology. In this paper, we have developed a new neuron tracing framework, which is based on a sliding filter. We improved every step of the traditional framework compared to the OCS framework. First, given a non-circular cross-section of a neuron, the sliding filter method was introduced to the proposed seeding method (SVF) and radius estimation method (SBF), which is critical for accurately tracing skeletons and reconstructing real morphology. Second, on the basis of better seeding results, the traditional open curve snake model was improved by introducing a new external force to aid the curve evolution for neuron skeleton tracing and a new strategy for collision detection. Finally, a surface reconstruction method based on contour lines was used to generate whole neuron morphology.

A series of experiments have proved that the proposed framework has higher efficiency, stability and robustness in tracing accuracy. In addition, the proposed estimation method and adopted neuron reconstruction method can obtain more accurate neuron morphology, which is meaningful for future works such as simulation and analysis of neuron function in the field of neuroscience research.

Availability of supporting data

The source code can be available in the website [52]. The OP and NC datasets come from DIADEM challenge project, it can be downloaded from [53]. The checked6_frog_scrippts datasets, the checked6_human_culturedcell_Cambridge_in_vitro_confocal_GFP datasets, the checked6_human_allen_confocal datasets and the checked6_fruitfly_larvae_gmu datasets are available in the BigNeuron project whose website is [54].


VCI: Voxel Convergence Index

SBF: Sliding Band Filter

SVF: Sliding Volume Filter

SEF-OCS: SVF external force open curve snake

OCS: Open Curve Snake

MA: Medial axes

NCT: Neural Circuit Tracer method

APP: All-path pruning method

APP2: Improved all-path pruning method

DF: Distance-field based method

TM: 3D tubular models based method

S: Seeding

T: Tracing

RE: Radius estimation

SR: Surface reconstruction

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

GL and DS carried out the program of the neuron tracing and reconstruction and finish the work of writing the draft, KW designed the workflow of neuron cell tracing and reconstruction pipeline. JC provided the suggestion of manuscript revision and pipeline optimization. All authors read and approved the final manuscript.

Additional file

Additional file 1:. This document includes additional figures not included in the paper. Some other tracing results are shown in the supplementary material (12 figures). S1-S7 are some tracing results of BigNeuron datasets. S8-S9 are some other tracing results of NC datasets. S10-S12 are some other tracing results of OP datasets. (PDF 1439 kb)

Format: PDF Size: 1.4MB Download file

This file can be viewed with: Adobe Acrobat ReaderOpen Data


This work was supported by the Incheon National University International Cooperative Research Grant in 2013. Our deepest gratitude goes to the anonymous reviewers for their careful work and constructive suggestions that have helped us to improve this paper substantially. We also appreciate the support of softwares and datasets from the team of the BigNeuron project.


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Oscillometric measurement of systolic and diastolic blood pressures validated in a physiologic mathematical model

Charles F Babbs

Correspondence: Charles F Babbs

Author Affiliations
Department of Basic Medical Sciences, Weldon School of Biomedical Engineering, Purdue University, 1426 Lynn Hall, West Lafayette, IN, 47907-1246, USA

BioMedical Engineering OnLine 2012, 11:56 doi:10.1186/1475-925X-11-56

The electronic version of this article is the complete one and can be found online at:

Received: 28 June 2012
Accepted: 3 August 2012
Published: 22 August 2012
© 2012 Babbs; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Formula display:
The oscillometric method of measuring blood pressure with an automated cuff yields valid estimates of mean pressure but questionable estimates of systolic and diastolic pressures. Existing algorithms are sensitive to differences in pulse pressure and artery stiffness. Some are closely guarded trade secrets. Accurate extraction of systolic and diastolic pressures from the envelope of cuff pressure oscillations remains an open problem in biomedical engineering.

A new analysis of relevant anatomy, physiology and physics reveals the mechanisms underlying the production of cuff pressure oscillations as well as a way to extract systolic and diastolic pressures from the envelope of oscillations in any individual subject. Stiffness characteristics of the compressed artery segment can be extracted from the envelope shape to create an individualized mathematical model. The model is tested with a matrix of possible systolic and diastolic pressure values, and the minimum least squares difference between observed and predicted envelope functions indicates the best fit choices of systolic and diastolic pressure within the test matrix.

The model reproduces realistic cuff pressure oscillations. The regression procedure extracts systolic and diastolic pressures accurately in the face of varying pulse pressure and arterial stiffness. The root mean squared error in extracted systolic and diastolic pressures over a range of challenging test scenarios is 0.3 mmHg.

A new algorithm based on physics and physiology allows accurate extraction of systolic and diastolic pressures from cuff pressure oscillations in a way that can be validated, criticized, and updated in the public domain.

The ejection of blood from the left ventricle of the heart into the aorta produces pulsatile blood pressure in arteries. Systolic blood pressure is the maximum pulsatile pressure and diastolic pressure is the minimum pulsatile pressure in the arteries, the minimum occurring just before the next ventricular contraction. Normal systolic/diastolic values are near 120/80 mmHg. Normal mean arterial pressure is about 95 mmHg [1].

Blood pressure is measured noninvasively by occluding a major artery (typically the brachial artery in the arm) with an external pneumatic cuff. When the pressure in the cuff is higher than the blood pressure inside the artery, the artery collapses. As the pressure in the external cuff is slowly decreased by venting through a bleed valve, cuff pressure drops below systolic blood pressure, and blood will begin to spurt through the artery. These spurts cause the artery in the cuffed region to expand with each pulse and also cause the famous characteristic sounds called Korotkoff sounds. The pressure in the cuff when blood first passes through the cuffed region of the artery is an estimate of systolic pressure. The pressure in the cuff when blood first starts to flow continuously is an estimate of diastolic pressure. There are several ways to detect pulsatile blood flow as the cuff is deflated: palpation, auscultation over the artery with a stethoscope to hear the Korotkoff sounds, and recording cuff pressure oscillations. These correspond to the three main techniques for measuring blood pressure using a cuff [2].

