Neurodegenerative Conditions

Kramer, Abigail; Alioto, Andrea G.; Kramer, Joel H.

doi:10.1002/9781119057840.ch25

Cited by 2 publications

(1 citation statement)

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“…Third, our models should be compared in future studies to standard biomarkers such as PET and plasma indicators to evaluate potential synergistic biomarker combinations. Fourth, although the cognitive test employed in this study was the MMSE, additional specific assessments for FTD are available, such as particular executive function and language tasks ( Custodio et al, 2016 ; Kramer, Alioto, & Kramer, 2020 ; Torralva et al, 2009 ; Younes & Miller, 2020 ), which may be added to the model. Fifth, despite the AAL being one of the most widely assess atlases in dementia research ( Elsheikh et al, 2021 ; Ibañez et al, 2021a , 2021b ; Lee et al, 2018 ; Liu et al, 2012 ; Lord et al, 2016 ; Reyes et al, 2018 ; Saba et al, 2019 ; Sedeño et al, 2016 , 2017 ), future research may compare classification performance across different brain parcellations in the dementia population.…”

Section: Discussionmentioning

confidence: 99%

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

Gonzalez-Gomez

Ibáñez

Moguilner

2023

Network Neuroscience

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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 subtlety 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, semantic variant primary progressive aphasia; and healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity 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 brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through the feature importance analysis. If replicated and validated, this approach may potentially help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

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