A generalized workflow for conducting electric field-optimized, fMRI-guided, transcranial magnetic stimulation

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

OBJECTIVE

Transcranial 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).

METHODS

rs-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).

RESULTS

For 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.

CONCLUSIONS

These 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.

SIGNIFICANCE

This finding lays the groundwork for future studies to individualize TMS targeting based on how predicted rs-fMRI changes might impact psychiatric symptoms.

Intermittent theta-burst stimulation to the right dorsolateral prefrontal cortex may increase potentiated startle in healthy individuals

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.

A New Angle on Transcranial Magnetic Stimulation Coil Orientation: A Targeted Narrative Review

Transcranial magnetic stimulation (TMS) is used to treat several neuropsychiatric disorders including depression, where it is effective in approximately one half of patients for whom pharmacological approaches have failed. Treatment response is related to stimulation parameters such as the stimulation frequency, pattern, intensity, location, total number of pulses and sessions applied, and target brain network engagement. One critical but underexplored component of the stimulation procedure is the orientation or yaw angle of the commonly used figure-of-eight TMS coil, which is known to impact neuronal response to TMS. However, coil orientation has remained largely unchanged since TMS was first used to treat depression and continues to be based on motor cortex anatomy, which may not be optimal for the dorsolateral prefrontal cortex treatment site. In this targeted narrative review, we evaluate experimental, clinical, and computational evidence indicating that optimizing coil orientation may improve TMS treatment outcomes. The properties of the electric field induced by TMS, the changes to this field caused by the differing conductivities of head tissues, and the interaction between coil orientation and the underlying cortical anatomy are summarized. We describe evidence that the magnitude and site of cortical activation, surrogate markers of TMS dosing and brain network targeting considered central in clinical response to TMS, are influenced by coil orientation. We suggest that coil orientation should be considered when applying therapeutic TMS and propose several approaches to optimizing this potentially important treatment parameter.

Noninvasive brain stimulation in autism: review and outlook for personalized interventions in adult patients

Noninvasive brain stimulation (NIBS) has been increasingly investigated during the last decade as a treatment option for persons with autism spectrum disorder (ASD). Yet, previous studies did not reach a consensus on a superior treatment protocol or stimulation target. Persons with ASD often suffer from social isolation and high rates of unemployment, arising from difficulties in social interaction. ASD involves multiple neural systems involved in perception, language, and cognition, and the underlying brain networks of these functional domains have been well documented. Aiming to provide an overview of NIBS effects when targeting these neural systems in late adolescent and adult ASD, we conducted a systematic search of the literature starting at 631 non-duplicate publications, leading to six studies corresponding with inclusion and exclusion criteria. We discuss these studies regarding their treatment rationale and the accordingly chosen methodological setup. The results of these studies vary, while methodological advances may allow to explain some of the variability. Based on these insights, we discuss strategies for future clinical trials to personalize the selection of brain stimulation targets taking into account intersubject variability of brain anatomy as well as function.

Bayesian Optimization of Neurostimulation (BOONStim)

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.

Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions

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.

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

Personalized connectivity-based network targeting model of transcranial magnetic stimulation for treatment of psychiatric disorders: computational feasibility and reproducibility

Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial. Compared to group-averaged functional connectivity (FC), individual FC provides specific information about a person's brain functional architecture, offering the potential for more accurate network targeting for personalized TMS. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC. To overcome this challenge, the proposed solutions include increasing the scan duration and employing the cluster method to enhance the stability of FC. This study aimed to evaluate the stability of a personalized FC-based network targeting model in individuals with major depressive disorder or schizophrenia with auditory verbal hallucinations. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we assessed the model's stability. We employed longer scan durations and cluster methodologies to improve the precision in identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the cluster method achieved stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. The current model provides a feasible approach to obtaining stable personalized TMS targets from the scalp, offering a more accurate method of TMS targeting in clinical applications.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

The effect of deep repetitive transcranial magnetic stimulation with an H1 coil on hopelessness in patients with major depressive disorder: A randomized controlled trial

OBJECTIVES

To assess the efficacy of repetitive transcranial magnetic stimulation (rTMS) with an H1 coil as a treatment for hopelessness in patients with major depressive disorder (MDD).

METHODS

We conducted a randomised controlled trial in a tertiary psychiatric institution in Croatia, including patients diagnosed with MDD without psychotic symptoms and with clinically relevant hopelessness. High-frequency (18 Hz) rTMS with an H1 coil was administered over four weeks on the left dorsolateral prefrontal cortex. We examined changes in the Beck Hopelessness Scale (BHS) scores.

