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

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Abstract

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 < 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.

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Data availability

The raw imaging data were measured in the Institute for Clinical and Experimental Medicine and are not publicly available due to restrictions imposed by the administering institution. Raw anonymized data can be shared based on a formal data sharing agreement based on a submission of a project outline, that may - depending on the nature of the data requested - require project amendment approval from the local ethics committee. Informal requests for information shall be sent to the corresponding author. Part of the preprocessed fMRI data is available for download (Bučková et al. 2021).

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Funding

This work was supported by Ministry of Health of the Czech Republic – DRO (Kralovske Vinohrady University Hospital – FNKV, IN: 00064173, National Institute of Mental Health – NIMH, IN: 00023752, and Institute for Clinical and Experimental Medicine – IKEM, IN: 00023001), Charles University (112616/GAUK/2016, 260533/SVV/2022, Q35, Q37, Q41, Cooperatio - Neuroscience); the Czech Technical University Internal Grant Agency (SGS19/169/OHK3/3T/13, SGS22/062/OHK3/1T/13), the Czech Science Foundation project No. 13-23940S, Czech Health Research Council Project No. NU21-08-00432, the Czech Academy of Sciences Strategy AV21 Research Programmes “Hopes and Risks of the Digital Age” and “Breakthrough Technologies for the Future – Sensing, Digitisation, Artificial Intelligence and Quantum Technologies”, and by the long-term strategic development financing of the Institute of Computer Science (RVO:67985807) of the Czech Academy of Sciences.

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Authors and Affiliations

  1. The Czech Technical University in Prague, Karlovo namesti 13, 121 35, Prague, Czech Republic

    Barbora Rehák Bučková

  2. Institute of Computer Science of the Czech Academy of Sciences, Pod Vodarenskou vezi 2/271, 182 00, Prague, Czech Republic

    Barbora Rehák Bučková, Jakub Kopal & Jaroslav Hlinka

  3. National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic

    Barbora Rehák Bučková, Jan Mareš, Antonín Škoch, Jaroslav Tintěra & Jaroslav Hlinka

  4. Institute for Clinical and Experimental Medicine, Videnska 1958, 140 21, Prague, Czech Republic

    Jan Mareš, Antonín Škoch & Jaroslav Tintěra

  5. University of Nottingham, Queen’s Medical Centre, NG7 2UH, Nottingham, UK

    Robert Dineen

  6. National Institute for Health Research, Nottingham Biomedical Research Centre, NG1 5DU, Nottingham, UK

    Robert Dineen

  7. Charles University, Ruska 87, 100 00, Prague, Czech Republic

    Kamila Řasová

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  1. Barbora Rehák Bučková
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  2. Jan Mareš
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Contributions

BRB: Methodology, Formal analysis, Writing - Original Draft, Visualization JM: Data Curation AS: Software, Data Curation JK: Software, JT: Investigation RAD: Conceptualization, KR: Conceptualization, Project administration, Funding acquisition, JH: Conceptualization, Project administration, Funding acquisition, Methodology, Supervision. All authors participated on editing the manuscript and approved its final version.

Corresponding author

Correspondence to Jaroslav Hlinka.

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The study design was approved by the Ethics Committee of the Faculty Hospital Královské Vinohrady.

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All participants were informed about the experimental setup and provided written informed consent in accordance with the Declaration of Helsinki.

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Appendix

Appendix

Tables 2, 3, 4, Figs. 5 and Tables 5, 6 and  7. 

Table 2 Pairs of regions of AAL atlas, where FC significantly differs between patients and controls
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Table 3 The results of the Support Vector Machine models across modalities for all thresholds: FA – Fractional Anisotropy; FC – Functional Connectivity; GMV – Grey Matter Volume; all – combination of all three modalities
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Table 4 The results of the Logistic Regression models combined with Principal Component Analysis features across modalities for all thresholds: FA – Fractional Anisotropy; FC – Functional Connectivity; GMV – Grey Matter Volume; all – combination of all three modalities
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Fig. 5
figure 5

P-values of McNemar test between all thresholds and classifiers: Support Vector Machines (left), Logistic Regression (right). Threshold values in Support Vector Machines stand for the percentage of all features, in Logistic regression they represent the number of PCA component added to the model. FA – Fractional Anisotropy; FC – Functional Connectivity; GMV – Grey Matter Volume; all – combination of all three modalities

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Table 5 The results of the Support Vector Regression models across modalities for all thresholds: FA – Fractional Anisotropy; FC – Functional Connectivity; GMV – Grey Matter Volume; all – combination of all three modalities; EDSS – Expanded Disability Status Scale; BBS – Berg Balance Scale; TUG – Timed Up and Go Test; MSIS – Multiple Sclerosis Impact Scale; MSWS – Twelve Item Multiple Sclerosis Walking Scale
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Table 6 The results of the Linear Regression models combined with Principal Component Analysis components across modalities for all thresholds: FA – Fractional Anisotropy; FC – Functional Connectivity; GMV – Grey Matter Volume; all – combination of all three modalities; EDSS – Expanded Disability Status Scale; BBS – Berg Balance Scale; TUG – Timed Up and Go Test; MSIS – Multiple Sclerosis Impact Scale; MSWS – Twelve Item Multiple Sclerosis Walking Scale
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Table 7 The results of the LASSO Logistic Regression. LASSO had been fitted separately for every fold in leave-one-out crossvalidation, and the best model was chosen in each fold using Bayesian Information Criterion. FA – Fractional Anisotropy; FC – Functional Connectivity; GMV – Grey Matter Volume; all – combination of all three modalities;
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Rehák Bučková, B., Mareš, J., Škoch, A. et al. Multimodal-neuroimaging machine-learning analysis of motor disability in multiple sclerosis. Brain Imaging and Behavior 17, 18–34 (2023). https://doi.org/10.1007/s11682-022-00737-3

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