Cortical thickness in Parkinson disease: A coordinate-based meta-analysis

Reproducibility of cerebellar involvement as quantified by consensus structural MRI biomarkers in advanced essential tremor

Essential tremor (ET) is the most prevalent movement disorder with poorly understood etiology. Some neuroimaging studies report cerebellar involvement whereas others do not. This discrepancy may stem from underpowered studies, differences in statistical modeling or variation in magnetic resonance imaging (MRI) acquisition and processing. To resolve this, we investigated the cerebellar structural differences using a local advanced ET dataset augmented by matched controls from PPMI and ADNI. We tested the hypothesis of cerebellar involvement using three neuroimaging biomarkers: VBM, gray/white matter volumetry and lobular volumetry. Furthermore, we assessed the impacts of statistical models and segmentation pipelines on results. Results indicate that the detected cerebellar structural changes vary with methodology. Significant reduction of right cerebellar gray matter and increase of the left cerebellar white matter were the only two biomarkers consistently identified by multiple methods. Results also show substantial volumetric overestimation from SUIT-based segmentation-partially explaining previous literature discrepancies. This study suggests that current estimation of cerebellar involvement in ET may be overemphasized in MRI studies and highlights the importance of methods sensitivity analysis on results interpretation. ET datasets with large sample size and replication studies are required to improve our understanding of regional specificity of cerebellum involvement in ET. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 21 March 2022. The protocol, as accepted by the journal, can be found at: https://doi.org/10.6084/m9.figshare.19697776 .

Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease

Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI) based CAD methods can help reveal structural changes in brain. Classifier is a key component in CAD system, which directly affects the classification performance. Privileged information (PI) can assist to train the classifier by providing additional information, which makes test samples have less error and improves the classification accuracy. In this paper, we proposed a PI based kernel ridge regression plus (KRR+) in diagnosis of PD. Specifically, morphometric features and brain network features are extracted from MRI. Then, empirical kernel mapping feature expression method is used to make the data separable in high-dimensional space. Besides, we introduce self-paced learning that can adaptively select the sample in training of the model, which can further improve the classification performance. The experimental results show that the proposed method is effective for PD diagnosis, its performance is superior to existing classification model. This method is helpful to assist clinicians to find out possible neuroimaging biomarkers in the diagnosis of PD.

Structural and functional brain alterations in anorexia nervosa:A multimodal meta-analysis of neuroimaging studies

Anorexia nervosa (AN) is a complex psychiatric disorder with poorly understood etiology. Numerous voxel‐based morphometry (VBM) and resting‐state functional imaging studies have provided strong evidence of abnormal brain structure and intrinsic and functional activities in AN, but with inconsistent conclusions. Herein, a whole‐brain meta‐analysis was conducted on VBM (660 patients with AN, and 740 controls) and resting‐state functional imaging (425 patients with AN, and 461 controls) studies that measured differences in the gray matter volume (GMV) and intrinsic functional activity between patients with AN and healthy controls (HCs). Overall, patients with AN displayed decreased GMV in the bilateral median cingulate cortex (extending to the bilateral anterior and posterior cingulate cortex), and left middle occipital gyrus (extending to the left inferior parietal lobe). In resting‐state functional imaging studies, patients with AN displayed decreased resting‐state functional activity in the bilateral anterior cingulate cortex and bilateral median cingulate cortex, and increased resting‐state functional activity in the right parahippocampal gyrus. This multimodal meta‐analysis identified reductions of gray matter and functional activity in the anterior and median cingulate in patients with AN, which contributes to further understanding of the pathophysiology of AN.

Gray Matter Atrophy in Amnestic Mild Cognitive Impairment: A Voxel-Based Meta-Analysis

Background: Voxel-based morphometry (VBM) has been widely used to investigate structural alterations in amnesia mild cognitive impairment (aMCI). However, inconsistent results have hindered our understanding of the exact neuropathology related to aMCI. Objectives: Our aim was to systematically review the literature reporting VBM on aMCI to elucidate consistent gray matter alterations, their functional characterization, and corresponding co-activation patterns. Methods: The PubMed, Web of Science, and EMBASE databases were searched for VBM studies on aMCI published from inception up to June 2020. Peak coordinates were extracted from clusters that showed significant gray matter differences between aMCI patients and healthy controls (HC). Meta-analysis was performed using seed-based d mapping with the permutation of subject images (SDM-PSI), a newly improved meta-analytic method. Functional characterization and task-based co-activation patterns using the BrainMap database were performed on significant clusters to explore their functional roles. Finally, VBM was performed based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to further support the findings. Results: A total of 31 studies with 681 aMCI patients and 837 HC were included in this systematic review. The aMCI group showed significant gray matter atrophy in the left amygdala and right hippocampus, which was consistent with results from the ADNI dataset. Functional characterization revealed that these regions were mainly associated with emotion, cognition, and perception. Further, meta-regression analysis demonstrated that gray matter atrophy in the left inferior frontal gyrus and the left angular gyrus was significantly associated with cognitive impairment in the aMCI group. Conclusions: The findings of gray matter atrophy in the left amygdala and right hippocampus are highly consistent and robust, and not only offer a better understanding of the underlying neuropathology but also provide accurate potential biomarkers for aMCI.

Cortical thickness in Parkinson's disease: a coordinate-based meta-analysis

Parkinson's disease (PD) is a common age-related neurodegenerative disease that affects the structural architecture of the cerebral cortex. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis is a popular measure to assess brain structural alterations in the gray matter in PD. However, the results of CTh analysis in PD lack consistency and have not been systematically reviewed. We conducted a comprehensive coordinate-based meta-analysis (CBMA) of 38 CTh studies (57 comparison datasets) in 1,843 patients with PD using the latest seed-based d mapping software. Compared with 1,172 healthy controls, no significantly consistent CTh alterations were found in patients with PD, suggesting CTh as an unreliable neuroimaging marker for PD. The lack of consistent CTh alterations in PD could be ascribed to the heterogeneity in clinical populations, variations in imaging methods, and underpowered small sample sizes. These results highlight the need to control for potential confounding factors to produce robust and reproducible CTh results in PD.