RESULTS

We randomly assigned 51 participants to the intervention group (rTMS plus standard therapy) and 52 to the control group (standard therapy). The mean (SD) ages were 50 (12.3) and 50 (10.4) years, and 47% and 52% of the participants were females in the intervention and control groups, respectively. Following treatment, the BHS scores decreased (unadjusted bivariate analysis, p = 0.043; false discovery rate (FDR) >5%). Multivariate analysis revealed that the BHS score was reduced by 10.8% (95% confidence interval (CI: -17.8% to -3.9%) in the rTMS group and 0.7% (95% CI: 7.5% -6.1%) in the control group (p = 0.037; FDR < 5%).

CONCLUSIONS

rTMS with an H1 coil improved the symptoms of hopelessness in patients with MDD.

Accelerated TMS - moving quickly into the future of depression treatment

Accelerated TMS is an emerging application of Transcranial Magnetic Stimulation (TMS) aimed to reduce treatment length and improve response time. Extant literature generally shows similar efficacy and safety profiles compared to the FDA-cleared protocols for TMS to treat major depressive disorder (MDD), yet accelerated TMS research remains at a very early stage in development. The few applied protocols have not been standardized and vary significantly across a set of core elements. In this review, we consider nine elements that include treatment parameters (i.e., frequency and inter-stimulation interval), cumulative exposure (i.e., number of treatment days, sessions per day, and pulses per session), individualized parameters (i.e., treatment target and dose), and brain state (i.e., context and concurrent treatments). Precisely which of these elements is critical and what parameters are most optimal for the treatment of MDD remains unclear. Other important considerations for accelerated TMS include durability of effect, safety profiles as doses increase over time, the possibility and advantage of individualized functional neuronavigation, use of biological readouts, and accessibility for patients most in need of the treatment. Overall, accelerated TMS appears to hold promise to reduce treatment time and achieve rapid reduction in depressive symptoms, but at this time significant work remains to be done. Rigorous clinical trials combining clinical outcomes and neuroscientific measures such as electroencephalogram, magnetic resonance imaging and e-field modeling are needed to define the future of accelerated TMS for MDD.

In-vivoverified anatomically aware deep learning for real-time electric field simulation

Objective.Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it enables targeted stimulation of specific brain regions. However, current computational methods employed for E-field simulations necessitate extensive preprocessing and simulation time, limiting their fast applications in the determining the optimal coil placement.Approach.We present an attentional deep learning network to simulate E-fields. This network takes individual magnetic resonance images and coil configurations as inputs, firstly transforming the images into explicit brain tissues and subsequently generating the local E-field distribution near the target brain region. Main results. Relative to the previous deep-learning simulation method, the presented method reduced the mean relative error in simulated E-field strength of gray matter by 21.1%, and increased the correlation between regional E-field strengths and corresponding electrophysiological responses by 35.0% when applied into another dataset.In-vivoTMS experiments further revealed that the optimal coil placements derived from presented method exhibit comparable stimulation performance on motor evoked potentials to those obtained using computational methods. The simplified preprocessing and increased simulation efficiency result in a significant reduction in the overall time cost of traditional TMS coil placement optimization, from several hours to mere minutes.Significance.The precision and efficiency of presented simulation method hold promise for its application in determining individualized coil placements in clinical practice, paving the way for personalized TMS treatments.

Interleaved TMS/fMRI shows that threat decreases dlPFC-mediated top-down regulation of emotion processing

Background

The right dorsolateral prefrontal cortex (dlPFC) has been indicated to be a key region in the cognitive regulation of emotion by many previous neuromodulation and neuroimaging studies. However, there is little direct causal evidence supporting this top-down regulation hypothesis. Furthermore, it is unclear whether contextual threat impacts this top-down regulation. By combining TMS/fMRI, this study aimed to uncover the impact of unpredictable threat on TMS-evoked BOLD response in dlPFC-regulated emotional networks. Based on the previous findings linking the dlPFC to the downregulation of emotional network activity, we hypothesized TMS pulses would deactivate activity in anxiety expression regions, and that threat would reduce this top-down regulation.

Methods

44 healthy controls (no current or history of psychiatric disorders) were recruited to take part in a broader study. Subjects completed the neutral, predictable, and unpredictable (NPU) threat task while receiving TMS pulses to either the right dlPFC or a control region. dlPFC targeting was based on data from a separate targeting session, where subjects completed the Sternberg working memory (WM) task inside the MRI scanner.

Results

When compared to safe conditions, subjects reported significantly higher levels of anxiety under threat conditions. Additionally, TMS-evoked responses in the left insula (LI), right sensory/motor cortex (RSM), and a region encompassing the bilateral SMA regions (BSMA) differed significantly between safe and threat conditions. There was a significant TMS-evoked deactivation in safe periods that was significantly attenuated in threat periods across all 3 regions.

