Reduced alpha2 power is associated with slowed information processing speed in multiple sclerosis

Abstract

Objective Cognitive impairment is common in multiple sclerosis (MS), significantly impacts daily functioning, is time‐consuming to assess, and is prone to practice effects. We examined whether the alpha band power measured with magnetoencephalography (MEG) is associated with the different cognitive domains affected by MS. Methods Sixty‐eight MS patients and 47 healthy controls underwent MEG, T1‐ and FLAIR‐weighted magnetic resonance imaging (MRI), and neuropsychological testing. Alpha power in the occipital cortex was quantified in the alpha1 (8–10 Hz) and alpha2 (10–12 Hz) bands. Next, we performed best subset regression to assess the added value of neurophysiological measures to commonly available MRI measures. Results Alpha2 power significantly correlated with information processing speed (p < 0.001) and was always retained in all multilinear models, whereas thalamic volume was retained in 80% of all models. Alpha1 power was correlated with visual memory (p < 0.001) but only retained in 38% of all models. Conclusions Alpha2 (10–12 Hz) power in rest is associated with IPS, independent of standard MRI parameters. This study stresses that a multimodal assessment, including structural and functional biomarkers, is likely required to characterize cognitive impairment in MS. Resting‐state neurophysiology is thus a promising tool to understand and follow up changes in IPS.

Impaired activation of the prefrontal executive network during working memory processing in multiple sclerosis

“…Afterwards, we decomposed the frequency band into four components (spectral mode) in a data-driven way using a non-negative matrix factorization of the coherence measure, as described in. 25,30 The number of components (4) was chosen considering the number of conventional frequency bands included in the 1-40 Hz spectrum: theta/delta [1][2][3][4][5][6][7][8] Hz, alpha [8][9][10][11][12] Hz, beta [12][13][14][15][16][17][18][19][20][21][22][23][24][25] Hz, and low gamma [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] Hz. By multiplying each spectral mode for the PSD and COH of each subject and state, we extracted the spatial (PSD) and phase-coupling (COH) maps for each datadriven spectral mode.…”