Introduction: Previous work indicates that Multiple Sclerosis (MS) patients are characterized by significant alterations in brain functional connectivity relative to healthy control subjects of matched gender and age. However, the potential relationship between connectivity measures and functional improvement induced by rehabilitative strategies remains unknown. Here, we specifically explored such interactions using two advanced EEG-based connectivity measures: the weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI). These two measures are expected to have a different sensitivity for detecting linear and non-linear relationships between neural source dynamics. Therefore, we hypothesized that wPLI and wSMI may account for different aspects of training-related changes in motor performance in MS patients. Methods: Sixteen MS patients completed a two-week task-oriented circuit training (TOCT). Before (T0) and after (T1) the training period, the Timed Up and Go (TUG) test was used to assess relative variations in motor performance. Moreover, patients completed 10-min EEG resting-state recordings (64 electrodes; Micromed) with eyes closed. For both EEG recordings (T0 and T1), indices of ‘global’ (whole-brain) connectivity strength were computed as the average wPLI and median wSMI connectivity for all channel combinations. To evaluate the relationship between connectivity values at T0 or T1, or connectivity variations T1-T0, and changes in motor performance (ΔTUG), an ANCOVA model was used, in which patients’ age was included as between-subjects covariate. Results: We observed a significant correlation between the alpha-band (8-12Hz) wPLI connectivity and ΔTUG at T0 (r = 0.61; p = 0.017). However, no significant correlations were observed between T1 and T1-T0 variation in connectivity and ΔTUG. Further analyses showed that only anteroposterior connectivity was correlated to improvement after treatment. The whole-brain broadband (1-45Hz) wSMI connectivity was found to be correlated with ΔTUG at T1 (r = 0.67; p = 0.009) but not at T0. Finally, a significant correlation was observed between the T1-T0 variation in wSMI connectivity and ΔTUG (r = 0.70, p = 0.005). Conclusions: Our observations suggest that the tested connectivity measures account for different aspects of training-related functional changes. In particular, alpha-band-wPLI may represent a good indicator of whether a patient will positively respond to treatment, but does not reflect treatment-based changes in neural activity. On the contrary, broadband wSMI seems to account for changes induced by the treatment (e.g., increase in system complexity). Changes in broadband wSMI connectivity likely reflect the implementation or unmasking of (compensatory) mechanisms that are not active in patients before treatment.
Predictive value of EEG-based functional connectivity measures on the outcome of rehabilitation in Multiple Sclerosis patients
Laura Imperatori;Giada Lettieri;Luca Cecchetti;Giulio Bernardi;Emiliano Ricciardi;
2018-01-01
Abstract
Introduction: Previous work indicates that Multiple Sclerosis (MS) patients are characterized by significant alterations in brain functional connectivity relative to healthy control subjects of matched gender and age. However, the potential relationship between connectivity measures and functional improvement induced by rehabilitative strategies remains unknown. Here, we specifically explored such interactions using two advanced EEG-based connectivity measures: the weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI). These two measures are expected to have a different sensitivity for detecting linear and non-linear relationships between neural source dynamics. Therefore, we hypothesized that wPLI and wSMI may account for different aspects of training-related changes in motor performance in MS patients. Methods: Sixteen MS patients completed a two-week task-oriented circuit training (TOCT). Before (T0) and after (T1) the training period, the Timed Up and Go (TUG) test was used to assess relative variations in motor performance. Moreover, patients completed 10-min EEG resting-state recordings (64 electrodes; Micromed) with eyes closed. For both EEG recordings (T0 and T1), indices of ‘global’ (whole-brain) connectivity strength were computed as the average wPLI and median wSMI connectivity for all channel combinations. To evaluate the relationship between connectivity values at T0 or T1, or connectivity variations T1-T0, and changes in motor performance (ΔTUG), an ANCOVA model was used, in which patients’ age was included as between-subjects covariate. Results: We observed a significant correlation between the alpha-band (8-12Hz) wPLI connectivity and ΔTUG at T0 (r = 0.61; p = 0.017). However, no significant correlations were observed between T1 and T1-T0 variation in connectivity and ΔTUG. Further analyses showed that only anteroposterior connectivity was correlated to improvement after treatment. The whole-brain broadband (1-45Hz) wSMI connectivity was found to be correlated with ΔTUG at T1 (r = 0.67; p = 0.009) but not at T0. Finally, a significant correlation was observed between the T1-T0 variation in wSMI connectivity and ΔTUG (r = 0.70, p = 0.005). Conclusions: Our observations suggest that the tested connectivity measures account for different aspects of training-related functional changes. In particular, alpha-band-wPLI may represent a good indicator of whether a patient will positively respond to treatment, but does not reflect treatment-based changes in neural activity. On the contrary, broadband wSMI seems to account for changes induced by the treatment (e.g., increase in system complexity). Changes in broadband wSMI connectivity likely reflect the implementation or unmasking of (compensatory) mechanisms that are not active in patients before treatment.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
