Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross‐sectional and longitudinal magnetic resonance imaging data

Abstract

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T‐T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2‐year follow‐up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross‐sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross‐sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross‐sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross‐sectional and longitudinal data.

The pursuit for markers of disease progression in behavioral variant frontotemporal dementia: a scoping review to optimize outcome measures for clinical trials

“…While these are promising, at present, literature mostly involves cross-sectional studies in AD. Examples are speech-based artificial intelligence (AI) applications predicting cognitive decline ( Fristed et al, 2022 ), biometric measures (e.g., skin conduction, pupillometry and eye-tracking patterns) reflecting social-emotional and/or linguistic deficits ( Mendez et al, 2018 ; Singleton et al, 2022 ; El Haj et al, 2024 ), AI-based imaging algorithms for longitudinal brain mapping ( Pérez-Millan et al, 2023 ), and proteomics technology detecting protein profiles ( Katzeff et al, 2022 ).…”

“…Since clinical trials intend to intervene in early and intermediate stages, characterized by relatively diverse behavioral symptoms, behavioral inflection points should be taken into account. For instance, a crescendo-decrescendo pattern, including dominating apathy (measured with DAS or sub scores of FBI or CBI) in late stages, (Fristed et al, 2022), biometric measures (e.g., skin conduction, pupillometry and eye-tracking patterns) reflecting social-emotional and/or linguistic deficits (Mendez et al, 2018;Singleton et al, 2022;El Haj et al, 2024), AI-based imaging algorithms for longitudinal brain mapping (Pérez-Millan et al, 2023), and proteomics technology detecting protein profiles (Katzeff et al, 2022).…”

Transdiagnostic brain module dysfunctions across sub-types of frontotemporal dementia: a connectome-based investigation

“…Machine learning, tailored for intricate multivariate data, holds promise as an adjunct in the diagnostic process by offering decision support [25]. Previous studies have use machine learning algorithms to classify between the subtypes of dementia [25][26][27] and subtypes of FTD [28,29] in structural MRI. The accuracy of machine learning model between subtypes of FTD was ranged from 0.7-0.94.…”

Beyond group classification: probabilistic differential diagnosis of frontotemporal dementia and Alzheimer’s disease with MRI and CSF biomarkers.

“…A growing body of evidence supports the role of machine learning (ML) techniques using brain MRI (15)(16)(17) to support the clinical diagnosis of these two dementias (18)(19)(20)(21)(22). Many studies have shown that a support vector machine (SVM) with neuroimaging data differentiates AD or FTD patients from healthy controls (22)(23)(24)(25)(26)(27).…”

“…A growing body of evidence supports the role of machine learning (ML) techniques using brain MRI (15)(16)(17) to support the clinical diagnosis of these two dementias (18)(19)(20)(21)(22). Many studies have shown that a support vector machine (SVM) with neuroimaging data differentiates AD or FTD patients from healthy controls (22)(23)(24)(25)(26)(27). However, fewer studies exist on the differential diagnosis of these two dementias, even though the clinical symptoms of FTD and AD can display a substantial overlap between them (28-30).…”