Source-based artifact-rejection techniques for TMS–EEG

Aberra, 2020, Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons, Brain Stimul., 13, 175, 10.1016/j.brs.2019.10.002 Bagattini, 2019, Predicting Alzheimer’s disease severity by means of TMS–EEG coregistration, Neurobiol. Aging, 80, 38, 10.1016/j.neurobiolaging.2019.04.008 Bai, 2021, Spontaneous transient brain states in EEG source space in disorders of consciousness, NeuroImage, 240, 10.1016/j.neuroimage.2021.118407 Belardinelli, 2019, Reproducibility in TMS–EEG studies: A call for data sharing, standard procedures and effective experimental control, Brain Stimul.: Basic, Transl. Clin. Res. Neuromodulation, 12, 787, 10.1016/j.brs.2019.01.010 Benda, 2019, Peak detection with online electroencephalography (EEG) artifact removal for brain–computer interface (BCI) purposes, Brain Sci., 9, 347, 10.3390/brainsci9120347 Berg, 1994, A multiple source approach to the correction of eye artifacts, Electroencephalogr. Clin. Neurophysiol., 90, 229, 10.1016/0013-4694(94)90094-9 Bertazzoli, 2021, The impact of artifact removal approaches on TMS–EEG signal, NeuroImage, 10.1016/j.neuroimage.2021.118272 Biabani, 2019, Characterizing and minimizing the contribution of sensory inputs to TMS-evoked potentials, Brain Stimul., 12, 1537, 10.1016/j.brs.2019.07.009 Bortoletto, 2021, Asymmetric transcallosal conduction delay leads to finer bimanual coordination, Brain Stimul., 14, 379, 10.1016/j.brs.2021.02.002 ter Braack, 2013, Reduction of TMS induced artifacts in EEG using principal component analysis, IEEE Trans. Neural Syst. Rehabil. Eng., 21, 376, 10.1109/TNSRE.2012.2228674 Buzsáki, 2004, Neuronal oscillations in cortical networks, Science, 304, 1926, 10.1126/science.1099745 Cline, 2021, Advanced artifact removal for automated TMS-EEG data processing, 1039 Conde, 2019, The non-transcranial TMS-evoked potential is an inherent source of ambiguity in TMS–EEG studies, NeuroImage, 185, 300, 10.1016/j.neuroimage.2018.10.052 Curran, 2003, Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems, Brain Cognit., 51, 326, 10.1016/S0278-2626(03)00036-8 Daskalakis, 2008, Long-interval cortical inhibition from the dorsolateral prefrontal cortex: A TMS–EEG study, Neuropsychopharmacology, 33, 2860, 10.1038/npp.2008.22 Fernandez, 2021, Assessing cerebellar-cortical connectivity using concurrent TMS-EEG: A feasibility study, J. Neurophysiol., 125, 1768, 10.1152/jn.00617.2020 Ferrarelli, 2010, Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness, Proc. Natl. Acad. Sci., 107, 2681, 10.1073/pnas.0913008107 Gordon, 2018, Comparison of cortical EEG responses to realistic sham versus real TMS of human motor cortex, Brain Stimul., 11, 1322, 10.1016/j.brs.2018.08.003 Grasso, 2021, tDCS over posterior parietal cortex increases cortical excitability but decreases learning: An ERPs and TMS-EEG study, Brain Res., 1753, 10.1016/j.brainres.2020.147227 Guo, 2010, Real-time robust signal space separation for magnetoencephalography, IEEE Trans. Biomed. Eng., 57, 1856, 10.1109/TBME.2010.2043358 Hämäläinen, 1994, Interpreting magnetic fields of the brain: Minimum norm estimates, Med. Biol. Eng. Comput., 32, 35, 10.1007/BF02512476 Hauk, 2014, A framework for the design of flexible cross-talk functions for spatial filtering of EEG/MEG data: DeFleCT, Human Brain Mapping, 35, 1642, 10.1002/hbm.22279 Hauk, 2019, 672956 Hauk, 2022, Towards an objective evaluation of eeg/meg source estimation methods–the linear approach, NeuroImage, 255, 119177, 10.1016/j.neuroimage.2022.119177 Hedrich, 2017, Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG, NeuroImage, 157, 531, 10.1016/j.neuroimage.2017.06.022 Helfrich, 2014, Entrainment of brain oscillations by transcranial alternating current stimulation, Curr. Biol., 24, 333, 10.1016/j.cub.2013.12.041 Hernandez-Pavon, 2022, Removing artifacts from TMS-evoked EEG: A methods review and a unifying theoretical framework, J. Neurosci. Methods, 