scholarly journals Looking from the top: enhanced top-down sensorimotor processing in somatic anxiety

2021 ◽  
Author(s):  
Ismail Bouziane ◽  
Moumita Das ◽  
Cesar Caballero-Gaudes ◽  
Dipanjan Ray
Keyword(s):  
Empirical Bayes ◽  
Hierarchical Modeling ◽  
Functional Neuroimaging ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Cortical Processing ◽  
Top Down ◽  
Somatic Anxiety ◽  
Novel Approach ◽  
Human Connectome Project

AbstractBackgroundFunctional neuroimaging research on anxiety has traditionally focused on brain networks associated with the complex psychological aspects of anxiety. In this study, instead, we target the somatic aspects of anxiety. Motivated by the growing recognition that top-down cortical processing plays crucial roles in perception and action, we investigate effective connectivity among hierarchically organized sensorimotor regions and its association with (trait) anxiety.MethodsWe selected 164 participants from the Human Connectome Project based on psychometric measures. We used their resting-state functional MRI data and Dynamic Causal Modeling (DCM) to assess effective connectivity within and between key regions in the exteroceptive, interoceptive, and motor hierarchy. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes we first established the architecture of effective connectivity in sensorimotor networks and investigated its association with fear somatic arousal (FSA) and fear affect (FA) scores. To probe the robustness of our results, we implemented a leave-one-out cross validation analysis.ResultsAt the group level, the top-down connections in exteroceptive cortices were inhibitory in nature whereas in interoceptive and motor cortices they were excitatory. With increasing FSA scores, the pattern of top-down effective connectivity was enhanced in all three networks: an observation that corroborates well with anxiety phenomenology. Anxiety associated changes in effective connectivity were of effect size sufficiently large to predict whether somebody has mild or severe somatic anxiety. Interestingly, the enhancement in top-down processing in sensorimotor cortices were associated with FSA but not FA scores, thus establishing the (relative) dissociation between somatic and cognitive dimensions of anxiety.ConclusionsOverall, enhanced top-down effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of trait somatic anxiety. These results pave the way for a novel approach into investigating the neural underpinnings of anxiety based on the recognition of anxiety as an embodied phenomenon and the emerging interest in top-down cortical processing.

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

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.

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

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.

scholarly journals State-Dependent Effective Connectivity in Resting-State fMRI

2021 ◽  
Vol 15 ◽  
Author(s):  
Hae-Jeong Park ◽  
Jinseok Eo ◽  
Chongwon Pae ◽  
Junho Son ◽  
Sung Min Park ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Empirical Bayes ◽  
Time Course ◽  
Effective Connectivity ◽  
The State ◽  
Causal Modeling ◽  
Occupation Times ◽  
State Dependent ◽  
Mental Diseases

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.

scholarly journals Dynamic causal modeling for calcium imaging data reveals differential effective connectivity for sensory processing in a barrel cortical column

2019 ◽  
Author(s):  
Kyesam Jung ◽  
Jiyoung Kang ◽  
Seungsoo Chung ◽  
Hae-Jeong Park
Keyword(s):  
Calcium Imaging ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Ion Concentration ◽  
Hierarchical Architecture ◽  
Cortical Column ◽  
Top Down ◽  
External Mode ◽  
Neural Populations ◽  
Layer 2

AbstractMulti-photon calcium imaging (CaI) is an important tool to assess activity among neural populations within a column in the sensory cortex. However, the complex asymmetrical interactions among neural populations, termed effective connectivity, cannot be directly assessed by measuring the activity of each neuron using CaI but calls for computational modeling. To estimate effective connectivity among neural populations, we proposed a dynamic causal model (DCM) for CaI by combining a convolution-based dynamic neural state model and a dynamic calcium ion concentration model for CaI signals. After conducting a simulation study to evaluate DCM for CaI, we applied it to an experimental CaI data measured at the layer 2/3 of a barrel cortical column that differentially responds to hit and error whisking trails in mice. We first identified neural populations and constructed computational models with intrinsic connectivity of neural populations within the layer 2/3 of the barrel cortex and extrinsic connectivity with latent external modes. Bayesian model inversion and comparison shows that a top-down model with latent inhibitory and excitatory external modes explains the observed CaI signals during hit and error trials better than any other model, with a single external mode or without any latent modes. The best model also showed differential intrinsic and extrinsic effective connectivity between hit and error trials (corresponding to the bottom-up and top-down processes) in the functional hierarchical architecture. Both simulation and experimental results suggest the usefulness of DCM for CaI in terms of exploration of the hierarchical interactions among neural populations observed in CaI.

