Dataset Information

A Voxel-Wise Meta-Analysis of Gray Matter Abnormalities in Essential Tremor.

ABSTRACT: Objective: To identify the consistent gray matter (GM) volume changes from the whole brain voxel-based morphometry (VBM) studies on essential tremor (ET). Methods: The whole brain VBM studies comparing ET patients and healthy controls (HCs) were systematically searched in the PubMed, Embase and Web of Science from January 2000 to December 2017. Coordinates with significant differences in regional GM volume between ET patients and HCs were extracted from included studies and the meta-analysis was performed using effect size-based signed differential mapping (ES-SDM). Results: A total of 10 studies with 241 ET patients and 213 HCs were included in the meta-analysis. The consistent GM volume reduction was detected in the left precuneus extending to the left posterior cingulate gyrus. The subgroup meta-analysis which included studies performed on a 3.0 T scanner revealed significant GM volume increases in the bilateral frontal lobes, bilateral temporal lobes, left insula, left striatum and left pons, but obvious publication biases of these findings were detected through funnel plots and Egger's tests. Conclusions: The consistent result of our meta-analysis showed a structural damage in the left precuneus extending to the left posterior cingulate gyrus, which possibly played a role in the cognitive dysfunction and depression in ET patients. It might enhance our understanding of the pathophysiological mechanisms underlying ET.

SUBMITTER: Han Q

PROVIDER: S-EPMC6028592 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Voxel-Wise Meta-Analysis of Gray Matter Abnormalities in Essential Tremor.

Han Qing Q Hou Yanbing Y Shang Huifang H

Frontiers in neurology 20180626

<b>Objective:</b> To identify the consistent gray matter (GM) volume changes from the whole brain voxel-based morphometry (VBM) studies on essential tremor (ET). <b>Methods:</b> The whole brain VBM studies comparing ET patients and healthy controls (HCs) were systematically searched in the PubMed, Embase and Web of Science from January 2000 to December 2017. Coordinates with significant differences in regional GM volume between ET patients and HCs were extracted from included studies and the met ...[more]

PMID: 29997568

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Project description:Purpose: This study aimed to identify consistent gray matter volume (GMV) changes in the two subtypes of multiple system atrophy (MSA), including parkinsonism subtype (MSA-P), and cerebellar subtype (MSA-C), by conducting a voxel-wise meta-analysis of whole brain voxel-based morphometry (VBM) studies. Method: VBM studies comparing MSA-P or MSA-C and healthy controls (HCs) were systematically searched in the PubMed, Embase, and Web of Science published from 1974 to 20 October 2020. A quantitative meta-analysis of VBM studies on MSA-P or MSA-C was performed using the effect size-based signed differential mapping (ES-SDM) method separately. A complementary analysis was conducted using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) method, which allows a familywise error rate (FWE) correction for multiple comparisons of the results, for further validation of the results. Results: Ten studies were included in the meta-analysis of MSA-P subtype, comprising 136 MSA-P patients and 211 HCs. Five studies were included in the meta-analysis of MSA-C subtype, comprising 89 MSA-C patients and 134 HCs. Cerebellum atrophy was detected in both MSA-P and MSA-C, whereas basal ganglia atrophy was only detected in MSA-P. Cerebral cortex atrophy was detected in both subtypes, with predominant impairment of the superior temporal gyrus, inferior frontal gyrus, temporal pole, insula, and amygdala in MSA-P and predominant impairment of the superior temporal gyrus, middle temporal gyrus, fusiform gyrus, and lingual gyrus in MSA-C. Most of these results survived the FWE correction in the complementary analysis, except for the bilateral amygdala and the left caudate nucleus in MSA-P, and the right superior temporal gyrus and the right middle temporal gyrus in MSA-C. These findings remained robust in the jackknife sensitivity analysis, and no significant heterogeneity was detected. Conclusion: A different pattern of brain atrophy between MSA-P and MSA-C detected in the current study was in line with clinical manifestations and provided the evidence of the pathophysiology of the two subtypes of MSA.

Project description:Background and Objective: Although depression is one of the most common non-motor symptoms in essential tremor (ET), its pathogenesis and diagnosis biomarker are still unknown. Recently, machine learning multivariate pattern analysis (MVPA) combined with connectivity mapping of resting-state fMRI has provided a promising way to identify patients with depressed ET at the individual level and help to reveal the brain network pathogenesis of depression in patients with ET. Methods: Based on global brain connectivity (GBC) mapping from 41 depressed ET, 49 non-depressed ET, 45 primary depression, and 43 healthy controls (HCs), multiclass Gaussian process classification (GPC) and binary support vector machine (SVM) algorithms were used to identify patients with depressed ET from non-depressed ET, primary depression, and HCs, and the accuracy and permutation tests were used to assess the classification performance. Results: While the total accuracy (40.45%) of four-class GPC was poor, the four-class GPC could discriminate depressed ET from non-depressed ET, primary depression, and HCs with a sensitivity of 70.73% (P < 0.001). At the same time, the sensitivity of using binary SVM to discriminate depressed ET from non-depressed ET, primary depression, and HCs was 73.17, 80.49, and 75.61%, respectively (P < 0.001). The significant discriminative features were mainly located in cerebellar-motor-prefrontal cortex circuits (P < 0.001), and a further correlation analysis showed that the GBC values of significant discriminative features in the right middle prefrontal gyrus, bilateral cerebellum VI, and Crus 1 were correlated with clinical depression severity in patients with depressed ET. Conclusion: Our findings demonstrated that GBC mapping combined with machine learning MVPA could be used to identify patients with depressed ET, and the GBC changes in cerebellar-prefrontal cortex circuits not only posed as the significant discriminative features but also helped to understand the network pathogenesis underlying depression in patients with ET.

Dataset Information

A Voxel-Wise Meta-Analysis of Gray Matter Abnormalities in Essential Tremor.

Publications

A Voxel-Wise Meta-Analysis of Gray Matter Abnormalities in Essential Tremor.

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