Braskie, M. N. | bioRxiv Science

bioRxiv2025

White Matter Tract Vulnerability to Amyloid Pathology on the Alzheimer's Disease Continuum

Alzheimers disease (AD) is marked by progressive cognitive decline and memory loss, due to the abnormal accumulation of amyloid-beta (A{beta}) plaques, which facilitate the spread of tau pathology, and a gradually spreading pattern of neuronal loss. Understanding how amyloid positivity affects the brains neural pathways is critical for understanding how the brain changes with AD pathology. Tractometry offers a powerful approach for the in vivo, 3D quantitative assessment of white matter tracts, enabling the localization of microstructural abnormalities in diseased populations and those at risk. In this study, we applied BUAN (Bundle Analytics) tractometry to multi-cohort diffusion MRI data from a total of 1,908 participants: 606 participants in ADNI3 (Alzheimers Disease Neuroimaging Initiative Phase 3) and 1,302 participants from the HABS-HD (Health and Aging Brain Study-Health Disparities). Using BUAN and along-tract statistical analysis, we assessed the localized effects of amyloid positivity on white-matter pathways, which may be further influenced by downstream tau accumulation. Amyloid positivity was quantified via amyloid-sensitive positron emission tomography (PET). BUAN enables tract-specific quantification of white matter microstructure and supports statistical testing along the full length of fiber bundles to detect subtle, spatially localized associations. We present 3D visualizations of tractwise amyloid associations, highlighting distinct patterns of white matter degeneration in AD.

neuroscience↗

bioRxiv2020

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population

As stroke mortality rates decrease, there has been a surge of effort to study post-stroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in post-stroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas-based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric and spatial comparisons to the manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control and spatial accuracy. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.

neuroscience↗