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

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.

scholarly journals The Role of Unimodal Feedback Pathways in Gender Perception During Activation of Voice and Face Areas

2021 ◽  
Vol 15 ◽  
Author(s):  
Clement Abbatecola ◽  
Peggy Gerardin ◽  
Kim Beneyton ◽  
Henry Kennedy ◽  
Kenneth Knoblauch
Keyword(s):  
Imaging Techniques ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Conjoint Measurement ◽  
Top Down ◽  
Model Framework ◽  
Face Area ◽  
Gender Perception ◽  
The Face ◽  
Feedback Pathways

Cross-modal effects provide a model framework for investigating hierarchical inter-areal processing, particularly, under conditions where unimodal cortical areas receive contextual feedback from other modalities. Here, using complementary behavioral and brain imaging techniques, we investigated the functional networks participating in face and voice processing during gender perception, a high-level feature of voice and face perception. Within the framework of a signal detection decision model, Maximum likelihood conjoint measurement (MLCM) was used to estimate the contributions of the face and voice to gender comparisons between pairs of audio-visual stimuli in which the face and voice were independently modulated. Top–down contributions were varied by instructing participants to make judgments based on the gender of either the face, the voice or both modalities (N = 12 for each task). Estimated face and voice contributions to the judgments of the stimulus pairs were not independent; both contributed to all tasks, but their respective weights varied over a 40-fold range due to top–down influences. Models that best described the modal contributions required the inclusion of two different top–down interactions: (i) an interaction that depended on gender congruence across modalities (i.e., difference between face and voice modalities for each stimulus); (ii) an interaction that depended on the within modalities’ gender magnitude. The significance of these interactions was task dependent. Specifically, gender congruence interaction was significant for the face and voice tasks while the gender magnitude interaction was significant for the face and stimulus tasks. Subsequently, we used the same stimuli and related tasks in a functional magnetic resonance imaging (fMRI) paradigm (N = 12) to explore the neural correlates of these perceptual processes, analyzed with Dynamic Causal Modeling (DCM) and Bayesian Model Selection. Results revealed changes in effective connectivity between the unimodal Fusiform Face Area (FFA) and Temporal Voice Area (TVA) in a fashion that paralleled the face and voice behavioral interactions observed in the psychophysical data. These findings explore the role in perception of multiple unimodal parallel feedback pathways.

scholarly journals Functional neuroimaging and schizophrenia: a view towards effective connectivity modeling and polygenic risk

2013 ◽  
Vol 15 (3) ◽  
pp. 279-289 ◽  
Keyword(s):  
Functional Neuroimaging ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Imaging Genetics ◽  
Causal Modeling ◽  
Intermediate Phenotype ◽  
Polygenic Risk ◽  
Intermediate Phenotypes ◽  
Psychiatric Syndrome ◽  
The Impact

We review critical trends in imaging genetics as applied to schizophrenia research, and then discuss some future directions of the field. A plethora of imaging genetics studies have investigated the impact of genetic variation on brain function, since the paradigm of a neuroimaging intermediate phenotype for schizophrenia first emerged. It was initially posited that the effects of schizophrenia susceptibility genes would be more penetrant at the level of biologically based neuroimaging intermediate phenotypes than at the level of a complex and phenotypically heterogeneous psychiatric syndrome. The results of many studies support this assumption, most of which show single genetic variants to be associated with changes in activity of localized brain regions, as determined by select cognitive controlled tasks. From these basic studies, functional neuroimaging analysis of intermediate phenotypes has progressed to more complex and realistic models of brain dysfunction, incorporating models of functional and effective connectivity, including the modalities of psycho-physiological interaction, dynamic causal modeling, and graph theory metrics. The genetic association approaches applied to imaging genetics have also progressed to more sophisticated multivariate effects, including incorporation of two-way and three-way epistatic interactions, and most recently polygenic risk models. Imaging genetics is a unique and powerful strategy for understanding the neural mechanisms of genetic risk for complex CNS disorders at the human brain level.

