Altered Brain Network Degree Centrality in Major Depressive Disorder via Electro-Acupuncture Stimulation at Baihui(gv20)

2021 ◽  
Author(s):  
Shasha Li ◽  
Ya Chen ◽  
Gaoxiong Duan ◽  
Yong Pang ◽  
Huimei Liu ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Brain Function ◽  
Brain Regions ◽  
Brain Network ◽  
Degree Centrality ◽  
Major Depressive ◽  
Acupuncture Stimulation ◽  
Therapeutic Mechanisms ◽  
Abnormal Brain

Abstract Background: Although the acupuncture treatment of major depressive disorder(MDD) has been recognized by the latest clinical practice guidelines of the American Academy of Internal Medicine, complex therapeutic mechanisms need further to clarify. The aim of the study is investigate whether the aberrant resting state brain network in MDD patients could be regulated by acupuncture at GV20 using functional magnetic resonance imaging(fMRI) combined with degree centrality(DC) method. Results: Compared to healthy subjects, MDD patients exhibited significantly aberrant DC in widely brain regions, including cortical(PFC, precuneus, temporal, insula) and sub-cortical (thalamus, putamen and caudate) structures. Furthermore, results showed that acupuncture at GV20 induced down-regulation the DC of abnormal brain regions in MDD patients. Conclusions: Our findings provide imaging evidence to support that GV20-related acupuncture stimulation may modulate the abnormal brain function state in MDD patients by using fMRI technique combined with DC analysis. This study may partly interpret the neural mechanisms of acupuncture at GV20 which is used to treat patients with MDD in clinical. Trial registration: ChiCTR, ChiCTR-IOR-15006357. Registered 05 May 2015, http://www.chictr.org.cn/showproj.aspx?proj=10922.

Machine learning classifier using abnormal brain network topological metrics in major depressive disorder

Neuroreport ◽  
2012 ◽  
Vol 23 (17) ◽  
pp. 1006-1011 ◽  
Author(s):  
Hao Guo ◽  
Xiaohua Cao ◽  
Zhifen Liu ◽  
Haifang Li ◽  
Junjie Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Brain Network ◽  
Major Depressive ◽  
Learning Classifier ◽  
Abnormal Brain

scholarly journals Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiao Li ◽  
Jakob Seidlitz ◽  
John Suckling ◽  
Feiyang Fan ◽  
Gong-Jun Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Structural Changes ◽  
Brain Regions ◽  
Cell Type ◽  
Major Depressive ◽  
Structural Differences ◽  
Neuroimaging Data ◽  
Cell Type Specific

AbstractMajor depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD.

scholarly journals Microstructural deficits in the thalamus of major depressive disorder

2021 ◽  
Author(s):  
Pengfei Xu ◽  
Gangqiang Hou ◽  
Yuxuan Zhang ◽  
Yingli Zhang ◽  
Hui Ai ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Large Scale ◽  
Gray Matter Volume ◽  
Structural Connectivity ◽  
Brain Network ◽  
Microstructural Properties ◽  
Major Depressive ◽  
T1 Relaxation ◽  
Structural Abnormalities

Macroscopic structural abnormalities in the thalamus and thalamic circuits have been shown to contribute to the neuropathology of major depressive disorder (MDD). However, cytoarchitectonic properties underlying these macroscopic abnormalities remain unknown. The purpose of this study was to identify systematic deficits of brain architecture in depression, from structural brain network organization to microstructural properties. A multi-modal neuroimaging approach including diffusion, anatomical and quantitative magnetic resonance imaging (MRI) was used to examine structural-related alternations in 56 MDD patients compared with 35 age- and sex-matched controls. Structural networks were constructed and analyzed using seed-based probabilistic tractography. Morphometric measurements, including cortical thickness and voxel-based morphometry (VBM), were evaluated across the whole brain. A conjunction analysis was then conducted to identify key regions showing common structural alternations across modalities. The microstructural properties, macromolecular tissue volume (MTV) and T1 relaxation times of identified key regions were then calculated. Results showed multiple alterations of structural connectivity within a set of subcortical areas and their connections to cortical regions in MDD patients. These subcortical regions included the putamen, thalamus and caudate, which are predominately involved in the limbic-cortical-striatal-pallidal-thalamic network (LCSPT). Structural connectivity was disrupted within and between large-scale networks, mainly including subcortical networks, default mode networks and salience/ventral attention networks. Consistently, these regions also exhibited widespread volume reductions in MDD patients, specifically the bilateral thalamus, left putamen and right caudate. Importantly, the microstructural properties, T1 relaxation time of left thalamus were increased and negatively correlated with its gray matter volume in MDD patients. The present work to date sheds light on the neuropathological disruptions of LCSPT circuit in MDD, providing the first multi-modal neuroimaging evidence for the macro-micro structural abnormalities of the thalamus in patients with MDD. These findings have implications in understanding the abnormal changes of brain structures across development of MDD.

scholarly journals Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Naho Ichikawa ◽  
Giuseppe Lisi ◽  
Noriaki Yahata ◽  
Go Okada ◽  
Masahiro Takamura ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Learning Algorithm ◽  
Inferior Frontal Gyrus ◽  
Resting State Fmri ◽  
Posterior Cingulate Cortex ◽  
Brain Network ◽  
Major Depressive ◽  
Functional Connections ◽  
Limited Efficacy

Abstract The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second ‘most important’ FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

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