Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury

Jessica L. Nielson; Jesse Paquette; Aiwen W. Liu; Cristian F. Guandique; C. Amy Tovar; Tomoo Inoue; Karen Amanda Irvine; John C. Gensel; Jennifer Kloke; Tanya C. Petrossian; Pek Y. Lum; Gunnar E. Carlsson; Geoffrey T. Manley; Wise Young; Michael S. Beattie; Jacqueline C. Bresnahan; Adam R. Ferguson

doi:10.1038/ncomms9581

Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury

Jessica L. Nielson, Jesse Paquette, Aiwen W. Liu, Cristian F. Guandique, C. Amy Tovar, Tomoo Inoue, Karen Amanda Irvine, John C. Gensel, Jennifer Kloke, Tanya C. Petrossian, Pek Y. Lum, Gunnar E. Carlsson, Geoffrey T. Manley, Wise Young, Michael S. Beattie, Jacqueline C. Bresnahan, Adam R. Ferguson

Research output: Contribution to journal › Article › peer-review

160 Scopus citations

Abstract

Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.

Original language	English
Article number	8581
Journal	Nature Communications
Volume	6
DOIs	https://doi.org/10.1038/ncomms9581
State	Published - Oct 14 2015

Bibliographical note

Publisher Copyright:
© 2015 Macmillan Publishers Limited.

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

Access to Document

10.1038/ncomms9581

Cite this

Nielson, J. L., Paquette, J., Liu, A. W., Guandique, C. F., Tovar, C. A., Inoue, T., Irvine, K. A., Gensel, J. C., Kloke, J., Petrossian, T. C., Lum, P. Y., Carlsson, G. E., Manley, G. T., Young, W., Beattie, M. S., Bresnahan, J. C., & Ferguson, A. R. (2015). Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury. Nature Communications, 6, Article 8581. https://doi.org/10.1038/ncomms9581

@article{c6af0e3c143e451a8970e972d2484d9f,

title = "Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury",

abstract = "Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.",

author = "Nielson, {Jessica L.} and Jesse Paquette and Liu, {Aiwen W.} and Guandique, {Cristian F.} and Tovar, {C. Amy} and Tomoo Inoue and Irvine, {Karen Amanda} and Gensel, {John C.} and Jennifer Kloke and Petrossian, {Tanya C.} and Lum, {Pek Y.} and Carlsson, {Gunnar E.} and Manley, {Geoffrey T.} and Wise Young and Beattie, {Michael S.} and Bresnahan, {Jacqueline C.} and Ferguson, {Adam R.}",

note = "Publisher Copyright: {\textcopyright} 2015 Macmillan Publishers Limited.",

year = "2015",

month = oct,

day = "14",

doi = "10.1038/ncomms9581",

language = "English",

volume = "6",

}

Nielson, JL, Paquette, J, Liu, AW, Guandique, CF, Tovar, CA, Inoue, T, Irvine, KA, Gensel, JC, Kloke, J, Petrossian, TC, Lum, PY, Carlsson, GE, Manley, GT, Young, W, Beattie, MS, Bresnahan, JC & Ferguson, AR 2015, 'Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury', Nature Communications, vol. 6, 8581. https://doi.org/10.1038/ncomms9581

TY - JOUR

T1 - Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury

AU - Nielson, Jessica L.

AU - Paquette, Jesse

AU - Liu, Aiwen W.

AU - Guandique, Cristian F.

AU - Tovar, C. Amy

AU - Inoue, Tomoo

AU - Irvine, Karen Amanda

AU - Gensel, John C.

AU - Kloke, Jennifer

AU - Petrossian, Tanya C.

AU - Lum, Pek Y.

AU - Carlsson, Gunnar E.

AU - Manley, Geoffrey T.

AU - Young, Wise

AU - Beattie, Michael S.

AU - Bresnahan, Jacqueline C.

AU - Ferguson, Adam R.

PY - 2015/10/14

Y1 - 2015/10/14

N2 - Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.

AB - Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.

UR - http://www.scopus.com/inward/record.url?scp=84945197673&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84945197673&partnerID=8YFLogxK

U2 - 10.1038/ncomms9581

DO - 10.1038/ncomms9581

M3 - Article

C2 - 26466022

AN - SCOPUS:84945197673

SN - 2041-1723

VL - 6

JO - Nature Communications

JF - Nature Communications

M1 - 8581

ER -

Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury

Abstract

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this