Multimodal-neuroimaging machine-learning analysis of motor disability in multiple sclerosis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F23%3A43920985" target="_blank" >RIV/00023752:_____/23:43920985 - isvavai.cz</a>

Alternative codes found

RIV/67985807:_____/23:00564950 RIV/00023001:_____/23:00083800 RIV/68407700:21230/23:00361000 RIV/00216208:11120/23:43924322

Result on the web

<a href="https://link.springer.com/article/10.1007/s11682-022-00737-3" target="_blank" >https://link.springer.com/article/10.1007/s11682-022-00737-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11682-022-00737-3" target="_blank" >10.1007/s11682-022-00737-3</a>

Result language

angličtina

Original language name

Multimodal-neuroimaging machine-learning analysis of motor disability in multiple sclerosis

Original language description

Motor disability is a dominant and restricting symptom in multiple sclerosis, yet its neuroimaging correlates are not fully understood. We apply statistical and machine learning techniques on multimodal neuroimaging data to discriminate between multiple sclerosis patients and healthy controls and to predict motor disability scores in the patients. We examine the data of sixty-four multiple sclerosis patients and sixty-five controls, who underwent the MRI examination and the evaluation of motor disability scales. The modalities used comprised regional fractional anisotropy, regional grey matter volumes, and functional connectivity. For analysis, we employ two approaches: high-dimensional support vector machines run on features selected by Fisher Score (aiming for maximal classification accuracy), and low-dimensional logistic regression on the principal components of data (aiming for increased interpretability). We apply analogous regression methods to predict symptom severity. While fractional anisotropy provides the classification accuracy of 96.1% and 89.9% with both approaches respectively, including other modalities did not bring further improvement. Concerning the prediction of motor impairment, the low-dimensional approach performed more reliably. The first grey matter volume component was significantly correlated (R = 0.28-0.46, p &lt; 0.05) with most clinical scales. In summary, we identified the relationship between both white and grey matter changes and motor impairment in multiple sclerosis. Furthermore, we were able to achieve the highest classification accuracy based on quantitative MRI measures of tissue integrity between patients and controls yet reported, while also providing a low-dimensional classification approach with comparable results, paving the way to interpretable machine learning models of brain changes in multiple sclerosis.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Brain Imaging and Behavior

ISSN

1931-7557

e-ISSN

1931-7565

Volume of the periodical

17

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

17

Pages from-to

18-34

UT code for WoS article

000884940700001

EID of the result in the Scopus database

2-s2.0-85142175876