Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.
The distinction of a neurodegenerative condition against multiple related diseases simultaneously, with overlapping features and high levels of heterogeneity in behavioral, clinical, and neuropathological markers, remains a challenge. Here, we combined structural and functional connectivity markers with diverse methods including graph theory and cognitive markers to perform a multiclass classification of five FTD variants. An optimum set of features was obtained through progressive feature elimination by removing redundant and uninformative variables. Our results for the simultaneous multiclass categorization of each FTD variant and healthy controls achieved a performance up to an area under the curve of 0.95. This approach can help develop clinical decision support tools to detect specific affectations in the context of overlapping neurodegenerative diseases.
Competing Interests: The authors have declared that no competing interests exist.
Handling Editor: Mikail Rubinov