ID 249 – Corrupted fractal organization of EEG topographical fluctuations predict disease state in minimally disabled multiple sclerosis patients. Issue 3 (March 2016) - Gschwind, M.; De Ville, D. Van; Hardmeier, M.; Fuhr, P.; Michel, C.; Seeck, M.
Abstract : Objective: Topographical EEG analysis gives access to the coherent activation within global functional networks in resting-state. For these so-called microstates a temporal organization according to fractal structure has been demonstrated in healthy participants. Here we tested whether these fractal properties would be affected in multiple sclerosis (MS) patients. Methods: We acquired 256-channel EEG in 53 minimally-disable relapsing-remitting MS patients (mean age 37.6 y, EDSS ⩽ 4, mean disease duration 9.9 y, 45 females), and 49 healthy controls (mean age 36.4 y, 33 females). We analyzed 5 min of wakefulness at rest, and isolated the 4 predominant topographies, well known from literature. Using a wavelet framework and detrended fluctuation analysis we estimated temporal fractality and long-range dependency. Results: We found significant differences between MS patients and controls in terms of a decreased Hurst exponent ( p = 0.01), and an increased entropy ( p = 0.01) in patients, indicating disturbed fractal order. Moreover, a high Hurst exponent strongly predicted a high depression score in patients ( p < 0.000; R 2 = 0.245), but not in controls. Conclusions: In MS patients, higher order temporal complexity of topographical EEG fluctuations is disturbed, predicting patient's depression score. Key message: Features of temporal complexity in topographical EEG fluctuations could serve as a potential marker of brain state in relapsing-remitting MS.