In the palpatory method the appearance of a distal pulse indicates that cuff pressure has just fallen below systolic arterial pressure. In the auscultatory method the appearance of the Korotkoff sounds similarly denotes systolic pressure, and disappearance or muffling of the sounds denotes diastolic pressure. In the oscillometric method the cuff pressure is high pass filtered to extract the small oscillations at the cardiac frequency and the envelope of these oscillations is computed, for example as the area obtained by integrating each pulse [3]. These oscillations in cuff pressure increase in amplitude as cuff pressure falls between systolic and mean arterial pressure. The oscillations then decrease in amplitude as cuff pressure falls below mean arterial pressure. The corresponding oscillation envelope function is interpreted by computer aided analysis to extract estimates of blood pressure.

The point of maximal oscillations corresponds closely to mean arterial pressure [4-6]. Points on the envelope corresponding to systolic and diastolic pressure, however, are less well established. Frequently a version of the maximum amplitude algorithm [7] is used to estimate systolic and diastolic pressure values. The point of maximal oscillations is used to divide the envelope into rising and falling phases. Then characteristic ratios or fractions of the peak amplitude are used to find points corresponding to systolic pressure on the rising phase of the envelope and to diastolic pressure on the falling phase of the envelope.

The characteristic ratios (also known as oscillation ratios or systolic and diastolic detection ratios [8]) have been obtained experimentally by measuring cuff oscillation amplitudes at independently determined systolic or diastolic points, divided by the maximum cuff oscillation amplitude. The systolic point is found at about 50% of the peak height on the rising phase of the envelope. The diastolic point is found at about 70 percent of the peak height on the falling phase of the envelope [7]. These empirical ratios are sensitive however to changes in physiological conditions, including most importantly the pulse pressure (systolic minus diastolic blood pressure) and the degree of arterial stiffness [9,10]. Moreover, a rational physical explanation for any particular ratio has been lacking. Since cuff pressure oscillations continue when cuff pressure falls beneath diastolic blood pressure, the endpoint for diastolic pressure is indistinct. Most practical algorithms used in commercially available devices are closely guarded trade secrets that are not subject to independent critique and validation. Hence the best way to determine systolic and diastolic arterial pressures from cuff pressure oscillations remains an open scientific problem.

The present study addresses this problem with a new approach based upon the underlying physics, anatomy, and physiology. This task requires modeling the cuff and arm and the dynamics of a partially occluded artery within the arm during cuff deflation. A second phase of the problem is the development of a regression procedure for analysis of recorded cuff pressure oscillations to extract model parameters and predict the unique systolic and diastolic pressure levels that would produce the observed cuff pressure oscillations.

Methods Part 1: Modeling cuff pressure oscillations
Model of the cuff and arm
As shown in Figure 1, one can regard the cuff as an air filled balloon of dimensions on the order of 30 cm x 10 cm x 1 cm, which is wrapped in a non-expanding fabric around the arm. After inflation the outer wall of the cuff becomes rigid and the compliance of the cuff is entirely due to the air it contains. During an oscillometric run the cuff is inflated to a pressure well above systolic, say 150 to 200 mmHg, and then vented gradually at a bleed rate of r = 3 mmHg / second [11]. Small oscillations in cuff pressure happen when the artery fills and empties with blood as cuff pressure passes between systolic and diastolic pressure in the artery.

thumbnailFigure 1. Arrangement of cuff, skin, muscle, bone, and artery for a simple model of the arm during oscillometric blood pressure recording.
Let P0 be the maximal inflation pressure of the cuff at the beginning of a run. The pressure is bled down slowly at rate, r mmHg/sec (about 3 mmHg/sec [11]). During the brief period of one heartbeat the amount of air inside the cuff is roughly constant. In addition to smooth cuff deflation, small cuff pressure oscillations are caused by pulsatile expansion of the artery and the corresponding compression of the air in the cuff. One can model the cuff as a pressure vessel having nearly fixed volume, V0 − ΔVa, where V0 is cuff volume between heartbeats and ΔVa is the small incremental volume of blood in the artery beneath the cuff as it expands with the arterial pulse.

To compute cuff pressure oscillations from the volume changes, ΔVa, in the occluded artery segment it is necessary to know the compliance of the cuff, C = ΔV/ΔP, which is obtainable from Boyle’s law as follows. Boyle’s law is PV = nRT, where P is the absolute pressure (760 mmHg plus cuff pressure with respect to atmospheric), V is the volume of air within the cuff, n is the number of moles of gas, R is the universal gas constant, and T is the absolute temperature. During the time of one heartbeat, n, R, and T are constants and n is roughly constant owing to the slow rate of cuff deflation. Hence to relate the change in cuff pressure, ΔP to the small change in cuff volume, ΔV, from artery expansion we may write PV≈(P+ΔP)⋅(V+ΔV)≈PV+PΔV+VΔP, for absolute cuff pressure P. So

0≈PΔV+VΔPandCcuff=−ΔVΔP=ΔVaΔP≈V0P. (1)
The negative change in cuff volume represents indentation by the expanding arm when the artery inside fills with blood. The effective cuff compliance, Ccuff , or more precisely the time-varying and pressure-varying dynamic compliance of the sealed air inside the cuff, is

Ccuff=dVcuffdP=V0P+760mmHg, (2)
with cuff pressure, P, expressed in normal clinical units of mmHg relative to atmospheric pressure. In turn, the time rate of change in cuff pressure is

dPdt=−r+1CcuffdVa(t)dt≅−r+(P0+760−rtV0)dVa(t)dt. (3)
In this problem as cuff pressure is slowly released, even as cuff volume remains nearly constant, the dynamic compliance of the cuff increases significantly and its stiffness decreases. Hence a suitably exact statement of the physics requires a differential equation (1a), rather than the constant compliance approximation P=P0−rt+Va(t)/Ccuff. However, equation (1a) may be integrated numerically to obtain a sufficiently exact representation of cuff pressure changes with superimposed cardiogenic oscillations.

