The Stroop task is a well-established tool to investigate the influence of competing visual categories on decision making. Neuroimaging as well as rTMS studies have demonstrated the involvement of parietal structures, particularly the intraparietal sulcus (IPS), in this task. Given its reliability, the numerical Stroop task was used to compare the effects of different TMS targeting approaches by Sack and colleagues (Sack AT 2009), who elegantly demonstrated the superiority of individualized fMRI targeting. We performed the present study to test whether fMRI-guided rTMS effects on numerical Stroop task performance could still be observed while using more advanced techniques that have emerged in the last decade (e.g., electrical sham, robotic coil holder system, etc.). To do so we used a traditional reaction time analysis and we performed, post-hoc, a more advanced comprehensive drift diffusion modeling approach. Fifteen participants performed the numerical Stroop task while active or sham 10 Hz rTMS was applied over the region of the right intraparietal sulcus (IPS) showing the strongest functional activation in the Incongruent > Congruent contrast. This target was determined based on individualized fMRI data collected during a separate session. Contrary to our assumption, the classical reaction time analysis did not show any superiority of active rTMS over sham, probably due to confounds such as potential cumulative rTMS effects, and the effect of practice. However, the modeling approach revealed a robust effect of rTMS on the drift rate variable, suggesting differential processing of congruent and incongruent properties in perceptual decision-making, and more generally, illustrating that more advanced computational analysis of performance can elucidate the effects of rTMS on the brain where simpler methods may not.
Magnetic Resonance Imaging , Reaction Time , Stroop Test , Transcranial Magnetic Stimulation , Humans , Magnetic Resonance Imaging/methods , Transcranial Magnetic Stimulation/methods , Male , Female , Adult , Reaction Time/physiology , Young Adult , Parietal Lobe/physiology , Parietal Lobe/diagnostic imaging , Decision Making/physiology , Brain Mapping/methods
Repetitive transcranial magnetic stimulation (rTMS) treatment protocols targeting the right dlPFC have been effective in reducing anxiety symptoms comorbid with depression. However, the mechanism behind these effects is unclear. Further, it is unclear whether these results generalize to non-depressed individuals. We conducted a series of studies aimed at understanding the link between anxiety potentiated startle and the right dlPFC, following a previous study suggesting that continuous theta burst stimulation (cTBS) to the right dlPFC can make people more anxious. Based on these results we hypothesized that intermittent TBS (iTBS), which is thought to have opposing effects on plasticity, may reduce anxiety when targeted at the same right dlPFC region. In this double-blinded, cross-over design, 28 healthy subjects underwent 12 study visits over a 4-week period. During each of their 2 stimulation weeks, they received four 600 pulse iTBS sessions (2/day), with a post-stimulation testing session occurring 24 h following the final iTBS session. One week they received active stimulation, one week they received sham. Stimulation weeks were separated by a 1-week washout period and the order of active/sham delivery was counterbalanced across subjects. During the testing session, we induced anxiety using the threat of unpredictable shock and measured anxiety potentiated startle. Contrary to our initial hypothesis, subjects showed increased startle reactivity following active compared to sham stimulation. These results replicate work from our two previous trials suggesting that TMS to the right dlPFC increases anxiety potentiated startle, independent of both the pattern of stimulation and the timing of the post stimulation measure. Although these results confirm a mechanistic link between right dlPFC excitability and startle, capitalizing upon this link for the benefit of patients will require future exploration.
Across all domains of brain stimulation (neuromodulation), conventional analysis of neuron activation involves two discrete steps: i) prediction of macroscopic electric field, ignoring presence of cells and; ii) prediction of cell activation from tissue electric fields. The first step assumes that current flow is not distorted by the dense tortuous network of cell structures. The deficiencies of this assumption have long been recognized, but - except for trivial geometries - ignored, because it presented intractable computation hurdles. This study introduces a novel approach for analyzing electric fields within a microscopically realistic brain volume. Our pipeline overcomes the technical intractability that prevented such analysis while also showing significant implications for brain stimulation. Contrary to the standard finite element method (FEM), we suggest using a nested iterative boundary element method (BEM) coupled with the fast multipole method (FMM). The nested iterative BEM-FMM approach allows for solving problems with multiple length scales efficiently. It could potentially handle any number of electromagnetically coupled closely spaced neurons and blood microcapillaries. A target application is a subvolume of the L2/3 P36 mouse primary visual cortex containing approximately 400 detailed neuronal meshes at a resolution of 100 nm, which is obtained from scanning electron microscopy data. Our immediate result is a reduction of the stimulation field strength necessary for neuron activation by a factor of 0.85-0.55 (by 15%-45%) as compared to macroscopic predictions. This is in line with experimental data stating that existing macroscopic theories substantially overestimate electric field levels necessary for brain stimulation.