10.1016/j.jneumeth.2022.109591 Ilmoniemi, 2010, Methodology for combined TMS and EEG, Brain Topogr., 22, 233, 10.1007/s10548-009-0123-4 Ilmoniemi, 1997, Neuronal responses to magnetic stimulation reveal cortical reactivity and connectivity, Neuroreport, 8, 3537, 10.1097/00001756-199711100-00024 Kaukoranta, 1986, Mixed and sensory nerve stimulations activate different cytoarchitectonic areas in the human primary somatosensory cortex SI, Exp. Brain Res., 63, 60, 10.1007/BF00235646 Klomjai, 2015, Basic principles of transcranial magnetic stimulation (TMS) and repetitive TMS (rTMS), Ann. Phys. Rehabil. Med., 58, 208, 10.1016/j.rehab.2015.05.005 Kohl, 2021, Neural mechanisms underlying human auditory evoked responses revealed by human neocortical neurosolver, Brain Topogr., 1 Koponen, 2018, Multi-locus transcranial magnetic stimulation—theory and implementation, Brain Stimul., 11, 849, 10.1016/j.brs.2018.03.014 Korhonen, 2011, Removal of large muscle artifacts from transcranial magnetic stimulation-evoked EEG by independent component analysis, Med. Biol. Eng. Comput., 49, 397, 10.1007/s11517-011-0748-9 Litvak, 2007, Artifact correction and source analysis of early electroencephalographic responses evoked by transcranial magnetic stimulation over primary motor cortex, NeuroImage, 37, 56, 10.1016/j.neuroimage.2007.05.015 Mäki, 2011, Projecting out muscle artifacts from TMS-evoked EEG, NeuroImage, 54, 2706, 10.1016/j.neuroimage.2010.11.041 Makkonen, 2021, Real-time artifact detection and removal for closed-loop EEG–TMS, Int. J. Bioelectromagn., 23, 12/1 Mancuso, 2021, Transcranial evoked potentials can be reliably recorded with active electrodes, Brain Sci., 11, 145, 10.3390/brainsci11020145 Massimini, 2005, Breakdown of cortical effective connectivity during sleep, Science, 309, 2228, 10.1126/science.1117256 Mennes, 2010, Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP, Psychophysiology, 47, 1142 Metsomaa, 2014, Multi-trial evoked EEG and independent component analysis, J. Neurosci. Methods, 228, 15, 10.1016/j.jneumeth.2014.02.019 Mutanen, 2020, Source-based artifact-rejection techniques available in TESA, an open-source TMS–EEG toolbox, Brain Stimul.: Basic, Transl., Clin. Res. Neuromodulation, 13, 1349, 10.1016/j.brs.2020.06.079 Mutanen, 2016, Recovering TMS-evoked EEG responses masked by muscle artifacts, NeuroImage, 139, 157, 10.1016/j.neuroimage.2016.05.028 Mutanen, 2013, The effect of stimulus parameters on TMS–EEG muscle artifacts, Brain Stimul., 6, 371, 10.1016/j.brs.2012.07.005 Mutanen, 2018, Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm, NeuroImage, 166, 135, 10.1016/j.neuroimage.2017.10.021 Nieminen, 2016, Consciousness and cortical responsiveness: A within-state study during non-rapid eye movement sleep, Sci. Rep., 6, 1, 10.1038/srep30932 Niessen, 2021, An analytical approach to identify indirect multisensory cortical activations elicited by TMS?, Brain Stimul.: Basic, Transl., Clin. Res. Neuromodulation, 14, 376, 10.1016/j.brs.2021.02.003 Nikulin, 2003, Modulation of electroencephalographic responses to transcranial magnetic stimulation: Evidence for changes in cortical excitability related to movement, Eur. J. Neurosci., 18, 1206, 10.1046/j.1460-9568.2003.02858.x Numminen, 1995, Transformation of multichannel magnetocardiographic signals to standard grid form, IEEE Trans. Biomed. Eng., 42, 72, 10.1109/10.362916 Nunez, 2006 Pascual-Marqui, 2002, Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details, Methods Findings Exp. Clin. Pharmacol., 24, 5 Pellicciari, 2017, Characterizing the cortical oscillatory response to TMS pulse, Front. Cell. Neurosci., 11, 38, 10.3389/fncel.2017.00038 Pievani, 2021, Targeting default mode network dysfunction in persons at risk of Alzheimer’s disease with transcranial magnetic stimulation (NEST4AD): Rationale and study design, J. Alzheimer’s Dis., 1 Premoli, 2014, TMS-EEG signatures of GABAergic neurotransmission in the human cortex, J. Neurosci., 34, 