Model of the artery segment
Next to characterize the time rate of volume expansion of the artery, dVa/dt, one can regard the artery as an elastic tube with a dynamic compliance, Ca, which varies with volume and with internal minus external pressure. The dynamic compliance Ca = dVa/d(Pa – Po), where Pt = Pa – Po is the transmural pressure or the difference between pressure inside the artery and outside the artery. Then

dVadt=dVad(Pa−Po)⋅d(Pa−Po)dt≅Ca(dPadt+r), (4)
where the artery “feels” the prevailing difference between internal blood pressure and external cuff pressure, neglecting the small cuff pressure oscillations. The time derivative of arterial pressure can be determined from a characteristic blood pressure waveform and the known rate, r, of cuff deflation. Hence, the crucial variable to be specified next is the dynamic arterial compliance, Ca.

Specifying the compliance of the artery is more difficult than specifying the cuff compliance, because the pressure across the artery wall during an oscillometric measurement varies over a wide range from negative to positive. Most research studies, such as the classical ones of Geddes and Posey [12], explore only positive distending pressures. A few sources however [9,13] describe pressure-volume functions like the one sketched in Figure 2 for arteries subjected to both positive and negative distending pressure.

thumbnailFigure 2. Hypothetical pressure-volume relationship for an artery including negative transmural pressures and collapse. Pc is collapse pressure, and Pmid is normal mid-level arterial pressur.
For classical biomaterials one can use two exponential functions to model the nonlinear volume vs. pressure relationship over a wide range of distending pressures. Here we shall use two exponential functions: one for negative pressure range and another for the positive pressure range in a manner similar to that described by Jeon et al. [13]. The first exponential function for negative transmural pressure, Pt<0, is easy to imagine. For the negative transmural pressure domain artery volume Va=Va0eaPt for positive constant, a, and zero pressure volume, Va0, of the artery. Here the dynamic compliance is clearly

dVadPt=aVa0eaPtfor Pt<0. (5)
For the positive transmural pressure domain one can use a similar, but decelerating exponential function [12]. However, there should be no discontinuity at the zero-transmural pressure point (0, V0). This means that for positive constant, b, and typically b < a,

dVadPt=aVa0e−bPtfor Pt≥0. (6)
These two exponential functions can be used to characterize the dynamic compliance of the artery model in terms of easily obtained data, including the collapse pressure, Pc < 0, defined as the pressure when the artery volume is reduced to 0.1Va0 (for example, Pc = −20 mmHg) and the normal pressure arterial compliance, Cn, measured at normal mid-level arterial pressure, Pmid , halfway between systolic and diastolic pressure.

Solving for constant, a, we have

a=ln(0.1)Pc. (7)
Solving for constant, b, we have

b=−ln(CnaVa0)Pmid. (8)
The zero pressure volume, Va0, can be known from anatomy if necessary, but as shown later is not needed if one is interested only in the relative amplitude of cuff pressure oscillations.

One can integrate the expressions (2a) and (2b) to obtain analytical volume versus pressure functions similar to Figure 2. Thus for Pt < 0

Va=Va0+aVa0∫Pt0eaPtdPt=Va0eaPt, (9)
and for Pt ≥ 0

Va=Va0+aVa0∫Pt0e−bPtdPt=Va0−abVa0(e−bPt−1)=Va0(1+ab(1−e−bPt)). (10)
Figure 3 shows a plot of the resulting pressure-volume curve for a normal 10-cm long artery segment and constants a and b as described for initial conditions below. The form of the function is quite reasonable and consistent with prior work [9,13]. When bi-exponential constants a and b are varied, a wide variety of shapes for the pressure-volume curve can be represented. When volume changes more rapidly with pressure, the artery is more compliant. When volume changes less rapidly with pressure, the artery is stiffer. Increasing a and b in proportion allows greater volume change for a given pressure change and represents a more compliant artery. Decreasing a and b in proportion reduces the volume change for a given pressure change and represents a stiffer artery. Increasing the ratio a/b represents a greater maximal distension. Decreasing the ratio a/b represents a smaller maximal distension.

thumbnailFigure 3. Representative volume vs. pressure curves for an artery segment over a wide range of positive and negative transmural pressure. Standard normal variables a = 0.11 mmHg-1 , b = 0.03/mmHg-1, Va0 = 0.3 ml. Variations in shape occur with combinations of increased (2x normal) and decreased (1/2 normal) values of parameters a and b.
Forcing function—the time domain blood pressure waveform
For proof of concept and validity testing one can use a Fourier series to represent blood pressure waveforms in these models [2]. A suitable and simple one for initial testing here is

