EPICURUS: E-field-based spatial filtering procedure for an accurate estimation of local EEG activity evoked by Transcranial Magnetic Stimulation

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Mutanen , View ORCID Profile Carlo Leto , View ORCID Profile Maria Teresa Colomina , View ORCID Profile Cécile Gallea , View ORCID Profile Martina Bracco , View ORCID Profile Antoni Valero-Cabré doi: https://doi.org/10.1101/2025.02.16.638512 Xavier Corominas-Teruel 1 Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, INSERM 1127, CNRS, 7225, APHP-Hôpital de la Pitié Salpêtrière, Causal Dynamics, Plasticity and Rehabilitation Group, FRONTLAB Team , Paris, France 2 Department of Psychology and Research Center for Behaviour Assessment (CRAMC), Universitat Rovira i Virgili, Neurobehavior and Health Research Group, NEUROLAB , Tarragona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xavier Corominas-Teruel For correspondence: xavier.corominas{at}icm-institute.org xc.teruel{at}gmail.com antoni.valerocabre{at}icm-institute.org avalerocabre{at}gmail.com Tuomas P. Mutanen 3 Department of Neuroscience and Biomedical Engineering, Aalto University School of Science , Espoo, Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tuomas P. Mutanen Carlo Leto 1 Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, INSERM 1127, CNRS, 7225, APHP-Hôpital de la Pitié Salpêtrière, Causal Dynamics, Plasticity and Rehabilitation Group, FRONTLAB Team , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carlo Leto Maria Teresa Colomina 2 Department of Psychology and Research Center for Behaviour Assessment (CRAMC), Universitat Rovira i Virgili, Neurobehavior and Health Research Group, NEUROLAB , Tarragona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maria Teresa Colomina Cécile Gallea 4 Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, INSERM 1127, CNRS 7225, APHP-Hôpital de la Pitié Salpêtrière, MOV’IT Team , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cécile Gallea Martina Bracco 1 Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, INSERM 1127, CNRS, 7225, APHP-Hôpital de la Pitié Salpêtrière, Causal Dynamics, Plasticity and Rehabilitation Group, FRONTLAB Team , Paris, France 4 Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, INSERM 1127, CNRS 7225, APHP-Hôpital de la Pitié Salpêtrière, MOV’IT Team , Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martina Bracco Antoni Valero-Cabré 5 Cognitive Neuroscience and Information Tech. Research Program, Open University of Catalonia (UOC) , Barcelona, SPAIN 6 Dept. Anatomy and Neurobiology, Laboratory of Cerebral Dynamics, Boston University School of Medicine , Boston, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Antoni Valero-Cabré For correspondence: xavier.corominas{at}icm-institute.org xc.teruel{at}gmail.com antoni.valerocabre{at}icm-institute.org avalerocabre{at}gmail.com Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Background The concurrent use of Transcranial magnetic stimulation (TMS) with electroencephalography (EEG) is increasingly integrated in research and clinical protocols to provide proof of effect by magnetic pulses. However, a reliable identification of evoked local EEG activity over TMS targeted cortical sites remains still challenging. Methods Here we present EPICURUS, a novel EEG spatial filtering approach, by which, individual MRI based simulations of TMS- electrical fields (E-fields) guide the reconstruction of TMS evoked o EEG signals originated in the primary motor cortex, minimizing crosstalk from non relevant more distant sources. Results A reduction of late Transcranial Evoked-Potentials (TEPs) components (>100 ms post pulse onset) suggest our E-field-based spatial filter approach efficiently reduced intrusion of non-locally relevant distant sources engaged by TMS, particularly when combined with a suppression of auditory entries. Conclusion The individually customized E-field-based spatial filtering procedure here developed for TMS- EEG datasets shows promise improving the spatio-temporal mapping of primary sources activated by magnetic pulses. HIGHLIGHTS Concurrent TMS-EEG recordings are a well-established tool to monitor brain state of activity and provide proof of effect and target engagement. The lack of spatial specificity of EEG prevents an accurate estimation of the locally activated EEG sources, hindering the interpretation of TMS effects. We here developed a spatial filtering approach for TMS-EEG based on individual estimation of E- field spread following stimulation. Our E-field spatial filter combined with auditory constraints reduced non-relevant distant signal components and improved the reliability of local EEG estimates. INTRODUCTION Electroencephalography (EEG) is a neurophysiological approach increasingly employed as the optimal companion to monitor the impact of Transcranial Magnetic Stimulation (TMS) on brain activity. However, the activity recorded by such a high-temporal resolution technique after a TMS pulse remains spatially unspecific, and