Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI.
Classification
Functional network
Obsessive compulsive disorder
Psychiatry
Sub-graph entropy
Journal
NeuroImage. Clinical
ISSN: 2213-1582
Titre abrégé: Neuroimage Clin
Pays: Netherlands
ID NLM: 101597070
Informations de publication
Date de publication:
2020
2020
Historique:
received:
13
10
2019
revised:
17
01
2020
accepted:
04
02
2020
pubmed:
18
2
2020
medline:
27
3
2021
entrez:
18
2
2020
Statut:
ppublish
Résumé
This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD) in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing OCD in youth involves interviews with adolescent patients and their parents by an experienced clinician, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), and behavioral observation. Discovering signal processing and network-based biomarkers from functional magnetic resonance imaging (fMRI) scans of patients has the potential to assist clinicians in their diagnostic assessments of adolescents suffering from OCD. This paper investigates the clinical diagnostic utility of a set of univariate, bivariate and multivariate features extracted from resting-state fMRI using an information-theoretic approach in 15 adolescents with OCD and 13 matched healthy controls. Results indicate that an information-theoretic approach based on sub-graph entropy is capable of classifying OCD vs. healthy subjects with high accuracy. Mean time-series were extracted from 85 brain regions and were used to calculate Shannon wavelet entropy, Pearson correlation matrix, network features and sub-graph entropy. In addition, two special cases of sub-graph entropy, namely node and edge entropy, were investigated to identify important brain regions and edges from OCD patients. A leave-one-out cross-validation method was used for the final predictor performance. The proposed methodology using differential sub-graph (edge) entropy achieved an accuracy of 0.89 with specificity 1 and sensitivity 0.80 using leave-one-out cross-validation with in-fold feature ranking and selection. The high classification accuracy indicates the predictive power of the sub-network as well as edge entropy metric.
Identifiants
pubmed: 32065968
pii: S2213-1582(20)30045-0
doi: 10.1016/j.nicl.2020.102208
pmc: PMC7025090
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
102208Informations de copyright
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.
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