E-field Models Research Articles

Exploring potential working mechanisms of accelerated HF-rTMS in refractory major depression with a focus on locus coeruleus connectivity.

This study investigates the effects of accelerated high-frequency repetitive transcranial magnetic stimulation (aHF-rTMS), applied to the left dorsolateral prefrontal cortex (DLPFC), on locus coeruleus (LC) functional connectivity in the treatment of refractory medication-resistant major depression (MRD). We studied 12 antidepressant-free refractory MRD patients, focusing on how aHF-rTMS affects the LC, a central component of the brain's noradrenergic system and key to mood regulation and stress response. A stronger decrease in LC functional connectivity following aHF-rTMS treatment resulted in better clinical improvement. We observed such LC functional connectivity decreases with several brain regions, including the superior frontal gyrus, precentral gyrus, middle occipital gyrus, and cerebellum. Moreover, our exploratory analyses hint at a possible role for E-field modeling in forecasting clinical outcomes. Additional analyses suggest potential genetic and dopaminergic factors influencing changes in LC functional connectivity in relation to clinical response. The findings of this study underscore the pivotal role of the LC in orchestrating higher cognitive functions through its extensive connections with the prefrontal cortices, facilitating decision-making, influencing attention, and addressing depressive rumination. Additionally, the observed enhancement in LC-(posterior) cerebellar connectivity not only highlights the cerebellum's role in moderating clinical outcomes through noradrenergic system modulation but also suggests its potential as a predictive marker for aHF-rTMS efficacy. These results reveal new insights into the neural mechanisms of refractory depression and suggest therapeutic targets for enhancing noradrenergic activity, thereby addressing both cognitive and psychomotor symptoms associated with the disorder.

Safety and efficacy of targeting the supplementary motor area with double-cone deep transcranial magnetic stimulation vs figure-eight coil in treatment of obsessive-compulsive disorder with comorbid major depressive disorder

Background and objectiveThe Supplementary Motor Area (SMA), a relatively large brain structure predominantly located along the interhemispheric fissure, is an established target for repetitive Transcranial Magnetic Stimulation (rTMS) treatment of Obsessive-Compulsive Disorder (OCD). We investigated the feasibility, safety, and efficacy of targeting SMA using a double-cone “deep” TMS coil compared to conventional figure-eight coil for treatment of OCD with comorbid Major Depressive Disorder (MDD). MethodsSixty-two patients with treatment-resistant OCD and comorbid MDD participated in the study. All patients received high-frequency rTMS over the left dorsolateral prefrontal cortex (DLPFC) with a figure-eight coil (MagVenture B70), followed by 1 Hz rTMS over the bilateral SMA using either the B70 (N = 25) or double-cone deep coil (MagVenture DB80) (n = 23) for 36 treatment sessions. Weekly clinical assessments were conducted. ResultsSubjects overall had significant reductions in OCD and depressive symptom severity at the primary endpoint. Subjects stimulated at SMA with the double-cone deep coil had statistically significantly lesser reductions in overall OCD and depression symptom severity compared to the figure-eight group. The intensity of stimulation at SMA was significantly greater with the double-cone deep than figure-eight coil and e-field modeling showed that it affected broader regions beyond SMA (off-target stimulation). There was no significant difference in reported tolerability between groups. ConclusionsSMA stimulation using either a double-cone deep or conventional figure-of-eight coil was safe and was associated with a significant reduction in comorbid OCD and depression symptoms, but the higher intensities of stimulation with the double-cone deep coil used in this study were significantly less clinically beneficial than figure-eight coil stimulation.

Neuromodulatory transcranial magnetic stimulation (TMS) changes functional connectivity proportional to the electric-field induced by the TMS pulse