reflects a combination of mixed cerebral sources directly or indirectly activated by TMS. This phenomena occurs because besides relevant local direct effects on the targeted cortical site TMS engages activity in interconnected distant areas and networks [ 1 ], while also increasing function in non relevant regions driven by TMS-generated sensory input in the auditory, tactile and vestibular domains [ 2 , 3 ]. Here, we present an innovative approach to isolate activity genuinely elicited locally by TMS pulses within the modeled boundaries of a targeted cortical region, while minimizing unspecific contributions from non-relevant distant sources. The method dubbed EPICURUS ( E -field based s P atial f I ltering for C oncurrent EEG-TMS data U sed to ta R get so U rce S ignal) has been designed as an spatial filter for TMS-EEG datasets, and combines E-field simulations [ 4 ] with a Minimum Norm-like EEG source Estimator (MNE), minimizing signal leakage and crosstalk from non-stimulated brain locations [ 5 - 7 ]. MATERIALS AND METHODS Participants To test EPICURUS performance, we used it to analyze a TMS-EEG dataset of n=18 healthy participants (10 females, 8 males, 26±5 years old), all right-handed, meeting safety criteria for MRI [ 8 ] and TMS [ 9 ], who provided written consent to participate in the study. Our protocol followed the Declaration of Helsinki and received approval by a local ethical committee ( Comité de Protection des Personnes, Ile de France I ). TMS-EEG protocol and data processing Participants received 80 single TMS pulses (with a variable interpulse interval of 5-7sec) over the left primary motor cortex (M1) while we continuously recorded (25kHz sampling rate) scalp EEG activity with a 65 electrode amplifier (ActiChamp, Brain Products) and active electrodes placed following the International 10/10 system coordinates. The left M1 ‘hotspot’ was defined as the optimal cortical location for eliciting at rest Motor-Evoked Potentials in the First Dorsal Interosseous (FDI) muscle of the right hand [ 10 ]. TMS was administered with a D70 mm figure-of-eight coil attached to a biphasic stimulator (Magstim Rapid2) at a fixed intensity of (60% of the maximal stimulator output; MSO). On each participant, TMS coil position was maintained consistently throughout the experiment using an MRI-frameless stereotactic system (Brainsight, Rogue Solutions Inc., Montréal, QC, Canada). The TMS-EEG dataset was processed using the MATLAB-based toolbox EEGLAB [ 11 ] and its TMS– EEG signal analyzer plugin (TESA [ 12 ]). Data was epoched around the TMS pulse (from -1200 to 1200ms – w.r.t. – the TMS pulse) and baseline corrected (from -500 to -10ms). Artifacted channels were temporarily removed from analyses. TMS-pulse electromagnetic (from -2ms to 12ms w.r.t the TMS pulse) and recharging artifacts (visually identified [ 13 ]) were removed and interpolated using a spline function. Data was downsampled to 1kHz and decomposed into independent components (ICA) to remove ocular artifacts. Next, the SOUND algorithm was used for noise suppression, replacement of removed channels, and signal re- referencing to a common average [ 14 ]. The SSP-SIR algorithm was then used to suppress TMS-evoked muscle artifacts [ 15 ] and a second ICA was computed to remove residual continuous muscle artifacts. Finally, data was bandpass filtered with a fourth-order Butterworth filter (2-45Hz) and baseline corrected. E-field-based spatial filter Our pipeline integrates a previously developed spatial filter approach based on cross-talk functions (DeFleCT; [ 5 ]) with E-field distribution modeled using SimNIBS 4.0 [ 4 ]. First, we reconstructed individual finite element head models (FEM [ 16 ]) for each participant and computed the leadfield matrix employing the Helsinki BEM framework [ 6 ] from the FEM model. We simulated the E-field distribution induced by TMS at the intensity of 60% MSO with SimNIBS (version 4.0, 114.7 dI/dtmax=100% MSO; [ 17 ]), using TMS coil positions recorded during the experimental session. We employed the spatial distribution of each participant’s E-field to identify the cortical area most impacted by TMS (>80% E-field peak strength; region of interest– ROI). Next, a spatial filter with distributed source minimization constraints (i.e., crosstalk from outside the established ROI) was built for each participant. The spatial filtering strategy mirrors Special Case 2 of the DeFleCT implementation [ 5 ], using an L2 minimum-norm estimate with a normalization constraint tailored to the TMS targeted sources combined with a noise minimization constraint (See Figure1). To validate our approach and explore the capabilities of the filter, we implemented an additional filtering procedure; a discrete source constraint minimizing signal components arising from sources in the auditory cortex (∼Brodman areas 22,41 and 42; [ 18 ]; bilaterally delineated on each the individual head models). Hence, the original EEG signals were filtered to generate two dataset conditions: one using the