An enriched granger causal model allowing variable static anatomical constraints

Abstract

The anatomical connectivity constrains but does not fully determine functional connectivity, especially when one explores into the dynamics over the course of a trial. Therefore, an enriched granger causal model (GCM) integrated with anatomical prior information is proposed in this study, to describe the dynamic effective connectivity to distinguish the depression and explore the pathogenesis of depression. In the proposed frame, the anatomical information was converted via an optimized transformation model, which was then integrated into the normal GCM by variational bayesian model. Magnetoencephalography (MEG) signals and diffusion tensor imaging (DTI) of 24 depressive patients and 24 matched controls were utilized for performance comparison. Together with the sliding windowed MEG signals under sad facial stimuli, the enriched GCM was applied to calculate the regional-pair dynamic effective connectivity, which were repeatedly sifted via feature selection and fed into different classifiers. From the aspects of model errors and recognition accuracy rates, results supported the superiority of the enriched GCM with anatomical priors over the normal GCM. For the effective connectivity with anatomical priors, the best subject discrimination accuracy of SVM was 85.42% (the sensitivity was 87.50% and the specificity was 83.33%). Furthermore, discriminative feature analysis suggested that the enriched GCM that detect the variable anatomical constraint on function could better detect more stringent and less dynamic brain function in depression. The proposed approach is valuable in dynamic functional dysfunction exploration in depression and could be useful for depression recognition.

The thalamus is the causal hub of intervention in patients with major depressive disorder: Evidence from the Granger causality analysis

“…The GC approach is nonparametric and computationally straightforward ( Roebroeck et al, 2005 , Seth et al, 2013 ), thus providing a unique, unbiased solution for effective connection ( Bielczyk et al, 2019 ). In recent years, the GC approach has been widely used to investigate the intrinsic effective connectivity (EC) network in resting-state functional magnetic resonance imaging (rs-fMRI) data ( Bi et al, 2019 , Iwabuchi et al, 2017 , Jiang et al, 2017 ). The alteration of EC networks in MDD patients is viewed as an important aspect of pathogenesis and potential intervention targets of MDD ( Feng et al, 2016 , Hamilton et al, 2011 , Yang et al, 2021a ).…”

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

“…Second, our methodology offers a framework for exploring the brains' functional integration under pharmacological-based interventions or in psychiatric disorders, where deviations from typical structural-effective connectivity relationships might elucidate (for example) the underlying neuronal mechanisms of disease pathophysiology. Recently, structurally informed directed connectivity models have been used to identify case-control differences in depression, autism, and schizophrenia, 20,[44][45][46][47] and may prove useful in disease subtyping. Furthermore, research has suggested that the coupling between structural and functional connectivity decrease under the influence of psychedelics, and it will be important to investigate whether this pattern is evident using the hierarchical empirical Bayes method utilized here.…”

“…First, a Bayesian approach that involves constraining the inversion of generative models with structural connectivity-based (i.e., structure-based) priors. [13][14][15][16][17][18][19][20][21][22] Second, a mechanistic approach, via which structural connectivity is incorporated directly into a generative model's equations (rather than being incorporated into priors over the equations' parameters). [23][24][25] Finally, a data-driven machine learning (ML) approach, which leverages various ML techniques to infer a map of directed interactions from both structural and functional connectivity taken together.…”

Sub‐second transient activated patterns to sad expressions in major depressive disorders discovered via hidden Markov model

“…Since the different temporal stages for the processing of the negative emotion were closely associated with impaired neural mechanism for MDD patients, aberrant spatial patterns underlying different periods deserved further analyses. Based on this, literature reported that the hyper responsiveness of brain regions like amygdala and hippocampus, which were involved in limbic system, was associated with depression severity (Bi et al., 2019; Suslow et al., 2010). Previous sensor‐level analysis has found that patients with MDD expressed increased activations in occipital lobes when dealing with sad faces (Jiang et al., 2019).…”