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
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author(s) and the copyright owner(s) are credited and that the original publication
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distribution or reproduction is permitted which does not comply with these terms.
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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