E-field-based filter alone and another adding to the former the above-mentioned auditory constraints. Download figure Open in new tab Figure 1. Methodological overview. Illustrative step-by-step procedure of the E-field-based spatial filtering pipeline EPICURUS. Raw TMS-EEG data ( a ) was preprocessed and cleaned to remove artifacts from each of the n=18 participant datasets ( b ). In parallel, MRI-based head models and E-field distributions on each MRI volume were computed for each participant. Finally, local ROIs corresponding to the stimulated left M1 and derived from >80% E-Field strength model, were used to reconstruct the signal directly evoked by TMS ( c ). From the preprocessed data, a minimum norm-based (MNE) reconstruction was first estimated ( d ). This was followed by the implementation of a novel estimator with noise and distributed source constraints to minimize crosstalk leakage from any other sources than those from our ROI ( e ). CTF: CrossTalk Function employed for spatial filtering ; FDI: First Dorsal Interosseous; MNE: Minimum Norm Estimator; TMS-EEG: concurrent Transcranial Magnetic Stimulation with ElectroEncephaloGraphy. Spatial filter validation and statistical analysis To validate our spatial filtering pipeline EPICURUS, we first identified TEPs from all available EEG sensors ( Figure 2.A.1, A.2 ) and characterized their latency, morphology and topographies. Next, we analyzed TEPs from a cluster of electrodes in the vicinity of the TMS stimulation site (i.e., mean signal from C1, C3, CP1 and CP3; sensor-level original signal; Figure 2.A.3 ). Additionally, we employed a classical MNE estimator, without noise minimization (i.e., source-level original signal), to reconstruct EEG time-series directly from the sources impacted by the stimulation (i.e., the sources identified within the E-field-simulation- based ROI; ( Figure 2.A.3 )). Download figure Open in new tab Figure 2. Group average results. (a) Reference data. 1 : Sensor-level time-course across the 64-channels corresponding to “reference data” TEPs. 2: Sensor-level Butterfly plot (superior panel) and topographic sensor and source source-level projection (inferior panel) across the 64-channels corresponding to the original “reference data” TEPs. 3 : Sensor-level original “reference data” TEPs from a cluster of electrodes under the stimulated left M1 (mean of C1,C3,CP1,CP3; sensor-level original; blue trace), and from the region most impacted by TMS (ROI) using a standard MNE (mean signal from the ROI; source-level original; red trace). Group-level source space data are visualized by projecting group-averaged signals into a standard MNI152 mesh. (b) Filters’ validation . Statistical comparisons of original “reference data” sensor- and source-level (blue and red traces respectively) and E-field-based filtered TEPs (yellow trace) (left panel). Statistical comparisons of original “reference data” sensor- and source-level (blue and red traces respectively) and E- field-based filtered TEPs combined with auditory constraints (green trace) (central panel). Statistical comparisons of filtered (with and without constraints; greed and yellow traces respectively) TEPs (right panel). TEPs group mean and standard deviation across subjects are showed as a thick centered line and a background shaded line, respectively. Vertical grey bars highlight statistically significant clusters (p<0.05). The sensor-level and source-level original TEPs served as reference data conditions for our analysis, and were statistically compared to the spatially filtered signals (E-field-based and E-field-based with auditory constraints). Comparisons were performed with non-parametric cluster-based permutation statistics employing one-way 1×3 ANOVA (FieldTrip; [ 19 , 20 ]; 50000 iterations, α = 0.05, p < 0.05, time-window: from 0ms to 400ms -w.r.t. TMS pulse). Whenever needed, post-hoc tests were conducted using cluster-based non- parametric statistics with two-tailed dependent-samples t-tests (50000 iterations, α = 0.05, p < 0.05), employing Bonferroni correction for multiple comparisons. RESULTS Comparisons between E-field-based filtered datasets and the original source-level and sensor-level signals revealed significant differences across, identifying a significant cluster ∼175ms following TMS pulse onset (F(2, 32) = 9.1, p = 0.002; Figure 2.B , left panel). Post-hoc comparisons indicated significant differences between the source-level and the sensor-level original signals (paired t-test: t =3.6, p corrected = 0.006), and between the E-field-based filtered signal and sensor-level original signal (t = 2.9, p corrected = 0.04). However, no significant differences were observed between the E-field-based filtered signal and the original source-level dataset (t = 2.3, p corrected = 0.3). Comparisons between E-field-based filtered signal with auditory constraints and the original source- level, and sensor-level signals revealed significant differences across signals, and revealed two clusters with different latencies (cluster 