Altered Effective Connectivity of Central Autonomic Network in Response to Negative Facial Expression in Adults With Cannabis Use Disorder

Disrupted Brain Functional Status in Patients with Reversible Cerebral Vasoconstriction Syndrome

OBJECTIVES

The aim of this study was to investigate the functional networks in subjects with reversible cerebral vasoconstriction syndrome (RCVS) using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

We prospectively recruited patients with RCVS and healthy controls (HCs) between February 2017 and April 2021. The rs-fMRI data were analyzed using graph theory methods. We compared node-based global and regional topological metrics (Bundle 1) and network-based intranetwork and internetwork connectivity (Bundle 2) between RCVS patients and HCs. We also explored the associations of clinical and vascular (ie, the Lindegaard index, LI) parameters with significant rs-fMRI metrics.

RESULTS

A total of 104 RCVS patients and 93 HCs were included in the final analysis. We identified significantly decreased local efficiency of the left dorsal anterior insula (dAI; p = 0.0005) in RCVS patients within 30 days after disease onset as compared to HCs, which improved 1 month later. RCVS patients also had increased global efficiency (p = 0.009) and decreased average degree centrality (p = 0.045), clustering coefficient (p = 0.033), and assortativity values (p = 0.003) in node-based analysis. In addition, patients with RCVS had increased internetwork connectivity of the default mode network (DMN) with the salience (p = 0.027) and dorsal attention (p = 0.016) networks. Significant correlations between LI and regional local efficiency in left dAI (rs  = -0.418, p = 0.042) was demonstrated.

INTERPRETATION

The significantly lower local efficiency of the left dAI, suggestive of impaired central autonomic modulation, was negatively correlated with vasoconstriction severity, which is highly plausible for the pathogenesis of RCVS. ANN NEUROL 2023;94:772-784.

Altered amygdala-cortical connectivity in individuals with Cannabis use disorder

BACKGROUND

Cannabis is one of the most commonly used substances in the United States. Prior literature using task-based functional magnetic resonance imaging (fMRI) has identified that individuals with Cannabis use disorder (CUD) show impairments in emotion processing circuitry. However, whether the functional networks involving these regions are also altered in CUD remains poorly understood.

AIMS

Investigate changes in resting-state functional connectivity (rsFC) in regions related to emotional processing in CUD.

METHODS

Sixty-two participants completed resting-state fMRI, including 21 with CUD, 20 with histories of illicit substance use but no current CUD diagnosis, and 21 with no history of illicit substance use. Whole-brain seed-based connectivity analyses were performed and one-way analyses of covariance (ANCOVAs) were conducted to detect group differences in the bilateral amygdalae, hippocampi, and the anterior and posterior cingulate cortices.

RESULTS

The CUD group exhibited significant reductions in rsFC between the amygdala and the cuneus, paracentral lobule, and supplementary motor area, and between the cingulate cortices and the occipital and temporal lobes. There were no significant group differences in hippocampal functional connectivity. In addition, CUD symptom counts based on the Structured Clinical Interview for DSM-5 (SCID) and the Cannabis Use Disorders Identification Test (CUDIT) significantly correlated with multiple connectivity metrics.

CONCLUSION

These data expand on emerging literature indicating that CUD is associated with dysfunction in the neural circuits underlying emotion processing. Dysfunction in emotion processing circuits may play a role in the behavioral impairments seen in emotion processing tasks in individuals with CUD, and the severity of CUD symptoms appears to be directly related to the degree of dysfunction in these circuits.

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

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

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.

Altered effective connectivity of the reward network during an incentive-processing task in adults with alcohol use disorder

BACKGROUND

Abnormalities of reward sensitivity and impulsivity are known to be correlated with each other and alcohol use disorder (AUD) risk, but the underlying aberrant neural circuitry involved is not clearly defined. We sought to extend the current knowledge of AUD pathophysiology by studying incentive processing in persons with AUD using functional neuroimaging data.

METHODS

We utilized functional MRI data from the Human Connectome Project Database obtained during performance of a number-guessing incentive-processing task with win, loss, and neutral feedback conditions in 78 participants with either DSM-IV alcohol abuse or dependence (combined as the AUD group) and 78 age- and sex-matched control (CON) participants. Within a network consisting of anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), insula, ventral striatum, and dorsal striatum (DS) in the right hemisphere, we performed dynamic causal modeling analysis to test group-level differences (AUD vs. CON) in effective directional connectivity (EC) as modulated by "win" and "loss" conditions. We used linear regression analyses to characterize the relations between each EC outcome and measures of cumulative alcohol exposure and impulsivity.

RESULTS

During wins, AUD participants had lower ECs from ACC to the other four nodes, greater ECs from insula to the other four nodes, greater ECs from DLPFC to the other four nodes, and greater DS to DS self-connection EC than CON participants. In the total sample, EC from the insula to the DLPFC (insula → DLPFC) during wins was positively correlated with both impulsivity (as measured by the delay-discounting task) and cumulative alcohol exposure. The DS to DS self-connection EC during wins was positively correlated with impulsivity. Many of the altered ECs from the ACC and insula to other nodes were correlated with cumulative alcohol exposure.

CONCLUSIONS

Individuals with AUD have disrupted EC in both instrumentally driven and automatized corticostriatal reward circuits during non-alcohol reward feedback. These results point to disrupted corticostriatal EC in both "top-down" and "bottom-up" pathways among individuals with AUD.