Brain Stimulation

Neural excitability fluctuates with sensory events, creating windows of opportunity to enhance brain stimulation. Repetitive transcranial magnetic stimulation (TMS), including intermittent theta burst stimulation (iTBS), is a promising treatment for neurological and psychiatric disorders, but does not account for fluctuations in neural excitability, likely contributing to variable outcomes. Sensory Entrained TMS (seTMS) leverages sensorimotor oscillations to enhance corticospinal responses, but the sustained effects as a repetitive protocol are unknown. We extend seTMS to iTBS, measuring motor-evoked potentials (MEPs) as a physiological readout. In a randomized crossover study comparing standard iTBS with sensory entrained iTBS (se-iTBS; n=20), we found that se-iTBS more than doubled the MEP effect (55% vs 26% MEP enhancement) and persisted for at least 30 minutes. Notably, at least 80% of participants showed larger responses with se-iTBS at all time points. se-iTBS may provide a robust and practical framework for optimizing TMS that bridges electrophysiological mechanisms and clinical applications.

ObjectiveComputational models and visualization toolboxes for Deep Brain Stimulation (DBS) increasingly rely on pre-computed electric field libraries to estimate the Volume of Tissue Activated (VTA). However, the boundary conditions (BCs) and source models used to generate these fields vary widely across studies, and there is currently no experimental consensus regarding which parameters most accurately reflect the physical device output. The objective of this study was to experimentally validate the electric potential distribution of directional DBS leads in order to determine the optimal Finite Element Method (FEM) configuration. ApproachThe voltage distribution surrounding a Boston Scientific Vercise Gevia directional lead was mapped in a saline phantom using a custom high-precision robotic scanning system. Experimental measurements were compared against six FEM configurations that varied in source formulation (Dirichlet vs. Neumann boundary conditions) and ground definitions. For each configuration, the resulting VTA volume was computed to assess the clinical impact of modeling assumptions. ResultsThe FEM configuration implementing a Dirichlet (voltage) boundary condition on the active contact with a grounded implantable pulse generator (IPG) surface demonstrated the highest accuracy, achieving a Symmetric Mean Absolute Percent Error (SMAPE) of less than 9% across all contact levels. In contrast, conventional current-controlled simulations employing Neumann boundary conditions with disparate ground definitions substantially overestimated electric field spread. Suboptimal boundary condition selection resulted in an approximate 67% overestimation of VTA volume (137 mm3 vs. 82 mm3) relative to the experimentally validated model. SignificanceAlthough clinical DBS systems operate as current sources, standard Neumann (current density) boundary conditions do not adequately represent the equipotential behavior of the electrode-tissue interface, resulting in nearly a two-fold error in predicted VTA volume. To improve the validity of predictive clinical models, we recommend the use of Dirichlet boundary conditions derived from the device operating impedance (V = Itarget x Zmeasured) rather than conventional current density specifications.

Optimization of deep brain stimulation (DBS) therapy for neurological and neuropsychiatric disorders depends on objective quantitative biomarkers that can guide stimulation parameter adjustments. With the recent introduction of new-generation DBS systems capable of simultaneously stimulating brain activity and recording local field potentials (LFP), there is increasing demand for platforms that enable efficient visualization and analysis of these signals for electrophysiological biomarkers identification. To address the limitations of currently available toolboxes that require advanced signal processing skills and rely on proprietary software, we present NeoDBS, an open-source Python platform designed for ingestion and advance signal visualization and processing of LFP signals from DBS systems through an easy-to-use graphical interface. NeoDBS is a user-centered platform that offers predefined analysis pipelines with the aim of facilitating electrophysiological biomarker investigation for DBS across different brain disorders. Custom analysis pipelines are also available for users to leverage the signal analysis tools to their research needs. Critical functionalities for longitudinal biomarker research are featured in NeoDBS, such as batch file processing and event-locked analysis for in-clinic and at-home recordings. This combination of accessibility, user-experience and advanced signal processing tools makes NeoDBS an environment that propels easy and fast electrophysiological biomarker research for DBS, across patients, sessions, and stimulation parameters.