ObjectiveTranscranial magnetic stimulation (TMS) can efficiently and robustly modulate synaptic plasticity, but little is known about how TMS affects functional connectivity (rs-fMRI). Accordingly, this project characterized TMS-induced rsFC changes in depressed patients who received 3 days of left prefrontal intermittent theta burst stimulation (iTBS). Methodsrs-fMRI was collected from 16 subjects before and after iTBS. Correlation matrices were constructed from the cleaned rs-fMRI data. Electric-field models were conducted and used to predict pre-post changes in rs-fMRI. Site by orientation heatmaps were created for vectors centered on the stimulation site and a control site (contralateral motor cortex). ResultsFor the stimulation site, there was a clear relationship between both site and coil orientation, and connectivity changes. As distance from the stimulation site increased, prediction accuracy decreased. Similarly, as eccentricity from the optimal orientation increased, prediction accuracy decreased. The systematic effects described above were not apparent in the heatmap centered on the control site. ConclusionsThese results suggest that rs-fMRI following iTBS changes systematically as a function of the distribution of electrical energy delivered from the TMS pulse, as represented by the e-field model. SignificanceThis finding lays the groundwork for future studies to individualize TMS targeting based on how predicted rs-fMRI changes might impact psychiatric symptoms.

A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) is a device-based neuromodulation technique increasingly used to treat brain diseases. Electric field (E-field) modeling is an important technique in several TMS clinical applications, including the precision stimulation of brain targets with accurate stimulation density for the treatment of mental disorders and the localization of brain function areas for neurosurgical planning. Classical methods for E-field modeling usually take a long computation time. Fast algorithms are usually developed with significantly lower spatial resolutions that reduce the prediction accuracy and limit their usage in real-time or near real-time TMS applications. This review paper discusses several modern algorithms for real-time or near real-time TMS E-field modeling and their advantages and limitations. The reviewed methods include techniques such as basis representation techniques and deep neural-network-based methods. This paper also provides a review of software tools that can integrate E-field modeling with navigated TMS, including a recent software for real-time navigated E-field mapping based on deep neural-network models.

Amplitude-determined seizure-threshold, electric field modeling, and electroconvulsive therapy antidepressant and cognitive outcomes

Electroconvulsive therapy (ECT) pulse amplitude, which dictates the induced electric field (E-field) magnitude in the brain, is presently fixed at 800 or 900 milliamperes (mA) without clinical or scientific rationale. We have previously demonstrated that increased E-field strength improves ECT’s antidepressant effect but worsens cognitive outcomes. Amplitude-determined seizure titration may reduce the E-field variability relative to fixed amplitude ECT. In this investigation, we assessed the relationships among amplitude-determined seizure-threshold (STa), E-field magnitude, and clinical outcomes in older adults (age range 50 to 80 years) with depression. Subjects received brain imaging, depression assessment, and neuropsychological assessment pre-, mid-, and post-ECT. STa was determined during the first treatment with a Soterix Medical 4×1 High Definition ECT Multi-channel Stimulation Interface (Investigation Device Exemption: G200123). Subsequent treatments were completed with right unilateral electrode placement (RUL) and 800 mA. We calculated Ebrain defined as the 90th percentile of E-field magnitude in the whole brain for RUL electrode placement. Twenty-nine subjects were included in the final analyses. Ebrain per unit electrode current, Ebrain/I, was associated with STa. STa was associated with antidepressant outcomes at the mid-ECT assessment and bitemporal electrode placement switch. Ebrain/I was associated with changes in category fluency with a large effect size. The relationship between STa and Ebrain/I extends work from preclinical models and provides a validation step for ECT E-field modeling. ECT with individualized amplitude based on E-field modeling or STa has the potential to enhance neuroscience-based ECT parameter selection and improve clinical outcomes.

Effect of non-invasive spinal cord stimulation in unmedicated adults with major depressive disorder: a pilot randomized controlled trial and induced current flow pattern

