Background and Purpose Although structural disconnection represents the hallmark of multiple sclerosis (MS) pathophysiology, classification attempts based on structural connectivity have achieved low accuracy levels. Here, we set out to fill this gap, exploring the performance of supervised classifiers on features derived from microstructure informed tractography and selected applying a novel robust approach. Methods Using microstructure informed tractography with diffusion MRI data, we created quantitative connectomes of 55 MS patients and 24 healthy controls. We then used a robust approach-based on two classical methods of feature selection- to select relevant features from three network representations (whole connectivity matrices, node strength, and local efficiency). Classification accuracy of the selected features was tested with five different classifiers, while their meaningfulness was tested via correlation with clinical scales. As a comparison, the same classifiers were run on features selected with the standard procedure in network analysis (thresholding). Results Our procedure identified 11 features for the whole net, five for local efficiency, and seven for node strength. For all classifiers, the accuracy was in the range 64.5%-91.1%, with features extracted from the whole net reaching the maximum, and overcoming results obtained with the standard procedure in all cases. Correlations with clinical scales were identified across functional domains, from motor and cognitive abilities to fatigue and depression. Conclusion Applying a robust feature selection procedure to quantitative structural connectomes, we were able to classify MS patients with excellent accuracy, while providing information on the white matter connections and gray matter regions more affected by MS pathology.

Classification of multiple sclerosis patients based on structural disconnection: A robust feature selection approach

Schiavi, Simona
;
Azzari, Alberto;Mensi, Antonella;Daducci, Alessandro;Bicego, Manuele;
2022

Abstract

Background and Purpose Although structural disconnection represents the hallmark of multiple sclerosis (MS) pathophysiology, classification attempts based on structural connectivity have achieved low accuracy levels. Here, we set out to fill this gap, exploring the performance of supervised classifiers on features derived from microstructure informed tractography and selected applying a novel robust approach. Methods Using microstructure informed tractography with diffusion MRI data, we created quantitative connectomes of 55 MS patients and 24 healthy controls. We then used a robust approach-based on two classical methods of feature selection- to select relevant features from three network representations (whole connectivity matrices, node strength, and local efficiency). Classification accuracy of the selected features was tested with five different classifiers, while their meaningfulness was tested via correlation with clinical scales. As a comparison, the same classifiers were run on features selected with the standard procedure in network analysis (thresholding). Results Our procedure identified 11 features for the whole net, five for local efficiency, and seven for node strength. For all classifiers, the accuracy was in the range 64.5%-91.1%, with features extracted from the whole net reaching the maximum, and overcoming results obtained with the standard procedure in all cases. Correlations with clinical scales were identified across functional domains, from motor and cognitive abilities to fatigue and depression. Conclusion Applying a robust feature selection procedure to quantitative structural connectomes, we were able to classify MS patients with excellent accuracy, while providing information on the white matter connections and gray matter regions more affected by MS pathology.
classification
machine learning
microstructure informed tractography
multiple sclerosis
quantitative structural connectivity
robust feature selection
Diffusion Magnetic Resonance Imaging
Gray Matter
Humans
Connectome
Multiple Sclerosis
White Matter
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/1072986
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