eprintid: 10104623 rev_number: 21 eprint_status: archive userid: 608 dir: disk0/10/10/46/23 datestamp: 2020-07-10 10:59:06 lastmod: 2021-10-11 22:47:21 status_changed: 2020-07-10 10:59:06 type: article metadata_visibility: show creators_name: Castellazzi, G creators_name: Cuzzoni, MG creators_name: Cotta Ramusino, M creators_name: Martinelli, D creators_name: Denaro, F creators_name: Ricciardi, A creators_name: Vitali, P creators_name: Anzalone, N creators_name: Bernini, S creators_name: Palesi, F creators_name: Sinforiani, E creators_name: Costa, A creators_name: Micieli, G creators_name: D'Angelo, E creators_name: Magenes, G creators_name: Gandini Wheeler-Kingshott, CAM title: A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features ispublished: pub divisions: UCL divisions: B02 divisions: C07 divisions: D07 divisions: F87 keywords: Alzheimer disease, vascular dementia, machine learning, resting state fMRI, DTI note: This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. https://creativecommons.org/licenses/by/4.0/ abstract: Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations. date: 2020-06-11 date_type: published official_url: https://doi.org/10.3389/fninf.2020.00025 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1795258 doi: 10.3389/fninf.2020.00025 lyricists_name: Castellazzi, Gloria lyricists_name: Ricciardi, Antonio lyricists_name: Wheeler-Kingshott, Claudia lyricists_id: GCAST91 lyricists_id: ARICC01 lyricists_id: CWHEE14 actors_name: Austen, Jennifer actors_id: JAUST66 actors_role: owner full_text_status: public publication: Frontiers in Neuroinformatics volume: 14 article_number: 25 citation: Castellazzi, G; Cuzzoni, MG; Cotta Ramusino, M; Martinelli, D; Denaro, F; Ricciardi, A; Vitali, P; ... Gandini Wheeler-Kingshott, CAM; + view all <#> Castellazzi, G; Cuzzoni, MG; Cotta Ramusino, M; Martinelli, D; Denaro, F; Ricciardi, A; Vitali, P; Anzalone, N; Bernini, S; Palesi, F; Sinforiani, E; Costa, A; Micieli, G; D'Angelo, E; Magenes, G; Gandini Wheeler-Kingshott, CAM; - view fewer <#> (2020) A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Frontiers in Neuroinformatics , 14 , Article 25. 10.3389/fninf.2020.00025 <https://doi.org/10.3389/fninf.2020.00025>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10104623/2/Wheeler-Kingshott_fninf-14-00025.pdf