A semi-automated pipeline for finite element modeling of electric field induced in nonhuman primates by transcranial magnetic stimulation.

BACKGROUND: Transcranial magnetic stimulation (TMS) is used to treat a range of brain disorders by inducing an electric field (E-field) in the brain. However, the precise neural effects of TMS are not well understood. Nonhuman primates (NHPs) are used to model the impact of TMS on neural activity, but a systematic method of quantifying the induced E-field in the cortex of NHPs has not been developed. NEW METHOD: The pipeline uses statistical parametric mapping (SPM) to automatically segment a structural MRI image of a rhesus macaque into five tissue compartments. Manual corrections are necessary around implants. The segmented tissues are tessellated into 3D meshes used in finite element method (FEM) software to compute the TMS induced E-field in the brain. The gray matter can be further segmented into cortical laminae using a volume preserving method for defining layers. RESULTS: Models of three NHPs were generated with TMS coils placed over the precentral gyrus. Two coil configurations, active and sham, were simulated and compared. The results demonstrated a large difference in E-fields at the target. Additionally, the simulations were calculated using two different E-field solvers and were found to not significantly differ. COMPARISON WITH EXISTING METHODS: Current methods segment NHP tissues manually or use automated methods for only the brain tissue. Existing methods also do not stratify the gray matter into layers. CONCLUSION: The pipeline calculates the induced E-field in NHP models by TMS and can be used to plan implant surgeries and determine approximate E-field values around neuron recording sites.

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.

How electroconvulsive therapy works in the treatment of depression: is it the seizure, the electricity, or both?

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.

Real-Time Computation of Brain E-Field for Enhanced Transcranial Magnetic Stimulation Neuronavigation and Optimization.

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).

Multimodal smoking cessation treatment combining transcranial magnetic stimulation, cognitive behavioral therapy, and nicotine replacement therapy in veterans with posttraumatic stress disorder: A feasibility randomized controlled trial protocol.

Tobacco-related deaths exceed those resulting from homicides, suicides, motor vehicle accidence, alcohol consumption, illicit substance use, and acquired immunodeficiency syndrome (AIDS), combined. Amongst U.S. veterans, this trend is particularly concerning given that those suffering from posttraumatic stress disorder (PTSD)-about 11% of those receiving care from the Department of Veterans Affairs (VA)-have triple the risk of developing tobacco use disorder (TUD). The most efficacious strategies being used at the VA for smoking cessation only result in a 23% abstinence rate, and veterans with PTSD only achieve a 4.5% abstinence rate. Therefore, there is a critical need to develop more effective treatments for smoking cessation. Recent studies have revealed the insula as integrally involved in the neurocircuitry of TUD, specifically showing that individuals with brain lesions involving this region had drastically improved quit rates. Some of these studies show a probability of quitting up to 5 times greater compared to non-insula lesioned regions). Altered activity of the insula may be involved in the disruption of the salience network's (SN) connectivity to the executive control network (ECN), which compromises that patient's ability to switch between interoceptive states focused on cravings to executive and cognitive control. Thus, we propose a feasibility phase II randomized controlled trial (RCT) to study a patterned form of repetitive transcranial magnetic stimulation (rTMS), intermittent theta burst stimulation (iTBS), at 90% of the subject's resting motor threshold (rMT) applied over a region in the right post-central gyrus most functionally connected to the right posterior insula. We hypothesize that by increasing functional connectivity between the SN with the ECN to enhance executive control and by decreasing connectivity with the default mode network (DMN) to reduce interoceptive focus on withdrawal symptoms, we will improve smoking cessation outcomes. Fifty eligible veterans with comorbid TUD and PTSD will be randomly assigned to two conditions: active-iTBS + cognitive behavioral therapy (CBT) + nicotine replacement therapy (NRT) (n=25) or sham-iTBS + CBT + NRT (n=25). The primary outcome, feasibility, will be determined by achieving a recruitment of 50 participants and retention rate of 80%. The success of iTBS will be evaluated through self-reported nicotine use, cravings, withdrawal symptoms, and abstinence following quit date (confirmed by bioverification) along with evaluation for target engagement through neuroimaging changes, specifically connectivity differences between the insula and other regions of interest.

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.

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.

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.

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.

Social appropriateness perception of dynamic interactions.