BACKGROUND: Transcranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed. OBJECTIVE: The current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline. METHODS: BOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to grid optimization. RESULTS: Bayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within a 5% error of the maxima detected by grid optimization, and requiring less time. CONCLUSIONS: BOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround.
Neurostimulation devices that use rotating permanent magnets are being explored for their potential therapeutic benefits in patients with psychiatric and neurological disorders. This study aims to characterize the electric field (E-field) for ten configurations of rotating magnets using finite element analysis and phantom measurements. Various configurations were modeled, including single or multiple magnets, and bipolar or multipolar magnets, rotated at 10, 13.3, and 350 revolutions per second (rps). E-field strengths were also measured using a hollow sphere (r=9.2 cm) filled with a 0.9% sodium chloride solution and with a dipole probe. The E-field spatial distribution is determined by the magnets' dimensions, number of poles, direction of the magnetization, and axis of rotation, while the E-field strength is determined by the magnets' rotational frequency and magnetic field strength. The induced E-field strength on the surface of the head ranged between 0.0092 and 0.52 V/m. In the range of rotational frequencies applied, the induced E-field strengths were approximately an order or two of magnitude lower than those delivered by conventional transcranial magnetic stimulation. The impact of rotational frequency on E-field strength represents a confound in clinical trials that seek to tailor rotational frequency to individual neural oscillations. This factor could explain some of the variability observed in clinical trial outcomes.
Neurostimulation devices that use rotating permanent magnets are being explored for their potential therapeutic benefits in patients with psychiatric and neurological disorders. This study aims to characterize the electric field (E-field) for ten configurations of rotating magnets using finite element analysis and phantom measurements. Various configurations were modeled, including single or multiple magnets, bipolar or multipolar magnets, rotated at 10, 13.3, and 400 Hz. E-field strengths were also measured using a hollow sphere ( r = 9.2 cm) filled with a 0.9% sodium chloride solution and with a dipole probe. The E-field spatial distribution is determined by the magnets' dimensions, number of poles, direction of the magnetization, and axis of rotation, while the E-field strength is determined by the magnets' rotational frequency and magnetic field strength. The induced E-field strength on the surface of the head ranged between 0.0092 and 0.59 V/m. At the range of rotational frequencies applied, the induced E-field strengths were approximately an order or two of magnitude lower than those delivered by conventional transcranial magnetic stimulation. The impact of rotational frequency on E-field strength represents a previously unrecognized confound in clinical trials that seek to personalize stimulation frequency to individual neural oscillations and may represent a mechanism to explain some clinical trial results.
Objective.In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems.Approach.We propose, describe, and systematically investigate an AMR method using the boundary element method with fast multipole acceleration (BEM-FMM) as the base numerical solver. The goal is to efficiently allocate additional unknowns to critical areas of the model, where they will best improve solution accuracy. The implemented AMR method's accuracy improvement is measured on head models constructed from 16 Human Connectome Project subjects under problem classes of TES, TMS, and EEG. Errors are computed between three solutions: an initial non-adaptive solution, a solution found after applying AMR with a conservative refinement rate, and a 'silver-standard' solution found by subsequent 4:1 global refinement of the adaptively-refined model.Main results.Excellent agreement is shown between the adaptively-refined and silver-standard solutions for standard head models. AMR is found to be vital for accurate modeling of TES and EEG forward problems for standard models: an increase of less than 25% (on average) in number of mesh elements for these problems, efficiently allocated by AMR, exposes electric field/potential errors exceeding 60% (on average) in the solution for the unrefined models.Significance.This error has especially important implications for TES dosing prediction-where the stimulation strength plays a central role-and for EEG lead fields. Though the specific form of the AMR method described here is implemented for the BEM-FMM, we expect that AMR is applicable and even required for accurate electromagnetic simulations by other numerical modeling packages as well.
