TY - JOUR
T1 - A method for evaluating dynamic functional network connectivity and task-modulation
T2 - Application to schizophrenia
AU - Sakoğlu, Ünal
AU - Pearlson, Godfrey D.
AU - Kiehl, Kent A.
AU - Wang, Y. Michelle
AU - Michael, Andrew M.
AU - Calhoun, Vince D.
N1 - Funding Information:
Acknowledgments This work was funded by National Institution of Health (NIH)/National Institute of Biomedical Imaging and Bio Engineering (NIBIB) grant 2RO1 EB000840-06 (Calhoun) and National Institute of Mental Health (NIMH) grant RO1 MH072681 (Kiehl). The authors would like to thank the Medical Image Analysis Lab staff (http://mialab.mrn.org) at Mind Research Network (MRN) for their valuable feedback.
PY - 2010/12
Y1 - 2010/12
N2 - Objective: In this paper, we develop a dynamic functional network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of functional connections. Materials and methods: We study the theoretical basis of the approach, perform a simulation analysis and apply it to fMRI data from schizophrenia patients (SP) and healthy controls (HC). Analyses on the fMRI data include: (a) group sICA to determine regions of significant task-related activity, (b) static and dynamic FNC analysis among these networks by using maximal lagged-correlation and time-frequency analysis, and (c) HC-SP group differences in functional network connections and in task-modulation of these connections. Results: This new approach enables an assessment of task-modulation of connectivity and identifies meaningful inter-component linkages and differences between the two study groups during performance of an auditory oddball task (AOT). The static FNC results revealed that connectivities involving medial visual-frontal, medial temporal-medial visual, parietal-medial temporal, parietal-medial visual and medial temporal-anterior temporal were significantly greater in HC, whereas only the right lateral fronto-parietal (RLFP)-orbitofrontal connection was significantly greater in SP. The dynamic FNC revealed that task-modulation of motor-frontal, RLFP-medial temporal and posterior default mode (pDM)-parietal connections were significantly greater in SP, and task modulation of orbitofrontal-pDM and medial temporal-frontal connections were significantly greater in HC (all P < 0.05). Conclusion: The task-modulation of dynamic FNC provided findings and differences between the two groups that are consistent with the existing hypothesis that schizophrenia patients show less segregated motor, sensory, cognitive functions and less segregated default mode network activity when engaged with a task. Dynamic FNC, based on sICA, provided additional results which are different than, but complementary to, those of static FNC. For example, it revealed dynamic changes in default mode network connectivities with other regions which were significantly different in schizophrenia in terms of task-modulation, findings which were not possible to discover by static FNC.
AB - Objective: In this paper, we develop a dynamic functional network connectivity (FNC) analysis approach using correlations between windowed time-courses of different brain networks (components) estimated via spatial independent component analysis (sICA). We apply the developed method to fMRI data to evaluate it and to study task-modulation of functional connections. Materials and methods: We study the theoretical basis of the approach, perform a simulation analysis and apply it to fMRI data from schizophrenia patients (SP) and healthy controls (HC). Analyses on the fMRI data include: (a) group sICA to determine regions of significant task-related activity, (b) static and dynamic FNC analysis among these networks by using maximal lagged-correlation and time-frequency analysis, and (c) HC-SP group differences in functional network connections and in task-modulation of these connections. Results: This new approach enables an assessment of task-modulation of connectivity and identifies meaningful inter-component linkages and differences between the two study groups during performance of an auditory oddball task (AOT). The static FNC results revealed that connectivities involving medial visual-frontal, medial temporal-medial visual, parietal-medial temporal, parietal-medial visual and medial temporal-anterior temporal were significantly greater in HC, whereas only the right lateral fronto-parietal (RLFP)-orbitofrontal connection was significantly greater in SP. The dynamic FNC revealed that task-modulation of motor-frontal, RLFP-medial temporal and posterior default mode (pDM)-parietal connections were significantly greater in SP, and task modulation of orbitofrontal-pDM and medial temporal-frontal connections were significantly greater in HC (all P < 0.05). Conclusion: The task-modulation of dynamic FNC provided findings and differences between the two groups that are consistent with the existing hypothesis that schizophrenia patients show less segregated motor, sensory, cognitive functions and less segregated default mode network activity when engaged with a task. Dynamic FNC, based on sICA, provided additional results which are different than, but complementary to, those of static FNC. For example, it revealed dynamic changes in default mode network connectivities with other regions which were significantly different in schizophrenia in terms of task-modulation, findings which were not possible to discover by static FNC.
KW - Auditory oddball task
KW - Brain
KW - Dynamic
KW - Functional magnetic resonance imaging
KW - Functional network connectivity
KW - Independent component analysis
KW - Schizophrenia
KW - fMRI
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U2 - 10.1007/s10334-010-0197-8
DO - 10.1007/s10334-010-0197-8
M3 - Article
C2 - 20162320
AN - SCOPUS:78651267773
SN - 0968-5243
VL - 23
SP - 351
EP - 366
JO - Magnetic Resonance Materials in Physics, Biology and Medicine
JF - Magnetic Resonance Materials in Physics, Biology and Medicine
IS - 5-6
ER -