State-Dependent Effective Connectivity in Resting-State fMRI

The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.

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Large-scale DCMs for resting-state fMRI

Network Neuroscience ◽

10.1162/netn_a_00015 ◽

2017 ◽

Vol 1 (3) ◽

pp. 222-241 ◽

Cited By ~ 63

Author(s):

Adeel Razi ◽

Mohamed L. Seghier ◽

Yuan Zhou ◽

Peter McColgan ◽

Peter Zeidman ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Large Scale ◽

Effective Connectivity ◽

Directed Graphs ◽

Resting State Fmri ◽

Causal Modeling ◽

Large Graphs ◽

Model Inversion ◽

Graph Theoretic

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

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O2.6. DELUSIONS ASSOCIATED WITH ABNORMAL FRONTOSTRIATAL EFFECTIVE CONNECTIVITY IN A SPECTRAL DCM ANALYSIS OF RESTING STATE FMRI

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa028.011 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S5-S5

Author(s):

Victoria Okuneye ◽

Brett Clementz ◽

Elliot Gershon ◽

Matcheri Keshavan ◽

Jennifer E McDowell ◽

...

Keyword(s):

Prefrontal Cortex ◽

Resting State ◽

Prediction Error ◽

Empirical Bayes ◽

Bayesian Model Averaging ◽

Effective Connectivity ◽

Resting State Fmri ◽

Causal Modeling ◽

Healthy Controls ◽

Intermediate Phenotypes

Abstract Background Delusions, false beliefs held in the face of disconfirming evidence, are a prevalent and highly distressing feature of psychotic disorders. The neurobiology of delusions remains unknown but recent evidence suggests a role for abnormal prediction error neural signaling. Prediction error is neurocognitive process in which the brain signals the need to update beliefs when presented with information that disconfirms expectations. Task based neuroimaging studies have identified delusional beliefs correlate with altered activation in frontal and subcortical brain regions during prediction error, though such work is limited in scope. In a large sample of transdiagnostic psychotic patients we modeled the resting state effective connectivity of the delusion-associated predication error (D-PE) circuit. Methods Resting state fMRI was obtained from 289 psychotic subjects (schizophrenia, schizoaffective disorder, bipolar disorder with psychotic features) and 219 healthy controls, recruited as part of the multisite Bipolar & Schizophrenia Network on Intermediate Phenotypes (BSNIP1) study. Neuroimaging data were processed using CONN software with strict quality control criteria. Five D-PE regions of interest (ROIs) were created based on peak coordinates from published task-based prediction error fMRI studies: right Dorsolateral Prefrontal Cortex [r DLPFC], r Ventrolateral Prefrontal Cortex [r VLPFC], r Caudate, l Caudate and l Midbrain. In each subject the first eigenvariate was extracted from the rs-fMRI timeseries of each D-PE ROI. Spectral Dynamic Causal Modeling (spDCM) was performed on a fully connected model of the 5 ROIs. Parameters for the full model were fit using Parameter Empirical Bayes (PEB) and then passed to the group level where they were reduced using Bayesian Model Averaging (BMA). The association of effective connectivity with current delusional severity was tested using PEB-BMA controlling for antipsychotic medication, sex, age and scanner site. Significant effective connectivity was identified as parameters with free energy evidence greater than 95% probability. Additionally, we assessed the effective connectivity differences of this circuit between psychotic probands and healthy controls. Results Greater delusional severity was significantly associated with inhibition of the r Caudate by the r VLPFC, excitation of the r DLPFC by the l Caudate, and decreased self-inhibition of the r VLPFC and r DLPFC. Effective connectivity of the D-PE network in psychotic probands compared to healthy controls was associated with inhibition of the r Caudate by the r VLPFC, the r DLPFC by the l Midbrain, the l Midbrain by the r Caudate, and decreased self-inhibition of the r Caudate, r VLPFC, and r DLPFC. Discussion We found that resting state effective connectivity of the prediction error circuit is disrupted in psychotic subjects experiencing delusions. Specifically, delusion severity was associated with both increased bottom-up and decreased top-down frontostriatal connectivity along with greater disinhibition of the r VLPFC and r DLPFC. These effective connectivity results provide novel insight into the causal paths which may underlie delusion neural circuitry. This provides further evidence that dysconnectivity of prediction error system is a biomarker of delusions in psychosis. Furthermore, these transdiagnostic results implicate frontostriatal dysconnectivity as common neuropathology in delusions.

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Altered hypothalamus functional connectivity with limbic system in fibromyalgia and the modulation effect of intervention

10.21203/rs.2.20627/v1 ◽

2020 ◽

Author(s):

Jian Kong ◽

Yiting Huang ◽

Jiao Liu ◽

Siyi Yu ◽

Ming Cheng ◽

...