Pa=DBP+0.5PP+0.36PP[sin(ωt)+12sin(2ωt)+14sin(3ωt)] (11)
for arterial pressure, Pa, as a function of time, t, with ω being the angular frequency of the heartbeat, that is ω = 2πf for cardiac frequency, f, in Hz. Here SBP is systolic blood pressure, DBP is diastolic blood pressure, and PP is pulse pressure (SBP − DBP). In turn, the derivative of the arterial pressure waveform is

dPadt=0.36ω[cos(ωt)+cos(2ωt)+34cos(3ωt)]. (12)
Combining the cuff compliance, pressure-volume functions for the artery, and the arterial pressure waveform, one can write a set of equations for the rate of change in cuff pressure during an oscillometric pressure measurement in terms of P0, r, Va, Ccuff, and time. We must work with the time derivative of cuff pressure, rather than absolute cuff pressure, because the compliance of the cuff and also the form of the artery volume vs. pressure function vary with time and pressure during a run. Cuff pressure can then be computed numerically by integrating equation (1a),

dPdt≅−r+(P0+760−rtV0)dVa(t)dt. (13)
Using the chain rule of calculus, and taking transmural pressure as arterial blood pressure minus cuff pressure,

dVadt=aVa0ea(Pa−P0+rt)⋅(dPadt+r)for Pa–P0+rt<0 (14)
dVadt=aVa0e−b(Pa−P0+rt)⋅(dPadt+r)for Pa–P0+rt≥0, (15)
with artery pressure, Pa, and its time derivative given by equations (5). Combining equations (1a) and (6) gives a precise model for cuff pressure oscillations.

Initial conditions
Artery dimensions: As a standard normal model consider a brachial artery with internal radius of 0.1 cm under zero distending pressure. The resting artery volume is Va0 = πr2L or

Va0=3.14⋅(0.1cm)2⋅10cm=0.3cm3. (16)
Stiffness constant a: For collapse to 10 percent at −20 mmHg transmural pressure we have

a=ln(0.1)Pc=−2.3−20mmHg=0.11mmHg−1. (17)
Stiffness constant b: It is easy to estimate the normal pressure compliance of the brachial artery in humans, Cn , from experiments using ultrasound. For example, using the data of Mai and Insana [14], the brachial artery strain (Δr/r) during a normal pulse is 4 percent for a blood pressure of 130/70 mmHg with pulse pressure 60 mmHg. In turn the volume of expansion during a pulse is 2πrΔrL, where r is the radius and L is the length of the compressed artery segment. Hence for a normal pressure radius of 0.2 cm the change in volume would be

ΔVa=6.28⋅0.2cm⋅0.04⋅0.2cm⋅10cm=0.10cm3. (18)
The normal pressure compliance for the artery segment is the volume change divided by pulse pressure or

Cn = 0.10 ml / 60 mmHg = 0.0016 ml/mmHg.

For normal artery the pressure halfway between systolic and diastolic pressure, Pmid , would be 100 mmHg, so

b=−ln(CnaVa0)Pmid=−ln⎛⎝⎜⎜⎜0.0016cm3mmHg0.11mmHg⋅0.3cm3⎞⎠⎟⎟⎟100mmHg=0.03mmHg−1. (19)
Jeon et al. [13] working with a similar model used a = 0.09 mmHg-1., b = 0.03 mmHg.

Numerical methods
In this model equations (1), (5), and (6) govern the evolution of cuff pressure as a function of time during cuff deflation. Equation (1) can be integrated numerically using techniques such as the simple Euler method coded in Microsoft Visual Basic, Matlab, or “C”. In the results that follow cuff deflation is started from a maximal level of 150 mmHg and continues over a period of 40 sec. Pressures are plotted every 1/20th second. To extract the small oscillations from the larger cuff pressure signal, as would be done in an automatic instrument by an analog high pass filter, cuff pressure at time, t, is subtracted from the average of pressures recorded between times t − Δt/2 and t + Δt/2 , where Δt is the period of the pulse. For simplicity, filtered oscillations are not computed for time points that are Δt/2 seconds from the beginning or from the end of the time domain sample.

Methods Part 2: Interpreting cuff pressure oscillations
Given this model and the associated insight into the physics of cuff pressure oscillations, one can also devise a scheme for estimating true systolic and diastolic blood pressures from an observed time domain record of cuff pressure and filtered cuff pressure oscillations. The method is based upon the ability, just described, to predict the amplitude of pulse pressure oscillations for a given diastolic pressure and pulse pressure and the ability to deduce exponential constants, a and b, from the rising and falling regions of the oscillation amplitude envelope. Details are as follows.

Artery motion during cuff deflation
The shape of the volume vs. pressure curve for arteries determines the driving signal for cuff pressure oscillations during an oscillometric measurement, as shown in Figure 4.

thumbnailFigure 4. Pressure-volume relationship for an artery (solid curve) including positive and negative transmural pressures. Dashed triangles have equal bases indicating the range of transmural pressure (internal artery blood pressure minus cuff pressure) that determines the change in volume with each pulse. (a) Cuff pressure well above systolic with net distending pressure always negative. (b) Cuff pressure close to systolic. (c) Cuff pressure near mean arterial pressure with maximal volume changes. (d) Cuff pressure just below diastolic. (e) Cuff pressure well below diastolic.
The pulsatile component of transmural pressure causes the artery to change in volume with each heartbeat. The magnitude of the change in transmural pressure is always equal to the pulse pressure (say, 40 mmHg) which is assumed to be constant during cuff deflation. As cuff pressure gradually decreases from well above systolic to well below diastolic pressure, the range of transmural pressure, Pt, experienced by the artery changes. At (a) cuff pressure is well above systolic and net distending pressure is always negative. There is a small change in arterial volume because the artery becomes less collapsed as each arterial pulse makes the transmural pressure less negative. As cuff pressure approaches systolic the relative unloading of negative pressure becomes more profound. Because of the exponential shape of the arterial pressure-volume curve, the amount of volume change accelerates. At (b) cuff pressure is close to systolic. After this point the volume change continues to increase but at a decelerating rate, because of the shape of the pressure-volume curve. Hence (b) is the inflection point for systolic pressure. At (c) cuff pressure is near mean arterial pressure and the volume change is maximal. At (d) cuff pressure is just below diastolic. After this point, as shown in (e), the volume change becomes less and less with each pulse as the increasingly distended artery becomes stiffer. Hence (d) is the inflection point for diastolic pressure. Thus the nonlinear compliance of arteries and the shape of the arterial pressure-volume curve govern the amplitude of cuff pressure oscillations.