Converging theoretical frameworks suggest a role and a therapeutic potential for spinal interoceptive pathways in major depressive disorder (MDD). Here, we aimed to evaluate the antidepressant effects and tolerability of transcutaneous spinal direct current stimulation (tsDCS) in MDD. This was a double-blind, randomized, sham-controlled, parallel group, pilot clinical trial in unmedicated adults with moderate MDD. Twenty participants were randomly allocated (1:1 ratio) to receive “active” 2.5 mA or “sham” anodal tsDCS sessions with a thoracic (anode; T10)/right shoulder (cathode) electrode montage 3 times/week for 8 weeks. Change in depression severity (MADRS) scores (prespecified primary outcome) and secondary clinical outcomes were analyzed with ANOVA models. An E-Field model was generated using the active tsDCS parameters. Compared to sham (n = 9), the active tsDCS group (n = 10) showed a greater baseline to endpoint decrease in MADRS score with a large effect size (−14.6 ± 2.5 vs. −21.7 ± 2.3, p = 0.040, d = 0.86). Additionally, compared to sham, active tsDCS induced a greater decrease in MADRS “reported sadness” item (−1.8 ± 0.4 vs. −3.2 ± 0.4, p = 0.012), and a greater cumulative decrease in pre/post tsDCS session diastolic blood pressure change from baseline to endpoint (group difference: 7.9 ± 3.7 mmHg, p = 0.039). Statistical trends in the same direction were observed for MADRS “pessimistic thoughts” item and week-8 CGI-I scores. No group differences were observed in adverse events (AEs) and no serious AEs occurred. The current flow simulation showed electric field at strength within the neuromodulation range (max. ~0.45 V/m) reaching the thoracic spinal gray matter. The results from this pilot study suggest that tsDCS is feasible, well-tolerated, and shows therapeutic potential in MDD. This work also provides the initial framework for the cautious exploration of non-invasive spinal cord neuromodulation in the context of mental health research and therapeutics. The underlying mechanisms warrant further investigation. Clinicaltrials.gov registration: NCT03433339 URL: https://clinicaltrials.gov/ct2/show/NCT03433339.

Outcome measures for electric field modeling in tES and TMS: A systematic review and large-scale modeling study

BackgroundElectric field (E-field) modeling is a potent tool to estimate the amount of transcranial magnetic and electrical stimulation (TMS and tES, respectively) that reaches the cortex and to address the variable behavioral effects observed in the field. However, outcome measures used to quantify E-fields vary considerably and a thorough comparison is missing. ObjectivesThis two-part study aimed to examine the different outcome measures used to report on tES and TMS induced E-fields, including volume- and surface-level gray matter, region of interest (ROI), whole brain, geometrical, structural, and percentile-based approaches. The study aimed to guide future research in informed selection of appropriate outcome measures. MethodsThree electronic databases were searched for tES and/or TMS studies quantifying E-fields. The identified outcome measures were compared across volume- and surface-level E-field data in ten tES and TMS modalities targeting two common targets in 100 healthy individuals. ResultsIn the systematic review, we extracted 308 outcome measures from 202 studies that adopted either a gray matter volume-level (n = 197) or surface-level (n = 111) approach. Volume-level results focused on E-field magnitude, while surface-level data encompassed E-field magnitude (n = 64) and normal/tangential E-field components (n = 47). E-fields were extracted in ROIs, such as brain structures and shapes (spheres, hexahedra and cylinders), or the whole brain. Percentiles or mean values were mostly used to quantify E-fields. Our modeling study, which involved 1,000 E-field models and > 1,000,000 extracted E-field values, revealed that different outcome measures yielded distinct E-field values, analyzed different brain regions, and did not always exhibit strong correlations in the same within-subject E-field model. ConclusionsOutcome measure selection significantly impacts the locations and intensities of extracted E-field data in both tES and TMS E-field models. The suitability of different outcome measures depends on the target region, TMS/tES modality, individual anatomy, the analyzed E-field component and the research question. To enhance the quality, rigor, and reproducibility in the E-field modeling domain, we suggest standard reporting practices across studies and provide four recommendations.

Template MRI scans reliably approximate individual and group-level tES and TMS electric fields induced in motor and prefrontal circuits.

Electric field (E-field) modeling is a valuable method of elucidating the cortical target engagement from transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES), but it is typically dependent on individual MRI scans. In this study, we systematically tested whether E-field models in template MNI-152 and Ernie scans can reliably approximate group-level E-fields induced in N = 195 individuals across 5 diagnoses (healthy, alcohol use disorder, tobacco use disorder, anxiety, depression). We computed 788 E-field models using the CHARM-SimNIBS 4.0.0 pipeline with 4 E-field models per participant (motor and prefrontal targets for TMS and tES). We additionally calculated permutation analyses to determine the point of stability of E-fields to assess whether the 152 brains represented in the MNI-152 template is sufficient. Group-level E-fields did not significantly differ between the individual vs. MNI-152 template and Ernie scans for any stimulation modality or location (p > 0.05). However, TMS-induced E-field magnitudes significantly varied by diagnosis; individuals with generalized anxiety had significantly higher prefrontal and motor E-field magnitudes than healthy controls and those with alcohol use disorder and depression (p < 0.001). The point of stability for group-level E-field magnitudes ranged from 42 (motor tES) to 52 participants (prefrontal TMS). MNI-152 and Ernie models reliably estimate group-average TMS and tES-induced E-fields transdiagnostically. The MNI-152 template includes sufficient scans to control for interindividual anatomical differences (i.e., above the point of stability). Taken together, using the MNI-152 and Ernie brains to approximate group-level E-fields is a valid and reliable approach.

