Background: Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification.
Methods: Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC).
Results: The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.570, p = 0.11). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.646 (p = 0.021). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.680, p = 0.005).
Conclusions: FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.
Keywords: (bv)FTD, (behavioural variant) Frontotemporal dementia; (rs-f)MRI, (resting-state functional) Magnetic resonance imaging; 3DT1w, 3-dimensional T1-weighted; AUC, Area under the receiver operating characteristics curve; AxD, Axial diffusivity; C9orf72, Chromosome 9 open reading frame 72; C9orf72, human; DTI, Diffusion tensor imaging; DWI, Diffusion-weighted imaging; Diffusion Tensor Imaging; FA, Fractional anisotropy; FCor, Full correlations; Frontotemporal dementia; GM, Grey matter; GMD, Grey matter density; GRN protein, human; GRN, Progranulin; ICA, Independent component analysis; MAPT protein, human; MAPT, Microtubule-associated protein Tau; MD, Mean diffusivity; MMSE, Mini-mental state examination; Multimodal MRI; Pcor, Sparse L1-regularised partial correlations; RD, Radial diffusivity; ROC, Receiver operating characteristics; Resting-state functional MRI; TBSS, Tract-based spatial statistics; WM, White matter; WMD, White matter density; classification; machine learning.