Conclusions

These findings suggest that threat decreases dlPFC-regulated emotional processing by attenuating the top-down control of emotion, like the left insula. Critically, these findings provide support for the use of right dlPFC stimulation as a potential intervention in anxiety disorders.

Functional connectomics in depression: insights into therapies

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.

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.

Electric field distribution predicts efficacy of accelerated intermittent theta burst stimulation for late-life depression

Introduction

Repetitive transcranial magnetic stimulation (rTMS) is a promising intervention for late-life depression (LLD) but may have lower rates of response and remission owing to age-related brain changes. In particular, rTMS induced electric field strength may be attenuated by cortical atrophy in the prefrontal cortex. To identify clinical characteristics and treatment parameters associated with response, we undertook a pilot study of accelerated fMRI-guided intermittent theta burst stimulation (iTBS) to the right dorsolateral prefrontal cortex in 25 adults aged 50 or greater diagnosed with LLD and qualifying to receive clinical rTMS.

Methods

Participants underwent baseline behavioral assessment, cognitive testing, and structural and functional MRI to generate individualized targets and perform electric field modeling. Forty-five sessions of iTBS were delivered over 9 days (1800 pulses per session, 50-min inter-session interval). Assessments and testing were repeated after 15 sessions (Visit 2) and 45 sessions (Visit 3). Primary outcome measure was the change in depressive symptoms on the Inventory of Depressive Symptomatology-30-Clinician (IDS-C-30) from Visit 1 to Visit 3.

Results

Overall there was a significant improvement in IDS score with the treatment (Visit 1: 38.6; Visit 2: 31.0; Visit 3: 21.3; mean improvement 45.5%) with 13/25 (52%) achieving response and 5/25 (20%) achieving remission (IDS-C-30 < 12). Electric field strength and antidepressant effect were positively correlated in a subregion of the ventrolateral prefrontal cortex (VLPFC) (Brodmann area 47) and negatively correlated in the posterior dorsolateral prefrontal cortex (DLPFC).

Conclusion

Response and remission rates were lower than in recently published trials of accelerated fMRI-guided iTBS to the left DLPFC. These results suggest that sufficient electric field strength in VLPFC may be a contributor to effective rTMS, and that modeling to optimize electric field strength in this area may improve response and remission rates. Further studies are needed to clarify the relationship of induced electric field strength with antidepressant effects of rTMS for LLD.

Revisiting the focality of non-invasive brain stimulation - implications for studies of human cognition

Non-invasive brain stimulation techniques are popular tools to investigate brain function in health and disease. Although transcranial magnetic stimulation (TMS) is widely used in cognitive neuroscience research to probe causal structure-function relationships, studies often yield inconclusive results. To improve the effectiveness of TMS studies, we argue that the cognitive neuroscience community needs to revise the stimulation focality principle - the spatial resolution with which TMS can differentially stimulate cortical regions. In the motor domain, TMS can differentiate between cortical muscle representations of adjacent fingers. However, this high degree of spatial specificity cannot be obtained in all cortical regions due to the influences of cortical folding patterns on the TMS-induced electric field. The region-dependent focality of TMS should be assessed a priori to estimate the experimental feasibility. Post-hoc simulations allow modeling of the relationship between cortical stimulation exposure and behavioral modulation by integrating data across stimulation sites or subjects.

Distinct reserve capacity impacts on default-mode network in response to left angular gyrus-navigated repetitive transcranial magnetic stimulation in the prodromal Alzheimer disease

Default-mode network (DMN) may be the earliest affected network and is associated with cognitive decline in Alzheimer's disease (AD). Repetitive transcranial magnetic stimulation (rTMS) may help to modulate DMN plasticity. Still, stimulation effects substantially vary across studies and individuals. Global left frontal cortex (gLFC) connectivity, a substitute for reserve capacity, may contribute to the heterogeneous physiological effects of neuro-navigated rTMS. This study investigated the effects of left angular gyrus-navigated rTMS on DMN connectivity in different reserve capacity participants. gLFC connectivity, was computed through resting-state fMRI correlations. Thirty-one prodromal AD patients were divided into low connection group (LCG) and high connection group (HCG) by the median of gLFC connectivity. Distinct reserve capacity impacts on DMN in response to rTMS were identified in these two groups. Then, brain-behavior relationships were examined. gLFC connectivity within a certain range is directly proportional to cognitive reserve ability (i.e., LCG), and the effectiveness of functional connectivity beyond this range decreases (i.e, HCG). Moreover, LCG exhibited increased DMN connectivity and significantly positive memory improvements, while HCG showed a contrary connectivity decline and maintained or slightly improved their cognitive function after neuro-navigated rTMS treatment. The prodromal AD patients with the distinct reserve capacity may benefit differently from left angular gyrus-navigated rTMS, which may lead to increasing attention in defining personalized medicine approach of AD.

Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM)

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that uses a coil to induce an electric field (E-field) in the brain and modulate its activity. Many applications of TMS call for the repeated execution of E-field solvers to determine the E-field induced in the brain for different coil placements. However, the usage of solvers for these applications remains impractical because each coil placement requires the solution of a large linear system of equations. We develop a fast E-field solver that enables the rapid evaluation of the E-field distribution for a brain region of interest (ROI) for a large number of coil placements, which is achieved in two stages. First, during the pre-processing stage, the mapping between coil placement and brain ROI E-field distribution is approximated from E-field results for a few coil placements. Specifically, we discretize the mapping into a matrix with each column having the ROI E-field samples for a fixed coil placement. This matrix is approximated from a few of its rows and columns using adaptive cross approximation (ACA). The accuracy, efficiency, and applicability of the new ACA approach are determined by comparing its E-field predictions with analytical and standard solvers in spherical and MRI-derived head models. During the second stage, the E-field distribution in the brain ROI from a specific coil placement is determined by the obtained rows and columns in milliseconds. For many applications, only the E-field distribution for a comparatively small ROI is required. For example, the solver can complete the pre-processing stage in approximately 4 hours and determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error enabling its use for neuro-navigation and other applications. Highlight: We developed a fast solver for TMS computational E-field dosimetry, which can determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error.

Comparison of coil placement approaches targeting dorsolateral prefrontal cortex in depressed adolescents receiving repetitive transcranial magnetic stimulation: an electric field modeling study

Background

A promising treatment option for adolescents with treatment-resistant depression is high-frequency repetitive transcranial magnetic stimulation (rTMS) delivered to the left dorsolateral prefrontal cortex (L-DLPFC). Conventional coil placement strategies for rTMS in adults include the 5-cm rule, the Beam F3 method, and the magnetic resonance imaging (MRI) neuronavigation method. The purpose of this study was to compare the three targeting approaches to a computational E-field optimization coil placement method in depressed adolescents.

Methods

Ten consenting and assenting depressed adolescents (4 females, age: 15.9 ± 1.1) participated in an open-label rTMS treatment study. Participants were offered MRI-guided rTMS 5 times per week over 6-8 weeks. To compute the induced E-field, a head model was generated based on MRI images, and a figure-8 TMS coil (Neuronetics) was placed over the L-DLPFC using the four targeting approaches.

Results

Results show that there was a significant difference in the induced E-field at the L-DLPFC between the four targeting methods ( χ 2 = 24.7, p < 0.001). Post hoc pairwise comparisons show 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 ± 17.4 V/m), followed by the F3 method (87.4 ± 35.4 V/m), followed by the MRI-guided (112.1 ± 14.6 V/m), and followed by the computationally optimized method (130.1 ± 18.1 V/m). The Bartlett test of homogeneity of variances show that there was a significant difference in sample variance between the groups ( K 2 = 8.0, p < 0.05), with F3 having the largest variance. In participants who completed the full course of treatment, the median E-field strength in the L-DLPFC was correlated with the change in depression severity ( r = - 0.77, p < 0.05).

Conclusions

The E-field models revealed inadequacies of scalp-based targeting methods compared to MRI-guidance. Computational optimization may further enhance E-field dose delivery to the treatment target.

Automated optimization of TMS coil placement for personalized functional network engagement

Transcranial magnetic stimulation (TMS) is used to treat multiple psychiatric and neurological conditions by manipulating activity in particular brain networks and circuits, but individual responses are highly variable. In clinical settings, TMS coil placement is typically based on either group average functional maps or scalp heuristics. Here, we found that this approach can inadvertently target different functional networks in depressed patients due to variability in their functional brain organization. More precise TMS targeting should be feasible by accounting for each patient's unique functional neuroanatomy. To this end, we developed a targeting approach, termed targeted functional network stimulation (TANS). The TANS approach improved stimulation specificity in silico in 8 highly sampled patients with depression and 6 healthy individuals and in vivo when targeting somatomotor functional networks representing the upper and lower limbs. Code for implementing TANS and an example dataset are provided as a resource.