The particular volume change of the artery from the nadir of diastolic pressure to the subsequent peak of systolic pressure can be specified analytically from Equations (4a) and (4b) as follows. Consider Pt as the transmural pressure at the diastolic nadir of the arterial blood pressure wave and let PP be the pulse pressure. One can imagine three domains of transmural pressure. In Domain (1) Pt + PP < 0. In Domain (2) Pt < 0 and Pt + PP ≥ 0. In Domain (3) Pt > 0. The largest artery volume oscillations occur in Domain (2) when transmural pressure oscillates between positive and negative values. Doman (1) represents the head of the oscillation envelope in time, and Domain (3) represents the tail.

Using equations (4), the artery volume changes during the rising phase of the arterial pulse in each of the three domains are

Domain (1):

ΔVa=Va0[ea(Pt+PP)−eaPt] (20)
Domain (2):

ΔVa=Va0[1+ab(1−e−b(Pt+PP))−eaPt] (21)
Domain (3):

ΔVa=Va0[ab(1−e−b(Pt+PP))−ab(1−e−bPt)], (22)
where for cuff pressure, P, systolic blood pressure SBP, and diastolic blood pressure DBP, the transmural pressure Pt = DBP − P, and the pulse pressure PP = SBP − DBP.

It is easy to show by differentiating expressions (7) for Domains (1), (2), and (3) that the systolic and diastolic pressure points correspond exactly to the maximal and minimal slopes d(ΔVa)/dPt. Therefore a simple analysis for finding systolic and diastolic pressures points would involve taking local slopes of the oscillation envelope vs. pressure function. Slope taking, however, is vulnerable to noise in practical applications. An alternative approach that does not involve slope taking creates a model of each individual subject’s arm in terms of exponential constants a and b and then numerically finds the unique combination of systolic and diastolic arterial pressures that best reproduces the observed oscillation envelope.

Regression analysis for exponential constants
To obtain exponential constant, a, note that in the leading edge of the amplitude envelope at pressures near systolic blood pressure in Domain (1) the pulsatile change in cuff pressure is

ΔP=ΔVaCcuff=Va0Ccuff[ea(Pt+PP)−eaPt]=Va0Ccuff[ea(PP)−1]eaPt=Va0Ccuff[ea(PP)−1]ea(DBP−P)=Va0Ccuff[ea(PP)−1]ea(DBP)e−aP=k1e−aP (23)
for constant, k1, during a cuff deflation scan in which cuff pressure, P, varies and the other variables are constant. (Note that here Ccuff is very nearly constant because the rising phase of the pulse happens in a very short time, roughly 0.1 sec.) Hence, ln(ΔP)=ln(k1)−aP, and a regression plot of the natural logarithm of the amplitude of pulse oscillations in the leading region of the envelope versus the instantaneous cuff pressure, P, yields a plot with slope − a. Thus we can obtain by linear regression an estimate of stiffness constant, a, as aˆ=slope1. The range of the rising phase of the oscillation envelope from the beginning of the envelope to the first inflection point (maximal slope) can be used for the first semi-log regression. More simply, the range of the rising phase of the oscillation envelope from its beginning to one third of the peak height provides reasonable estimates of slope1.

Similarly in Domain (3) during the tail region of the amplitude envelope at cuff pressures less than the maximal negative slope of the falling phase

ΔP=ΔVaCcuff=Va0Ccuff[ab(1−e−b(Pt+PP))−ab(1−e−bPt)]=Va0Ccuffab[1+e−b(PP)]e−bPt=Va0Ccuffab[1+e−b(PP)]e−b(DBP−P)=k3ebP (24)
hence, ln(ΔP)=ln(k3)+bP, and a regression plot of the natural logarithm of the amplitude of pulse oscillations in the envelope tail versus cuff pressure at the time of each pulse yields a plot with slope b. In turn, we can obtain by linear regression an estimate of stiffness constant, b, as bˆ=slope3. The range of the falling phase of the oscillation envelope from the second inflection point (maximal negative slope) of the oscillation envelope to the end of the envelope can be used to define the range of the second semi-log regression. More simply, the range of the falling phase of the oscillation envelope from two thirds of the peak height to the end of the envelope provides reasonable estimates of slope3. The slope estimates from the head and tail regions of the amplitude envelope include multiple points and so are relatively noise resistant. Other variables involved in the lumped constants, k1 and k3, are not relevant to the estimation of exponential constants a and b.

