Use Of Functional Connectivity Research Articles

Whole-brain intrinsic functional connectivity predicts symptoms and functioning in early psychosis

Theories of psychotic illness suggest that abnormal intrinsic functional connectivity may explain its characteristic positive and disorganization symptoms as well as lead to impaired general functioning. Here we used resting state functional magnetic resonance imaging (fMRI) to evaluate associations between these symptoms and the degree to which global connectivity is abnormal in early psychosis (EP). Eighty-six healthy controls (HCs) and 108 individuals with EP with resting state fMRI data were included in primary analyses. The EP group included 83 participants with schizophrenia-spectrum disorders and 25 with bipolar disorder type I with psychotic features. A global intrinsic connectivity “similarity index” for each EP individual was determined by calculating its correlation with the average HC connectivity matrix extracted using Schaefer atlases of multiple parcellations (100, 200, 300, and 400 region parcellations). As hypothesized, connectivity similarity with the average HC matrix was negatively associated with Brief Psychiatric Rating Scale total score, Scale for the Assessment of Positive Symptoms total score, and disorganization symptoms. Similarity was also positively associated with Global Assessment of Functioning score. Results were not driven by sex or diagnosis effects and were consistent across parcellation schemes. These results support the hypothesis that changes in whole-brain connectivity patterns are associated with psychosis symptoms and support the use of functional connectivity as a biomarker for these symptoms in EP.

Hitting the Target of Image-Guided Psychiatry?

Hitting the Target of Image-Guided Psychiatry?

GNN-Based Depression Recognition Using Spatio-Temporal Information: A fNIRS Study.

In recent years, depression has become an increasingly serious problem globally. Previous studies of automatic depression recognition based on functional near-Infrared spectroscopy (fNIRS) or other brain imaging techniques have shown potential to serve as auxiliary diagnosis methods that provide assistance to medical professionals. Recently, some studies have found that, besides directly using the data themselves (temporal data), the use of functional connectivity among channels (spatial data) also can be effective. In this paper, we propose a method based on Graph Neural Network (GNN) that combines both temporal and spatial features of fNIRS data for automatic depression recognition. Specifically, fNIRS data of 96 subjects were collected and pre-processed. Basic statistical metrics of each channel were extracted as temporal features, and channel connectivity (coherence and correlation) were calculated as spatial features. Point-biserial analysis was conducted on these features and depression labels as a data-driven motivation. For classification, we considered data of each subject as a graph, with temporal features as node features and spatial features as edge weights. The graphs were fed into GNNs for training and testing. Experimental results showed that our GNN-based methods realized the best depression recognition performance compared with classical machine-learning methods regarding accuracy, F1 score, and precision, especially in F1 score for over 10%.

Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions.

Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis.

Selective TMS-induced modulation of functional connectivity correlates with changes in behavior

Despite the increasing use of functional connectivity (FC) studies in fundamental and clinical research, the link between FC and behavior is still poorly understood. To test the hypothesis that artificial modulation of FC correlates with changes in behavior in a quantitative manner, we performed behavioral and resting state fMRI experiments in monkeys while perturbing, offline, the frontal eye fields (FEF) using unilateral continuous theta burst transcranial magnetic stimulation (FEF-cTBS). Stimulation of left and right FEF caused remarkably specific decreases in FC, which were symmetric for intra-hemispheric and asymmetric for inter-hemispheric FC. Surprisingly, FEF-cTBS improved the performance and compensated intrinsic choice biases in saccadic behavior of four monkeys, independent of the initial bias direction. Moreover, the direction of the stimulation-induced effects on both behavior (i.e. bias compensation) and FC (i.e. decrease) were independent of the stimulated hemisphere, while their magnitude depended on the side of stimulation, choice bias and monkey. Overall, the naturally-occurring saccade biases determined the FC changes following FEF-cTBS. Finally, we showed that the average decreases in FC in the FEF network induced by cTBS can be used to predict, with high specificity, both the direction (opposite to the saccadic biases) and the magnitude of the shift in saccadic choice preference relative to the unperturbed state. To reconcile the apparent contradiction between improved performance and bias compensation vs. decrease in functional connectivity, we propose that the main functional consequences of FEF-cTBS relate to adjusting inter-hemispheric imbalances.

EEG Connectivity Patterns in Childhood Sexual Abuse: A Multivariate Application Considering Curvature of Brain Space

ABSTRACT Introduction. A limitation of the bivariate electroencephalogram (EEG) coherence measure is low precision in location specification in anatomical space and functional connectivity. A more powerful use of functional connectivity of distributed brain systems maybe evaluation of patterns of correlations obtained through the functional connectivity matrix of Principal Component Analysis. The eigenimages that result from such analysis represent a descriptive characterization of anatomically distributed changes in the brain. There is little research exploring the relationship between childhood sexual abuse (CSA) and connectivity patterns in the brain. This study explored the connectivity patterns between 24 high-functioning, unmedicated adults with a history of CSA and age, gender, and handedness matched high-functioning adults with no history of CSA. Method. Resting eyes closed quantitative EEG (QEEG) was recorded from 19 scalp locations with a linked ears reference from 60 unmedicated adult research ...