Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI

Abstract

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 = 0.582, p = 0.078). 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 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004).

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: C9orf72 human; Diffusion Tensor Imaging; Frontotemporal dementia; GRN protein human; MAPT protein human; Multimodal MRI; Resting-state functional MRI; classification; machine learning.

Fig. 1

Classification results bvFTD model. Box and scatter plot of each subject's bvFTD probability score on a scale from 0 (representing control) to 1 (representing bvFTD patient) after application of the bvFTD model. Groups are defined by carrier status (Fig. 1A) and genetic status (Fig. 1B). Probability scores were not significantly different for carriers and controls (p = 0.15), and did not differ between the four genetic groups (p = 0.37). Probability score results of the bvFTD patients and controls on which the bvFTD model was cross-validated were added for reference (Fig. 1C, data courtesy of Bouts et al. (2018) (Bouts et al., 2018)). Abbreviations: C9orf72: chromosome 9 open reading frame 72; GRN: progranulin; MAPT: microtubule-associated protein tau.

Fig. 2

Classification results carrier-control model. Box and scatter plot of each subject's carrier probability score on a scale from 0 (representing control) to 1 (representing presymptomatic FTD mutation carrier) after application of the best performing carrier-control model including the features RD and WMD. Carriers had significantly higher scores than controls (Fig. 2A, p < 0.001). Furthermore, there was an omnibus difference between the four genetic groups (Fig. 2B, p = 0.008), and post-hoc tests revealed higher scores for GRN carriers than for controls (p = 0.009). Abbreviations: C9orf72: chromosome 9 open reading frame 72; GRN: progranulin; MAPT: microtubule-associated protein tau.