Contributions
Abstract: P1013
Type: Poster
Abstract Category: Pathology and pathogenesis of MS - Imaging
Introduction: Recent studies have shown alterations in coordinated patterns of grey matter morphology in multiple sclerosis (MS) suggesting disconnection of structural networks. However, it remains unknown how these abnormalities relate to cognitive impairment in individual patients, as such studies used a group level approach. Here, we represent structural networks as nodes (GM structures) and edges (co-variation between structures), in order to study structural network abnormalities in MS and their relationship with cognitive decline.
Methods: A total of 148 MS patients (99 female, mean age 41.5 ± 8.5, mean EDSS 2.9 ± 1.6) (122 cognitively preserved (CP), and 26 cognitively impaired (CI)) and 33 matched healthy controls (HC) were included in the study. Single-subject GM graphs were constructed from 3DT1-weighted MRI scans, based on GM morphological similarity. Network properties (size, degree, connectivity density, clustering coefficient (C), path-length (L), normalized clustering (γ) and normalized path-length (λ)) were compared between groups and correlated with scores of cognitive functioning (normalized by age, educational level, gender, normalized GM volume and T2 lesion volumes). Predictors that explained the most variance in average cognition and cognitive functioning within 7 domains were identified with stepwise regression models.
Results: All MS groups showed lower connectivity density, compared to HC. The CI group also showed decreased size, degree, C and a tendency to lower λ, compared to HC and CP. Lower C and λ were selected as significant predictors of worse cognitive functioning. Lower λ was associated with more impaired executive functioning (b=70.2; p=.011); lower C was associated with slower information processing speed (b=42.5; p=.039) and more impaired working memory (b=10.9; p=.045) and attention (b=10.8; p=.004).
Conclusion: Our study shows that MS patients have less connections than controls, which was more prominent in the CI group. As a result, the CI subjects also showed lower clustering, path-length and lambda values, which is indicative of a more random topology. Lower values of C and λ were associated with a more severe cognitive impairment. These findings suggest that MS is associated with structural network disconnection, and a more pronounced loss of connections, observed in CI subjects, might deviate the network topology towards a more random topology.
Disclosure: Carolina M Rimkus received a grant from São Paulo Research Foundation (FAPESP 2014/23299-4).
Menno M Schooenheim: nothing to disclose.
Martijn M Steenwijk: nothing to disclose.
Hugo Vrenken: nothing to disclose.
Jeroen Geurts: nothing to disclose.
Mike Wattjes: nothing to disclose.
Joep Killestein: nothing to disclose.
Claudia C Leite: nothing to disclose.
Frederik Berkhof: Consultant to: Biogen-Idec, Janssen Alzheimer Immunotherapy, Bayer-Schering, Merck-Serono, Roche, Novartis, Genzyme, Sanofi-aventis
Sponsoring: EU-H2020, NWO, SMSR, EU-FP7, TEVA, Novartis, Toshiba
Editorial boards: Radiology, Brain, Neuroradiology, MSJ, Neurology
Betty M Tijms: nothing to disclose.
Abstract: P1013
Type: Poster
Abstract Category: Pathology and pathogenesis of MS - Imaging
Introduction: Recent studies have shown alterations in coordinated patterns of grey matter morphology in multiple sclerosis (MS) suggesting disconnection of structural networks. However, it remains unknown how these abnormalities relate to cognitive impairment in individual patients, as such studies used a group level approach. Here, we represent structural networks as nodes (GM structures) and edges (co-variation between structures), in order to study structural network abnormalities in MS and their relationship with cognitive decline.
Methods: A total of 148 MS patients (99 female, mean age 41.5 ± 8.5, mean EDSS 2.9 ± 1.6) (122 cognitively preserved (CP), and 26 cognitively impaired (CI)) and 33 matched healthy controls (HC) were included in the study. Single-subject GM graphs were constructed from 3DT1-weighted MRI scans, based on GM morphological similarity. Network properties (size, degree, connectivity density, clustering coefficient (C), path-length (L), normalized clustering (γ) and normalized path-length (λ)) were compared between groups and correlated with scores of cognitive functioning (normalized by age, educational level, gender, normalized GM volume and T2 lesion volumes). Predictors that explained the most variance in average cognition and cognitive functioning within 7 domains were identified with stepwise regression models.
Results: All MS groups showed lower connectivity density, compared to HC. The CI group also showed decreased size, degree, C and a tendency to lower λ, compared to HC and CP. Lower C and λ were selected as significant predictors of worse cognitive functioning. Lower λ was associated with more impaired executive functioning (b=70.2; p=.011); lower C was associated with slower information processing speed (b=42.5; p=.039) and more impaired working memory (b=10.9; p=.045) and attention (b=10.8; p=.004).
Conclusion: Our study shows that MS patients have less connections than controls, which was more prominent in the CI group. As a result, the CI subjects also showed lower clustering, path-length and lambda values, which is indicative of a more random topology. Lower values of C and λ were associated with a more severe cognitive impairment. These findings suggest that MS is associated with structural network disconnection, and a more pronounced loss of connections, observed in CI subjects, might deviate the network topology towards a more random topology.
Disclosure: Carolina M Rimkus received a grant from São Paulo Research Foundation (FAPESP 2014/23299-4).
Menno M Schooenheim: nothing to disclose.
Martijn M Steenwijk: nothing to disclose.
Hugo Vrenken: nothing to disclose.
Jeroen Geurts: nothing to disclose.
Mike Wattjes: nothing to disclose.
Joep Killestein: nothing to disclose.
Claudia C Leite: nothing to disclose.
Frederik Berkhof: Consultant to: Biogen-Idec, Janssen Alzheimer Immunotherapy, Bayer-Schering, Merck-Serono, Roche, Novartis, Genzyme, Sanofi-aventis
Sponsoring: EU-H2020, NWO, SMSR, EU-FP7, TEVA, Novartis, Toshiba
Editorial boards: Radiology, Brain, Neuroradiology, MSJ, Neurology
Betty M Tijms: nothing to disclose.