Parietal-hippocampal rTMS improves cognitive function in Alzheimer's disease and increases dynamic functional connectivity of default mode network

Parietal-hippocampal repetitive transcranial magnetic stimulation (rTMS) improves cognitive function in Alzheimer's disease (AD), however, the underlying therapeutic mechanism has not been elucidated. A double-blind, randomized, sham-controlled parietal-hippocampal rTMS trial (five sessions/week for a total of 10 sessions) of mild-to-moderate AD patients was conducted in the study. High-frequency rTMS was applied to a subject-specific left lateral parietal region with the highest functional connectivity with the hippocampus based on resting-state fMRI. A multimodal MRI scan and a complete neuropsychological battery of tests were conducted at baseline, immediately after the intervention and 12-week follow-up after the rTMS treatment. Compared to sham treatment (n = 27), patients undergoing active rTMS treatment (n = 29) showed higher Mini Mental State Examination (MMSE) score and dynamic functional connectivity (dFC) magnitude of the default mode network (DMN) after two weeks of rTMS treatment, but not at 12-week follow-up. A significant positive correlation was observed between changes in MMSE and changes in the dFC magnitude of DMN in patients who underwent active-rTMS treatment, but not in those who received sham-rTMS treatment. The findings of the current study indicate that fMRI-guided rTMS treatment improves cognitive function of AD patients in the short term, and DMN functional connectivity contributes to therapeutic effectiveness of rTMS.

Personalizing Repetitive Transcranial Magnetic Stimulation Parameters for Depression Treatment Using Multimodal Neuroimaging

Repetitive transcranial magnetic stimulation (rTMS) is a tool that can be used to administer treatment for neuropsychiatric disorders such as major depressive disorder, although the clinical efficacy is still rather modest. Overly general stimulation protocols that consider neither patient-specific depression symptomology nor individualized brain characteristics, such as anatomy or structural and functional connections, may be the cause of the high inter- and intraindividual variability in rTMS clinical responses. Multimodal neuroimaging can provide the necessary insights into individual brain characteristics and can therefore be used to personalize rTMS parameters. Optimal coil positioning should include a three-step process: 1) identify the optimal (indirect) target area based on the exact symptom pattern of the patient; 2) derive the cortical (direct) target location based on functional and/or structural connectomes derived from functional and diffusion magnetic resonance imaging data; and 3) determine the ideal coil position by computational modeling, such that the electric field distribution overlaps with the cortical target. These TMS-induced electric field simulations, derived from anatomical and diffusion magnetic resonance imaging data, can be further applied to compute optimal stimulation intensities. In addition to magnetic resonance imaging, electroencephalography can provide complementary information regarding the ongoing brain oscillations. This information can be used to determine the optimal timing and frequency of the stimuli. The heightened benefits of these personalized stimulation approaches are logically reasoned, but speculative. Randomized clinical trials will be required to compare clinical responses from standard rTMS protocols to personalized protocols. Ultimately, an optimized clinical response may result from precision protocols derived from combinations of personalized stimulation parameters.

TAP: targeting and analysis pipeline for optimization and verification of coil placement in transcranial magnetic stimulation

Objective.Transcranial magnetic stimulation (TMS) can modulate brain function via an electric field (E-field) induced in a brain region of interest (ROI). The ROI E-field can be computationally maximized and set to match a specific reference using individualized head models to find the optimal coil placement and stimulus intensity. However, the available software lacks many practical features for prospective planning of TMS interventions and retrospective evaluation of the experimental targeting accuracy.Approach.The TMS targeting and analysis pipeline (TAP) software uses an MRI/fMRI-derived brain target to optimize coil placement considering experimental parameters such as the subject's hair thickness and coil placement restrictions. The coil placement optimization is implemented in SimNIBS 3.2, for which an additional graphical user interface (TargetingNavigator) is provided to visualize/adjust procedural parameters. The coil optimization process also computes the E-field at the target, allowing the selection of the TMS device intensity setting to achieve specific E-field strengths. The optimized coil placement information is prepared for neuronavigation software, which supports targeting during the TMS procedure. The neuronavigation system can record the coil placement during the experiment, and these data can be processed in TAP to quantify the accuracy of the experimental TMS coil placement and induced E-field.Main results.TAP was demonstrated in a study consisting of three repetitive TMS sessions in five subjects. TMS was delivered by an experienced operator under neuronavigation with the computationally optimized coil placement. Analysis of the experimental accuracy from the recorded neuronavigation data indicated coil location and orientation deviations up to about 2 mm and 2°, respectively, resulting in an 8% median decrease in the target E-field magnitude compared to the optimal placement.Significance.TAP supports navigated TMS with a variety of features for rigorous and reproducible stimulation delivery, including planning and evaluation of coil placement and intensity selection for E-field-based dosing.

