Advanced MRI Biomarkers and Deep Learning for Efficient Clinical Trials in Frontotemporal Dementia Subtypes

This study highlights the utility of advanced imaging biomarkers in differentiating FTD subtypes and monitoring progression. The integration of volumetric and dMRI metrics, along with deep learning segmentation, offers precise, early detection of changes, thereby reducing sample size requirements and enabling cost-effective, efficient clinical trials. Furthermore, this approach offers insights into the unique spatial and temporal progression patterns of each FTD subtype, paving the way for personalized intervention strategies.

Authors

Publication Details

Journal
Alzheimer s & Dementia
Published
2025-12-01
DOI
https://doi.org/10.1002/alz70856_103177
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
0.00
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Advanced MRI Biomarkers and Deep Learning for Efficient Clinical Trials in Frontotemporal Dementia Subtypes

Jean‐Philippe Coutu, Alex Zijdenbos, Simone P. Zehntner, Felix Carbonell et al.
Alzheimer s & Dementia
Functional Brain Connectivity Studies
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Advanced MRI Biomarkers and Deep Learning for Efficient Clinical Trials in Frontotemporal Dementia Subtypes

Jean‐Philippe Coutu, Alex Zijdenbos, Simone P. Zehntner, Felix Carbonell, Barry J Bedell
article en

Abstract

This study highlights the utility of advanced imaging biomarkers in differentiating FTD subtypes and monitoring progression. The integration of volumetric and dMRI metrics, along with deep learning segmentation, offers precise, early detection of changes, thereby reducing sample size requirements and enabling cost-effective, efficient clinical trials. Furthermore, this approach offers insights into the unique spatial and temporal progression patterns of each FTD subtype, paving the way for personalized intervention strategies.

Alzheimer s & DementiaVol. 21(S2)
Openalex Percentile: Top 33%
Functional Brain Connectivity Studies
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