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Gleave, E.

Publications and source records attributed to Gleave, E..

3 recordsLinked to original sources
bioRxiv2026

Structural brain alterations in autism: A large-scale voxel-based morphometry mega-analysis

BackgroundPrevious large-scale structural MRI analyses of the brain in autism have identified gray matter (GM) differences when using region-of-interest analyses based on gross anatomical regions. However, such analyses have limited spatial specificity and may obscure subtle focal differences. Whole brain voxel-based morphometry (VBM) analyses enable greater spatial precision to identify and localize previously undetected neuroanatomical alterations. PurposeTo rigorously identify voxel-wise GM and white matter (WM) volume differences in autism in the largest VBM mega-analysis to date. Materials and MethodsThis retrospective mega-analysis included structural 3D volumetric T1-weighted MRI brain scans from 3,051 participants (15.0 {+/-} 8.2 yrs; 76.8% male; 1,519 autism; 1,532 neurotypicals) collected across 51 sites/scanners. Voxel-wise GM and WM volumes were quantified using the ENIGMA CAT12 VBM pipeline. Linear mixed-effects regression was performed at each voxel to evaluate the association between diagnostic group and voxel-wise volume while adjusting for standard nuisance covariates ResultsAutism was associated with widespread lower GM volume involving cortical, subcortical, and cerebellar regions (peak t=7.39, peak {beta}=0.13); such GM differences were most notably detected in the bilateral orbitofrontal cortex, amygdala, thalamus, and posterior lobes of the cerebellum. WM volume was lower in autism across major projection, commissural, association, and cerebellar/brainstem tracts (peak t=6.74, peak {beta}=0.08), including the corona radiata, internal capsule, corpus callosum, and cerebellar peduncles. These findings remained consistent in sensitivity analyses, including covarying for full-scale IQ and the application of increasingly strict motion exclusion criteria. ConclusionAutism is associated with smaller voxel-wise GM and WM volume involving widespread cortical, subcortical, and cerebellar regions. This high-resolution identification and localization of structural brain differences support the involvement of distributed neural systems in autism that underlie reward processing, sensory integration, and motor functioning in autism. Summary statementIn the largest voxel-based morphometry study of autism to date, widespread smaller gray and white matter volumes were identified across distributed brain regions implicated in reward and sensorimotor function. Key ResultsO_LIOur largest voxel-based morphometry mega-analysis to date in autism (N=3,051), identified widespread gray and white matter alterations (peak t=7.39, peak {beta}=0.13). C_LIO_LIWe reveal novel associations within thalamic mediodorsal nuclei by leveraging finer voxel-wise volumetric analyses. C_LIO_LIAdditional associations in orbitofrontal cortex, amygdala, and cerebellum, along with extensive white matter alterations across projection, commissural, association fibers, reinforce reward-processing, sensory and motor development theories in autism. C_LI

neuroscience↗
bioRxiv2024

Comparison of Explainable AI Models for MRI-based Alzheimer's Disease Classification

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimers disease or infer dementia severity from 3D T1-weighted brain MRI scans. Here, we examine the value of adding occlusion sensitivity analysis (OSA) and gradient-weighted class activation mapping (Grad-CAM) to these models to make the results more interpretable. Much research in this area focuses on specific datasets such as the Alzheimers Disease Neuroimaging Initiative (ADNI) or National Alzheimers Coordinating Center (NACC), which assess people of North American, predominantly European ancestry, so we examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort). We also evaluate the benefit of using a combined dataset to train the CNN models. Our experiments show feature localization consistent with knowledge of AD from other methods. OSA and Grad-CAM resolve features at different scales to help interpret diagnostic inferences made by CNNs.

neuroscience↗
bioRxiv2024

Stepping dynamics of dynein characterized by MINFLUX

Cytoplasmic dynein is the principal motor for minus-end-directed motility and force generation functions along microtubules (MTs)1. Dynein converts the chemical energy of ATP hydrolysis into coordinated structural changes to step processively along MTs, but how dynein couples ATP hydrolysis to a minus-end-directed step remains controversial2, 3. The dynamics of dynein stepping have previously been characterized by tracking flexible regions of the motor with limited resolution4-6. Here, we site-specifically labeled yeast dynein at its MT-binding domain by developing a cysteine-light mutant and tracked its stepping at sub-millisecond and nanometer resolution at physiological ATP concentrations using MINFLUX7. We show that dynein hydrolyzes one ATP per step and takes multiples of 8 nm steps. Steps are preceded by a transient movement towards the plus end. These backward "dips" correspond to MT release upon ATP binding and subsequent diffusion of the stepping monomer around its MT-bound partner. Functional assays showed that dips terminate with a minus-end-directed movement upon ATP hydrolysis. These results provide critical insights into the order of mechanochemical events that result in a productive step of dynein.

biophysics↗