Keyword(s):

Functional Connectivity ◽

Limbic System ◽

Resting State ◽

Tai Chi ◽

Effective Connectivity ◽

Resting State Fmri ◽

Trial Registration ◽

Modulation Effect ◽

Resting State Functional Connectivity ◽

Before And After

Abstract Background: This study aims to investigate the resting state functional connectivity (rsFC) changes of the hypothalamus in Fibromyalgia patients and the modulation effect of effective treatments. Methods: Fibromyalgia patients and matched healthy controls (HC’s) were recruited. Resting state fMRI data were collected from fibromyalgia patients before and after a 12-week Tai Chi intervention and once from HC’s. Results: Data analysis showed that fibromyalgia patients displayed significantly decreased medial hypothalamus (MH) rsFC with the thalamus and amygdala when compared to HC’s at baseline. After the intervention, fibromyalgia patients showed increased (normalized) MH rsFC in the thalamus and amygdala. Effective connectivity analysis showed disrupted MH and thalamus interaction in fibromyalgia, which nonetheless could be partially restored by Tai Chi. Conclusions: Elucidating the role of the diencephalon and limbic system in the pathophysiology and development of fibromyalgia may facilitate the development of new treatment methods for this prevalent disorder. Trial registration: Trial registration ClinicalTrials.gov Identifier: NCT02407665. Registered 3 April 2015 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02407665

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The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

10.1101/634063 ◽

2019 ◽

Author(s):

Hannes Almgren ◽

Frederik Van de Steen ◽

Adeel Razi ◽

Karl Friston ◽

Daniele Marinazzo

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Effective Connectivity ◽

Network Connectivity ◽

Resting State Fmri ◽

Measurement Noise ◽

Resting State Networks ◽

Cross Sectional ◽

Global Signal ◽

Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

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Time course of the state-dependent effect of transcranial magnetic stimulation in the TMS-adaptation paradigm

Neuroscience Letters ◽

10.1016/j.neulet.2008.07.051 ◽

2008 ◽

Vol 443 (2) ◽

pp. 82-85 ◽

Cited By ~ 29

Author(s):

Zaira Cattaneo ◽

Juha Silvanto

Keyword(s):

Transcranial Magnetic Stimulation ◽

Magnetic Stimulation ◽

Time Course ◽

The State ◽

Dependent Effect ◽

State Dependent

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Hierarchical Dynamic Causal Modeling of Resting-State fMRI Reveals Longitudinal Changes in Effective Connectivity in the Motor System after Thalamotomy for Essential Tremor

Frontiers in Neurology ◽

10.3389/fneur.2017.00346 ◽

2017 ◽

Vol 8 ◽

Cited By ~ 10

Author(s):

Hae-Jeong Park ◽

Chongwon Pae ◽

Karl Friston ◽

Changwon Jang ◽

Adeel Razi ◽

...

Keyword(s):

Essential Tremor ◽

Resting State ◽

Motor System ◽

Effective Connectivity ◽

Resting State Fmri ◽

Causal Modeling ◽

Dynamic Causal Modeling ◽

Longitudinal Changes

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Effective Connectivity Measured Using Optogenetically Evoked Hemodynamic Signals Exhibits Topography Distinct from Resting State Functional Connectivity in the Mouse

Cerebral Cortex ◽

10.1093/cercor/bhx298 ◽

2017 ◽

Vol 28 (1) ◽

pp. 370-386 ◽

Cited By ~ 16

Author(s):

Adam Q Bauer ◽

Andrew W Kraft ◽

Grant A Baxter ◽

Patrick W Wright ◽

Matthew D Reisman ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Effective Connectivity ◽

Resting State Functional Connectivity

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Altered Effective Connectivity in Sensorimotor Cortices: a Novel Signature of Severity and Clinical Course in Depression

10.1101/2021.03.14.435324 ◽

2021 ◽

Author(s):

Dipanjan Ray ◽

Dmitry Bezmaternykh ◽

Mikhail Mel’nikov ◽

Karl J Friston ◽

Moumita Das

Keyword(s):

Empirical Bayes ◽

Hierarchical Modeling ◽

Depressive Symptomatology ◽

Functional Neuroimaging ◽

Effective Connectivity ◽

Causal Modeling ◽

Depression Severity ◽

Sensory Cortices ◽

Leave One Out

AbstractFunctional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, in stead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and Dynamic Causal Modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory whereas the forward connections are mainly excitatory in nature. In the motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in interoceptive cortex: an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (non-pharmacological) treatment, depression associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.

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Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2105730118 ◽

2021 ◽

Vol 118 (40) ◽

pp. e2105730118

Author(s):

Dipanjan Ray ◽

Dmitry Bezmaternykh ◽

Mikhail Mel’nikov ◽

Karl J. Friston ◽

Moumita Das

Keyword(s):

Empirical Bayes ◽

Hierarchical Modeling ◽

Depressive Symptomatology ◽

Functional Neuroimaging ◽

Effective Connectivity ◽

Causal Modeling ◽

Depression Severity ◽

Sensory Cortices ◽

Leave One Out

Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with parametric empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory, whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex, an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross-validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (nonpharmacological) treatment, depression-associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.

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