ArticleScientific reports2019
Modified connectivity of vulnerable brain nodes in multiple sclerosis, their impact on cognition and their discriminative value.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Evaluating topological and graph-theoretical approaches to extract complex multimodal brain connectivity patterns in multiple sclerosis.Health information science and systems · 2025Article
- Retinal Damage and Visual Network Reconfiguration Defines Visual Function Recovery in Optic Neuritis.Neurology(R) neuroimmunology & neuroinflammation · 2024Article
- Microscopic fractional anisotropy outperforms multiple sclerosis lesion assessment and clinical outcome associations over standard fractional anisotropy tensor.Human brain mapping · 2024Article
- Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns.Network neuroscience (Cambridge, Mass.) · 2022Article
- Structural and Functional Connectivity Substrates of Cognitive Impairment in Multiple Sclerosis.Frontiers in neurology · 2021Review
- Robustness of Brain Structural Networks Is Affected in Cognitively Impaired MS Patients.Frontiers in neurology · 2020Article
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Authors and funding
14 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain structural network modifications in multiple sclerosis (MS) seem to be clinically relevant. The discriminative ability of those changes to identify MS patients or their cognitive status remains unknown. Therefore, this study aimed to investigate connectivity changes in MS patients related to their cognitive status, and to define an automatic classification method to classify subjects as patients and healthy volunteers (HV) or as cognitively preserved (CP) and impaired (CI) patients. We analysed structural brain connectivity in 45 HV and 188 MS patients (104 CP and 84 CI). A support vector machine with k-fold cross-validation was built using the graph metrics features that best differentiate the groups (p < 0.05). Local efficiency (LE) and node strength (NS) network properties showed the largest differences: 100% and 69.7% of nodes had reduced LE and NS in CP patients compared to HV. Moreover, 55.3% and 57.9% of nodes had decreased LE and NS in CI compared to CP patients, in associative multimodal areas. The classification method achieved an accuracy of 74.8-77.2% to differentiate patients from HV, and 59.9-60.8% to discriminate CI from CP patients. Structural network integrity is widely reduced and worsens as cognitive function declines. Central network properties of vulnerable nodes can be useful to classify MS patients.
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