BackgroundPersonalized optimization of 4x1 high-definition transcranial electrical stimulation (HD-tES) faces inherent trade-offs between montage flexibility, computational efficiency, and implementation accessibility. Conventional 10-10 electrode systems constrain placement to discrete landmark positions, while unconstrained optimization relies on stochastic algorithms that risk converging to local optima and requires neuronavigation equipment often unavailable in rehabilitation settings. Here we introduce a scalp geometry-based parameter space (SGP) that parameterizes 4x1 HD-tES montages using three intuitive scalp-defined parameters--position, radius, and orientation--and characterize parameter-performance regularities through exhaustive electric field simulations across 30 subjects and 624 cortical targets (>3.6 million configurations). ResultsPosition primarily determined proximity to optimal performance, radius governed the intensity-focality trade-off, and orientation served as fine-tuning. Exploiting these regularities, a minimal search space (SGP-MSS) was constructed that reduced computational complexity by over 90% while guaranteeing global optima identification. Compared with standard 10-10 montages, SGP-MSS achieved up to 99% higher targeting intensity and 126% higher focality (all p < 0.0001). Compared with lead-field-free optimization, SGP-MSS achieved comparable performance with greater cross-subject stability. ConclusionsThe SGP framework enables efficient individualized HD-tES optimization without neuronavigation. Its scalp-based parameterization supports electrode positioning via standard cranial landmark measurements, facilitating translation to routine clinical and home-based rehabilitation settings.

Background: Transcranial temporal interference stimulation (tTIS) is an emerging noninvasive neuromodulation approach that enables focal, frequency-specific modulation of deep brain regions, offering a novel method for investigating therapeutic mechanisms underlying brain and mental health disorders. Pain is a key target because it is a feature of multiple disorders and is increasingly understood to depend on brain circuits. Here, we tested the effects of tTIS on bilateral evoked pain, capitalizing on converging evidence from human and animal studies indicating that the primary motor cortex (M1) contains body-wide inter-effector regions and has descending projections to regions implicated in nociceptive, motivational, and autonomic processing, making it a key cortical target for pain modulation. Methods: We conducted a pre-registered, triple-blind, randomized crossover study (N = 32, 160 study sessions), investigating frequency-dependent effects of tTIS applied to the left M1 on experimentally evoked thermal pain in healthy adults. We tested four stimulation frequencies (10 Hz, 20 Hz, 70 Hz, and sham) on separate days (>10,000 pain trials total). Noxious heat was applied to both the right and left forearms using individually calibrated temperatures both pre- and post-stimulation. Results: Active tTIS produced significant analgesia at all stimulation frequencies (10 Hz, 20 Hz, and 70 Hz) relative to sham (Cohens d = 0.46-0.82, all p < 0.05). 10 Hz produced the greatest reduction (d = 0.82), and both 10 Hz and 20 Hz produced more analgesia than 70 Hz (d = 0.44 and 0.38, respectively; p < 0.05). Stimulation-related sensations were equivalent across frequencies, and participants were blind to condition. Pain reductions remained stable over a [~]40-min post-stimulation period and were bilateral, consistent with stimulation of body-wide inter-effector regions. Conclusions: These results provide the first evidence that tTIS can reliably reduce experimental pain perception in humans in a frequency-dependent manner, providing a foundation for noninvasive pain modulation with tTIS.

Background: Gamma oscillation dysfunction has been implicated in neuropsychiatric disorders. Restoring gamma oscillations via brain stimulation represents an emerging therapeutic approach. However, the strength of its clinical effects and treatment moderators remain unclear. Method: We conducted a systematic review and meta-analysis to examine the clinical effects of gamma neuromodulation in neuropsychiatric disorders. A literature search for controlled trials using gamma stimulation was performed across five databases up until April 2025. Effect sizes were calculated using Hedge's g. Separate analyses using the random-effects model examined the clinical effects in schizophrenia (SZ), major depressive disorder (MDD), bipolar disorder, and autism spectrum disorder. For SZ and MDD, subgroup analyses evaluated the effects of stimulation modality, stimulation frequency, treatment duration, and pulses per session. Result: Fifty-six studies met the inclusion criteria (NSZ = 943, NMDD = 916, NBD = 175, NASD = 232). In SZ, gamma stimulation was associated with improvements in positive (k = 10, g = -0.60, p < 0.001), negative (k = 12, g = -0.37, p = 0.03), depressive (k = 8, g = -0.39, p < 0.001), anxious symptoms (k = 5, g = -0.59, p < 0.001), and overall cognitive function (k = 7, g = 0.55, p < 0.001). Stimulation frequency and treatment duration moderated therapeutic effects. In MDD, reductions in depressive symptoms were observed (k = 23, g = -0.34, p = 0.007). Conclusion: Gamma neuromodulation showed moderate therapeutic benefits in SZ and MDD. Substantial heterogeneity likely reflects protocol differences, highlighting the need for well-powered future trials.