Optimizing TMS Coil Placement Approaches for Targeting the Dorsolateral Prefrontal Cortex in Depressed Adolescents: An Electric Field Modeling Study.

High-frequency repetitive transcranial magnetic stimulation (rTMS) to the left dorsolateral prefrontal cortex (L-DLPFC) shows promise as a treatment for treatment-resistant depression in adolescents. Conventional rTMS coil placement strategies include the 5 cm, the Beam F3, and the magnetic resonance imaging (MRI) neuronavigation methods. The purpose of this study was to use electric field (E-field) models to compare the three targeting approaches to a computational E-field optimization coil placement method in depressed adolescents. Ten depressed adolescents (4 females, age: 15.9±1.1) participated in an open-label rTMS treatment study and were offered MRI-guided rTMS five times per week over 6-8 weeks. Head models were generated based on individual MRI images, and E-fields were simulated for the four targeting approaches. Results showed a significant difference in the induced E-fields at the L-DLPFC between the four targeting methods (χ2=24.7, p<0.001). Post hoc pairwise comparisons showed that there was a significant difference between any two of the targeting methods (Holm adjusted p<0.05), with the 5 cm rule producing the weakest E-field (46.0±17.4V/m), followed by the F3 method (87.4±35.4V/m), followed by MRI-guided (112.1±14.6V/m), and followed by the computational approach (130.1±18.1V/m). Variance analysis showed that there was a significant difference in sample variance between the groups (K2=8.0, p<0.05), with F3 having the largest variance. Participants who completed the full course of treatment had median E-fields correlated with depression symptom improvement (r=-0.77, p<0.05). E-field models revealed limitations of scalp-based methods compared to MRI guidance, suggesting computational optimization could enhance dose delivery to the target.

Functional connectivity explains how neuronavigated TMS of posterior temporal subregions differentially affect language processing

Background“Wernicke's area” is most often used to describe the posterior superior temporal gyrus (STG) and refers to a region traditionally thought to support language comprehension. However, the posterior STG additionally plays a critical role in language production. The purpose of the current study was to determine to what extent regions within the posterior STG are selectively recruited during language production. Methods23 healthy right-handed participants completed an auditory fMRI localizer task, resting-state fMRI and underwent neuronavigated TMS language mapping. We applied repetitive TMS bursts during a picture naming paradigm to probe speech disruptions of different categories (anomia, speech arrest, semantic paraphasia and phonological paraphasia). We combined an in-house built high precision stimulation software suite with E-field modeling to map the naming errors to cortical regions and revealed a dissociation of language functions within the temporal gyrus. Resting state fMRI was used to explain how E-field peaks of different categories differentially affected language production. ResultsPeaks for phonological and semantic errors were found in the STG while those for anomia and speech arrest were located in the MTG. Seed-based connectivity analysis revealed a local connectivity pattern for phonological and semantic errors, while anomia and speech arrest seeds resulted in a larger network between IFG and posterior MTG. ConclusionsOur study provides important insights into the functional neuroanatomy of language production and might help to increase the current understanding of specific language production difficulties on a causal level.

Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders.