5603, 10.1523/JNEUROSCI.5089-13.2014 Ragazzoni, 2013, Vegetative versus minimally conscious states: A study using TMS-EEG, sensory and event-related potentials, PLoS One, 8, 10.1371/journal.pone.0057069 Ramakrishnan, 2019, Neurophysiological effect of ketamine on prefrontal cortex in treatment-resistant depression: A combined transcranial magnetic stimulation–electroencephalography study, Chronic Stress, 3, 10.1177/2470547019861417 Rodríguez-González, 2021, Exploring the interactions between neurophysiology and cognitive and behavioral changes induced by a non-pharmacological treatment: A network approach, Front. Aging Neurosci., 483 Rodríguez-González, 2020, Consistency of local activation parameters at sensor-and source-level in neural signals, J. Neural Eng., 17, 10.1088/1741-2552/abb582 Rodríguez-González, 2019, Towards automatic artifact rejection in resting-state MEG recordings: Evaluating the performance of the SOUND algorithm, 4807 Rogasch, 2014, Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties, NeuroImage, 101, 425, 10.1016/j.neuroimage.2014.07.037 Salo, 2020, EEG artifact removal in TMS studies of cortical speech areas, Brain Topogr., 33, 1, 10.1007/s10548-019-00724-w Salo, 2019, Transcranial magnetic stimulation-evoked potentials after the stimulation of the right-hemispheric homologue of Broca’s area, NeuroReport, 30, 1110, 10.1097/WNR.0000000000001337 Salo, 2018, Individual activation patterns after the stimulation of different motor areas: A transcranial magnetic stimulation–electroencephalography study, Brain Connect., 8, 420, 10.1089/brain.2018.0593 Shirinpour, 2021, Multi-scale modeling toolbox for single neuron and subcellular activity under transcranial magnetic stimulation, Brain Stimul. Stenroos, 2013, Minimum-norm cortical source estimation in layered head models is robust against skull conductivity error, NeuroImage, 81, 265, 10.1016/j.neuroimage.2013.04.086 Stenroos, 2016, Incorporating and compensating cerebrospinal fluid in surface-based forward models of magneto- and electroencephalography, PLoS One, 11, 10.1371/journal.pone.0159595 Taulu, 2006, Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements, Phys. Med. Biol., 51, 1759, 10.1088/0031-9155/51/7/008 Thielscher, 2015, Field modeling for transcranial magnetic stimulation: A useful tool to understand the physiological effects of TMS?, 222 Todaro, 2019, Mapping brain activity with electrocorticography: Resolution properties and robustness of inverse solutions, Brain Topogr., 32, 583, 10.1007/s10548-018-0623-1 Tremblay, 2019, Clinical utility and prospective of TMS–EEG, Clin. Neurophysiol., 130, 802, 10.1016/j.clinph.2019.01.001 Uusitalo, 1997, Signal-space projection method for separating MEG or EEG into components, Med. Biol. Eng. Comput., 35, 135, 10.1007/BF02534144 Van Veen, 1997, Localization of brain electrical activity via linearly constrained minimum variance spatial filtering, IEEE Trans. Biomed. Eng., 44, 867, 10.1109/10.623056 Veniero, 2013, Paired associative stimulation enforces the communication between interconnected areas, J. Neurosci., 33, 13773, 10.1523/JNEUROSCI.1777-13.2013 Vorwerk, 2019, Influence of head tissue conductivity uncertainties on EEG dipole reconstruction, Front. Neurosci., 13, 531, 10.3389/fnins.2019.00531 Vosskuhl, 2020, Signal-space projection suppresses the tACS artifact in EEG recordings, Front. Human Neurosci., 14, 525, 10.3389/fnhum.2020.536070 Zazio, 2021, Alpha-band cortico-cortical phase synchronization is associated with effective connectivity in the motor network, Clin. Neurophysiol., 132, 2473, 10.1016/j.clinph.2021.06.025 Ziegler, 2014, A finite-element reciprocity solution for EEG forward modeling with realistic individual head models, NeuroImage, 103, 542, 10.1016/j.neuroimage.2014.08.056 Zrenner, 2018, Real-time EEG-defined excitability states determine efficacy of TMS-induced plasticity in human motor cortex, Brain Stimul., 11, 374, 10.1016/j.brs.2017.11.016