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 ◽  
...  

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.

2017 ◽  
Vol 1 (3) ◽  
pp. 222-241 ◽  
Author(s):  
Adeel Razi ◽  
Mohamed L. Seghier ◽  
Yuan Zhou ◽  
Peter McColgan ◽  
Peter Zeidman ◽  
...  

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.


2021 ◽  
Vol 15 ◽  
Author(s):  
Hae-Jeong Park ◽  
Jinseok Eo ◽  
Chongwon Pae ◽  
Junho Son ◽  
Sung Min Park ◽  
...  

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.


2021 ◽  
Author(s):  
Nathan T Hall ◽  
Michael N Hallquist

Background: Borderline personality disorder (BPD) is associated with altered activity in the prefrontal cortex (PFC) and amygdala, yet no studies have examined fronto-limbic circuitry in borderline adolescents. Here, we examined the contribution of fronto-limbic connectivity to the longitudinal stability of emotion-related impulsivity (ERI), a key feature of BPD, in symptomatic adolescents and young adults. Methods: We compared resting-state effective connectivity (EC) in 82 adolescents and emerging adults with and without clinically significant borderline symptoms (n BPD = 40, ages 13-30). Group-specific directed networks were estimated amongst fronto-limbic nodes including PFC, ventral striatum (VS), central amygdala (CeN), and basolateral amygdala (BLA). We calculated directed centrality metrics and examined if these values were associated with initial levels and rates of change in ERI symptoms over a one-year follow-up using latent growth curve models (LGCMs). Results: In the healthy group, ventromedial prefrontal cortex (vmPFC) and dorsal ACC had a directed influence on CeN and VS respectively. In the borderline group bilateral BLA had a directed influence on CeN, whereas in the healthy group CeN influenced BLA. LGCMs revealed that in borderline adolescents, ERI remained stable across follow-ups. Further, higher output of R CeN in controls was associated with stronger within-person decreases in ERI. Conclusions: Functional inputs from BLA and vmPFC appear to play competing roles in influencing CeN activity. In borderline adolescents BLA may predominate over CeN activity, while in controls the ability of CeN to conversely influence BLA activity is associated with more rapid reductions in ERI.


Author(s):  
Maksim G. Sharaev ◽  
Viktoria V. Zavyalova ◽  
Vadim L. Ushakov ◽  
Sergey I. Kartashov ◽  
Boris M. Velichkovsky

Author(s):  
Stefan Frässle ◽  
Samuel J. Harrison ◽  
Jakob Heinzle ◽  
Brett A. Clementz ◽  
Carol A. Tamminga ◽  
...  

Abstract“Resting-state” functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks.Here, we show that a method recently developed for task-fMRI – regression dynamic causal modeling (rDCM) – extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.


Author(s):  
Liucija Vaisvilaite ◽  
Vetle Hushagen ◽  
Janne Gronli ◽  
Karsten Specht

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.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jian Kong ◽  
Yiting Huang ◽  
Jiao Liu ◽  
Siyi Yu ◽  
Cheng Ming ◽  
...  

AbstractThe hypothalamus links the nervous system to the endocrine system and plays a crucial role in maintaining the human body's homeostasis. This study aims to investigate the resting state functional connectivity (rsFC) changes of the hypothalamus in fibromyalgia patients. 24 Fibromyalgia patients and 24 matched healthy controls (HCs) were recruited. Resting state fMRI data were collected from the fibromyalgia patients and HC’s. Fibromyalgia patients went through a second scan after 12 weeks of Tai Chi mind–body intervention. Data analysis showed that fibromyalgia patients displayed less medial hypothalamus (MH) rsFC with the thalamus and amygdala when compared to the functional connectivity in the HCs. After the Tai Chi mind–body intervention, fibromyalgia patients showed increased MH rsFC with the thalamus and amygdala accompanied by clinical improvement. Effective connectivity analysis showed disrupted MH and thalamus interaction in the fibromyalgia patients, which was altered by mind–body exercise. Our findings suggest that fibromyalgia is associated with altered functional connectivity within the diencephalon and limbic system. Elucidating the roles of the diencephalon and limbic system in the pathophysiology and development of fibromyalgia may facilitate the development of a new biomarker and effective treatment methods for this prevalent disorder.Trial Registration ClinicalTrials.gov, NCT02407665. Registered: 3 April 2015, https://clinicaltrials.gov/ct2/show/NCT02407665?term=NCT02407665&draw=2&rank=1


Author(s):  
Sevdalina Kandilarova ◽  
Drozdstoy Stoyanov ◽  
Katrin Aryutova ◽  
Rossitsa Paunova ◽  
Mladen Mantarkov ◽  
...  

Background & Objective: We have previously identified aberrant connectivity of the left precuneus, ventrolateral prefrontal cortex, anterior cingulate cortex, and anterior insula in patients with either a paranoid (schizophrenia), or a depressive syndrome (both unipolar and bipolar). In the current study, we attempted to replicate and expand these findings by including a healthy control sample and separating the patients in a depressive episode into two groups: unipolar and bipolar depression. We hypothesized that the connections between those major nodes of the resting state networks would demonstrate different patterns in the three patient groups compared to the healthy subjects. Method: Resting-state functional MRI was performed on a sample of 101 participants, of which 26 patients with schizophrenia (current psychotic episodes), 24 subjects with bipolar disorder (BD), 33 with major depressive disorder (MDD) (both BD and MDD patients were in a current depressive episode), and 21 healthy controls. Spectral Dynamic Causal Modeling was used to calculate the coupling values between eight regions of interest, including the anterior precuneus (PRC), anterior hippocampus, anterior insula, angular gyrus, lateral orbitofrontal cortex (OFC), middle frontal gyrus, planum temporale, and anterior thalamus. Results & Conclusion: We identified disturbed effective connectivity from the left lateral orbitofrontal cortex to the left anterior precuneus that differed significantly between unipolar depression, where the influence was inhibitory, and bipolar depression, where the effect was excitatory. A logistic regression analysis correctly classified 75% of patients with unipolar and bipolar depression based solely on the coupling values of this connection. In addition, patients with schizophrenia demonstrated negative effective connectivity from the anterior PRC to the lateral OFC, which distinguished them from healthy controls and patients with major depression. Future studies with unmedicated patients will be needed to establish the replicability of our findings.


2021 ◽  
Author(s):  
Stefan Frässle ◽  
Samuel J. Harrison ◽  
Jakob Heinzle ◽  
Brett A. Clementz ◽  
Carol A. Tamminga ◽  
...  

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