A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia

By U. Sakoglu, Godfrey D. Pearlson, Kent A. Kiehl, Michelle Y. Wang, Andrew M. Michael, Vince D. Calhoun. Magnetic Resonance Materials in Physics, Biology and Medicine, 2010

634 citations recorded by the source corpus.

ObjectiveIn 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 methodsWe 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.ResultsThis 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).ConclusionThe 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.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/e91481e78e3b82ed62b12b64f40e3389f1e90cec

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Citations in the corpus

Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, positive): “As with recent studies exploring resting-state dynamics [85], [86], [88], ICA can be used to capture and exploit the dynamic changes and variability over time in brain activity.”
Association between heart rate variability and fluctuations in resting-state functional connectivity (2012, background): “…work. rights reserved. relation to sleep stages (Horovitz et al., 2009), previous history of activation (Grigg and Grady, 2010; Waites et al., 2005), and in resting state, as seen with fMRI (Chang and Glover, 2010; Kiviniemi et al., 2011; Sakoglu et al., 2010) and MEG (de Pasquale et al., 2010).”
Tracking whole-brain connectivity dynamics in the resting state. (2014, neutral): “Keywords: Dynamics, fMRI, Functional connectivity, Independent component analysis, Intrinsic activity, Resting state, State variability”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “…ﬂexibility of the window (ﬁxed, Allen et al., 2014; or adaptive, Lindquist et al., 2014; Yaesoubi et al., 2015), as well as the length of the window (Leonardi & Van De Ville, 2015; Liegeois et al., 2016; Sakoglu et al., 2010; V. M. Vergara, Mayer, Damaraju, & Calhoun, 2017; Zalesky & Breakspear,”
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging (2018, methodology): “In recent years, there have been much interests in computing time-resolved connectivity measures and successful applications in identifying biomarkers from dynamic connectivity (Chang and Glover, 2010; Sakoglu et al., 2010; Allen et al., 2014; Zalesky et al., 2014; Du et al., 2015a; Sadaghiani et al”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, background): “…the course of the scan (within a few seconds) and reveal a number of functional connectivity (FC) states in the brain, which can be sensitive to changes related to neurological disorders (Sakoğlu et al., 2010; Leonardi et al., 2013a, 2013b; Damaraju et al., 2014; Li et al., 2014; Ou et al., 2015).”
CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, background): “Connectivity dynamics capture uncontrolled but reoccurring patterns of interactions among intrinsic networks during task engagement or at rest (Allen et al., 2012; Calhoun et al., 2014; Hutchison et al., 2013; Rashid et al., 2014; Sakoğlu et al., 2010).”
EEG correlates of time-varying BOLD functional connectivity (2013, neutral): “Similar fluctuations in correlation patterns have been observed across other networks in humans with fMRI (Kiviniemi et al., 2011; Rack-Gomer and Liu, 2012; Sakoglu et al., 2010) and MEG (de Pasquale et al., 2010), and in the BOLD signal of anesthetized monkeys (Hutchison et al., 2012).”
On spurious and real fluctuations of dynamic functional connectivity during rest (2015, positive): “To study changes in FC over time sliding-window correlation analysis, where the correlation is estimated for brain activity duringmultiple, possibly overlapping temporal segments (typically 30–60 s), has been widely deployed (Allen et al., 2014; Chang and Glover, 2010; Hutchison et al., 2013a; Sakog”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “Specifically, a change in large-scale FC and a change in the activation (depolarization) state of a local cortical column may interact through two principal, complementary routes: a local state change can be the source for long-range changes in FC, or the local state change is a reflection of distan”
Decreased Functional Connectivity by Aging Is Associated with Cognitive Decline (2012, background): “Although the components are spatially independent, significant temporal correlations can exist between them (Sakoglu et al., 2010; Jafri, Pearlson, Stevens, & Calhoun, 2008).”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, methodology): “Several test statistics have been proposed to test for the presence of dFC, including the variance of the FC time series (Sakoglu et al., 2010), test statistics based on the FC time series’ Fourier-transform (Handwerker et al., 2012), and non-linear test statistics (Zalesky et al., 2014), among…”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, methodology): “Finally, model-driven explorations could be used to investigate how modi ﬁ cations of SC or the working point of brain dynamics (Deco et al., 2013b) translate into aberrant FC non-stationarity patternsasin Schizo-phrenia (Sakoglu et al., 2010) and Alzheimer's disease (Jones et al., 2012).”