Suchergebnisse

| openaire: EC/H2020/810377/EU//ConnectToBrain Funding Information: This research has received funding from the Academy of Finland (Decisions No. 255347, 265680, 294625, and 306845), the Finnish Cultural Foundation, Jane and Aatos Erkko Foundation, Erasmus Mundus SMART2 (No. 552042-EM-1-2014-1-FR-ERA MUNDUSEMA2), the Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq; grant number 140787/2014-3), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 810377), and the Research, Innovation and Dissemination Center for Neuromathematics (FAPESP; grant number 2013/07699-0). ; Background: Transcranial magnetic stimulation (TMS) coils allow only a slow, mechanical adjustment of the stimulating electric field (E-field) orientation in the cerebral tissue. Fast E-field control is needed to synchronize the stimulation with the ongoing brain activity. Also, empirical models that fully describe the relationship between evoked responses and the stimulus orientation and intensity are still missing. Objective: We aimed to (1) develop a TMS transducer for manipulating the E-field orientation electronically with high accuracy at the neuronally meaningful millisecond-level time scale and (2) devise and validate a physiologically based model describing the orientation selectivity of neuronal excitability. Methods: We designed and manufactured a two-coil TMS transducer. The coil windings were computed with a minimum-energy optimization procedure, and the transducer was controlled with our custom-made electronics. The electronic E-field control was verified with a TMS characterizer. The motor evoked potential amplitude and latency of a hand muscle were mapped in 3° steps of the stimulus orientation in 16 healthy subjects for three stimulation intensities. We fitted a logistic model to the motor response amplitude. Results: The two-coil TMS transducer allows one to manipulate the pulse orientation accurately without manual coil movement. The motor response amplitude followed a logistic function of the stimulus orientation; this dependency was strongly affected by the stimulus intensity. Conclusion: The developed electronic control of the E-field orientation allows exploring new stimulation paradigms and probing neuronal mechanisms. The presented model helps to disentangle the neuronal mechanisms of brain function and guide future non-invasive stimulation protocols. ; Peer reviewed

| openaire: EC/H2020/810377/EU//ConnectToBrain Funding Information: This project has received funding from the Finnish Cultural Foundation , Academy of Finland (decisions No. 294625 , 306845 , and 327326 ), Instrumentarium Science Foundation , Emil Aaltonen Foundation , Finnish Science Foundation for Technology and Economics , Jane and Aatos Erkko Foundation , and European Research Council (ERC Synergy) under the European Union's Horizon 2020 research and innovation programme (ConnectToBrain; grant agreement No. 810377 ). Publisher Copyright: © 2022 The Author(s) ; Background: Transcranial magnetic stimulation (TMS) is widely used in brain research and treatment of various brain dysfunctions. However, the optimal way to target stimulation and administer TMS therapies, for example, where and in which electric field direction the stimuli should be given, is yet to be determined. Objective: To develop an automated closed-loop system for adjusting TMS parameters (in this work, the stimulus orientation) online based on TMS-evoked brain activity measured with electroencephalography (EEG). Methods: We developed an automated closed-loop TMS–EEG set-up. In this set-up, the stimulus parameters are electronically adjusted with multi-locus TMS. As a proof of concept, we developed an algorithm that automatically optimizes the stimulation orientation based on single-trial EEG responses. We applied the algorithm to determine the electric field orientation that maximizes the amplitude of the TMS–EEG responses. The validation of the algorithm was performed with six healthy volunteers, repeating the search twenty times for each subject. Results: The validation demonstrated that the closed-loop control worked as desired despite the large variation in the single-trial EEG responses. We were often able to get close to the orientation that maximizes the EEG amplitude with only a few tens of pulses. Conclusion: Optimizing stimulation with EEG feedback in a closed-loop manner is feasible and enables effective coupling to brain activity. ; Peer reviewed

Funding Information: This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korean government (Nos. 2017-0-00451; 2017-0-01779; 2019-0-00079; 2019-0-01371; and 2021-0-02068), the University and University Hospital of Liège, Belgian National Fund for Scientific Research (F.R.S-FNRS), the Italian Ministry of Health, GR-2016–02361494 (to S.C.), the Canadian Institute for Advanced Research (CIFAR) (to M.M.), European Union's Horizon 2020 Framework Program for Research and Innovation under the Specific Grant Agreement (No. 945539, Human Brain Project SGA3) (to M.M. and S.L.), BIAL Foundation, AstraZeneca Foundation, Fund Generate, King Baudouin Foundation, DOCMA project [EU-H2020-MSCA-RISE-–778234], James McDonnell Foundation, Mind Science Foundation, Fondazione Europea di Ricerca Biomedica, National Institutes of Health (No. R01MH064498), Academy of Finland (Nos. 265680 and 294625), Tiny Blue Dot Foundation (to M.M.), and grant EraPerMed JTC 2019 "PerBrain" (to M.R.). L.R.D.S. and R.P. are PhD fellows, O.G. and A. T. are research associates, and S.L. is research director at the F.R.S.–FNRS. We thank S. Lapuschkin for sharing the code; further, we thank all the healthy participants, patients, and their families who participated in this study. Publisher Copyright: © 2022, The Author(s). ; Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness. ; Peer reviewed