A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers
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 ('converters') and non-converting carriers ('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.
|Keywords||c9orf72, human, classification, diffusion tensor imaging, frontotemporal dementia, grn protein, human, machine learning, mapt protein, human, multimodal mri, resting-state functional mri|
|Persistent URL||dx.doi.org/10.1136/jnnp-2019-320774, hdl.handle.net/1765/117395|
|Series||VSNU Open Access deal|
|Journal||Journal of Neurology, Neurosurgery and Psychiatry: an international peer-reviewed journal for health professionals and researchers in all areas of neurology and neurosurgery|
|Note||corresponding author at Leiden University|
Feis, R.A, Bouts, M.J.R.J. (Mark J.R.J.), de Vos, F, Schouten, T.M. (Tijn M.), Panman, J.L. (Jessica L.), Jiskoot, L.C, … Rombouts, S.A.R.B. (2019). A multimodal MRI-based classification signature emerges just prior to symptom onset in frontotemporal dementia mutation carriers. Journal of Neurology, Neurosurgery and Psychiatry: an international peer-reviewed journal for health professionals and researchers in all areas of neurology and neurosurgery. doi:10.1136/jnnp-2019-320774