Using the Schmahmann Syndrome Scale to Assess Cognitive Impairment in Young Adults with Metabolic Syndrome: A Hypothesis-Generating Report

The posterior cerebellum is the most significantly compromised brain structure in individuals with metabolic syndrome (MetS) (Kotkowski et al., 2019). In light of this, we hypothesized that cognitive decline reported in patients with MetS is likely related to posterior cerebellar atrophy. In this study, we performed a post-hoc analyses using T1-weighted magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) in the form of voxel-wise tract-based spatial statistics (TBSS), biometric, and psychometric data from young participants with (n = 52, aged 18–35 years) and without MetS (n = 52, aged 18–35 years). To test the predictive value of components of the Schmahmann Syndrome scale (SSS), also known as the cerebellar cognitive affective syndrome scale, we used structural equation modeling to adapt available psychometric scores in our participant sample to the SSS and compare them to the composite score of all psychometric data available. Our key findings point to a statistically significant correlation between TBSS fractional anisotropy (FA) values from DTI and adapted SSS psychometric scores in individuals with MetS (r2 = .139, 95% CI = 0.009, .345). This suggests that the SSS could be applied to assess cognitive and likely neuroanatomical effects associated with MetS. We strongly suggest that future work aimed at investigating the neurocognitive effects of MetS and related comorbidities (i.e. dyslipidemia, diabetes, obesity) would benefit from implementing and further exploring the validity of the SSS scale in this patient population

Metabolic syndrome predictors of brain gray matter volume in an age-stratified community sample of 776 Mexican- American adults: Results from the genetics of brain structure image archive

Introduction: This project aimed to investigate the association between biometric components of metabolic syndrome (MetS) with gray matter volume (GMV) obtained with magnetic resonance imaging (MRI) from a large cohort of community-based adults (n = 776) subdivided by age and sex and employing brain regions of interest defined previously as the “Neural Signature of MetS” (NS-MetS). Methods: Lipid profiles, biometrics, and regional brain GMV were obtained from the Genetics of Brain Structure (GOBS) image archive. Participants underwent T1-weighted MR imaging. MetS components (waist circumference, fasting plasma glucose, triglycerides, HDL cholesterol, and blood pressure) were defined using the National Cholesterol Education Program Adult Treatment Panel III. Subjects were grouped by age: early adult (18–25 years), young adult (26–45 years), and middle-aged adult (46–65 years). Linear regression modeling was used to investigate associations between MetS components and GMV in five brain regions comprising the NS-MetS: cerebellum, brainstem, orbitofrontal cortex, right insular/limbic cluster and caudate. Results: In both men and women of each age group, waist circumference was the single component most strongly correlated with decreased GMV across all NS-MetS regions. The brain region most strongly correlated to all MetS components was the posterior cerebellum. Conclusion: The posterior cerebellum emerged as the region most significantly associated with MetS individual components, as the only region to show decreased GMV in young adults, and the region with the greatest variance between men and women. We propose that future studies investigating neurological effects of MetS and its comorbidities—namely diabetes and obesity—should consider the NS-MetS and the differential effects of age and sex

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. Keywords: Anatomic likelihood estimation, ALE, BrainMap, Functional covariance, Functional MRI, Gray matter density, Hippocampal network model, Hippocampus, Magnetic resonance imaging, MRI, Meta-analysis, Meta-analytic connectivity modeling, MACM, Structural covariance, Structural MRI, Voxel-based morphometry, VB

A neural signature of metabolic syndrome

That metabolic syndrome (MetS) is associated with age‐related cognitive decline is well established. The neurobiological changes underlying these cognitive deficits, however, are not well understood. The goal of this study was to determine whether MetS is associated with regional differences in gray‐matter volume (GMV) using a cross‐sectional, between‐group contrast design in a large, ethnically homogenous sample. T1‐weighted MRIs were sampled from the genetics of brain structure (GOBS) data archive for 208 Mexican‐American participants: 104 participants met or exceeded standard criteria for MetS and 104 participants were age‐ and sex‐matched metabolically healthy controls. Participants ranged in age from 18 to 74 years (37.3 ± 13.2 years, 56.7% female). Images were analyzed in a whole‐brain, voxel‐wise manner using voxel‐based morphometry (VBM). Three contrast analyses were performed, a whole sample analysis of all 208 participants, and two post hoc half‐sample analyses split by age along the median (35.5 years). Significant associations between MetS and decreased GMV were observed in multiple, spatially discrete brain regions including the posterior cerebellum, brainstem, orbitofrontal cortex, bilateral caudate nuclei, right parahippocampus, right amygdala, right insula, lingual gyrus, and right superior temporal gyrus. Age, as shown in the post hoc analyses, was demonstrated to be a significant covariate. A further functional interpretation of the structures exhibiting lower GMV in MetS reflected a significant involvement in reward perception, emotional valence, and reasoning. Additional studies are needed to characterize the influence of MetS\u27s individual clinical components on brain structure and to explore the bidirectional association between GMV and MetS