Head , Silver , Humans , Head/physiology , Transcranial Magnetic Stimulation/methods , Electroencephalography/methods , Electromagnetic Phenomena , Brain/physiology
It has been suggested that aberrant excitation/inhibition (E/I) balance and dysfunctional structure and function of relevant brain networks may underlie the symptoms of autism spectrum disorder (ASD). However, the nomological network linking these constructs to quantifiable measures and mechanistically relating these constructs to behavioral symptoms of ASD is lacking. Herein we describe a within-subject, controlled, proof-of-mechanism study investigating the pathophysiology of auditory/language processing in adolescents with ASD. We utilize neurophysiological and neuroimaging techniques including magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG) metrics of language network structure and function. Additionally, we apply a single, individually targeted session of continuous theta burst stimulation (cTBS) as an experimental probe of the impact of perturbation of the system on these neurophysiological and neuroimaging outcomes. MRS, fMRI, and MEG measures are evaluated at baseline and immediately prior to and following cTBS over the posterior superior temporal cortex (pSTC), a region involved in auditory and language processing deficits in ASD. Also, behavioral measures of ASD and language processing and DWI measures of auditory/language network structures are obtained at baseline to characterize the relationship between the neuroimaging and neurophysiological measures and baseline symptom presentation. We hypothesize that local gamma-aminobutyric acid (GABA) and glutamate concentrations (measured with MRS), and structural and functional activity and network connectivity (measured with DWI and fMRI), will significantly predict MEG indices of auditory/language processing and behavioral deficits in ASD. Furthermore, a single session of cTBS over left pSTC is hypothesized to lead to significant, acute changes in local glutamate and GABA concentration, functional activity and network connectivity, and MEG indices of auditory/language processing. We have completed the pilot phase of the study (n=20 Healthy Volunteer adults) and have begun enrollment for the main phase with adolescents with ASD (n=86; age 14-17). If successful, this study will establish a nomological network linking local E/I balance measures to functional and structural connectivity within relevant brain networks, ultimately connecting them to ASD symptoms. Furthermore, this study will inform future therapeutic trials using cTBS to treat the symptoms of ASD.
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.
Electroconvulsive Therapy , Humans , Aged , Middle Aged , Aged, 80 and over , Electroconvulsive Therapy/methods , Brain/diagnostic imaging , Brain/physiology , Seizures/therapy , Antidepressive Agents/therapeutic use , Cognition , Treatment Outcome
The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.
Brain , Transcranial Magnetic Stimulation , Humans , Transcranial Magnetic Stimulation/methods , Brain/physiology , Neuroimaging
INTRODUCTION: The effects of body mass index (BMI) on the core symptoms of bipolar disorder (BD) and its implications for disease trajectory are largely unexplored. OBJECTIVE: To examine whether BMI impacted hospitalization rate, medical and psychiatric comorbidities, and core symptom domains such as depression and suicidality in BD. METHODS: Participants (15 years and older) were 2790 BD outpatients enrolled in the longitudinal STEP-BD study; all met DSM-IV criteria for BD-I, BD-II, cyclothymia, BD NOS, or schizoaffective disorder, bipolar subtype. BMI, demographic information, psychiatric and medical comorbidities, and other clinical variables such as bipolarity index, history of electroconvulsive therapy (ECT), and history of suicide attempts were collected at baseline. Longitudinal changes in Montgomery-Åsberg Depression Rating Scale (MADRS) score, Young Mania Rating Scale (YMRS) score, and hospitalizations during the study were also assessed. Depending on the variable of interest, odds-ratios, regression analyses, factor analyses, and graph analyses were applied. RESULTS: A robust increase in psychiatric and medical comorbidities was observed, particularly for baseline BMIs >35. A significant relationship was noted between higher BMI and history of suicide attempts, and individuals with BMIs >40 had the highest prevalence of suicide attempts. Obese and overweight individuals had a higher bipolarity index (a questionnaire measuring disease severity) and were more likely to have received ECT. Higher BMIs correlated with worsening trajectory of core depression symptoms and with worsening lassitude and inability to feel. CONCLUSIONS: In BD participants, elevated BMI was associated with worsening clinical features, including higher rates of suicidality, comorbidities, and core depression symptoms.
