Nodal centrality of functional network in the differentiation of schizophrenia

Hu Cheng, Sharlene Newman, Joaquín Goñi, Jerillyn S. Kent, Josselyn Howell, Amanda Bolbecker, Aina Puce, Brian O'Donnell, William P. Hetrick

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.

Original languageEnglish (US)
Article number6499
Pages (from-to)345-352
Number of pages8
JournalSchizophrenia Research
Volume168
Issue number1-2
DOIs
StatePublished - Oct 1 2015

Fingerprint

Schizophrenia
Brain
Communication
Magnetic Resonance Imaging
Control Groups

Keywords

  • Betweenness centrality
  • Functional network
  • Machine learning
  • Resting state fMRI
  • Schizophrenia
  • Support vector machine

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Biological Psychiatry

Cite this

Cheng, H., Newman, S., Goñi, J., Kent, J. S., Howell, J., Bolbecker, A., ... Hetrick, W. P. (2015). Nodal centrality of functional network in the differentiation of schizophrenia. Schizophrenia Research, 168(1-2), 345-352. [6499]. https://doi.org/10.1016/j.schres.2015.08.011

Nodal centrality of functional network in the differentiation of schizophrenia. / Cheng, Hu; Newman, Sharlene; Goñi, Joaquín; Kent, Jerillyn S.; Howell, Josselyn; Bolbecker, Amanda; Puce, Aina; O'Donnell, Brian; Hetrick, William P.

In: Schizophrenia Research, Vol. 168, No. 1-2, 6499, 01.10.2015, p. 345-352.

Research output: Contribution to journalArticle

Cheng, H, Newman, S, Goñi, J, Kent, JS, Howell, J, Bolbecker, A, Puce, A, O'Donnell, B & Hetrick, WP 2015, 'Nodal centrality of functional network in the differentiation of schizophrenia', Schizophrenia Research, vol. 168, no. 1-2, 6499, pp. 345-352. https://doi.org/10.1016/j.schres.2015.08.011
Cheng H, Newman S, Goñi J, Kent JS, Howell J, Bolbecker A et al. Nodal centrality of functional network in the differentiation of schizophrenia. Schizophrenia Research. 2015 Oct 1;168(1-2):345-352. 6499. https://doi.org/10.1016/j.schres.2015.08.011
Cheng, Hu ; Newman, Sharlene ; Goñi, Joaquín ; Kent, Jerillyn S. ; Howell, Josselyn ; Bolbecker, Amanda ; Puce, Aina ; O'Donnell, Brian ; Hetrick, William P. / Nodal centrality of functional network in the differentiation of schizophrenia. In: Schizophrenia Research. 2015 ; Vol. 168, No. 1-2. pp. 345-352.
@article{d0db3d4840044bd7ac0b60553684d2f7,
title = "Nodal centrality of functional network in the differentiation of schizophrenia",
abstract = "A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80{\%}, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.",
keywords = "Betweenness centrality, Functional network, Machine learning, Resting state fMRI, Schizophrenia, Support vector machine",
author = "Hu Cheng and Sharlene Newman and Joaqu{\'i}n Go{\~n}i and Kent, {Jerillyn S.} and Josselyn Howell and Amanda Bolbecker and Aina Puce and Brian O'Donnell and Hetrick, {William P.}",
year = "2015",
month = "10",
day = "1",
doi = "10.1016/j.schres.2015.08.011",
language = "English (US)",
volume = "168",
pages = "345--352",
journal = "Schizophrenia Research",
issn = "0920-9964",
publisher = "Elsevier",
number = "1-2",

}

TY - JOUR

T1 - Nodal centrality of functional network in the differentiation of schizophrenia

AU - Cheng, Hu

AU - Newman, Sharlene

AU - Goñi, Joaquín

AU - Kent, Jerillyn S.

AU - Howell, Josselyn

AU - Bolbecker, Amanda

AU - Puce, Aina

AU - O'Donnell, Brian

AU - Hetrick, William P.

PY - 2015/10/1

Y1 - 2015/10/1

N2 - A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.

AB - A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.

KW - Betweenness centrality

KW - Functional network

KW - Machine learning

KW - Resting state fMRI

KW - Schizophrenia

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=84942368274&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84942368274&partnerID=8YFLogxK

U2 - 10.1016/j.schres.2015.08.011

DO - 10.1016/j.schres.2015.08.011

M3 - Article

C2 - 26299706

AN - SCOPUS:84942368274

VL - 168

SP - 345

EP - 352

JO - Schizophrenia Research

JF - Schizophrenia Research

SN - 0920-9964

IS - 1-2

M1 - 6499

ER -