Biology subjects

Razi, A.

Publications and source records attributed to Razi, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Context Shapes Alpha-Band Electrophysiological Signatures Under Psilocybin

Background: Psilocybin is a serotonergic psychedelic that provides a pharmacological tool for examining neural activity associated with altered states of consciousness. Existing studies report reduced power (4-30 Hz) in posterior regions and altered functional connectivity (FC) at rest, but have typically involved small samples and coarse anatomical parcellations. Additionally, perceptual engagement has been shown to attenuate psilocybin-related whole-brain FC changes. However, how naturalistic context modifies psilocybin's frequency-specific electrophysiological effects remains incompletely characterised. Methods: We analysed the largest psilocybin electroencephalography (EEG) dataset to date (n = 56), with matched baseline recordings acquired before psilocybin administration. EEG was recorded during movie viewing, eyes-closed rest, guided meditation, and music listening. Cortical source activity was estimated using minimum-norm estimation and analysed across six canonical frequency bands (1-80 Hz). Spectral power, alpha peak frequency (APF), and magnitude-squared coherence within and between seven functional networks were quantified. Psilocybin effects were assessed using permutation testing with FDR correction, and linear mixed-effects models separated the contributions of drug, context, and their interaction to alpha-band connectivity. Results: At rest, psilocybin decreased alpha (8-13 Hz) power, increased gamma-range power, and elevated APF. Alpha-band FC decreased within and between higher-order networks implicated in top-down control. Relative to eyes-closed rest, psilocybin-context interactions showed that structured sensory environments attenuated or reversed psilocybin-related alpha-band connectivity changes. Conclusions: These findings identify alpha-band activity as a key electrophysiological correlate of psilocybin-related changes in cortical communication. Structured sensory input reshapes psilocybin-related cortical dynamics, particularly alpha-band connectivity across large-scale cortical networks, with the strength and direction of this modulation differing by context.

neuroscience↗

bioRxiv2026

Language-associated brain representations covary with disruption of selfhood under psilocybin

Language helps organise experience into an idiosyncratic, temporally extended sense of self. We therefore used fMRI to investigate whether greater similarity between participants in the activity of language-associated brain regions accompanied psilocybin-induced loss of selfhood. Participants heard music and viewed moving clouds at baseline and after 19 mg psilocybin. At each brain location and time point, we tested whether similarity increased with each pair's summed rank on the Ego Dissolution Inventory (EDI; N=43). We grouped independent components of these associations by time course and decoded each group's map with Neurosynth. Elementary visual imagery (SI; N=49) and baseline activity served as comparators. Under psilocybin, one group formed a distributed, left-lateralised frontotemporal network matching the Neurosynth language map (r=.382, spatial-null p<.001, q=.003), nearly twice the largest comparator estimate (SI: r=.208, p=.004, q=.028; baseline: r<.12). In exploratory analyses, resting-state connectivity of this network decreased with posterior visual cortex and increased with thalamic and frontal regions. Participants with higher EDI scores showed larger changes (Spearman's rho=.455, Bonferroni-adjusted p=.048; SI: rho=.008), most strongly for items describing loss of ego or identity (rho=.43 to .51). We propose that language-associated systems incorporate each person's semantic and autobiographical context into experience, and that disrupting them contributes to loss of ego and identity.

neuroscience↗

bioRxiv2026

What Is a Generative Model? Definitions, Disagreements, and Evaluation in Human Neuroimaging

The term generative model is widely used in human neuroimaging; however, its meaning is often left implicit. Prompted by observations and discussions at the 2025 Organization for Human Brain Mapping (OHBM) Annual Meeting, we surveyed members of the neuroimaging community to examine how generative models are defined, used, and evaluated in practice. Responses revealed some agreement on functional criteria-- such as a models ability to simulate data--alongside marked disagreement about whether specific, widely used methods should be considered generative models. Evaluative priorities also varied across respondents, though out-of-sample generalization and interpretability were consistently emphasized. Rather than proposing a single definition, this perspective highlights the diversity of current usage and argues for greater clarity when the term is invoked.