Bipolar Disorder , Humans , Bipolar Disorder/psychology , Body Mass Index , Psychiatric Status Rating Scales , Suicide, Attempted/psychology , Comorbidity
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) has been increasingly used for treating obsessive-compulsive disorder (OCD). Although several meta-analyses have explored its effectiveness and safety, there is no umbrella review specifically focused on rTMS for OCD. This umbrella review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and analyzed relevant meta-analyses on rTMS for OCD. METHODS: Twenty-three articles were identified from PubMed, and after screening, 12 meta-analyses were included in the review. The studies analyzed in the meta-analyses ranged from 10 to 27, with total participants ranging from 282 to 791. The most commonly studied regions were the dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), and orbito-frontal cortex (OFC). RESULT: The majority of the meta-analyses consistently supported the effectiveness of rTMS in reducing OCD symptoms when applied to the DLPFC and SMA. Encouraging results were also observed when targeting the medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC) through deep transcranial magnetic stimulation (dTMS). However, there was a high level of heterogeneity in the findings of nine out of 12 meta-analyses. CONCLUSION: In conclusion, existing evidence suggests that rTMS targeting the DLPFC and SMA consistently reduces OCD symptoms, but targeting the mPFC and ACC through dTMS shows variable results. However, the high heterogeneity in the study findings indicates a need for further research and standardization in the field.
Motor Cortex , Obsessive-Compulsive Disorder , Humans , Obsessive-Compulsive Disorder/therapy , Prefrontal Cortex , Transcranial Magnetic Stimulation/methods , Treatment Outcome , Meta-Analysis as Topic
Importance: Electroconvulsive therapy (ECT) is highly effective and rapid in treating depression, but it carries a risk of significant cognitive adverse effects. Magnetic seizure therapy (MST), an investigational antidepressant treatment, may maintain the robust antidepressant efficacy of ECT while substantially reducing adverse effects due to its enhanced focality and weaker stimulation strength; however, previous clinical trials of MST were limited by small sample sizes. Objective: To compare the antidepressant efficacy of MST vs ultrabrief pulse right unilateral (RUL) ECT. Design, Setting, and Participants: A between-participants, double-blinded, randomized clinical trial was conducted at 3 academic hospitals from June 2007 to August 2012. Adults aged 18 to 90 years who were referred for treatment with ECT, had a major depressive episode in the context of major depressive disorder or bipolar disorder, and had a baseline 24-item Hamilton Depression Rating Scale (HDRS-24) total score of 18 or higher were included. Participants were randomly assigned 1:1 to treatment with MST or ultrabrief pulse RUL ECT. After the treatment course, patients were naturalistically followed up for up to 6 months to examine the durability of clinical effects. Interventions: Treatment with MST, applied at 100 Hz at 100% of the maximum device power for 10 seconds, or ultrabrief pulse RUL ECT, applied at 6 times seizure threshold. Main Outcomes and Measures: The primary outcome was change from baseline in HDRS-24 total score, with patients followed up for up to 6 months. A reduction of at least 50% in the HDRS-24 score indicated response, and at least a 60% decrease in the HDRS-24 score and a total score of 8 or less indicated remission. Results: Of the 73 participants (41 [56.2%] female; mean [SD] age, 48 [14.1] years), 35 were randomized to MST and 38 to ECT. Among them, 53 (72.6%) were classified as completers (29 in the MST group and 24 in the ECT group). Both MST and ECT demonstrated clinically meaningful antidepressant effects. In the intent-to-treat sample, 18 participants (51.4%) in the MST group and 16 (42.1%) in the ECT group met response criteria; 13 (37.1%) in the MST group and 10 (26.3%) in the ECT group met remission criteria. Among completers, 17 of 29 (58.6%) in the MST group and 15 of 24 (62.5%) in the ECT group met response criteria; 13 of 29 (44.8%) in the MST group and 10 of 24 (41.7%) in the ECT group met remission criteria. There was no significant difference between MST and ECT for either response or remission rates. However, the mean (SD) number of treatments needed to achieve remission was 9.0 (3.1) with MST and 6.7 (3.3) with ECT, a difference of 2.3 treatments (t71.0 = 3.1; P = .003). Both MST and ECT showed a sustained benefit over a 6-month follow-up period, again with no significant difference between them. Compared with MST, ECT had significantly longer time to orientation after treatment (threshold level: F1,56 = 10.0; P = .003) and greater severity of subjective adverse effects, particularly in the physical and cognitive domains. Conclusions and Relevance: This randomized clinical trial found that the efficacy of MST was indistinguishable from that of ultrabrief pulse RUL ECT, the safest form of ECT currently available. These results support the continued development of MST and provide evidence for advantages relative to state-of-the-art ECT. Trial Registration: ClinicalTrials.gov Identifier: NCT00488748.
