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Hamalainen, M.

Publications and source records attributed to Hamalainen, M..

2 recordsLinked to original sources

Maturation Trajectories of Cortical Resting-State Networks Depend on the Mediating Frequency Band

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13-30Hz) and gamma (31-80Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.

neuroscience

MEG/EEG group study with MNE: recommendations, quality assessments and best practices

Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets.\n\nWhile there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data are often require subject-specific decisions which are hard to document.\n\nThis work presents the results obtained by the reanalysis of an open dataset from Wakeman and Henson (2015) using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space. Results with possible alternative strategies are presented and discussed at different stages such as the use of high-pass filtering versus baseline correction, tSSS versus SSS, the use of a minimum norm inverse versus LCMV beamformer, and the use of univariate or multivariate statistics. This aims to provide a comparative study of different stages of M/EEG analysis pipeline on the same dataset, with open access to all of the scripts necessary to reproduce this analysis.

neuroscience