A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers

Rogier A. Feis, Mark J. R. J. Bouts, Frank de Vos, Tijn M. Schouten, Jessica L. Panman, Lize C. Jiskoot, Elise G. P. Dopper, Jeroen van der Grond, John C. van Swieten, Serge A. R. B. Rombouts

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Background Multimodal MRI-based classification may aid early frontotemporal dementia (FTD) diagnosis. Recently, presymptomatic FTD mutation carriers, who have a high risk of developing FTD, were separated beyond chance level from controls using MRI-based classification. However, it is currently unknown how these scores from classification models progress as mutation carriers approach symptom onset. In this longitudinal study, we investigated multimodal MRI-based classification scores between presymptomatic FTD mutation carriers and controls. Furthermore, we contrasted carriers that converted during follow-up (â € converters') and non-converting carriers (â € non-converters'). Methods We acquired anatomical MRI, diffusion tensor imaging and resting-state functional MRI in 55 presymptomatic FTD mutation carriers and 48 healthy controls at baseline, and at 2, 4, and 6 years of follow-up as available. At each time point, FTD classification scores were calculated using a behavioural variant FTD classification model. Classification scores were tested in a mixed-effects model for mean differences and differences over time. Results Presymptomatic mutation carriers did not have higher classification score increase over time than controls (p=0.15), although carriers had higher FTD classification scores than controls on average (p=0.032). However, converters (n=6) showed a stronger classification score increase over time than non-converters (p<0.001). Conclusions Our findings imply that presymptomatic FTD mutation carriers may remain similar to controls in terms of MRI-based classification scores until they are close to symptom onset. This proof-of-concept study shows the promise of longitudinal MRI data acquisition in combination with machine learning to contribute to early FTD diagnosis.

Original languageEnglish
Pages (from-to)1207-1214
Number of pages8
JournalJournal of Neurology, Neurosurgery and Psychiatry
Issue number11
Publication statusPublished - 1 Nov 2019

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