From the Group to the Individual in Schizophrenia Spectrum Disorders: Biomarkers of Social Cognitive Impairments and Therapeutic Translation

People with schizophrenia spectrum disorders (SSDs) often experience persistent social cognitive impairments, associated with poor functional outcome. There are currently no approved treatment options for these debilitating symptoms, highlighting the need for novel therapeutic strategies. Work to date has elucidated differential social processes and underlying neural circuitry affected in SSDs, which may be amenable to modulation using neurostimulation. Further, advances in functional connectivity mapping and electric field modeling may be used to identify individualized treatment targets to maximize the impact of brain stimulation on social cognitive networks. Here, we review literature supporting a roadmap for translating functional connectivity biomarker discovery to individualized treatment development for social cognitive impairments in SSDs. First, we outline the relevance of social cognitive impairments in SSDs. We review machine learning approaches for dimensional brain-behavior biomarker discovery, emphasizing the importance of individual differences. We synthesize research showing that brain stimulation techniques, such as repetitive transcranial magnetic stimulation, can be used to target relevant networks. Further, functional connectivity-based individualized targeting may enhance treatment response. We then outline recent approaches to account for neuroanatomical variability and optimize coil positioning to individually maximize target engagement. Overall, the synthesized literature provides support for the utility and feasibility of this translational approach to precision treatment. The proposed roadmap to translate biomarkers of social cognitive impairments to individualized treatment is currently under evaluation in precision-guided trials. Such a translational approach may also be applicable across conditions and generalizable for the development of individualized neurostimulation targeting other behavioral deficits.

Continuous Theta Burst Stimulation to the Right Dorsolateral Prefrontal Cortex may increase Potentiated Startle in healthy individuals

Background

Convergent neuroimaging and neuromodulation studies implicate the right dorsolateral prefrontal cortex (dlPFC) as a key region involved in anxiety-cognition interactions. However, neuroimaging data are correlational, and neuromodulation studies often lack appropriate methodological controls. Accordingly, this work was designed to explore the role of right prefrontal cognitive control mechanisms in the expression/regulation of anxiety using continuous theta-burst transcranial magnetic stimulation (cTBS) and threat of unpredictable shock. Based on prior neuromodulation studies, we hypothesized that the right dlPFC contributed to anxiety expression, and that cTBS should downregulate this expression.

Methods

We measured potentiated startle and performance on the Sternberg working memory paradigm in 28 healthy participants before and after 4 sessions (600 pulses/session) of active or sham cTBS. Stimulation was individualized to the right dlPFC site of maximal working memory-related activity and optimized using electric-field modeling.

Results

Compared with sham cTBS, active cTBS, which is thought to induce long-term depression-like synaptic changes, increased startle during threat of shock, but the effect was similar for predictable and unpredictable threat. As a measure of target (dis)engagement, we also showed that active but not sham cTBS decreased accuracy on the Sternberg task.

Conclusions

Counter to our initial hypothesis, cTBS to the right dlPFC made individuals more anxious, rather than less anxious. Although preliminary, these results are unlikely to be due to transient effects of the stimulation, because anxiety was measured 24 hours after cTBS. In addition, these results are unlikely to be due to off-target effects, because target disengagement was evident from the Sternberg performance data.

Can Transcranial Electrical Stimulation Facilitate Post-stroke Cognitive Rehabilitation? A Systematic Review and Meta-Analysis

Background

Non-invasive brain stimulation methods have been widely utilized in research settings to manipulate and understand the functioning of the human brain. In the last two decades, transcranial electrical stimulation (tES) has opened new doors for treating impairments caused by various neurological disorders. However, tES studies have shown inconsistent results in post-stroke cognitive rehabilitation, and there is no consensus on the effectiveness of tES devices in improving cognitive skills after the onset of stroke.

Objectives

We aim to systematically investigate the efficacy of tES in improving post-stroke global cognition, attention, working memory, executive functions, visual neglect, and verbal fluency. Furthermore, we aim to provide a pathway to an effective use of stimulation paradigms in future studies.

Methods

Preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines were followed. Randomized controlled trials (RCTs) were systematically searched in four different databases, including Medline, Embase, Pubmed, and PsychInfo. Studies utilizing any tES methods published in English were considered for inclusion. Standardized mean difference (SMD) for each cognitive domain was used as the primary outcome measure.

Results

The meta-analysis includes 19 studies assessing at least one of the six cognitive domains. Five RCTs studying global cognition, three assessing visual neglect, five evaluating working memory, three assessing attention, and nine studies focusing on aphasia were included for meta-analysis. As informed by the quantitative analysis of the included studies, the results favor the efficacy of tES in acute improvement in aphasic deficits (SMD = 0.34, CI = 0.02-0.67, p = 0.04) and attention deficits (SMD = 0.59, CI = -0.05-1.22, p = 0.07), however, no improvement was observed in any other cognitive domains.

