Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach.

Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging when symptoms overlap or the conditions present atypically. Resting-state fMRI (rs-fMRI) studies have demonstrated condition-specific alterations in AD, FTD, and mild cognitive impairment (MCI) compared to healthy controls (HC). Here, we used machine learning to build a diagnostic classification model based on these alterations. We curated all rs-fMRIs and their corresponding clinical information from the ADNI and FTLDNI databases. Imaging data underwent preprocessing, time course extraction, and feature extraction in preparation for the analyses. The imaging features data and clinical variables were fed into gradient-boosted decision trees with fivefold nested cross-validation to build models that classified four groups: AD, FTD, HC, and MCI. The mean and 95% confidence intervals for model performance metrics were calculated using the unseen test sets in the cross-validation rounds. The model built using only imaging features achieved 74.4% mean balanced accuracy, 0.94 mean macro-averaged AUC, and 0.73 mean macro-averaged F1 score. It accurately classified FTD (F1 = 0.99), HC (F1 = 0.99), and MCI (F1 = 0.86) fMRIs but mostly misclassified AD scans as MCI (F1 = 0.08). Adding clinical variables to model inputs raised balanced accuracy to 91.1%, macro-averaged AUC to 0.99, macro-averaged F1 score to 0.92, and improved AD classification accuracy (F1 = 0.74). In conclusion, a multimodal model based on rs-fMRI and clinical data accurately differentiates AD-MCI vs. FTD vs. HC.
Competing Interests: Declarations. Ethics Approval: This study was exempt from IRB review due to the public availability of ADNI and FTLDNI and the strict deidentification of data within them. Consent to Participate: The data used in this study was from the ADNI and FTLDNI databases which we obtained from the Laboratory of Neuroimaging at the University of Southern California. Data access was subject to data use agreements with ADNI and FTLDNI, both of which had obtained written informed consent forms from their participants regarding their participation in the studies and the use of their deidentified data in future studies by qualified investigators for research purposes. The contents of this manuscript were approved by these organizations before submission to this journal. Consent for Publication: The data used in this study was from the ADNI and FTLDNI databases which we obtained from the Laboratory of Neuroimaging at the University of Southern California. Data access was subject to data use agreements with ADNI and FTLDNI, both of which had obtained written informed consent forms from their participants regarding the use of their deidentified data in future studies for research and scientific publication by qualified investigators. The contents of this manuscript were approved by these organizations before submission to this journal. Competing Interests: The authors declare no competing interests.
(© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)