MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia

Ana L. Manera*, Mahsa Dadar, FTLDNI Investigators, GENFI Consortium, John Cornelis Van Swieten, Barbara Borroni, Raquel Sanchez-Valle, Fermin Moreno, Robert Laforce, Caroline Graff, Matthis Synofzik, Daniela Galimberti, James Benedict Rowe, Mario Masellis, Maria Carmela Tartaglia, Elizabeth Finger, Rik Vandenberghe, Alexandre de Mendonca, Fabrizio Tagliavini, Isabel Santana, Christopher R. ButlerAlex Gerhard, Adrian Danek, Johannes Levin, Markus Otto, Giovanni Frisoni, Roberta Ghidoni, Sandro Sorbi, Jonathan Daniel Rohrer, Simon Ducharme, D. Louis Collins

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)
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Introduction Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. Methods A total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation. Results Average 10-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores. Conclusion Our results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database.

Original languageEnglish
Pages (from-to)608-616
Number of pages9
JournalJournal of Neurology, Neurosurgery and Psychiatry
Issue number6
Publication statusPublished - 1 Jun 2021

Bibliographical note

Data collection and sharing for this project was funded by the
Frontotemporal Lobar Degeneration Neuroimaging Initiative (National Institutes of
Health Grant R01 AG032306). The study is coordinated through the University of
California, San Francisco, Memory and Aging Center. FTLDNI data are disseminated
by the Laboratory for Neuro Imaging at the University of Southern California. Brain
scan acquisition at the McConnell Brain Imaging was supported by the Brain Canada
Foundation with support from Health Canada and the Canada Foundation for
Innovation (CFI Project 34874). This work was supported by Italian Ministry of Health
(CoEN015 and Ricerca Corrente).

Publisher Copyright: © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.


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