Conclusion

The results favor the efficacy of tES in an improvement in aphasia and attentive deficits in stroke patients in acute, subacute, and chronic stages. However, the outcome of tES cannot be generalized across cognitive domains. The difference in the stimulation montages and parameters, diverse cognitive batteries, and variable number of training sessions may have contributed to the inconsistency in the outcome. We suggest that in future studies, experimental designs should be further refined, and standardized stimulation protocols should be utilized to better understand the therapeutic effect of stimulation.

Brain network effects by continuous theta burst stimulation in mal de débarquement syndrome: simultaneous EEG and fMRI study

Objective. Heterogeneous clinical responses to treatment with non-invasive brain stimulation are commonly observed, making it necessary to determine personally optimized stimulation parameters. We investigated neuroimaging markers of effective brain targets of treatment with continuous theta burst stimulation (cTBS) in mal de débarquement syndrome (MdDS), a balance disorder of persistent oscillating vertigo previously shown to exhibit abnormal intrinsic functional connectivity.Approach.Twenty-four right-handed, cTBS-naive individuals with MdDS received single administrations of cTBS over one of three stimulation targets in randomized order. The optimal target was determined based on the assessment of acute changes after the administration of cTBS over each target. Repetitive cTBS sessions were delivered on three consecutive days with the optimal target chosen by the participant. Electroencephalography (EEG) was recorded at single-administration test sessions of cTBS. Simultaneous EEG and functional MRI data were acquired at baseline and after completion of 10-12 sessions. Network connectivity changes after single and repetitive stimulations of cTBS were analyzed.Main results.Using electrophysiological source imaging and a data-driven method, we identified network-level connectivity changes in EEG that correlated with symptom responses after completion of multiple sessions of cTBS. We further determined that connectivity changes demonstrated by EEG during test sessions of single administrations of cTBS were signatures that could predict optimal targets.Significance.Our findings demonstrate the effect of cTBS on resting state brain networks and suggest an imaging-based, closed-loop stimulation paradigm that can identify optimal targets during short-term test sessions of stimulation.ClinicalTrials.gov Identifier:NCT02470377.

Degree of improving TMS focality through a geometrically stable solution of an inverse TMS problem

The Transcranial Magnetic Stimulation (TMS) inverse problem (TMS-IP) investigated in this study aims to focus the TMS induced electric field close to a specified target point defined on the gray matter interface in the M1_HAND area while otherwise minimizing it. The goal of the study is to numerically evaluate the degree of improvement of the TMS-IP solutions relative to the well-known sulcus-aligned mapping (a projection approach with the 90^∘ local sulcal angle). In total, 1536 individual TMS-IP solutions have been analyzed for multiple target points and multiple subjects using the boundary element fast multipole method (BEM-FMM) as the forward solver. Our results show that the optimal TMS inverse-problem solutions improve the focality - reduce the size of the field "hot spot" and its deviation from the target - by approximately 21-33% on average for all considered subjects, all observation points, two distinct coil types, two segmentation types, two intracortical observation surfaces under study, and three tested values of the field threshold. The inverse-problem solutions with the maximized focality simultaneously improve the TMS mapping resolution (differentiation between neighbor targets separated by approximately 10 mm) although this improvement is quite modest. Coil position/orientation and conductivity uncertainties have been included into consideration as the corresponding de-focalization factors. The present results will change when the levels of uncertainties change. Our results also indicate that the accuracy of the head segmentation critically influences the expected TMS-IP performance.

Combining transcranial magnetic stimulation with functional magnetic resonance imaging for probing and modulating neural circuits relevant to affective disorders

Combining transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging offers an unprecedented tool for studying how brain networks interact in vivo and how repetitive trains of TMS modulate those networks among patients diagnosed with affective disorders. TMS compliments neuroimaging by allowing the interrogation of causal control among brain circuits. Together with TMS, neuroimaging can provide valuable insight into the mechanisms underlying treatment effects and downstream circuit communication. Here we provide a background of the method, review relevant study designs, consider methodological and equipment options, and provide statistical recommendations. We conclude by describing emerging approaches that will extend these tools into exciting new applications. This article is categorized under: Psychology > Emotion and Motivation Psychology > Theory and Methods Neuroscience > Clinical Neuroscience.

