Differential insular cortex sub-regional atrophy in neurodegenerative diseases: a systematic review and meta-analysis

The insular cortex is proposed to function as a central brain hub characterized by wide-spread connections and diverse functional roles. As a result, its centrality in the brain confers high metabolic demands predisposing it to dysfunction in disease. However, the functional profile and vulnerability to degeneration varies across the insular sub-regions. The aim of this systematic review and meta-analysis is to summarize and quantitatively analyze the relationship between insular cortex sub-regional atrophy, studied by voxel based morphometry, with cognitive and neuropsychiatric deficits in frontotemporal dementia (FTD), Alzheimer’s disease (AD), Parkinson’s disease (PD), and dementia with Lewy bodies (DLB). We systematically searched through Pubmed and Embase and identified 519 studies that fit our criteria. A total of 41 studies (n = 2261 subjects) fulfilled the inclusion criteria for the meta-analysis. The peak insular coordinates were pooled and analyzed using Anatomic Likelihood Estimation. Our results showed greater left anterior insular cortex atrophy in FTD whereas the right anterior dorsal insular cortex showed larger clusters of atrophy in AD and PD/DLB. Yet contrast analyses did not reveal significant differences between disease groups. Functional analysis showed that left anterior insular cortex atrophy is associated with speech, emotion, and affective-cognitive deficits, and right dorsal atrophy with perception and cognitive deficits. In conclusion, insular sub-regional atrophy, particularly the anterior dorsal region, may contribute to cognitive and neuropsychiatric deficits in neurodegeneration. Our results support anterior insular cortex vulnerability and convey the differential involvement of the insular sub-regions in functional deficits in neurodegenerative diseases.

The hippocampal network model: A transdiagnostic metaconnectomic approach

Purpose

The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM) using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH) (Seeley et al., 2009) by identifying structural and functional covariance in this hippocampal network.

Methods

To generate our network model, we used BrainMap's VBM database to perform a region-to-whole-brain (RtWB) meta-analysis of 269 VBM experiments from 165 published studies across a range of 38 psychiatric and neurological diseases reporting hippocampal gray matter density alterations. This step identified 11 significant gray matter foci, or nodes. We subsequently used meta-analytic connectivity modeling (MACM) to define edges of structural covariance between nodes from VBM data as well as functional covariance using the functional task-activation database, also from BrainMap. Finally, we applied a correlation analysis using Pearson's r to assess the similarities and differences between the structural and functional covariance models.

Key findings

Our hippocampal RtWB meta-analysis reported consistent and significant structural covariance in 11 key regions. The subsequent structural and functional MACMs showed a strong correlation between HNM nodes with a significant structural-functional covariance correlation of r = .377 (p = .000049).

Significance

This novel method of studying network covariance using VBM and functional meta-analytic techniques allows for the identification of generalizable patterns of functional and structural abnormalities pertaining to the hippocampus. In accordance with the NDH, this framework could have major implications in studying and predicting spatial disease patterns using network-based assays.

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We derived regions that structurally co-vary with the hippocampus in a network model using a transdiagnostic meta-analytic approach.

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We used meta-analytic connectivity mapping to assess inter-regional connectivity from BrainMap's structural and functional databases.

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We tested the network degeneration hypothesis by identifying network correlations between structural and functional networks.

Voxelwise meta-analysis of gray matter anomalies in Parkinson variant of multiple system atrophy and Parkinson's disease using anatomic likelihood estimation

Numerous voxel-based morphometry (VBM) studies on gray matter (GM) in patients with the Parkinson variant of multiple system atrophy (MSA-P) and Parkinson's disease (PD) have been separately conducted. Identifying the different neuroanatomical changes in GM between MSA-P and PD through meta-analysis may aid the differential diagnosis of MSA-P and PD. A systematic review of VBM studies on patients with MSA-P and PD compared to healthy controls (HC) from the PubMed and Embase databases between January 1995 and June 2014 was conducted. Five studies comparing MSA-P with HC and twenty-three studies comparing PD with HC were included. The anatomical distribution of the coordinates of GM volume (GMV) differences was analyzed using the anatomical likelihood estimation (ALE) method. GMV reductions were present in the bilateral putamen, claustrum, insula, midbrain and left cerebellum in MSA-P. In PD, GMV decreases were present in the frontal, parietal, occipital and limbic lobes. Subtraction meta-analysis was performed to explore the differences in GM abnormalities between MSA-P and PD during the early stage of the disease. For patients with disease duration within 5 years, compared with PD, the decrease in GMV focused on the bilateral putamen and claustrum in MSA-P. In contrast, for patients with disease duration within 3 years, no significant GMV difference was found between MSA-P and PD. Our meta-analysis indicated that the atrophy of bilateral putamen or claustrum is not a neuroanatomical marker for distinguishing MSA-P from PD during the early stage by using the VBM method.