It has been recognized that the efficacy of TMS-based modulation may depend on the network profile of the stimulated regions throughout the brain. However, what profile of this stimulation network optimally benefits treatment outcomes is yet to be addressed. The answer to the question is crucial for informing network-based optimization of stimulation parameters, such as coil placement, in TMS treatments. In this study, we aimed to investigate the feasibility of taking a disease-specific network as the target of stimulation network for guiding individualized coil placement in TMS treatments. We present here a novel network-based model for TMS targeting of the pathological network. First, combining E-field modeling and resting-state functional connectivity, stimulation networks were modeled from locations and orientations of the TMS coil. Second, the spatial anti-correlation between the stimulation network and the pathological network of a given disease was hypothesized to predict the treatment outcome. The proposed model was validated to predict treatment efficacy from the position and orientation of TMS coils in two depression cohorts and one schizophrenia cohort with auditory verbal hallucinations. We further demonstrate the utility of the proposed model in guiding individualized TMS treatment for psychiatric disorders. In this proof-of-concept study, we demonstrated the feasibility of the novel network-based targeting strategy that uses the whole-brain, system-level abnormity of a specific psychiatric disease as a target. Results based on empirical data suggest that the strategy may potentially be utilized to identify individualized coil parameters for maximal therapeutic effects.

Effect of head positioning on TMS intensity: E-field modeling with BEM-FMM and modeling validation

Effect of head positioning on TMS intensity: E-field modeling with BEM-FMM and modeling validation

Combination of TMS mapping and E-field modeling reveals dissociation of language functions within the temporal gyrus

Combination of TMS mapping and E-field modeling reveals dissociation of language functions within the temporal gyrus

Accuracy and precision of navigated transcranial magnetic stimulation

Objective. Transcranial magnetic stimulation (TMS) induces an electric field (E-field) in the cortex. To facilitate stimulation targeting, image-guided neuronavigation systems have been introduced. Such systems track the placement of the coil with respect to the head and visualize the estimated cortical stimulation location on an anatomical brain image in real time. The accuracy and precision of the neuronavigation is affected by multiple factors. Our aim was to analyze how different factors in TMS neuronavigation affect the accuracy and precision of the coil–head coregistration and the estimated E-field. Approach. By performing simulations, we estimated navigation errors due to distortions in magnetic resonance images (MRIs), head-to-MRI registration (landmark- and surface-based registrations), localization and movement of the head tracker, and localization of the coil tracker. We analyzed the effect of these errors on coil and head coregistration and on the induced E-field as determined with simplistic and realistic head models. Main results. Average total coregistration accuracies were in the range of 2.2–3.6 mm and 1°; precision values were about half of the accuracy values. The coregistration errors were mainly due to head-to-MRI registration with average accuracies 1.5–1.9 mm/0.2–0.4° and precisions 0.5–0.8 mm/0.1–0.2° better with surface-based registration. The other major source of error was the movement of the head tracker with average accuracy of 1.5 mm and precision of 1.1 mm. When assessed within an E-field method, the average accuracies of the peak E-field location, orientation, and magnitude ranged between 1.5 and 5.0 mm, 0.9 and 4.8°, and 4.4 and 8.5% across the E-field models studied. The largest errors were obtained with the landmark-based registration. When computing another accuracy measure with the most realistic E-field model as a reference, the accuracies tended to improve from about 10 mm/15°/25% to about 2 mm/2°/5% when increasing realism of the E-field model. Significance. The results of this comprehensive analysis help TMS operators to recognize the main sources of error in TMS navigation and that the coregistration errors and their effect in the E-field estimation depend on the methods applied. To ensure reliable TMS navigation, we recommend surface-based head-to-MRI registration and realistic models for E-field computations.

Optimized APPS-tDCS electrode position, size, and distance doubles the on-target stimulation magnitude in 3000 electric field models

Transcranial direct current stimulation (tDCS) is a widely used noninvasive brain stimulation technique with mixed results to date. A potential solution is to apply more efficient stimulation to ensure that each participant receives sufficient cortical activation. In this four-part study, we used electric field (E-field) modeling to systematically investigate the cortical effects of conventional and novel tDCS electrode montages, with the goal of creating a new easily adoptable form of tDCS that induces higher and more focal E-fields. We computed 3000 anatomically accurate, MRI-based E-field models using 2 mA tDCS to target the left primary motor cortex in 200 Human Connectome Project (HCP) participants and tested the effects of: 1. Novel Electrode Position, 2. Electrode Size, and 3. Inter-Electrode Distance on E-field magnitude and focality. In particular, we examined the effects of placing electrodes surrounding the corticomotor target in the anterior and posterior direction (anterior posterior pad surround tDCS; APPS-tDCS). We found that electrode position, electrode size, and inter-electrode distance all significantly impact the cortical E-field magnitude and focality of stimulation (all p < 0.0001). At the same 2 mA scalp stimulation intensity, APPS-tDCS with smaller than conventional 1 × 1 cm electrodes surrounding the neural target deliver more than double the on-target cortical E-field (APPS-tDCS: average of 0.55 V/m from 2 mA; M1-SO and bilateral M1: both 0.27 V/m from 2 mA) while stimulating only a fraction of the off-target brain regions; 2 mA optimized APPS-tDCS produces 4.08 mA-like cortical E-fields. In sum, this new optimized APPS-tDCS method produces much stronger cortical stimulation intensities at the same 2 mA scalp intensity. APPS-tDCS also more focally stimulates the cortex at the intended target, using simple EEG coordinate locations and without MRI scans. This APPS-tDCS method is adoptable to any existing, commercially available tDCS device and can be used to ensure sufficient cortical activation in each person. Future directions include testing whether APPS-tDCS produces larger and more consistent therapeutic tDCS effects.

Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning

Electric fields (E-fields) induced by transcranial magnetic stimulation (TMS) can be modeled using partial differential equations (PDEs). Using state-of-the-art finite-element methods (FEM), it often takes tens of seconds to solve the PDEs for computing a high-resolution E-field, hampering the wide application of the E-field modeling in practice and research. To improve the E-field modeling's computational efficiency, we developed a self-supervised deep learning (DL) method to compute precise TMS E-fields. Given a head model and the primary E-field generated by TMS coils, a DL model was built to generate a E-field by minimizing a loss function that measures how well the generated E-field fits the governing PDE. The DL model was trained in a self-supervised manner, which does not require any external supervision. We evaluated the DL model using both a simulated sphere head model and realistic head models of 125 individuals and compared the accuracy and computational speed of the DL model with a state-of-the-art FEM. In realistic head models, the DL model obtained accurate E-fields that were significantly correlated with the FEM solutions. The DL model could obtain precise E-fields within seconds for whole head models at a high spatial resolution, faster than the FEM. The DL model built for the simulated sphere head model also obtained an accurate E-field whose average difference from the analytical E-fields was 0.0054, comparable to the FEM solution. These results demonstrated that the self-supervised DL method could obtain precise E-fields comparable to the FEM solutions with improved computational speed.

Addressing transcranial electrical stimulation variability through prospective individualized dosing of electric field strength in 300 participants across two samples: the 2-SPED approach

Objective. Transcranial electrical stimulation (tES) is a promising method for modulating brain activity and excitability with variable results to date. To minimize electric (E-)field strength variability, we introduce the 2-sample prospective E-field dosing (2-SPED) approach, which uses E-field strengths induced by tES in a first population to individualize stimulation intensity in a second population. Approach. We performed E-field modeling of three common tES montages in 300 healthy younger adults. First, permutation analyses identified the sample size required to obtain a stable group average E-field in the primary motor cortex (M1), with stability being defined as the number of participants where all group-average E-field strengths ± standard deviation did not leave the population’s 5–95 percentile range. Second, this stable group average was used to individualize tES intensity in a second independent population (n = 100). The impact of individualized versus fixed intensity tES on E-field strength variability was analyzed. Main results. In the first population, stable group average E-field strengths (V/m) in M1 were achieved at 74–85 participants, depending on the tES montage. Individualizing the stimulation intensity (mA) in the second population resulted in uniform M1 E-field strength (all p < 0.001) and significantly diminished peak cortical E-field strength variability (all p < 0.01), across all montages. Significance. 2-SPED is a feasible way to prospectively induce more uniform E-field strengths in a region of interest. Future studies might apply 2-SPED to investigate whether decreased E-field strength variability also results in decreased physiological and behavioral variability in response to tES.

