Characterization of the Somatosensory Mismatch Negativity in Proximal Muscles of the Upper Limb and Upper Body

The Mismatch Negativity (MMN) is a neurophysiological signal reflecting the brain's automatic response to any sudden change. This signal can be evaluated with electroencephalography (EEG) or magnetoencephalography (MEG) by measuring the event-related responses or fields (ERP, ERF) locked to deviant stimuli embedded in a sequence of standard ones. The MMN has been extensively investigated in the auditory domain and used as a tool for diagnostics and predictions in clinical populations – known to have an altered MMN, such as schizophrenia.
This project aims to better characterize the MMN in the somatosensory domain (sMMN) with a special focus on proximal muscles. This will allow us to establish normative values in the healthy population to use as reference for future comparisons with clinical populations affected by somatosensory and motor processing.
Using MEG recordings, we’re currently assessing the sMMN in healthy participants using ‘standard-omitted’ and ‘standard-deviant’ protocols. The target regions include distal muscles, such as abductor pollicis brevis, and proximal muscles (biceps brachii, pectoralis major). Ongoing results reveal a robust sMMN across all sets of locations, with variability in the direction of ERF change between deviant and standard stimuli across muscle groups. By describing the amplitude and topography patterns across all participants and muscle groups, this research will thus serve as reference for future work in clinical populations that may use the sMMN as a biomarker of abnormalities in somatosensory and motor processing.

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2. Restuccia, D., Marca, G.D., Valeriani, M., Leggio, M.G. and Molinari, M., 2007. Cerebellar damage impairs detection of somatosensory input changes. A somatosensory mismatch-negativity study. Brain, 130(1), pp.276-287.
3. Molinari, M. and Masciullo, M., 2019. The Implementation of Predictions During Sequencing. Frontiers in cellular neuroscience, 13, p.439.

Project Leaders - Maria Herrojo Ruiz, Anna Shestakova, Seymon Golosheykin.