Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia

Zarghami, Tahereh S.; Hossein-Zadeh, Gholam-Ali; Bahrami, Fariba

doi:10.3389/fnins.2020.00214

Cited by 15 publications

(11 citation statements)

References 110 publications

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“…Here, med(·) is the median function across time applied to the instantaneous phase synchrony IPS ( t ) (Zarghami et al, 2020 ) and φ i ( t ) is the phase angle of the i –th oscillator at time point t given in rad.…”

Section: Methodsmentioning

confidence: 99%

Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder

Bauer

Hirsch

Jones

et al. 2022

Front. Comput. Neurosci.

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Organized patterns of system-wide neural activity adapt fluently within the brain to adjust behavioral performance to environmental demands. In major depressive disorder (MD), markedly different co-activation patterns across the brain emerge from a rather similar structural substrate. Despite the application of advanced methods to describe the functional architecture, e.g., between intrinsic brain networks (IBNs), the underlying mechanisms mediating these differences remain elusive. Here we propose a novel complementary approach for quantifying the functional relations between IBNs based on the Kuramoto model. We directly estimate the Kuramoto coupling parameters (K) from IBN time courses derived from empirical fMRI data in 24 MD patients and 24 healthy controls. We find a large pattern with a significant number of Ks depending on the disease severity score Hamilton D, as assessed by permutation testing. We successfully reproduced the dependency in an independent test data set of 44 MD patients and 37 healthy controls. Comparing the results to functional connectivity from partial correlations (FC), to phase synchrony (PS) as well as to first order auto-regressive measures (AR) between the same IBNs did not show similar correlations. In subsequent validation experiments with artificial data we find that a ground truth of parametric dependencies on artificial regressors can be recovered. The results indicate that the calculation of Ks can be a useful addition to standard methods of quantifying the brain's functional architecture.

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Section: Methodsmentioning

confidence: 99%

Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder

Bauer

Hirsch

Jones

et al. 2022

Front. Comput. Neurosci.

View full text Add to dashboard Cite

show abstract

“…However, static FC relies on statistical relationships between fMRI signals throughout the complete scan, which forces it to discard critical information about the brain's dynamics. In contrast, it is reasonable to believe that dynamic approaches -which consider the temporal dynamics of fMRI signalsmay have the potential to discover more precise and informative biomarkers [16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Metastability as a neuromechanistic biomarker of schizophrenia pathology

Hancock

Rosas

McCutcheon

et al. 2022

Preprint

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The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain and is therefore well suited for the study of schizophrenia. In this study we investigate metastability as a neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, and pathophysiological relevance were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n=82, non-affective psychosis n=53) and the Cobre study (controls n=71, cases n=59). In this work we introduce a new framework that uses Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implements a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naive Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a neuromechanistic biomarker of schizophrenia pathology.

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“…We further defined mode-specific metrics by considering only the subsets of brain areas shifted in phase in each spatial mode, and compared their values across the 4 runs. Mode-specific metrics are commonly used to investigate differences between normal and abnormal functional brain activity (Kottaram et al, 2019;Zarghami et al, 2020). Using repeated measures ANOVA, we did not find any mode-specific metric that was reliable in all 5 modes across all 4 runs when excluding or including the cerebellar region as shown in Table 2.…”

Section: Mode-specific Metrics Do Not Appear Representative or Stable Across Runsmentioning

confidence: 75%

Metastability, fractal scaling, and synergistic information processing: what phase relationships reveal about intrinsic brain activity

Hancock

Cabral

Luppi

et al. 2022

Preprint

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Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originated from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical- and informational-complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.

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Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia

Cited by 15 publications

References 110 publications

Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder

Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder

Metastability as a neuromechanistic biomarker of schizophrenia pathology

Metastability, fractal scaling, and synergistic information processing: what phase relationships reveal about intrinsic brain activity

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