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

FTLDNI Investigators, GENFI Consortium

Research output: Contribution to journalArticlepeer-review


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 (US)
Pages (from-to)608-616
Number of pages9
JournalJournal of Neurology, Neurosurgery and Psychiatry
Issue number6
StatePublished - Jun 1 2021

ASJC Scopus subject areas

  • Surgery
  • Clinical Neurology
  • Psychiatry and Mental health


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