Depression recognition using functional connectivity based on dynamic causal model

Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.

Effective connectivity of dorsal and ventral visual pathways in chunk decomposition

Chunk decomposition is defined as a cognitive process which breaks up familiar items into several parts to reorganize them in an alternative approach.The present study investigated the effective connectivity of visual streams in chunk decomposition through dynamic causal modeling(DCM).The results revealed that chunk familiarity and perceptual tightness made a combined contribution to highlight not only the "what" and the "where" streams,but also the effective connectivity from the left inferior temporal gyrus to the left superior parietal lobule.

Abnormal Effective Connectivity of the Anterior Forebrain Regions in Disorders of Consciousness

A number of studies have indicated that disor- ders of consciousness result from multifocal injuries as well as from the impaired functional and anatomical connectivity between various anterior forebrain regions. However, the specific causal mechanism linking these regions remains unclear. In this study, we used spectral dynamic causal modeling to assess how the effective connections （ECs） between various regions differ between individuals. Next, we used connectome-based predictive modeling to evaluate the performance of the ECs in predicting the clinical scores of DOC patients. We found increased ECs from the striatum to the globus pallidus as well as from the globus pallidus to the posterior cingulate cortex, and decreased ECs from the globus pallidus to the thalamus and from the medial prefrontal cortex to the striatum in DOC patients as compared to healthy controls. Prediction of the patients＇ outcome was effective using the negative ECs as features. In summary, the present study highlights a key role of the thalamo-basal ganglia-cortical loop in DOCs and supports the anterior forebrain mesocircuit hypothesis. Furthermore, EC could be potentially used to assess the consciousness level.

Abnormal Effective Connectivity in the Brain is Involved in Auditory Verbal Hallucinations in Schizophrenia

Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stochastic dynamic causal modeling（s DCM） to quantify connections among the left dorsolateral prefrontal cortex（inner speech monitoring）, auditory cortex（auditory processing）, hippocampus（memory retrieval）, thalamus（information filtering）, and Broca＇s area（language production） in 17 first-episode drug-na？¨ve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging.Finally, we performed receiver operating characteristic（ROC） analysis and correlation analysis between image measures and symptoms. s DCM revealed an increasedsensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca＇s area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVHspecific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.

Separate Neural Networks for Gains and Losses in Intertemporal Choice

An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic causal modeling analyses, we investigated the functional interactions between regions involved in the decision- making process while participants performed temporal discounting tasks in both the gains and losses domains. We found two distinct intrinsic valuation systems underlying temporal discounting in the gains and losses domains： gains were specifically evaluated in the medial regions, including the medial prefrontal and orbitofrontal cortices, and losses were evaluated in the lateral dorsolateral prefrontal cortex. In addition, immediate reward or pun- ishment was found to modulate the functional interactions between the dorsolateral prefrontal cortex and distinct regions in both the gains and losses domains： in the gains domain, the mesolimbic regions; in the losses domain, the medial prefrontal cortex, anterior cingulate cortex, and insula. These findings suggest that intertemporal choice of gains and losses might involve distinct valuation systems, and more importantly, separate neural interactions may implement the intertemporal choices of gains and losses. These findings may provide a new biological perspective for understanding the neural mechanisms underlying intertemporal choice of gains and losses.

Instruction Cues Increase Brain Network Complexity During Movement Preparation

Instruction cues are widely employed for research on neural mechanisms during movement preparation.However,their influence on brain connectivity during movement has not received much attention.Herein,15 healthy subjects completed two experimental tasks including either instructed or voluntary movements;meanwhile electroencephalogram(EEG)data were synchronously recorded.Based on source analysis and related literature,six movement-related brain regions were selected,including the left/right supplementary motor area(SMA),left/right inferior frontal gyrus(iFg),and left/right postcentral gyrus(pCg).After assuming 10 a priori models of regional brain connectivity,we evaluated the optimal connectivity model between brain regions for the two scenarios using the dynamic causality model(DCM).During voluntary movement,the movement originated in the SMA,passed through the iFg of the prefrontal lobe,and then returned to the main sensory cortex of the pCg.In the instructed movement,the movement originated in the iFg,and then was transmitted to the pCg and the SMA,as well as from the pCg to the SMA.In contrast to the preparation process of voluntary movement,there were long-range information interactions between the iFg and pCg.Further,almost the same brain regions were active during movement preparation under both voluntary and instructed movement tasks,which evidences certain similarities in dynamic brain connectivity,that is,the brain has direct connections between the bilateral SMA,bilateral pCg,and bilateral SMA,indicating that the both brain hemispheres work together during the movement preparation phase.The results suggest that the network during the preparation process of instructed movements is more complex than voluntary movements.