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BACKGROUND: The incidence of behavioral addictions (BAs) associated with scientific and technological advances has been increasing steadily. Unfortunately, a large number of studies on the structural and functional abnormalities have shown poor reproducibility, and it remains unclear whether different addictive behaviors share common underlying abnormalities. Therefore, our objective was to conduct a quantitative meta-analysis of different behavioral addictions to provide evidence-based evidence of common structural and functional changes. METHODS: We conducted systematic searches in PubMed, Web of Science and Scopus from January 2010 to December 2023, supplementing reference lists of high-quality relevant meta-analyses and reviews, to identify eligible voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies. Using anisotropic seed-based D-Mapping (AES-SDM) meta-analysis methods, we compared brain abnormalities between BAs and healthy controls (HCs). RESULTS: There were 11 GMV studies (287 BAs and 292 HCs) and 26 fMRI studies (577 BAs and 545 HCs) that met inclusion criteria. Compared with HCs, BAs demonstrated significant reductions in gray matter volume (GMV) in (1) right anterior cingulate gyri extending into the adjacent superior frontal gyrus, as well as in the left inferior frontal gyrus and right striatum. (2) the bilateral precuneus, right supramarginal gyrus, and right fusiform gyrus were hyperfunction; (3) the left medial cingulate gyrus extended to the superior frontal gyrus, the left inferior frontal gyrus, and right middle temporal gyrus had hypofunction. CONCLUSIONS: Our study identified structural and functional impairments in brain regions involved in executive control, cognitive function, visual memory, and reward-driven behavior in BAs. Notably, fronto-cingulate regions may serve as common biomarkers of BAs.
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BACKGROUND: Major depressive disorder (MDD) is a serious psychiatric disorder marked by low mood and anhedonia. Understanding the neural mechanism of MDD is essential for the treatment of depression. White matter fibres, connecting different computational units in the brain, have an important effect on brain function; however, the mechanism of white matter fibre abnormality in MDD is still unclear. AIMS: Our study expected to find white matter abnormalities associated with the frontal lobe and hippocampus in individuals with MDD. METHOD: Using diffusion tensor imaging data and tract-based spatial statistics, we investigated the microstructural differences in white matter fibre tracts between 30 adults with MDD compared with 31 healthy controls, and calculated the association between MDD-related microstructural changes and illness duration. RESULTS: It was found that patients with MDD showed reduced fractional anisotropy in the genu and body of the corpus callosum, right corona radiata and part of the thalamic radiations, suggesting lower fibrous myelination levels in these regions; the decreased fractional anisotropy in these regions was associated with longer illness duration. CONCLUSIONS: Our results suggest that MDD may be associated with microstructural damage of key fibre tracts, which could provide insights into the understanding and treatment of MDD.
RESUMO
Objective: Currently, findings regarding resting-state functional magnetic resonance imaging studies of major depressive disorder (MDD) are inconsistent. In contrast to the previously used a priori seed-based functional connectivity analyses, this study employed whole-brain exploratory analyses and aimed to explore neural activity patterns in Chinese adults with MDD. Materials and methods: Specifically, this study examined the amplitude of low-frequency fluctuations within the whole brain and adopted a large-scale brain network template to explore the core dysfunctional brain regions in individuals with MDD. Results: Overall, 32 individuals with MDD and 32 healthy controls were evaluated. Compared to healthy controls, individuals with MDD showed more profound alterations in the amplitude of low-frequency fluctuations in the temporolimbic affective circuit (e.g., middle temporal gyrus and parahippocampus) and default mode network (e.g., precuneus and thalamus). Moreover, functional connectivity between the left mid-insula and parietal regions within the sensorimotor network was weaker in individuals with MDD than in healthy controls. Conclusion: In conclusion, the neural characteristics of MDD correspond to cognitive deficits in self-referential processing and emotional processing and are related to a risk of sensory disorders or psychomotor retardation. These findings present neural markers that may be used to identify MDD, contributing to clinical diagnosis.
