Dynamic Functional Connectivity States between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging

Research output: Contribution to journalArticle

Abstract

Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. However, recent studies have shown that robust temporal fluctuations of functional connectivity among as well as within functional networks exist, challenging this assumption. In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.

Original languageEnglish (US)
Pages (from-to)635-642
Number of pages8
JournalBrain Connectivity
Volume7
Issue number10
DOIs
StatePublished - Dec 1 2017

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Magnetic Resonance Imaging
Cluster Analysis
Healthy Volunteers
Brain

Keywords

  • dorsal motor network
  • dynamic conditional correlation
  • dynamic functional connectivity
  • resting-state fMRI
  • sensorimotor network
  • ventral motor network

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

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title = "Dynamic Functional Connectivity States between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging",
abstract = "Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. However, recent studies have shown that robust temporal fluctuations of functional connectivity among as well as within functional networks exist, challenging this assumption. In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.",
keywords = "dorsal motor network, dynamic conditional correlation, dynamic functional connectivity, resting-state fMRI, sensorimotor network, ventral motor network",
author = "Syed, {Maleeha F.} and Martin Lindquist and Jay Pillai and Shruti Agarwal and Gujar, {Sachin K} and Ann Choe and Caffo, {Brian S} and Haris Sair",
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AU - Syed, Maleeha F.

AU - Lindquist, Martin

AU - Pillai, Jay

AU - Agarwal, Shruti

AU - Gujar, Sachin K

AU - Choe, Ann

AU - Caffo, Brian S

AU - Sair, Haris

PY - 2017/12/1

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N2 - Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. However, recent studies have shown that robust temporal fluctuations of functional connectivity among as well as within functional networks exist, challenging this assumption. In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.

AB - Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. However, recent studies have shown that robust temporal fluctuations of functional connectivity among as well as within functional networks exist, challenging this assumption. In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.

KW - dorsal motor network

KW - dynamic conditional correlation

KW - dynamic functional connectivity

KW - resting-state fMRI

KW - sensorimotor network

KW - ventral motor network

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