Least squares analysis
Having estimated elastic constants a and b for a particular envelope of oscillations from a particular patient at a particular time, it is straightforward in a computer program to find SBP and DBP values that reproduce the observed envelope function most faithfully. Let y(P) be the observed envelope amplitude as a function of cuff pressure, P, and let ymax(Pmax) be the observed peak amplitude of oscillations at cuff pressure Pmax. Let yˆ(P, SBP, DBP) be the simulated envelope amplitude as a function of cuff pressure, P, for a particular pulse and a particular test set of systolic and diastolic pressure levels. The values of yˆ are obtained from equations (7) and the prevailing cuff compliance as follows

Domain (1):

yˆ=ΔVaCcuff=Va0[ea(SBP−P)−ea(DBP−P)]⋅P+760V0 (25)
Domain (2):

yˆ=ΔVaCcuff=Va0[1+ab(1−e−b(SBP−P))−ea(DBP−P)t]⋅P+760V0 (26)
Domain (3):

yˆ=ΔVaCcuff=Va0[ab(1−e−b(SBP−P))−ab(1−e−b(DBP−P))]⋅P+760V0. (27)
Let yˆmax(Pmax, SBP, DBP) be the predicted peak of the oscillation envelope at cuff pressure Pmax . A figure of merit for goodness of fit between modeled and observed oscillations for particular test values of SBP and DBP is the sum of squares over all measured pulses

SS(SBP,DBP)=∑allpulses(yymax−yˆyˆmax)2. (28)
The values of SBP and DBP that minimize this sum of squares are the taken as the best estimates of systolic and diastolic pressure by the oscillometric method.

Here cuff pressure, P, is the cuff pressure at the time of each oscillation. Use of the amplitude normalized ratios y/ymax and yˆ/ yˆmax, means that it is not necessary to know the zero pressure volume of the artery, Va0 , or cuff volume V0, which depend on anatomy and geometry of a particular arm and cuff and are constants. It is the shape of the amplitude envelope in the pressure domain that contains the relevant information. The least squares function, SS, includes information from all of the measured oscillations and so is relatively noise resistant.

A variety of numerical methods may be used to find the unique values of SBP and DBP corresponding to the minimum sum of squares. Here, to demonstrate proof of concept, we evaluate the sum of squares, SS, over a two-dimensional matrix of candidate systolic and diastolic pressures at 1 mmHg intervals and identify the minimum sum of squares by plotting. The values of SBP and DBP corresponding to this minimum sum of squares are the best fit estimates for a particular oscillometric pressure run. The best fit model takes into account the prevailing artery stiffness and also the prevailing pulse pressure.

Results and discussion
Normal model
Particular parameter values for the standard normal model are as shown in Table 1.

Table 1. Standard parameters for the oscillometric blood pressure model
Figures 5 (a) and (b) show plots of cuff pressure and arterial pressure vs. time and high pass filtered cuff pressure oscillations vs. time. Figure 6 shows cuff pressure oscillations vs. cuff pressure and the amplitude envelope of cuff pressure for the standard normal model. Cuff pressure oscillations were obtained by subtracting each particular value from the moving average value over a period of one heartbeat.

thumbnailFigure 5. Simulated oscillometric blood pressure determination in a normal patient. (a) Blood pressure and cuff pressure vs. time. (b) High pass filtered cuff pressure oscillations.
thumbnailFigure 6. Simulated oscillometric blood pressure determination in a normal patient. (a) Cuff pressure oscillations vs. pressure. (b) Amplitude envelope obtained from maximum minus minimum cuff pressure over each heartbeat.
Varying arterial compliance
Prior studies have suggested that variations in arterial wall stiffness and arterial pulse pressure cause errors in systolic and diastolic blood pressure estimates using the oscillometric method [4,14,15]. Hence, these variables were studied explicitly. Figure 7 shows effects of varying arterial stiffness, represented by the constants a and b in the bi-exponential artery model. Actual blood pressure was 120/80 mmHg. The cuff oscillation ratios for systolic pressure are similar with varying stiffness. However, the cuff oscillation ratios for diastolic pressure differ greatly among more compliant, normal, and stiffer arteries, indicating that the same oscillation ratios cannot be used to determine diastolic pressures from the amplitude envelope when artery stiffness varies. The diastolic oscillation ratios decrease from about 94% to 88% to 75% as stiffness decreases from high to normal to low. Oscillation amplitude ratios for diastolic pressure in particular are highly dependent upon the stiffness of arteries. Since artery stiffness varies with age, this phenomenon may be a problem clinically. Note, however, that the maximum and minimum slopes of the envelope in the pressure domain still correlate well with true systolic and diastolic pressures.

thumbnailFigure 7. Amplitude envelopes for varying arterial stiffness. Stiffness is represented as inverse compliance. Exponential constants a and b for 1/2 normal stiffness are multiplied by ln(2) = 1.44. Exponential constants a and b for 2x normal stiffness are divided by 1.44. In all cases actual blood pressure was 120/80 mmHg.
Varying pulse pressure
Figures 8 and 9 show raw data and amplitude oscillation envelopes for cases of high and low pulse pressure. The amplitude of cuff pressure oscillations is greater for widened pulse pressure than for narrowed pulse pressure. The shape of the amplitude envelope is distorted for widened pulse pressure; however the maximum and minimum slopes of the envelope in the pressure domain still correlate well with true systolic and diastolic pressures. Characteristic ratios for systolic and diastolic pressures vary with pulse pressure. The characteristic ratio for systolic pressure is substantially smaller for widened pulse pressure and significantly larger for narrowed pulse pressure. The characteristic ratio for diastolic pressure is substantially larger for widened pulse pressure than for narrowed pulse pressure.

thumbnailFigure 8. Simulations of varying arterial pulse pressure. (a) and (b) blood pressure and cuff pressure vs. time, 140/60 mmHg vs. 110/90 mmHg.
thumbnailFigure 9. Simulations of varying arterial pulse pressure. (a) and (b) amplitude envelopes for 140/60 mmHg vs. 110/90 mmHg.
Regression analysis for systolic and diastolic pressures
Figure 10 shows semi-log plots for the envelope functions shown in Figure 7 representing arteries of varying stiffness. The linear portions of the plots in the head and tail regions of log envelope amplitude vs. cuff pressure curves are evident. The artery stiffness constants a and b obtained from linear regression slopes for these head and tail regions are close to the nominal input values (data in Table 2).

