Background: This study aims to identify how the large-scale brain dynamic functional connectivity (dFC) differs between mood states in bipolar disorder (BD). The authors analyzed dFC in subjects with BD in depressed and euthymic states using resting-state functional magnetic resonance imaging (rsfMRI) data, and compared these states to healthy controls (HCs).
Method: 20 subjects with BD in a depressive episode, 23 euthymic BD subjects, and 31 matched HCs underwent rsfMRI scans. Using an existing parcellation of the whole brain, we measured dFC between brain regions and identified the different patterns of brain network connections between groups.
Results: In the analysis of whole brain dFC, the connectivity between the left Superior Temporal Gyrus (STG) in the somatomotor network (SMN), the right Middle Temporal Gyrus (MTG) in the default mode network (DMN) and the bilateral Postcentral Gyrus (PoG) in the DMN of depressed BD was greater than that of euthymic BD, while there was no significant difference between euthymic BD and HCs in these brain regions. Euthymic BD patients had abnormalities in the frontal-striatal-thalamic (FST) circuit compared to HCs.
Conclusions: Differences in dFC within and between DMN and SMN can be used to distinguish depressed and euthymic states in bipolar patients. The hyperconnectivity within and between DMN and SMN may be a state feature of depressed BD. The abnormal connectivity of the FST circuit can help identify euthymic BD from HCs.
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http://dx.doi.org/10.1016/j.brainres.2020.147143 | DOI Listing |
J Neural Eng
January 2025
Weldon School of Biomedical Engineering, Purdue University, 723 W. Michigan St., Indianapolis, Indiana, 46202, UNITED STATES.
Objective: Direct electrical neurostimulation using continuous sinusoidal low frequency alternating currents (LFAC) is an emerging modality for neuromodulation. As opposed to the traditional rectangular pulse stimulation, there is limited background on the characteristics of peripheral nerves responses to sinusoidal LFAC stimulation; especially within the low frequency range (<50Hz). In this study, we demonstrate LFAC activation as a means to activate motor nerves by direct bipolar nerve stimulation via cuff electrodes, and characterize the factors of activation.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Psychiatry, University of California San Diego, La Jolla, CA, United States of America.
Background: Bipolar Disorder (BD) is a complex disease. It is heterogeneous, both at the phenotypic and genetic level, although the extent and impact of this heterogeneity is not fully understood. One way to assess this heterogeneity is to look for patterns in the subphenotype data.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California.
Importance: Limited research explores mental health disparities between individuals in sexual and gender minority (SGM) populations and cisgender heterosexual (non-SGM) populations using national-level data.
Objective: To explore mental health disparities between SGM and non-SGM populations across sexual orientation, sex assigned at birth, and gender identity within the All of Us Research Program.
Design, Setting, And Participants: This cross-sectional study used survey data and linked electronic health records of eligible All of Us Research Program participants from May 31, 2017, to June 30, 2022.
Int J Clin Health Psychol
January 2025
Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China.
BMC Psychiatry
January 2025
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness.
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