Decreased Cross-Domain Mutual Information in Schizophrenia From Dynamic Connectivity States

Mustafa S. Salman, Victor M. Vergara, Eswar Damaraju, Vince Daniel Calhoun

Research output: Contribution to journalArticle

Abstract

The study of dynamic functional network connectivity (dFNC) has been important to understand the healthy and diseased brain. Recent developments model groups of functionally related brain structures (defined as functional domains) as entities that can send and receive information. A domain analysis starts by detecting a finite set of connectivity patterns known as domain states within each functional domain. Dynamic functional domain connectivity (DFDC) is a novel information theoretic framework for studying the temporal sequence of the domain states and the amount of information shared among domains. In this setting, the information flow among functional domains can be compared to the flow of bits among entities in a digital network. Schizophrenia is a chronic psychiatric disorder which is associated with how the brain processes information. Here, we employed the DFDC framework to analyze a dataset containing resting-state fMRI scans from 163 healthy controls (HCs) and 151 schizophrenia patients (SZs). As in other information theory methods, this study measured domain state probabilities, entropy within each DFDC and the cross-domain mutual information (CDMI) between pairs of DFDC. Results indicate that SZs show significantly higher (transformed) entropy than HCs in subcortical (SC)-SC; default mode network (DMN)-visual (VIS) and frontoparietal (FRN)-VIS DFDCs. SZs also show lower (transformed) CDMI between SC-VIS vs. SC-sensorimotor (SM), attention (ATTN)-VIS vs. ATTN-SM and ATTN-SM vs. ATTN-ATTN DFDC pairs after correcting for multiple comparisons. These results imply that different DFDC pairs function in a more independent manner in SZs compared to HCs. Our findings present evidence of higher uncertainty and randomness in SZ brain function.

Original languageEnglish (US)
Article number873
JournalFrontiers in Neuroscience
Volume13
DOIs
StatePublished - Aug 22 2019

Fingerprint

Schizophrenia
Entropy
Brain
Information Theory
Brain Diseases
Uncertainty
Psychiatry
Magnetic Resonance Imaging

Keywords

  • fMRI
  • functional domain
  • functional network connectivity
  • ICA
  • information theory
  • schizophrenia

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Decreased Cross-Domain Mutual Information in Schizophrenia From Dynamic Connectivity States. / Salman, Mustafa S.; Vergara, Victor M.; Damaraju, Eswar; Calhoun, Vince Daniel.

In: Frontiers in Neuroscience, Vol. 13, 873, 22.08.2019.

Research output: Contribution to journalArticle

@article{c0b730978e0a4a7680946b496f0da067,
title = "Decreased Cross-Domain Mutual Information in Schizophrenia From Dynamic Connectivity States",
abstract = "The study of dynamic functional network connectivity (dFNC) has been important to understand the healthy and diseased brain. Recent developments model groups of functionally related brain structures (defined as functional domains) as entities that can send and receive information. A domain analysis starts by detecting a finite set of connectivity patterns known as domain states within each functional domain. Dynamic functional domain connectivity (DFDC) is a novel information theoretic framework for studying the temporal sequence of the domain states and the amount of information shared among domains. In this setting, the information flow among functional domains can be compared to the flow of bits among entities in a digital network. Schizophrenia is a chronic psychiatric disorder which is associated with how the brain processes information. Here, we employed the DFDC framework to analyze a dataset containing resting-state fMRI scans from 163 healthy controls (HCs) and 151 schizophrenia patients (SZs). As in other information theory methods, this study measured domain state probabilities, entropy within each DFDC and the cross-domain mutual information (CDMI) between pairs of DFDC. Results indicate that SZs show significantly higher (transformed) entropy than HCs in subcortical (SC)-SC; default mode network (DMN)-visual (VIS) and frontoparietal (FRN)-VIS DFDCs. SZs also show lower (transformed) CDMI between SC-VIS vs. SC-sensorimotor (SM), attention (ATTN)-VIS vs. ATTN-SM and ATTN-SM vs. ATTN-ATTN DFDC pairs after correcting for multiple comparisons. These results imply that different DFDC pairs function in a more independent manner in SZs compared to HCs. Our findings present evidence of higher uncertainty and randomness in SZ brain function.",
keywords = "fMRI, functional domain, functional network connectivity, ICA, information theory, schizophrenia",
author = "Salman, {Mustafa S.} and Vergara, {Victor M.} and Eswar Damaraju and Calhoun, {Vince Daniel}",
year = "2019",
month = "8",
day = "22",
doi = "10.3389/fnins.2019.00873",
language = "English (US)",
volume = "13",
journal = "Frontiers in Neuroscience",
issn = "1662-4548",
publisher = "Frontiers Research Foundation",

}

TY - JOUR

T1 - Decreased Cross-Domain Mutual Information in Schizophrenia From Dynamic Connectivity States

AU - Salman, Mustafa S.

