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Single nucleotide polymorphisms (SNPs) in the ADGRL3 gene have been significantly associated with the development of ADHD, the aetiology of which remains poorly understood. The rs1397547 SNP has additionally been associated with significantly altered ADGRL3 transcription. We therefore generated iPSCs from two wild type ADHD patients, and two ADHD patients heterozygous for the risk SNP. With this resource we aim to facilitate further investigation into the complex and heterogenous pathology of ADHD. Furthermore, we demonstrate the feasibility of using magnetic activated cell sorting to allow the unbiased selection of fully reprogrammed iPSCs.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.