Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging

Abstract

Introduction: Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI).

Methods: Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification).

Results: The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration.

Discussion: Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.

Keywords: Dementia; Diagnosis; MRI; Machine learning; Neurodegeneration; Volumetry.

Fig. 1

Importance of brain regions for separating each dementia syndrome from healthy controls. Note: The scale on the right shows the importance for classification from 0 to 100 color coded from white to dark purple. Abbreviations: AD Alzheimer’s disease; bvFTD behavioral variant frontotemporal dementia; CBS corticobasal syndrome; GM gray matter; L left; lvPPA logopenic variant primary progressive aphasia; nfvPPA nonfluent variant primary progressive aphasia; PSP progressive supranuclear palsy; R right; svPPA semantic variant primary progressive aphasia.

Fig. 2

Spider plots with probability (in %) for different syndromes. Groups of patients are plotted together, and the respective correct diagnosis based on clinical criteria is written in red bold letters and framed within a box. Abbreviations: AD Alzheimer’s disease; bvFTD behavioral variant frontotemporal dementia; CBS corticobasal syndrome; lvPPA logopenic variant primary progressive aphasia; nfvPPA nonfluent variant primary progressive aphasia; PSP progressive supranuclear palsy; svPPA semantic variant primary progressive aphasia.

Fig. 3

Syndrome probabilities for correct disease classification in relation to severity and duration of disease. Scatterplots are shown for binary classification, i.e., respective disease vs controls (left), and differential diagnostic multiclass prediction, i.e., disease vs other diseases (right). Linear regression (left) or local polynomial regression (right). Abbreviations: AD Alzheimer’s disease; bvFTD behavioral variant frontotemporal dementia; CBS corticobasal syndrome; FTLD-CDR frontotemporal lobar degeneration-modified clinical dementia rating scale; lvPPA logopenic variant primary progressive aphasia; nfvPPA nonfluent variant primary progressive aphasia; PSP progressive supranuclear palsy; svPPA semantic variant primary progressive aphasia.