AU - Vergara, Victor M.

AU - Damaraju, Eswar

AU - Calhoun, Vince Daniel

PY - 2019/8/22

Y1 - 2019/8/22

N2 - The study of dynamic functional network connectivity (dFNC) has been important to understand the healthy and diseased brain. Recent developments model groups of functionally related brain structures (defined as functional domains) as entities that can send and receive information. A domain analysis starts by detecting a finite set of connectivity patterns known as domain states within each functional domain. Dynamic functional domain connectivity (DFDC) is a novel information theoretic framework for studying the temporal sequence of the domain states and the amount of information shared among domains. In this setting, the information flow among functional domains can be compared to the flow of bits among entities in a digital network. Schizophrenia is a chronic psychiatric disorder which is associated with how the brain processes information. Here, we employed the DFDC framework to analyze a dataset containing resting-state fMRI scans from 163 healthy controls (HCs) and 151 schizophrenia patients (SZs). As in other information theory methods, this study measured domain state probabilities, entropy within each DFDC and the cross-domain mutual information (CDMI) between pairs of DFDC. Results indicate that SZs show significantly higher (transformed) entropy than HCs in subcortical (SC)-SC; default mode network (DMN)-visual (VIS) and frontoparietal (FRN)-VIS DFDCs. SZs also show lower (transformed) CDMI between SC-VIS vs. SC-sensorimotor (SM), attention (ATTN)-VIS vs. ATTN-SM and ATTN-SM vs. ATTN-ATTN DFDC pairs after correcting for multiple comparisons. These results imply that different DFDC pairs function in a more independent manner in SZs compared to HCs. Our findings present evidence of higher uncertainty and randomness in SZ brain function.

AB - The study of dynamic functional network connectivity (dFNC) has been important to understand the healthy and diseased brain. Recent developments model groups of functionally related brain structures (defined as functional domains) as entities that can send and receive information. A domain analysis starts by detecting a finite set of connectivity patterns known as domain states within each functional domain. Dynamic functional domain connectivity (DFDC) is a novel information theoretic framework for studying the temporal sequence of the domain states and the amount of information shared among domains. In this setting, the information flow among functional domains can be compared to the flow of bits among entities in a digital network. Schizophrenia is a chronic psychiatric disorder which is associated with how the brain processes information. Here, we employed the DFDC framework to analyze a dataset containing resting-state fMRI scans from 163 healthy controls (HCs) and 151 schizophrenia patients (SZs). As in other information theory methods, this study measured domain state probabilities, entropy within each DFDC and the cross-domain mutual information (CDMI) between pairs of DFDC. Results indicate that SZs show significantly higher (transformed) entropy than HCs in subcortical (SC)-SC; default mode network (DMN)-visual (VIS) and frontoparietal (FRN)-VIS DFDCs. SZs also show lower (transformed) CDMI between SC-VIS vs. SC-sensorimotor (SM), attention (ATTN)-VIS vs. ATTN-SM and ATTN-SM vs. ATTN-ATTN DFDC pairs after correcting for multiple comparisons. These results imply that different DFDC pairs function in a more independent manner in SZs compared to HCs. Our findings present evidence of higher uncertainty and randomness in SZ brain function.

KW - fMRI

KW - functional domain

KW - functional network connectivity

KW - ICA

KW - information theory

KW - schizophrenia

UR - http://www.scopus.com/inward/record.url?scp=85072204904&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85072204904&partnerID=8YFLogxK

U2 - 10.3389/fnins.2019.00873

DO - 10.3389/fnins.2019.00873

M3 - Article

VL - 13

JO - Frontiers in Neuroscience

JF - Frontiers in Neuroscience

SN - 1662-4548

M1 - 873

ER -