The Relation between Induced Electric Field and TMS-Evoked Potentials: A Deep TMS-EEG Study

Transcranial magnetic stimulation (TMS) in humans induces electric fields (E-fields, EF) that perturb and modulate the brain’s endogenous neuronal activity and result in the generation of TMS-evoked potentials (TEPs). The exact relation of the characteristics of the induced E-field and the intensity of the brains’ response, as measured by electroencephalography (EEG), is presently unclear. In this pilot study, conducted on three healthy subjects and two patients with generalized epilepsy (total: 3 males, 2 females, mean age of 26 years; healthy: 2 males, 1 female, mean age of 25.7 years; patients: 1 male, 1 female, mean age of 26.5 years), we investigated the temporal and spatial relations of the E-field, induced by single-pulse stimuli, and the brain’s response to TMS. Brain stimulation was performed with a deep TMS device (BrainsWay Ltd., Jerusalem, Israel) and an H7 coil placed over the central area. The induced EF was computed on personalized anatomical models of the subjects through magneto quasi-static simulations. We identified specific time instances and brain regions that exhibit high positive or negative associations of the E-field with brain activity. In addition, we identified significant correlations of the brain’s response intensity with the strength of the induced E-field and finally prove that TEPs are better correlated with E-field characteristics than with the stimulator’s output. These observations provide further insight in the relation between E-field and the ensuing cortical activation, validate in a clinically relevant manner the results of E-field modeling and reinforce the view that personalized approaches should be adopted in the field of non-invasive brain stimulation.

Exploring the spatial resolution of TMS-EEG coupling on the sensorimotor region

The use of TMS-EEG coupling as a neuroimaging tool for the functional exploration of the human brain recently gained strong interest. If this tool directly inherits the fine temporal resolution from EEG, its spatial counterpart remains unknown. In this study, we explored the spatial resolution of TMS-EEG coupling by evaluating the minimal distance between two stimulated cortical sites that would significantly evoke different response dynamics. TMS evoked responses were mapped on the sensorimotor region in twenty participants. The stimulation grid was composed of nine targets separated between 10 and 15 mm on average. The dynamical signatures of TMS evoked activity were extracted and compared between sites using both local and remote linear regression scores and spatial generalized mixed models. We found a significant effect of the distance between stimulated sites on their dynamical signatures, neighboring sites showing differentiable response dynamics. Besides, common dynamical signatures were also found between sites up to 25-30 mm from each other. This overlap in dynamical properties decreased with distance and was stronger between sites within the same Brodmann area. Our results suggest that the spatial resolution of TMS-EEG coupling might be at least as high as 10 mm. Furthermore, our results reveal an anisotropic spatial resolution that was higher across than within the same Brodmann areas, in accordance with the TMS induced E-field modeling. Common cytoarchitectonic leading to shared dynamical properties within the same Brodmann area could also explain this anisotropy. Overall, these findings suggest that TMS-EEG benefits from the spatial resolution of TMS, which makes it an accurate technique for meso-scale brain mapping.

Differences in electric field strength between clinical and non-clinical populations induced by prefrontal tDCS: A cross-diagnostic, individual MRI-based modeling study

IntroductionPrefrontal cortex (PFC) regions are promising targets for therapeutic applications of non-invasive brain stimulation, e.g. transcranial direct current stimulation (tDCS), which has been proposed as a novel intervention for major depressive disorder (MDD) and negative symptoms of schizophrenia (SCZ). However, the effects of tDCS vary inter-individually, and dose–response relationships have not been established. Stimulation parameters are often tested in healthy subjects and transferred to clinical populations. The current study investigates the variability of individual MRI-based electric fields (e-fields) of standard bifrontal tDCS across individual subjects and diagnoses. MethodThe study included 74 subjects, i.e. 25 patients with MDD, 24 patients with SCZ, and 25 healthy controls (HC). Individual e-fields of a common tDCS protocol (i.e. 2 mA stimulation intensity, bifrontal anode-F3/cathode-F4 montage) were modeled by two investigators using SimNIBS (2.0.1) based on structural MRI scans. ResultOn a whole-brain level, the average e-field strength was significantly reduced in MDD and SCZ compared to HC, but MDD and SCZ did not differ significantly. Regions of interest (ROI) analysis for PFC subregions showed reduced e-fields in Sallet areas 8B and 9 for MDD and SCZ compared to HC, whereas there was again no difference between MDD and SCZ. Within groups, we generally observed high inter-individual variability of e-field intensities at a higher percentile of voxels. ConclusionMRI-based e-field modeling revealed significant differences in e-field strengths between clinical and non-clinical populations in addition to a general inter-individual variability. These findings support the notion that dose–response relationships for tDCS cannot be simply transferred from healthy to clinical cohorts and need to be individually established for clinical groups. In this respect, MRI-based e-field modeling may serve as a proxy for individualized dosing.