The intervention, the patient and the illness - Personalizing non-invasive brain stimulation in psychiatry

Current hypotheses on the therapeutic action of non-invasive brain stimulation (NIBS) in psychiatric disorders build on the abundant data from neuroimaging studies. This makes NIBS a very promising tool for developing personalized interventions within a precision medicine framework. NIBS methods fundamentally vary in their neurophysiological properties. They comprise repetitive transcranial magnetic stimulation (rTMS) and its variants (e.g. theta burst stimulation - TBS) as well as different types of transcranial electrical stimulation (tES), with the largest body of evidence for transcranial direct current stimulation (tDCS). In the last two decades, significant conceptual progress has been made in terms of NIBS targets, i.e. from single brain regions to neural circuits and to functional connectivity as well as their states, recently leading to brain state modulating closed-loop approaches. Regarding structural and functional brain anatomy, NIBS meets an individually unique constellation, which varies across normal and pathophysiological states. Thus, individual constitutions and signatures of disorders may be indistinguishable at a given time point, but can theoretically be parsed along course- and treatment-related trajectories. We address precision interventions on three levels: 1) the NIBS intervention, 2) the constitutional factors of a single patient, and 3) the phenotypes and pathophysiology of illness. With examples from research on depressive disorders, we propose solutions and discuss future perspectives, e.g. individual MRI-based electrical field strength as a proxy for NIBS dosage, and also symptoms, their clusters, or biotypes instead of disorder focused NIBS. In conclusion, we propose interleaved research on these three levels along a general track of reverse and forward translation including both clinically directed research in preclinical model systems, and biomarker guided controlled clinical trials. Besides driving the development of safe and efficacious interventions, this framework could also deepen our understanding of psychiatric disorders at their neurophysiological underpinnings.

Transcranial Magnetic Stimulation in Psychiatry: Is There a Need for Electric Field Standardization?

Single-pulse and repetitive transcranial magnetic stimulation (rTMS) are used in clinical practice for diagnostic and therapeutic purposes. However, rTMS-based therapies that lead to a significant and sustained reduction in neuropsychiatric symptoms remain scarce. While it is generally accepted that the stimulation frequency plays a crucial role in producing the therapeutic effects of rTMS, less attention has been dedicated to determining the role of the electric field strength. Conventional threshold-based intensity selection approaches, such as the resting motor threshold, produce variable stimulation intensities and electric fields across participants and cortical regions. Insufficient standardization of electric field strength may contribute to the variability of rTMS effects and thus therapeutic success. Computational approaches that can prospectively optimize the electric field and standardize it across patients and cortical targets may overcome some of these limitations. Here, we discuss these approaches and propose that electric field standardization will be instrumental for translational science frameworks (e.g., multiscale modeling and basic science approaches) aimed at deciphering the subcellular, cellular, and network mechanisms of rTMS. Advances in understanding these mechanisms will be important for optimizing rTMS-based therapies in psychiatry.

Fast computational optimization of TMS coil placement for individualized electric field targeting

Background:

During transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of interest (ROI). Determination of the coil position and orientation that best achieve this objective presently requires a large computational effort.

Objective:

To improve the accuracy of TMS we have developed a fast computational auxiliary dipole method (ADM) for determining the optimum coil position and orientation. The optimum coil placement maximizes the E-field along a predetermined direction or, alternatively, the overall E-field magnitude in the targeted ROI. Furthermore, ADM can assess E-field uncertainty resulting from precision limitations of TMS coil placement protocols.

Method:

ADM leverages the electromagnetic reciprocity principle to compute rapidly the TMS induced E-field in the ROI by using the E-field generated by a virtual constant current source residing in the ROI. The framework starts by solving for the conduction currents resulting from this ROI current source. Then, it rapidly determines the average E-field induced in the ROI for each coil position by using the conduction currents and a fast-multipole method. To further speed-up the computations, the coil is approximated using auxiliary dipoles enabling it to represent all coil orientations for a given coil position with less than 600 dipoles.

Results:

Using ADM, the E-fields generated in an MRI-derived head model when the coil is placed at 5900 different scalp positions and 360 coil orientations per position (over 2.1 million unique configurations) can be determined in under 15 min on a standard laptop computer. This enables rapid extraction of the optimum coil position and orientation as well as the E-field variation resulting from coil positioning uncertainty. ADM is implemented in SimNIBS 3.2.

Conclusion:

ADM enables the rapid determination of coil placement that maximizes E-field delivery to a specific brain target. This method can find the optimum coil placement in under 15 min enabling its routine use for TMS. Furthermore, it enables the fast quantification of uncertainty in the induced E-field due to limited precision of TMS coil placement protocols, enabling minimization and statistical analysis of the E-field dose variability.