neuroscience↗

bioRxiv2025

Depression reduces structurally informed network flexibility in premanifest Huntington's disease

1Background and objectivesThe extent to which structural connectivity constrains effective connectivity in both depression and neurodegenerative contexts remains poorly understood. In particular, the relationship between structural connectivity aberrations and effective dysconnectivity associated with depression in Huntingtins disease remains uncharacterized. Here, we applied a novel procedure that implements structural connectivity-informed spectral dynamic causal modelling to examine how structural connectivity shapes directed inter-regional influences in premanifest Huntingtons disease gene expansion carriers (HDGECs) with and without depression history. MethodsUsing spectral dynamic causal modeling embedded in a hierarchical empirical Bayes framework, we analyzed fMRI data from 98 premanifest HDGECs across default mode network and striatum (caudate and putamen). HDGECs were split into two groups based on either having a history of depression or not. Depression severity on both the Beck Depression Inventory, 2nd Edition (BDI-II) and Hospital Anxiety and Depression Scale, Depression Subscale (HADS-D) was used to measure clinically elevated depression symptoms. Leave-one-out cross-validation was implemented to test predictive validity. ResultsModel evidence substantially favored structurally informed over uninformed approaches across all participants. For HDGECs, having a history of depression was associated with reduced baseline variability in effective connectivity (decreased parameter), with particularly tight regularization of near-zero-valued structural connections toward zero effective connectivity values while leaving strongly connected pathways relatively unaffected. Effects converged on striatal self-connectivity and hippocampal-striatal pathways, with distinct patterns emerging between depression history groups. Notably, clinically elevated depression revealed differential connectivity signatures, with right caudate self-connectivity showing positive correlations with clinical cut-offs for HDGECs with and without depression history. In leave-one-out cross-validation, specific connections including DMN-to-striatum (BDI: r = -0.31, p = .002; HADS-D: r = -0.33, p = .001), right hippocampus-to-left caudate (BDI: r = -0.46, p < .001; HADS-D: r = -0.30, p = .002), and left caudate-to-left putamen (BDI: r = -0.48, p < .001; HADS-D: r = -0.30, p = .003) significantly predicted individual differences in depression severity scores. DiscussionTogether, these findings link reduced network flexibility to depression vulnerability in premanifest neurodegeneration, providing a mechanistic bridge between anatomical constraints, effective connectivity alterations, and clinical depression phenotypes.

neuroscience↗

bioRxiv2025

Effective connectivity of the human claustrum: Triple networks, subcortical circuits and psychedelic modulation

Decades of cross-species research highlight the claustrums extensive bidirectional connectivity with cortical and subcortical regions, implicating it in higher-order cognitive processes requiring synchronized brain states. Psychedelics may disrupt this synchrony by modulating claustro-cortical signaling, reflected by the dissolution of cortical network signatures. Using spectral dynamic causal modeling on resting-state fMRI data from the Human Connectome Project and PsiConnect datasets at 7T and 3T, we provide the first in vivo characterization of claustral effective connectivity with triple networks and subcortical regions in humans, both at rest and under the influence of psilocybin. Claustra displayed widespread bidirectional effective connectivity and a strong inhibitory influence on all target regions. Psilocybin enhanced claustral inhibition of cortical networks while disinhibiting subcortical areas, partially associated with psychedelic subjective effect scores. These findings are consistent with cellular and functional cross-species data, supporting the proposed mechanism of claustro-cortical inhibition in regulating network synchrony, while extending this influence to the subcortex, and revealing hierarchical and hemispheric asymmetries in claustral signaling modulation under psilocybin.

