Abstract
Magnetic Resonance Imaging (MRI) is one of the tools used to identify structural and functional changes caused by multiple sclerosis, and by processing MR images, connectivity networks can be obtained. The analysis of structural connectivity networks of multiple sclerosis patients usually employs network-derived metrics, which are computed independently for each subject. We propose a novel representation of connectivity networks that is extracted from a model trained on the whole multiple sclerosis population: RF-Isolation. RF-Isolation is a vector encoding the disconnection of each region of interest with respect to all other regions. This feature can be easily captured by isolation-based outlier detection methods. We therefore reformulate the task as an outlier detection problem and propose a novel approach, called MS-ProxIF, based on a variant of Isolation Forest, a Random Forest-based outlier detection system, from which the representation is extracted. We test the representation via a set of classification experiments, involving 79 subjects, 55 of which suffer from multiple sclerosis. In particular, we compare favourably to the most used network-derived metrics in multiple sclerosis.
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Notes
- 1.
The dataset has been collected at the Mount Sinai Hospital of New York (US) by the group of Matilde Inglese. The dataset is not publicly available.
- 2.
For the sake of clarity the remainder of the explanation will refer to tests characterized by P and \(\theta \), but an analogous reasoning would hold if the test consisted of choosing two prototypes \(P_L\) and \(P_R\).
- 3.
For more thorough descriptions, please refer to https://sites.google.com/site/bctnet/list-of-measures.
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Acknowledgments
The authors declare that there is no conflict of interest. This work was partly supported by grants from NMSS (RG 5120A3/1) and Teva Neuroscience (CNS-2014-221).
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Mensi, A. et al. (2022). RF-Isolation: A Novel Representation of Structural Connectivity Networks for Multiple Sclerosis Classification. In: Chicco, D., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science(), vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_13
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