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<p>Accurately characterizing white matter (WM) microstructure is critical for understanding neurodegenerative diseases such as semantic dementia (SD). Regionally constrained techniques like tract-based spatial statistics (TBSS) rely on diffusion-tensor imaging (DTI) and assume a single fiber population per voxel, limiting their sensitivity to complex architecture. Fixel-based analysis (FBA) overcomes these constraints by resolving multiple fiber populations (fixels) within a single voxel, enabling more anatomically specific assessment of WM organization. Multi-shell diffusion MRI from 16 semantic-variant PPA (svPPA) and 15 semantic-behavioral fronto-temporal dementia (sbvFTD) cases—with imaging-confirmed left- and right-predominant temporal atrophy, respectively—and 44 neurologically healthy controls were analyzed using both TBSS-DTI and whole-brain FBA. Fiber-specific metrics of fiber density and cross-section were evaluated alongside conventional DTI measures. Both methods confirmed damage to the anterior temporal lobe (ATL) connected tracts—the uncinate fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and temporal projections of the arcuate fasciculus. FBA further detected involvement of juxtacortical and other pathways that were not captured by TBSS, including the tapetum and anterior commissure, projections to the parahippocampal gyrus and amygdala, and longer-range parietal connections. By capturing multiple fiber populations within each voxel, FBA provides greater anatomical precision and sensitivity to micro- and macro-structural changes than TBSS. This fiber-specific framework enables a more comprehensive mapping of WM degeneration in SD and helps delineate early alterations in commissural and mesial-temporal pathways that may underlie disease spread and cognitive decline.</p>

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Fixel-based analysis reveals detailed white matter changes in semantic dementia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Maria Luisa Mandelli,
  • Yann Cobigo,
  • Ilaria Perretti,
  • Dana Leichter,
  • Celina Alba,
  • Rian Bogley,
  • Nick Wellman,
  • Siddarth Ramkrishnan,
  • Zachary A. Miller,
  • Bruce L. Miller,
  • William W. Seeley,
  • Howard J. Rosen,
  • Maria Luisa Gorno-Tempini

摘要

Accurately characterizing white matter (WM) microstructure is critical for understanding neurodegenerative diseases such as semantic dementia (SD). Regionally constrained techniques like tract-based spatial statistics (TBSS) rely on diffusion-tensor imaging (DTI) and assume a single fiber population per voxel, limiting their sensitivity to complex architecture. Fixel-based analysis (FBA) overcomes these constraints by resolving multiple fiber populations (fixels) within a single voxel, enabling more anatomically specific assessment of WM organization. Multi-shell diffusion MRI from 16 semantic-variant PPA (svPPA) and 15 semantic-behavioral fronto-temporal dementia (sbvFTD) cases—with imaging-confirmed left- and right-predominant temporal atrophy, respectively—and 44 neurologically healthy controls were analyzed using both TBSS-DTI and whole-brain FBA. Fiber-specific metrics of fiber density and cross-section were evaluated alongside conventional DTI measures. Both methods confirmed damage to the anterior temporal lobe (ATL) connected tracts—the uncinate fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and temporal projections of the arcuate fasciculus. FBA further detected involvement of juxtacortical and other pathways that were not captured by TBSS, including the tapetum and anterior commissure, projections to the parahippocampal gyrus and amygdala, and longer-range parietal connections. By capturing multiple fiber populations within each voxel, FBA provides greater anatomical precision and sensitivity to micro- and macro-structural changes than TBSS. This fiber-specific framework enables a more comprehensive mapping of WM degeneration in SD and helps delineate early alterations in commissural and mesial-temporal pathways that may underlie disease spread and cognitive decline.