1, ∼115ms, F(2, 32) = 7.7, p = 0.03; cluster 2, ∼175ms, F(2, 32) = 9.7, p = 0.001; Figure 2.B , central panel). Post-hoc comparisons indicated significant differences between the E-field-based filtered with auditory constraints and the sensor level original signal on both clusters (cluster 1, ∼115ms, t = - 4, p corrected = 0.01 / cluster 2, ∼175ms, t = 3.2, p corrected = 0.01), and between the E-field-based filtered with auditory constraints and the source level original signal on the first cluster (cluster 1, ∼115ms, t = -2.7, p corrected = 0.05). Finally, the comparison between the E-field-based filtered signal and the E-field-based with auditory constraints conditions revealed significant differences in the same two late latency clusters described in the previous analysis (cluster 1, ∼115ms, t = -4.5, p corrected = 0.0004; cluster 2, ∼175ms, t = 4.6, p corrected = 0.001; Figure 2.B , right panel). DISCUSSION Here we designed and implemented EPICURUS, a spatial filtering approach for TMS-EEG data that implements the TMS coil position during the experimental session, the E-field distribution biophysical model, and MNE source reconstruction methods minimizing signal leakage and crosstalk from distant brain sources. Our results revealed three key findings. First, the shape and latency of E-field-based filtered TEPs (with and without auditory constraints) were comparable to those originally recorded or estimated (source and sensor level), supporting the reliability and robustness of an approach that does not alter the overall time course of TMS evoked signals. Second, E-field-based filtered TEPs (with and without auditory constraints) showed lower amplitude than the sensor-level original TEPs, especially at later components (> 100 ms post TMS pulse onset). However, only TEPs processed with EPICURUS adding auditory constraints reached statistically significant differences compared to original source-level TEPs. Third, signals reconstructed with the E-field-based filtering method differed from those reconstructed with E-field-based filtering combined with auditory constraints, particularly ∼115ms. Prior work has shown that late TEP components, such as those present in our data at ∼115 and ∼175 ms (N115 and P175) with fronto-central topographies ( Figure 2.A.2 ), typically reflect the engagement of distant brain regions directly or indirectly activated by the original TMS pulse [1,2,21-23]. We here hypothesize that statistical differences between filtered and original signals here reported may arise from the attenuation of distant sources which, at least in part, contribute to indirect multisensory EEG responses to TMS. A likely direct link to auditory evoked activity is supported by our finding showing that the addition to EPICURUS of auditory constraints significantly reduces the amplitudes of such late TEP components. Although our filtering approach efficiently suppressed the contribution of TMS activity associated to multisensory responses, the primary aim of EPICURUS is to increase the reliability of local evoked responses minimizing the distant non-relevant sources. The suppression of multisensory responses in our TMS-EEG example is an indirect consequence of this aim. To effectively suppress multisensory responses, proper experimental procedures during experimental sessions (e.g., noise masking, control conditions; [ 21 ]) potentially combined with other analysis strategies, will be necessary [ 24 – 26 ]. COMPETING INTERESTS The authors declare no competing financial interests. DATA AND CODE AVAILABILITY The datasets produced and/or analyzed during the current study cannot be made publicly available due to institutional regulations. However, they can be obtained from the corresponding and/or first authors of the manuscript upon reasonable request. MATLAB codes for the pre-processing and filtering implementation can be found at the permanent link: https://github.com/XavierCorominas/Source-Based-filters-for-brain-stimulation-readouts . CREDIT AUTHORSHIP CONTRIBUTION STATEMENT Conceptualization: XC-T, TM, MB, AV-C. Methodology: XC-T, TM, MB. Data curation and analysis: XC-T. Funding acquisition: CG, MB, AV-C. Supervision: MB, AV-C. Writing – original draft: XC- T, AV-C & MB. Writing – review and editing: XC-T, TM, CL, CG, MT-C, MB, AV-C. ACKNOWLEDGEMENTS This work was supported by the Agence Nationale de la Recherche in France (BrainMAG; ANR-19- CE37-0021; AV-C), the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 897941 (MB), the Big Brain Theory (BBT-3; FORTE) internal ICM grant (CG, AV-C & MB), IHU-ICM CARNOT Maturation grant and additional funding came from Investissements d’avenir (ANR-10- IAIHU-0006) awarded to XC-T and AV-C for associated projects. REFERENCES [1]. ↵ Bortoletto M , Veniero D , Thut G , Miniussi C. The contribution of TMS–EEG coregistration in the exploration of the human cortical connectome . Neurosci Biobehav Rev 2015 ; 49 : 114 – 24 . doi: 10.1016/j.neubiorev.2014.12.014 . 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