Differentiation of frontotemporal dementia subtypes using neuroimaging‐based multi‐type parallel feature embedding

Abstract Background Frontotemporal dementia (FTD) represents a collection of neurocognitive syndromes with frontotemporal lobar degeneration (FTLD) neuropathology, and is associated with significant clinical, pathological, and genetic heterogeneity. We trained a deep neural network (DNN) classifier to differentiate behavioral‐variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA), and non‐fluent variant (nfvPPA) patients using MRI scans drawn from two multi‐site neuroimaging consortiums. Methods BvFTD (N = 173), nfvPPA (N = 63), and svPPA (N = 41) patients with T1‐MRI were extracted from the FTLD Neuroimaging Initiative (FTLDNI) and the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) databases. MRI data were preprocessed with FreeSurfer, with cortical thickness, cortical volume, and subcortical volumes extracted. Cortical measures were parcellated into 360 patches (i.e., ROIs) according to the HCP‐MMP1 atlas. Both the patch‐based cortical thickness and volume features were harmonized to control confounding effects of sex, age, total intracranial volume (TIV), cohort, and scanner. Multi‐type features were parallelly fed into a multi‐layer‐perceptron (MLP)‐based classifier. Weighted cross‐entropy loss function was used to account for unbalanced sample sizes across FTD subtypes. 10‐fold nested cross‐validation was used to evaluate the robustness of the classification model, with data split into 80/10/10 of training/validation/test data sets. Results Visual evaluation z‐scores of cortical features among FTLDNI and ALLFTD groups revealed site‐specific differences significantly reduced by feature harmonization, especially for volume (Figure 1). The balanced accuracy of the ensembled DNN classifier of each FTD subtype on test sets across all ten folds reached 0.76 ± 0.09 for bvFTD, 0.79 ± 0.07 for nfvPPA, and 0.88 ± 0.07 for svPPA (Figure 2). Figure 3 shows confusion matrices for classification performance in the test set across ten cross‐validation folds. Conclusion We developed a deep‐learning‐based framework to classify three FTD subtypes: bvFTD, nfvPPA, and svPPA. The combination of feature harmonization and parallel multi‐type feature embedding framework showed promising differentiation power. This work can be used to recognize at‐risk populations for early and precise diagnosis, to aid intervention planning. Future studies will compare classification based on different input features and use visualization methods to identify the most discriminative regions and explore their clinical relevance.

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