The current study explored the judgment of communicative appropriateness while processing a dialogue between two individuals. All stimuli were presented as audio-visual as well as audio-only vignettes and 24 young adults reported their social impression (appropriateness) of literal, blunt, sarcastic, and teasing statements. On average, teasing statements were rated as more appropriate when processing audio-visual statements compared to the audio-only version of a stimuli, while sarcastic statements were judged as less appropriate with additional visual information. These results indicate a rejection of the Tinge Hypothesis for audio-visual vignettes while confirming it for the reduced, audio-only counterparts. We also analyzed time-frequency EEG data of four frequency bands that have been related to language processing: alpha, beta, theta and low gamma. We found desynchronization in the alpha band literal versus nonliteral items, confirming the assumption that the alpha band reflects stimulus complexity. The analysis also revealed a power increase in the theta, beta and low gamma band, especially when comparing blunt and nonliteral statements in the audio-only condition. The time-frequency results corroborate the prominent role of the alpha and theta bands in language processing and offer new insights into the neural correlates of communicative appropriateness and social aspects of speech perception.

Network-based rTMS to modulate working memory: The difficult choice of effective parameters for online interventions.

BACKGROUND: Online repetitive transcranialmagnetic stimulation (rTMS) has been shown to modulate working memory (WM) performance in a site-specific manner, with behavioral improvements due to stimulation of the dorsolateral prefrontal cortex (DLPFC), and impairment from stimulation to the lateral parietal cortex (LPC). Neurobehavioral studies have demonstrated that subprocesses of WM allowing for the maintenance and manipulation of information in the mind involve unique cortical networks. Despite promising evidence of modulatory effects of rTMS on WM, no studies have yet demonstrated distinct modulatory control of these two subprocesses. The current study therefore sought to explore this possibility through site-specific stimulation during an online task invoking both skills. METHODS: Twenty-nine subjects completed a 4-day protocol, in which active or sham 5Hz rTMS was applied over the DLPFC and LPC in separate blocks of trials while participants performed tasks that required either maintenance alone, or both maintenance and manipulation (alphabetization) of information. Stimulation targets were defined individually based on fMRI activation and structural network properties. Stimulation amplitude was adjusted using electric field modeling to equate induced current in the target region across participants. RESULTS: Despite the use of advanced techniques, no significant differences or interactions between active and sham stimulation were found. Exploratory analyses testing stimulation amplitude, fMRI activation, and modal controllability showed nonsignificant but interesting trends with rTMS effects. CONCLUSION: While this study did not reveal any significant behavioral changes in WM, the results may point to parameters that contribute to positive effects, such as stimulation amplitude and functional activation.

Effects of a five-day HD-tDCS application to the right IFG depend on current intensity: A study in children and adolescents with ADHD.

Impaired executive functions in ADHD are associated with hypoactivity of the right inferior frontal gyrus (IFG). This region was targeted via repetitive applications of anodal, high-definition transcranial direct current simulation (HD-tDCS) on five consecutive days in 33 ADHD patients (10-17years) and in a healthy control group (n=13, only sham). Patients received either sham (n=13) or verum tDCS with 0.5mA (n=9) or 0.25mA (n=11) depending on individual cutaneous sensitivity. During stimulation, participants performed a combined working memory and response inhibition paradigm (n-back/nogo). At baseline, post, and a 4-month follow up, electroencephalography was recorded during this task. Moreover, interference control (flanker task) and spatial working memory (spanboard task) were assessed to explore possible transfer effects. Omission errors and reaction time variability in all tasks served as measures of attention. In the 0.25mA group increased nogo commission errors indicated a detrimental tDCS effect on response inhibition. After the 5-day stimulation, attentional improvements in the 0.5mA group were indicated by reduced omission errors and reaction time variability. Variability improvements were still evident at follow up. In all groups, nogo P3 amplitudes were reduced post-stimulation, but in the 0.5mA group this reduction was smaller than in the 0.25mA group. Results of the current study suggest distinct effects of tDCS with different current intensities demonstrating the importance of a deeper understanding on the impact of stimulation parameters and repeated tDCS applications to develop effective tDCS-based therapy approaches in ADHD.

Transcranial alternating current stimulation entrains alpha oscillations by preferential phase synchronization of fast-spiking cortical neurons to stimulation waveform.

Computational modeling and human studies suggest that transcranial alternating current stimulation (tACS) modulates alpha oscillations by entrainment. Yet, a direct examination of how tACS interacts with neuronal spiking activity that gives rise to the alpha oscillation in the thalamo-cortical system has been lacking. Here, we demonstrate how tACS entrains endogenous alpha oscillations in head-fixed awake ferrets. We first show that endogenous alpha oscillations in the posterior parietal cortex drive the primary visual cortex and the higher-order visual thalamus. Spike-field coherence is largest for the alpha frequency band, and presumed fast-spiking inhibitory interneurons exhibit strongest coupling to this oscillation. We then apply alpha-tACS that results in a field strength comparable to what is commonly used in humans (<0.5 mV/mm). Both in these ferret experiments and in a computational model of the thalamo-cortical system, tACS entrains alpha oscillations by following the theoretically predicted Arnold tongue. Intriguingly, the fast-spiking inhibitory interneurons exhibit a stronger entrainment response to tACS in both the ferret experiments and the computational model, likely due to their stronger endogenous coupling to the alpha oscillation. Our findings demonstrate the in vivo mechanism of action for the modulation of the alpha oscillation by tACS.