neuroscience↗

bioRxiv2025

Context-dependent structurally informed effective connectivity under psilocybin

The extent to which anatomical connectivity constrains pharmacologically altered brain dynamics remains poorly understood. Here, we combined psilocybin administration with a structurally informed effective-connectivity model to examine how structural connectivity shapes directed inter-regional influences across experiential contexts. Using dynamic causal modeling embedded in a hierarchical empirical Bayes framework, we analyzed fMRI data acquired from a hippocampo-thalamo-cortical network during rest, guided meditation, music listening and movie viewing. Across contexts, psilocybin reorganized directed interactions while preserving structure-based scaling. Effects converged on efferents (outgoing influences) from the left hippocampus--a hub interfacing mnemonic and associative systems with the default-mode network and thalamus. Notably, the left-hippocampus-to-thalamus pathway showed a sign-reversed association with mystical-experience scores (downregulation during guided meditation and upregulation during music listening). In model-based leave-one-out cross-validation, left-hippocampal efferents predicted individual differences in mystical-experience intensity. A minimal model-free benchmark (hippocampal signal variability) also showed modest associations with mystical experience. Together, these findings link context-specific, structurally informed effective connectivity to individual differences in the acute psychedelic experience, providing a mechanistic bridge between anatomy, neurodynamics, and phenomenology.

neuroscience↗

bioRxiv2025

Temporal Dynamics of Depression in Premanifest Huntington's Disease: A Network Dysconnection Approach

Background and objectivesDepression emerges years before motor signs in Huntington"s disease gene expansion carriers (HDGECs), yet how directional connectivity patterns evolve over time and relate to depression trajectories remains unknown. We hypothesized HDGECs with depression history would show accelerated grey matter atrophy and disinhibition of striatal-to-default mode network connections over time. MethodsThe study utilised the largest longitudinal study of premanifest HD, TrackOn-HD, including structural and resting-state brain scans. HDGECs were stratified by depression history and followed over three annual assessments. Analyses focused on 8 predefined regions spanning striatal and default mode network regions. Here, we applied spectral dynamic causal modelling to examine effective connectivity, alongside voxel-based morphometry of grey matter atrophy and linear mixed models of depressive symptoms. Spectral DCM employed Bayesian model reduction with free energy thresholds [≥]0.95 for strong evidence of connectivity changes. ResultsThe study included 105 HDGECs (53 females, mean age = 43, mean CAG = 43). Depression-related network dysconnection operated independently of regional grey matter atrophy, with HDGECs exhibiting widespread striatal volume loss but no differential atrophy patterns between depression groups. Larger posterior cingulate cortex volumes predicted increased depression severity specifically in HDGECs with depression history, across both Beck Depression Inventory-II ({beta} = 36.79, 95% CI = 2.33-71.25, p =.041) and Hospital Anxiety and Depression Scale-Depression Subscale ({beta} = 27.31, 95% CI = 11.12-43.50, p =.002). Spectral dynamic causal models explained 88.6% of variance in depression groups and 90.4% in non-depression groups. Longitudinal effective connectivity analyses revealed distinct dysconnection profiles: HDGECs with depression history showed widespread interhemispheric alterations including progressive inhibitory changes of striatal-DMN circuits and aberrant hippocampal regulatory control, while those without depression exhibited more focal dysconnection. These patterns occurred despite no group differences in grey matter atrophy trajectories over 24 months. DiscussionDepression in HDGECs emerges through network dysconnection that operates independently of regional atrophy. Functional alterations may precede detectable structural breakdown and represent pathological responses to subclinical neurodegenerative processes, suggesting functional connectivity markers may serve as early indicators of depression vulnerability in premanifest HD.

neuroscience↗

bioRxiv2025

Spectral imprint of structural embedding in effective connectivity

Neural fluctuations exhibit rich spectral profiles that reflects both local dynamics and structural (or anatomical) embedding. Yet, standard models of resting-state effective connectivity neglect structural embedding and assume uniformity in the timescales of regions endogenous fluctuations. We introduce a chromatic dynamic causal model (DCM) in which structural valency (or degree) modulates the spectral color of endogenous fluctuations. Specifically, we assume a linear mapping between regional structural valency and the spectral exponent of scale-free auto-spectra. Simulations show this mapping can emerge as a generic consequence of structural embedding under minimal coupling in a non-equilibrium regime. We show chromatic DCM reliably recovers ground-truth parameters across network sizes and noise conditions, outperforming standard spectral DCM. Applied to empirical data, chromatic DCM reveals that valency-exponent mappings vary across a cortical hierarchy, and that its parameters are conserved across a homologous network in humans, macaques, marmosets, and mice. These findings advance a generative account of structure-function coupling and expand the repertoire of biophysical mechanisms available for inference in effective connectivity modeling.