thumbnailFigure 10. Semi-log plots for determining model constants from amplitude envelope data. Note straight line regions in rising and falling phases of the curves.
Table 2. Validation of algorithm for estimation of systolic and diastolic pressures
Figure 11 shows a contour map of the sum of squares function in equation (11) for different test values of systolic and diastolic blood pressure using the previously determined regression values for stiffness constants a and b. The semi-log regression slopes give values for constants a and b of 0.1074 and 0.0303, respectively, versus the actual values of 0.110 and 0.030 used in the model to create the analyzed cuff pressure oscillations. The minimum sum of squares indicates the best fit between the oscillation envelope predicted by the mathematical model and the observed oscillation envelope. The minimum sum of squares is shown in Figure 11 as the center of the target-like pattern of colored, equal value contours. This point indicates the least squares solutions both for systolic pressure on the vertical scale and for diastolic pressure on the horizontal scale. Larger diameter ring-shaped contours indicate progressively greater sums of squares and therefore progressively greater disagreement between observed and predicted oscillation envelopes. The contour interval is 0.1 dimensionless units. The flat background indicates exceedingly large, off-scale sums of squares > 1.5 units. The minimum sum of squares occurs for test values SBP/DBP of 119/80 mmHg. The actual pressure was 120/80 mmHg.

thumbnailFigure 11. Contour plot of sum of squares goodness of fit measure showing a minimum value and best agreement at an estimated blood pressure of 119/80 mmHg, evaluated for input data computed with known pressure of 120/80 mmHg. Flat background indicates exceedingly large, off-scale sums of squares.
Figure 12 illustrates the sensitivity of the reconstruction algorithm to differences between various test levels and the actual values of systolic and diastolic blood pressure, in this case 120/80 mmHg. A low value of test pressure (110/70) creates a reconstructed envelope (dashed curve to left) that is clearly discordant with the observed normalized envelope values, E/Emax, shown as filled circles. A high value of test pressure (130/90) leads to equally discordant reconstructions in the opposite direction (heavy dashed curve to right). For both low and high test values the sum of squared differences is obviously large. The reconstructed model for the actual pressure (120/80) is shown as the solid curve. This illustration demonstrates the sensitivity of the least squares approach.

thumbnailFigure 12. Agreement of model (curves) and input (filled circles) amplitude functions in the normal pressure case.
Validation of the regression procedure
The cuff-arm-artery model of an oscillometric pressure measurement described in Methods Part One can be used to validate the regression and analysis procedure of Methods Part Two. An unlimited number and wide variety of test scenarios can be simulated in the model as unknowns for testing by the regression scheme, including a wide range of arterial stiffnesses and a wide range of pulse pressures, heart rates, blood pressure waveforms, cuff sizes, arm sizes, cuff lengths, artery diameters, etc. Importantly, the regression analysis assumes no prior knowledge of these model parameters or of the blood pressure used to generate the simulated oscillations. Cuff pressure oscillations and absolute cuff pressure are the only inputs to the algorithm for obtaining systolic and diastolic pressures.

The data summarized in Table 2 show the effectiveness of the proposed regression procedure in small sample of various possible test scenarios, including varying artery stiffness and varying pulse pressure. This small, systematic sample includes challenging cases for the algorithm. The accuracy is quite satisfactory, with reconstructed pressures within 0, 1, or 2 mmHg of the actual pressures in the face of varying artery stiffness and varying pulse pressure. The root mean squared error is 8√/10 = 0.28 mmHg.

The challenge of creating a satisfactory theoretical treatment of the genesis and interpretation of cuff pressure oscillations has attracted a diverse community of thinkers [4,5,7-10,16]. Nevertheless, specifying a valid method for extracting systolic and diastolic pressures from the envelope of cuff pressure oscillations remains an open problem. Here is presented a mathematical model incorporating anatomy, physiology, and biomechanics of arteries that predicts cuff pressure oscillations produced during noninvasive measurements of blood pressure using the oscillometric method. Understanding of the underlying mechanisms leads to a model-based algorithm for deducing systolic and diastolic pressures accurately from cuff pressure oscillations in the presence of varying arterial stiffness or varying pulse pressure.

The shape of the oscillation amplitude envelope dictates the stiffness parameters for the artery during both compression and distension. Semilog regression procedures give good estimates of the artery stiffness parameters that characterize each individual cuff deflation sequence. Using these parameters one can create and exercise an individualized cuff-arm-artery model for a wide range of possible systolic and diastolic pressures. The pair of systolic and diastolic pressures that best reproduces the observed oscillation envelope according to a least squares criterion constitutes the output of the algorithm.

When applied to amplitude normalized oscillation data the algorithm is insensitive to variations among subjects in zero pressure artery volume, Va0 , or initial cuff volume V0, since these terms are constants that are eliminated by the normalization procedure. Compression of the entire length of artery underlying the cuff is not necessary. Incomplete coupling of cuff pressure to the artery near the ends of the cuff merely decreases the ratio Va0 / V0 without effecting the extracted systolic and diastolic pressures.