scholarly journals Time-of-day effects in resting-state fMRI: changes in Effective Connectivity and BOLD signal

2020 ◽  
Author(s):  
Liucija Vaisvilaite ◽  
Vetle Hushagen ◽  
Janne Gronli ◽  
Karsten Specht
Keyword(s):  
Resting State ◽  
Empirical Bayes ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Time Of Day ◽  
Blood Oxygenation ◽  
Bold Signal ◽  
Human Connectome Project ◽  
Using Data ◽  
Density Dynamic

The current project explored the hypothesis that time-of-day dependent metabolic variations may contribute to reduced reliability in resting-state fMRI studies. We have investigated time-of-day effects in the spontaneous fluctuations (>0.1Hz) of the blood oxygenation level dependent (BOLD) signal. Using data from the human connectome project (HCP) release S1200, cross-spectral density dynamic causal modelling (DCM) was used to analyze time-dependent effects on the hemodynamic response and effective connectivity parameters. Hierarchical group-parametric empirical Bayes (PEB) found no support for changes in effective connectivity, whereas the hemodynamic parameters exhibited a significant time-of-day dependent effect. We conclude that these findings urge the need to account for the time of data acquisition in future MRI studies.

scholarly journals Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature

2021 ◽  
Vol 15 ◽  
Author(s):  
Andrew D. Snyder ◽  
Liangsuo Ma ◽  
Joel L. Steinberg ◽  
Kyle Woisard ◽  
Frederick G. Moeller
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Functional Neuroimaging ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Causal Modeling ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Dynamic Causal Modeling

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.

scholarly journals Voice and Face Gender Perception engages multimodal integration via multiple feedback pathways

2020 ◽  
Author(s):  
Clement Abbatecola ◽  
Kim Beneyton ◽  
Peggy Gerardin ◽  
Henry Kennedy ◽  
Kenneth Knoblauch
Keyword(s):  
Effective Connectivity ◽  
Causal Modeling ◽  
Conjoint Measurement ◽  
Auditory Modality ◽  
Multimodal Integration ◽  
Hierarchical Systems ◽  
Top Down ◽  
Face Area ◽  
Gender Perception ◽  
Gender Information

AbstractMultimodal integration provides an ideal framework for investigating top-down influences in perceptual integration. Here, we investigate mechanisms and functional networks participating in face-voice multimodal integration during gender perception by using complementary behavioral (Maximum Likelihood Conjoint Measurement) and brain imaging (Dynamic Causal Modeling of fMRI data) techniques. Thirty-six subjects were instructed to judge pairs of face-voice stimuli either according to the gender of the face (face task), the voice (voice task) or the stimulus (stimulus task; no specific modality instruction given). Face and voice contributions to the tasks were not independent, as both modalities significantly contributed to all tasks. The top-down influences in each task could be modeled as a differential weighting of the contributions of each modality with an asymmetry in favor of the auditory modality in terms of magnitude of the effect. Additionally, we observed two independent interaction effects in the decision process that reflect both the coherence of the gender information across modalities and the magnitude of the gender difference from neutral. In a second experiment we investigated with functional MRI the modulation of effective connectivity between the Fusiform Face Area (FFA) and the Temporal Voice Area (TVA), two cortical areas implicated in face and voice processing. Twelve participants were presented with multimodal face-voice stimuli and instructed to attend either to face, voice or any gender information. We found specific changes in effective connectivity between these areas in the same conditions that generated behavioral interactions. Taken together, we interpret these results as converging evidence supporting the existence of multiple parallel hierarchical systems in multi-modal integration.

Sign in / Sign up

Export Citation Format

Share Document