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.

Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach.

A deep neural network (DNN) that can reliably model muscle responses from corresponding brain stimulation has the potential to increase knowledge of coordinated motor control for numerous basic science and applied use cases. Such cases include the understanding of abnormal movement patterns due to neurological injury from stroke, and stimulation based interventions for neurological recovery such as paired associative stimulation. In this work, potential DNN models are explored and the one with the minimum squared errors is recommended for the optimal performance of the M2M-Net, a network that maps transcranial magnetic stimulation of the motor cortex to corresponding muscle responses, using: a finite element simulation, an empirical neural response profile, a convolutional autoencoder, a separate deep network mapper, and recordings of multi-muscle activation. We discuss the rationale behind the different modeling approaches and architectures, and contrast their results. Additionally, to obtain a comparative insight of the trade-o between complexity and performance analysis, we explore different techniques, including the extension of two classical information criteria for M2M-Net. Finally, we find that the model analogous to mapping the motor cortex stimulation to a combination of direct and synergistic connection to the muscles performs the best, when the neural response profile is used at the input.

Intensity- and timing-dependent modulation of motion perception with transcranial magnetic stimulation of visual cortex.

Despite the widespread use of transcranial magnetic stimulation (TMS) in research and clinical care, the dose-response relations and neurophysiological correlates of modulatory effects remain relatively unexplored. To fill this gap, we studied modulation of visual processing as a function of TMS parameters. Our approach combined electroencephalography (EEG) with application of single pulse TMS to visual cortex as participants performed a motion perception task. During each participants' first visit, motion coherence thresholds, 64-channel visual evoked potentials (VEPs), and TMS resting motor thresholds (RMT) were measured. In second and third visits, single pulse TMS was delivered at one of two latencies, either 30 ms before the onset of motion or at the onset latency of the N2 VEP component derived from the first session. TMS was delivered at 0%, 80%, 100%, or 120% of RMT over the site of N2 peak activity, or at 120% over vertex. Behavioral results demonstrated a significant main effect of TMS timing on accuracy, with better performance when TMS was applied at the N2-Onset timing versus Pre-Onset, as well as a significant interaction, indicating that 80% intensity produced higher accuracy than other conditions at the N2-Onset. TMS effects on the P3 VEP showed reduced amplitudes in the 80% Pre-Onset condition, an increase for the 120% N2-Onset condition, and monotonic amplitude scaling with stimulation intensity. The N2 component was not affected by TMS. These findings reveal the influence of TMS intensity and timing on visual perception and electrophysiological responses, with optimal facilitation at stimulation intensities below RMT.

Structural Controllability Predicts Functional Patterns and Brain Stimulation Benefits Associated with Working Memory.

The brain is an inherently dynamic system, and much work has focused on the ability to modify neural activity through both local perturbations and changes in the function of global network ensembles. Network controllability is a recent concept in network neuroscience that purports to predict the influence of individual cortical sites on global network states and state changes, thereby creating a unifying account of local influences on global brain dynamics. While this notion is accepted in engineering science, it is subject to ongoing debates in neuroscience as empirical evidence linking network controllability to brain activity and human behavior remains scarce. Here, we present an integrated set of multimodal brain-behavior relationships derived from fMRI, diffusion tensor imaging, and online repetitive transcranial magnetic stimulation (rTMS) applied during an individually calibrated working memory task performed by individuals of both sexes. The modes describing the structural network system dynamics showed direct relationships to brain activity associated with task difficulty, with difficult-to-reach modes contributing to functional brain states in the hard task condition. Modal controllability (a measure quantifying the contribution of difficult-to-reach modes) at the stimulated site predicted both fMRI activations associated with increasing task difficulty and rTMS benefits on task performance. Furthermore, fMRI explained 64% of the variance between modal controllability and the working memory benefit associated with 5 Hz online rTMS. These results therefore provide evidence toward the functional validity of network control theory, and outline a clear technique for integrating structural network topology and functional activity to predict the influence of stimulation on subsequent behavior.SIGNIFICANCE STATEMENT The network controllability concept proposes that specific cortical nodes are able to steer the brain into certain physiological states. By applying external perturbation to these control nodes, it is theorized that brain stimulation is able to selectively target difficult-to-reach states, potentially aiding processing and improving performance on cognitive tasks. The current study used rTMS and fMRI during a working memory task to test this hypothesis. We demonstrate that network controllability correlates with fMRI modulation because of working memory load and with the behavioral improvements that result from a multivisit intervention using 5 Hz rTMS. This study demonstrates the validity of network controllability and offers a new targeting approach to improve efficacy.