The cuff-arm-artery model can be used as well to test the validity of the algorithm for over a wide range of possible conditions by generating trial cuff pressure data for known arterial pressure waveforms. A stress test for the algorithm can be done by comparing systolic and diastolic pressure levels extracted from synthesized cuff pressure oscillations with the arterial pressure that generated the synthesized oscillations over a wide range of test conditions. These conditions may include extreme cases that are hard to reproduce experimentally, contamination with excessive noise, any conceivable blood pressure waveforms, cardiac arrhythmias such as atrial fibrillation, etc. Such computational experiments, in addition to future animal and clinical studies, can boost confidence in the reliability of the oscillometric method and can suggest further refinements.

Here for convenience we have used the bi-exponential model to generate cuff pressure oscillations for algorithm testing. However, the regression algorithm does not “know” where the sample data came from. It tries to extract constants a and b from the head and tail portions of the semi-log plot of oscillation amplitude versus cuff pressure. The resulting best fit values of a and b will still work for non-ideal or noise contaminated data to produce a model envelope that can be matched to the actual data. An extremely stiff artery with a linear pressure volume curve is easily accommodated by this process, since ex ≈ 1 + x for small values of x. In this limiting case the exponential pressure-volume curve becomes linear. An exceptionally flabby artery, rather like dialysis tubing, is well described by larger values of a and b and a larger ratio a/b. Thus the family of bi-exponential models is very inclusive of a wide range of arterial mechanical properties, as suggested in Figure 3.

Classically the oscillometric method has been relatively well validated as a measure of mean arterial pressure, which is indicated by the peak of the oscillation amplitude envelope [4]. Automated oscillometric pressure monitors have found use in hospitals for critical care monitoring in which the goal is to detect any worrisome trend in blood pressure more so than the exact absolute value. Out of hospital use of the oscillometric method in screening for high blood pressure is more problematic, because heretofore the accuracy of systolic and diastolic end points has been questioned and doubted. For example Stork and Jilek [17] studied two published algorithms, differing in detail and based on cuff oscillation ratios of either 50% for systolic and 80% for diastolic or 40% for systolic and 55% for diastolic. Compared to a reference pressure of 122/78 mmHg the algorithmic methods applied to oscillometric data gave pressures of 135/88 and 144/81 mmHg, respectively. An advisory statement from the Council for High Blood Pressure Research, American Heart Association [18] stressed the need for caution in the selection of all instruments used for blood pressure determination and the need for continuing studies to validate their the safety and reliability.

Accurate measurements of blood pressure in routine clinic and office settings are important because systemic arterial hypertension is a major cause of serious complications, including accelerated atherosclerosis, heart attacks, strokes, kidney disease, and death. These serious complications increase smoothly with every point above the nominal 120/80 mmHg, hence even small increases in blood pressure are important to detect. In screening for hypertension systematic bias or inaccuracy in blood pressure readings of a few mmHg can be significant, since the difference between high normal (85 diastolic) and abnormal (90 diastolic) is only a few mmHg. A recent 1 million-patient meta-analysis suggests that a 3–4 mmHg increase in systolic blood pressure would translate into 20% higher stroke mortality and a 12% higher mortality from ischemic heart disease [19].

False negative readings would be problematic because untreated high blood pressure can lead to strokes, blindness, kidney failure, and lethal heart attacks. False positive readings would be undesirable because the usual drugs for hypertension must be taken every day for life and can be expensive. They also have side effects. Hence accurate readings are essential. Given a reliable algorithm for extracting systolic and diastolic pressures, an automatic oscillometric device could provide screening for high blood pressure that is performed in the same way each time without inter-observer variation. The present research could lead to a wider role for oscillometric blood pressure monitors in physicians’ offices and clinics.

The analytical approach and algorithm presented here represent a solution to an open problem in biomedical engineering: how to determine systolic and diastolic blood pressures using the oscillometric method. Current algorithms for oscillometric blood pressure implemented in commercial devices may be quite valid but are closely held trade secrets and cannot be independently validated. The present paper provides a physically and physiologically reasonable approach in the public domain that can be independently criticized, tested, and refined. Future demonstration of real-world accuracy will require data comparing oscillometric and intra-arterial pressures in human beings over a range of test conditions including variable cuff size, arm diameter, cuff tightness, cuff deflation rate, etc. Further development and incorporation of this algorithm into commercial devices may lead to greater confidence in the accuracy of systolic and diastolic pressure readings obtained by the oscillometric method and, in turn, an expanded role for these devices.

Competing interests
The author declares that he has no competing interests.

Author’s contributions
CB is the only author and is responsible for all aspects of the research and the intellectual and technical content of the manuscript.

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Functional connectivity alteration after real-time fMRI motor imagery training through self-regulation of activities of the right premotor cortex

Functional connectivity alteration after real-time fMRI motor imagery training through self-regulation of activities of the right premotor cortex
Fufang Xie1, Lele Xu1, Zhiying Long2, Li Yao123 and Xia Wu123*

  • Corresponding author: Xia Wu

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BMC Neuroscience 2015, 16:29 doi:10.1186/s12868-015-0167-1

Published: 1 May 2015
Abstract (provisional)
Background Real-time functional magnetic resonance imaging technology (real-time fMRI) is a novel method that can be used to investigate motor imagery training, it has attracted increasing attention in recent years, due to its ability to facilitate subjects in regulating the activities of specific brain regions to influence their behaviors. Lots of researchers have demonstrated that the right premotor area play critical roles during real-time fMRI motor imagery training. Thus, it has been hypothesized that modulating the activity of right premotor area may result in an alteration of the functional connectivity between the premotor area and other motor-related regions. Results The results indicated that the functional connectivity between the bilateral premotor area and right posterior parietal lobe significantly decreased during the imagination task. Conclusions This finding is new evidence that real-time fMRI is effective and can provide a theoretical guidance for the alteration of the motor function of brain regions associated with motor imagery training.

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