neuroscience↗

bioRxiv2025

Predicting individual differences of fear and cognitive learning and extinction

The abilities to acquire new information and to modify previously learned knowledge are critical in an ever-changing world. However, the efficacy of learning is notably variable among individuals, with extinction learning being the epitome of such variability. Abundant studies have identified a core network of brain regions including amygdala, hippocampus, dorsal anterior cingulate cortex (ACC), ventromedial prefrontal cortex (PFC) and, more recently, the cerebellum, as key players in learning and extinction. Yet, the precise interactions within this network and their relationship to individual learning abilities and extinction have remained largely unexplored. In the present study, we examined how functional (FC), effective (EC), and structural (SC) connectivity patterns in the core learning network allow predicting individual differences in the efficacy of learning, extinction, and renewal. Analysing a large dataset of over 500 participants across a multitude of paradigms, our results revealed that FC predicted better acquisition, with a central role of ACC and hippocampus, whereas SC, involving ACC and amygdala, predicted higher levels of extinction learning. EC results suggested a predominantly inhibitory coupling among core learning network nodes, with paradigm-specific EC connectivity patterns predicting learning. Our predictions not only generalised between fear and cognitive predictive learning paradigms but were also successful in predicting learning from task-related FC and simulated data. Together, these results describe the multimodal neural determinants of learning, extinction, and renewal, and may inform individualised interventions for affective disorders based on neural connectivity patterns.

neuroscience↗

bioRxiv2025

PsiConnect: A Multimodal Neuroimaging Study of Psilocybin-Induced Changes in Brain and Behaviour

PsiConnect is a large-scale neuroimaging study designed to investigate the neural and subjective effects of psilocybin using multimodal neuroimaging. It combines functional, structural, and diffusion-weighted MRI with EEG to examine brain activity in 62 participants before and after a 19 mg dose of psilocybin. The design includes resting-state scans and three naturalistic conditions: guided meditation, music listening, and movie watching. Half of the cohort underwent an 8-week meditation training program, enabling the exploration of interactions among meditation, psilocybin, and brain function. The fMRI data was obtained through multi-echo fMRI, which enhances the signal-to-noise ratio and reduces susceptibility artifacts, thereby improving the reliability of the analyses. A comprehensive battery of behavioural and self-report measures captured both acute and longitudinal cognitive and subjective effects, with follow-ups extending to one year post-administration. The large sample size, multimodal neuroimaging, diversity of contexts, and longitudinal behavioural follow-ups enable the study of psilocybin-induced changes in brain and behaviour with an unprecedented level of detail and reliability. Furthermore, the data is curated according to open science principles to ensure accessibility and interoperability with established neuroimaging processing pipelines. These factors make PsiConnect a valuable and highly reusable resource for researchers in cognitive and computational neuroscience.

neuroscience↗

bioRxiv2025

Psychedelics Align Brain Activity with Context

Psychedelics can profoundly alter consciousness by reorganising brain connectivity; however, their effects are context-sensitive. To understand how this reorganisation depends on context, we collected and comprehensively analysed the largest psychedelic neuroimaging dataset to date. Sixty-two adults were scanned with functional MRI and EEG during rest and naturalistic stimuli (meditation, music, and movie), before and after ingesting 19 mg of psilocybin (functional MRI {approx}80 min post-dose; EEG {approx}150 min post-dose). Half the participants ranked the experience among the most meaningful of their lives. Under psilocybin, functional MRI and EEG signals recorded during eyes-closed conditions became similar to those recorded during an eyes-open condition. Global functional connectivity increased in associative regions and decreased in sensory areas. Using machine learning to represent neural activity as low-dimensional trajectories, we found that psilocybin reorganised these into structured, context-sensitive patterns of brain activity that reflected both experimental condition and the quality of subjective experience, revealing an organisation that was missed by time-averaged connectivity measures. Under psilocybin, brain networks that ordinarily segregate internal and external processing coherently integrated and aligned neural dynamics with context. This context-alignment manifested as distinct and cohesive neural trajectories in participants reporting positively felt self- and boundary-dissolving effects, corresponding to the felt experience of being part of the environment, which we refer to as embeddedness--the subjective experience of being continuous with, rather than separate from, the surrounding environment. The strength of this context-alignment was associated with next-day mindset change, bridging the neural, experiential, and therapeutic dimensions of the psychedelic state. These findings show that the organisation of brain activity covaries with the experiential coherence of the psychedelic state, and provide a systems-level framework for how context-sensitive brain dynamics link neurobiology to subjective experience and behavioural change.

