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

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.

Motor recovery and microstructural change in rubro-spinal tract in subcortical stroke

The mechanism of motor recovery after stroke may involve reorganization of the surviving networks. However, details of adaptive changes in structural connectivity are not well understood. Here, we show long-term changes in white matter microstructure that relate to motor recovery in stroke patients. We studied ten subcortical ischemic stroke patients who showed motor hemiparesis at the initial clinical examination and an infarcted lesion centered in the posterior limb of internal capsule of the unilateral hemisphere at the initial diffusion-weighted magnetic resonance imaging scan. The participants underwent serial diffusion tensor imaging and motor function assessments at three consecutive time points; within 2 weeks, and at 1 and 3 months after the onset. Fractional anisotropy (FA) was analyzed for regional differences between hemispheres and time points, as well as for correlation with motor recovery using a tract-based spatial statistics analysis. The results showed significantly increased FA in the red nucleus and dorsal pons in the ipsi-lesional side at 3 months, and significantly decreased FA in the ipsi-lesional internal capsule at all time points, and in the cerebral peduncle, corona radiata, and corpus callosum at 3 months. In the correlation analysis, FA values of clusters in the red nucleus, dorsal pons, midbody of corpus callosum, and cingulum were positively correlated with recovery of motor function. Our study suggests that changes in white matter microstructure in alternative descending motor tracts including the rubro-spinal pathway, and interhemispheric callosal connections may play a key role in compensating for motor impairment after subcortical stroke.