Depressive Disorder, Major , Electroconvulsive Therapy , Adult , Humans , Female , Middle Aged , Male , Depressive Disorder, Major/therapy , Depressive Disorder, Major/psychology , Electroconvulsive Therapy/adverse effects , Treatment Outcome , Antidepressive Agents , Seizures/therapy
We have known for nearly a century that triggering seizures can treat serious mental illness, but what we do not know is why. Electroconvulsive Therapy (ECT) works faster and better than conventional pharmacological interventions; however, those benefits come with a burden of side effects, most notably memory loss. Disentangling the mechanisms by which ECT exerts rapid therapeutic benefit from the mechanisms driving adverse effects could enable the development of the next generation of seizure therapies that lack the downside of ECT. The latest research suggests that this goal may be attainable because modifications of ECT technique have already yielded improvements in cognitive outcomes without sacrificing efficacy. These modifications involve changes in how the electricity is administered (both where in the brain, and how much), which in turn impacts the characteristics of the resulting seizure. What we do not completely understand is whether it is the changes in the applied electricity, or in the resulting seizure, or both, that are responsible for improved safety. Answering this question may be key to developing the next generation of seizure therapies that lack these adverse side effects, and ushering in novel interventions that are better, faster, and safer than ECT.
Electroconvulsive Therapy , Humans , Electroconvulsive Therapy/adverse effects , Electroconvulsive Therapy/methods , Depression , Electroencephalography , Seizures/therapy , Electricity , Treatment Outcome
When modeling transcranial magnetic stimulation (TMS) in the brain, a fast and accurate electric field solver can support interactive neuronavigation tasks as well as comprehensive biophysical modeling. We formulate, test, and disseminate a direct (i.e., non-iterative) TMS solver that can accurately determine global TMS fields for any coil type everywhere in a high-resolution MRI-based surface model with ~ 200,000 or more arbitrarily selected observation points within approximately 5 s, with the solution time itself of 3 s. The solver is based on the boundary element fast multipole method (BEM-FMM), which incorporates the latest mathematical advancement in the theory of fast multipole methods-an FMM-based LU decomposition. This decomposition is specific to the head model and needs to be computed only once per subject. Moreover, the solver offers unlimited spatial numerical resolution. Despite the fast execution times, the present direct solution is numerically accurate for the default model resolution. In contrast, the widely used brain modeling software SimNIBS employs a first-order finite element method that necessitates additional mesh refinement, resulting in increased computational cost. However, excellent agreement between the two methods is observed for various practical test cases following mesh refinement, including a biophysical modeling task. The method can be readily applied to a wide range of TMS analyses involving multiple coil positions and orientations, including image-guided neuronavigation. It can even accommodate continuous variations in coil geometry, such as flexible H-type TMS coils. The FMM-LU direct solver is freely available to academic users.
Brain , Transcranial Magnetic Stimulation , Transcranial Magnetic Stimulation/methods , Brain/physiology , Head/physiology , Software , Magnetic Resonance Imaging/methods
Transcranial Magnetic Stimulation (TMS) coil placement and pulse waveform current are often chosen to achieve a specified E-field dose on targeted brain regions. TMS neuronavigation could be improved by including real-time accurate distributions of the E-field dose on the cortex. We introduce a method and develop software for computing brain E-field distributions in real-time enabling easy integration into neuronavigation and with the same accuracy as 1st -order finite element method (FEM) solvers. Initially, a spanning basis set (< 400) of E-fields generated by white noise magnetic currents on a surface separating the head and permissible coil placements are orthogonalized to generate the modes. Subsequently, Reciprocity and Huygens' principles are utilized to compute fields induced by the modes on a surface separating the head and coil by FEM, which are used in conjunction with online (real-time) computed primary fields on the separating surface to evaluate the mode expansion. We conducted a comparative analysis of E-fields computed by FEM and in real-time for eight subjects, utilizing two head model types (SimNIBS's 'headreco' and 'mri2mesh' pipeline), three coil types (circular, double-cone, and Figure-8), and 1000 coil placements (48,000 simulations). The real-time computation for any coil placement is within 4 milliseconds (ms), for 400 modes, and requires less than 4 GB of memory on a GPU. Our solver is capable of computing E-fields within 4 ms, making it a practical approach for integrating E-field information into the neuronavigation systems without imposing a significant overhead on frame generation (20 and 50 frames per second within 50 and 20 ms, respectively).
Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.