neuroscience↗

bioRxiv2024

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as "default" or "visual". Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

neuroscience↗

bioRxiv2024

Structurally informed resting-state effective connectivity recapitulates cortical hierarchy

Neuronal communication relies on the anatomy of the brain, yet it remains unclear whether, at the macroscale, structural (or anatomical) connectivity provides useful constraints for modeling effective connectivity. Here, we assess a hierarchical empirical Bayes model that builds on a well-established dynamic causal model by integrating structural connectivity into resting-state effective connectivity via priors. In silico analyses show that the model successfully recovers ground-truth effective connectivity and compares favorably with a popular alternative. Analyses of empirical data reveal that a positive, monotonic relationship between structural connectivity and the prior variance of group-level effective connectivity generalizes across sessions and samples. Finally, attesting to the models biological plausibility, we show that inter-network differences in the coupling between structural and effective connectivity recapitulate a well-known unimodal- transmodal hierarchy. These findings underscore the value of integrating structural and effective connectivity to enhance understanding of functional integration, with implications for health and disease. Significance statementTo advance the understanding of how neuronal populations interact in vivo, it is essential to develop models that integrate neuroimaging modalities. Here, we show that integrating structural connectivity into dynamic causal modeling of resting-state effective connectivity substantially improves model evidence, yields reliable inferences, and demonstrates face and construct validity in silico. Furthermore, this integration reveals that structural connectivitys influence on effective connectivity varies along an established unimodal-transmodal cortical hierarchy. This finding provides the first evidence of a network-dependent modulation of the relationship between structural and effective connectivity in humans. Against the backdrop of sustained and widespread interest in dynamic causal modeling, this study highlights the added value of integrating structural connectivity-based constraints, offering a more biologically grounded account of brain dynamics.

neuroscience↗

bioRxiv2024

Sex-specific brain effective connectivity patterns associated with negative emotions

Sex differences in effective brain connectivity in emotional intelligence, emotional regulation, and stimuli-induced negative emotions have been highlighted in previous research. However, to our knowledge, no research has yet investigated the sex-specific effective connectivity related to negative emotions in healthy population during resting-state. The goal of this study is to find the association between sex-specific resting-state effective brain connectivity and basic negative emotions. For this, we have employed the NIH emotion battery of the three self-reported, basic negative emotions -- anger-affect, fear-affect, and sadness which we divided into high, moderate, and low emotion scores in each. The dataset comprises 1079 subjects (584 females) from HCP Young Adults. We selected large-scale resting-state brain networks important for emotional processing namely default mode, executive, and salience networks. We employed subject-level analysis using spectral dynamic causal modelling and group-level association analyses using parametric empirical Bayes. We report association of the self-connection of left hippocampus in females in high anger-affect, fear-affect, and sadness, whereas in males we found involvement of dorsal anterior cingulate cortex (dACC) in all three negative emotions - association of right amygdala to dACC in high anger-affect, association of the self-connection of dACC in high fear-affect, and association of dACC to left hippocampus in high sadness. Our findings primarily revealed the effective brain connectivity that is related to the higher levels of negative emotions that may lead to psychiatric disorders if not regulated. Sex-specific therapies and interventions that target psychopathology can be more beneficial when informed by the sex-specific resting-state effective connectivity.

