Reconstructing subcortical and cortical somatosensory activity via the RAMUS inverse source analysis technique using median nerve SEP dataReconstructing subcortical and cortical somatosensory activity via the RAMUS inverse source analysis technique using median nerve SEP data Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2022-08-042021enAR, JL, and SP were supported by the Academy of Finland Centre of Excellence in Inverse Modelling and Imaging 2018–2025 (CoE 336792) and DAAD project (334465), and by the ERA PerMed project PerEpi (344712). AR was also supported by the Vilho, Yrjö and Kalle Väisälä Foundation and Alfred Kordelini Foundation. JL is supported by Väisälä Fund’s (Finnish Academy of Science and Letters) one-year young researcher grant admitted in 2021. FN was supported by ERA PerMed project PerEpi (344712). AK was supported by the Academy of Finland Postdoctoral Researcher grant (326454). MA, MCP and CHW were supported by the Bundesministerium für Gesundheit (BMG) as project ZMI1-2521FSB006, under the frame of ERA PerMed as project ERAPERMED2020-227, by the Deutsche Forschungsgemeinschaft (DFG), project WO1425/10-1, and by DAAD project 57523877. This study concerns reconstructing brain activity at various depths based on non-invasive EEG (electroencephalography) scalp measurements. We aimed at demonstrating the potential of the RAMUS (randomized multiresolution scanning) technique in localizing weakly distinguishable far-field sources in combination with coinciding cortical activity. As we have shown earlier theoretically and through simulations, RAMUS is a novel mathematical method that by employing the multigrid concept, allows marginalizing noise and depth bias effects and thus enables the recovery of both cortical and subcortical brain activity. To show this capability with experimental data, we examined the 14–30 ms post-stimulus somatosensory evoked potential (SEP) responses of human median nerve stimulation in three healthy adult subjects. We aim at reconstructing the different response components by evaluating a RAMUS-based estimate for the primary current density in the nervous tissue. We present source reconstructions obtained with RAMUS and compare them with the literature knowledge of the SEP components and the outcome of the unit-noise gain beamformer (UGNB) and standardized low-resolution brain electromagnetic tomography (sLORETA). We also analyzed the effect of the iterative alternating sequential technique, the optimization technique of RAMUS, compared to the classical minimum norm estimation (MNE) technique. Matching with our previous numerical studies, the current results suggest that RAMUS could have the potential to enhance the detection of simultaneous deep and cortical components and the distinction between the evoked sulcal and gyral activity.http://creativecommons.org/licenses/by-nc-nd/4.0/NeuroImage245Rezaei_et_al_NeuroImage_245_2021.pdfChania [Greece]Library of TUC2022-08-04application/pdf5.1 MBfree Rezaei Atena Lahtinen Joonas Neugebauer Frank Antonakakis Marios Αντωνακακης Μαριος Piastra, Maria Carla 1988- Koulouri Alexandra Wolters Carsten H. Pursiainen, Sampsa Elsevier Electroencephalography (EEG) Somatosensory evoked potential (SEP) Median nerve stimulation Finite element method (FEM) Hierarchical Bayesian model (HBM) Deep brain activity