Examination of Cerebellar Grey-Matter Volume in Children with Neurodevelopmental Disorders: a Coordinated Analysis Using the ACAPULCO Algorithm.

Alterations in cerebellar morphology relative to controls have been identified in children with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and developmental coordination disorder (DCD). However, it is not clear if common cerebellar regions are affected in each neurodevelopmental disorder and whether cerebellar morphological changes reflect a generic developmental vulnerability, or disorder-specific characteristic. The present study concatenated anatomical MRI scans from five existing cohorts, resulting in data from 252 children between the age of 7 and 12 years (ASD = 58, ADHD = 86, DCD = 22, Controls = 86). The ACAPULCO processing pipeline for cerebellar segmentation was conducted on T1-weighted images. A voxel-wise approach with general linear model was used to compare grey-matter volume of the 27 cerebellar lobules between each clinical group and controls. Our findings revealed that the ADHD group showed lower grey-matter volume in the left Crus I - part of the executive/non-motor portion of the cerebellum, relative to controls (p = 0.02). This no longer remained significant after controlling for medication status. There were no regions of significant differences in volume of the cerebellar lobules in ASD or DCD compared to controls. Future work will conduct harmonisation of behavioural data (cognitive and motor outcomes) across cohorts, enabling more advanced analyses to identify symptom cluster across neurodevelopmental disorders.

Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities.

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.