neuroscience↗

bioRxiv2023

Genotype prediction of 336,463 samples from public expression data

Tens of thousands of RNA-sequencing experiments comprising hundreds of thousands of individual samples have now been performed. These data represent a broad range of experimental conditions, sequencing technologies, and hypotheses under study. The Recount project has aggregated and uniformly processed hundreds of thousands of publicly available RNA-seq samples. Most of these samples only include RNA expression measurements; genotype data for these same samples would enable a wide range of analyses including variant prioritization, eQTL analysis, and studies of allele specific expression. Here, we developed a statistical model based on the existing reference and alternative read counts from the RNA-seq experiments available through Recount3 to predict genotypes at autosomal biallelic loci in coding regions. We demonstrate the accuracy of our model using large-scale studies that measured both gene expression and genotype genome-wide. We show that our predictive model is highly accurate with 99.5% overall accuracy, 99.6% major allele accuracy, and 90.4% minor allele accuracy. Our model is robust to tissue and study effects, provided the coverage is high enough. We applied this model to genotype all the samples in Recount3 and provide the largest ready-to-use expression repository containing genotype information. We illustrate that the predicted genotype from RNA-seq data is sufficient to unravel the underlying population structure of samples in Recount3 using Principal Component Analysis.

genetics↗

bioRxiv2023

Neuron-specific chromatin disruption at CpG islands and aging-related regions in Kabuki syndrome mice

Many Mendelian developmental disorders caused by coding variants in epigenetic regulators have now been discovered. Epigenetic regulators are broadly expressed, and each of these disorders typically exhibits phenotypic manifestations from many different organ systems. An open question is whether the chromatin disruption - the root of the pathogenesis - is similar in the different disease-relevant cell types. This is possible in principle, since all these cell-types are subject to effects from the same causative gene, that has the same kind of function (e.g. methylates histones) and is disrupted by the same germline variant. We focus on mouse models for Kabuki syndrome types 1 and 2, and find that the chromatin accessibility abnormalities in neurons are mostly distinct from those in B or T cells. This is not because the neuronal abnormalities occur at regulatory elements that are only active in neurons. Neurons, but not B or T cells, show preferential chromatin disruption at CpG islands and at regulatory elements linked to aging. A sensitive analysis reveals that the regions disrupted in B/T cells do exhibit chromatin accessibility changes in neurons, but these are very subtle and of uncertain functional significance. Finally, we are able to identify a small set of regulatory elements disrupted in all three cell types. Our findings reveal the cellular-context-specific effect of variants in epigenetic regulators, and suggest that blood-derived "episignatures" may not be well-suited for understanding the mechanistic basis of neurodevelopment in Mendelian disorders of the epigenetic machinery.

genomics↗

bioRxiv2023

Investigating the association of resting-state brain effective connectivity with basic negative emotions

The neurotic personality has an impact on the regulation of basic negative emotions such as anger, fear, and sadness. There has been extensive research in search of functional connectivity biomarkers of neuroticism and basic negative emotions but there is a lack of research based on effective connectivity. In the current research, we intended to determine the significance of causal interaction of three large-scale resting-state networks - default mode, salience, and executive networks - to predict neuroticism and basic negative emotions. In this study, a large-scale human connectome project dataset comprising functional MRI scans and self-reported scores of neuroticism and negative emotions of 1079 subjects, was utilized. Spectral dynamic causal modelling and parametric empirical Bayes was used to estimate the subject-level effective connectivity parameters and their group-level associations with the neuroticism and emotional scores. Leave-one-out cross-validation using parametric empirical Bayes was employed for prediction analysis. Our results for heightened emotions showed that the self-connection of right hippocampus can predict individuals with high fear, self-connections of dorsal anterior cingulate cortex, posterior cingulate cortex and left dorsolateral prefrontal cortex can predict individuals with high sadness. High anger, low sadness, and neuroticism scores of any emotion category except low fear, could not be predicted using triple network effective connectivity. Our findings revealed that the causal (directed) connections of the resting-state triple network can potentially serve as a connectomic signature for people with high and low fear, high sadness, low anger, and neuroticism with low fear.

neuroscience↗

bioRxiv2022

Dysconnection and cognition in schizophrenia: a spectral dynamic causal modeling study

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC between the subnetworks of seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20% and 11% aberrant couplings. Overall, the proportion of diagnostic connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for cognitive assessment.

neuroscience↗