Site-Specific Effects of Online rTMS during a Working Memory Task in Healthy Older Adults.

The process of manipulating information within working memory is central to many cognitive functions, but also declines rapidly in old age. Improving this process could markedly enhance the health-span in older adults. The current pre-registered, randomized and placebo-controlled study tested the potential of online repetitive transcranial magnetic stimulation (rTMS) applied at 5 Hz over the left lateral parietal cortex to enhance working memory manipulation in healthy elderly adults. rTMS was applied, while participants performed a delayed-response alphabetization task with two individually titrated levels of difficulty. Coil placement and stimulation amplitude were calculated from fMRI activation maps combined with electric field modeling on an individual-subject basis in order to standardize dosing at the targeted cortical location. Contrary to the a priori hypothesis, active rTMS significantly decreased accuracy relative to sham, and only in the hardest difficulty level. When compared to the results from our previous study, in which rTMS was applied over the left prefrontal cortex, we found equivalent effect sizes but opposite directionality suggesting a site-specific effect of rTMS. These results demonstrate engagement of cortical working memory processing using a novel TMS targeting approach, while also providing prescriptions for future studies seeking to enhance memory through rTMS.

Application of long-interval paired-pulse transcranial magnetic stimulation to motion-sensitive visual cortex does not lead to changes in motion discrimination.

The perception of visual motion is dependent on a set of occipitotemporal regions that are readily accessible to neuromodulation. The current study tested if paired-pulse Transcranial Magnetic Stimulation (ppTMS) could modulate motion perception by stimulating the occipital cortex as participants viewed near-threshold motion dot stimuli. In this sham-controlled study, fifteen subjects completed two sessions. On the first visit, resting motor threshold (RMT) was assessed, and participants performed an adaptive direction discrimination task to determine individual motion sensitivity. During the second visit, subjects performed the task with three difficulty levels as TMS pulses were delivered 150 and 50 ms prior to motion stimulus onset at 120% RMT, under the logic that the cumulative inhibitory effect of these pulses would alter motion sensitivity. ppTMS was delivered at one of two locations: 3 cm dorsal and 5 cm lateral to inion (scalp-based coordinate), or at the site of peak activation for "motion" according to the NeuroSynth fMRI database (meta-analytic coordinate). Sham stimulation was delivered on one-third of trials by tilting the coil 90°. Analyses showed no significant active-versus-sham effects of ppTMS when stimulation was delivered to the meta-analytic (p = 0.15) or scalp-based coordinates (p = 0.17), which were separated by 29 mm on average. Active-versus-sham stimulation differences did not interact with either stimulation location (p = 0.12) or difficulty (p = 0.33). These findings fail to support the hypothesis that long-interval ppTMS recruits inhibitory processes in motion-sensitive cortex but must be considered within the limited parameters used in this design.

Comparison between conventional and HD-tDCS of the right inferior frontal gyrus in children and adolescents with ADHD.

OBJECTIVE: To investigate whether the effects of HD-tDCS and conventional tDCS of the right IFG are superior to the effects of sham stimulation for the improvement of working memory performance in ADHD. METHODS: 15 ADHD patients between 10 and 16 years underwent three tDCS sessions in which conventional, HD and sham tDCS of the right IFG were applied. In all sessions a 2-back working memory task was solved and EEG was recorded. Baseline data were assessed from 15 age matched healthy controls. RESULTS: In ADHD patients, increased positive values of P300 and N200 mean amplitudes were found after conventional and HD-tDCS. Thus, both components were more in resemblance to ERPs in healthy controls. Behavioral performance was not generally influenced by tDCS but effects of HD-tDCS depended on individual hyperactive/impulsive symptom load. The rate of responders for HD-tDCS was equivalent to the responder rate for conventional tDCS. CONCLUSIONS: ERP data indicate that HD-tDCS is equally suitable as conventional tDCS for the recruitment of the right IFG in the context of working memory processing. SIGNIFICANCE: HD-tDCS of the right IFG is a promising approach for neuromodulation in ADHD but further research is necessary to develop adaptations that produce reliable behavioral benefits.