Conventional temporal interference stimulation (TI, TIS, or tTIS) leverages two pairs of electrodes to induce an interfering electrical field in the brain. Both computational and experimental studies show that TI can stimulate deep brain regions without significantly affecting shallow areas. While promising, optimization of the locations and dosages on these two pairs of electrodes for maximal focal modulation remains computationally challenging. We are the first to propose two arrays of electrodes instead of two or multiple pairs of electrodes to boost modulation focality. However, the optimization algorithm outputs too many electrodes with overlaps across two frequencies, making it difficult to implement in practice. Based on recent progress in developing multi-channel TI devices and computational work on TI optimization, here we again advocate two-array TI, but with solid software and hardware evidence to show the feasibility. Specifically, we show that the latest optimization algorithm for two-pair TI innately works for two-array TI with the fastest speed (under 30s) among all major algorithms. With a similar amount of electrodes, two-array TI could achieve better focality (3.03 cm) at the hippocampus even than TI using up to 16 pairs of electrodes (3.19 cm) that takes days to optimize. We also show a hardware implementation of two-array TI using 10 electrodes on our 8-channel TI device. We argue that two-pair TI is only preferred when one does not care about modulation focality and promote two-array TI for its advantages in focality and lower cost in terms of both optimization time and electrodes needed. We restate the focality-intensity tradeoff but in the context of TI and provide a first voxel-level map of achievable focality and modulation strength by TI in the MNI-152 head template. We hope this work will pave the way for future adoptions of two-array TI for more focal non-invasive deep brain stimulation.

ObjectivePathological beta-band oscillations (13 to 30 Hz) in the subthalamic nucleus (STN) are a hallmark of Parkinsons disease and a primary target for deep brain stimulation therapy, yet the specific pattern of synaptic reorganization that drives their emergence remains incompletely understood. We developed a GPU-accelerated computational framework to systematically investigate combinations of synaptic changes across basal ganglia pathways that produce Parkinsonian beta oscillations while satisfying literature-based electrophysiology constraints. ApproachWe implemented a biophysically detailed spiking network model of the STN, external globus pallidus (GPe), and internal globus pallidus (GPi) in JAX (a high-performance numerical computing Python library), achieving a 490-fold speedup over conventional CPU-based simulation. Using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) we optimized 10 network parameters across two stages: first establishing a healthy baseline matching primate electrophysiology data, then searching within biologically motivated bounds for synaptic modifications that reproduce Parkinsonian firing rates and beta power. Fixed in-degree connectivity ensured optimized parameters produced scale-invariant dynamics from 450 to 45000 neurons. All simulations ran on a single cloud GPU instance at 84 cents per hour. Main ResultsThe optimizer converged on a coordinated pattern of synaptic reorganization dominated by asymmetric changes within the STN-GPe reciprocal loop: STN to GPe excitation increased 2.21-fold while GPe to STN inhibition collapsed to 0.11-fold of its healthy value. STN to GPi and GPe to GPi pathways changed minimally (1.06-fold and 1.45-fold respectively). This configuration transformed asynchronous firing (beta: 0.4 percent of spectral power) into synchronized bursting with prominent beta oscillations (49.4 percent), with firing rate changes matching experimental observations. Network dynamics were invariant across a 100-fold range of network sizes (firing rate deviation less than 2.4 Hz; all metrics p less than 0.001 across 10 random seeds at 45000 neurons). We implemented a simplified deep brain stimulation model for validation purposes, which achieved complete beta suppression (49.4 percent to 0.0 percent) and restored GPi output to healthy levels. SignificanceThese results suggest that pathological beta oscillations emerge from a specific pattern of synaptic reorganization, namely the reduction of GPe inhibitory feedback to STN. The GPU-accelerated optimization framework, running on commodity cloud infrastructure, demonstrates an accessible platform for parameter exploration in neural circuit models and a foundation for generating synthetic training data for adaptive deep brain stimulation algorithms.

Deep brain stimulation (DBS) of the ventral internal capsule/ventral striatum (VCVS) can alleviate symptoms of mental illness and may work in part by improving cognitive control, a feature of healthy decision making. The human VCVS DBS target and its mid-striatum analog in our preclinical rodent model contain both cortical axons and local striatal cell bodies. However, the specific neural components that stimulation acts on to mediate cognitive effects remain unclear. We addressed this by delivering high frequency optogenetic stimulation ("opto-DBS") to either medial prefrontal cortex (mPFC) axons or local mid-striatal (midSTR) neurons in a rodent Set-Shift task. Opto-DBS of mPFC axons reduced response times, effectively replicating the cognitive enhancement observed in a previous study that used electrical stimulation. Conversely, we observed cognitive impairment from sustained (>10 min) opto-DBS of midSTR neurons. In addition, the cognitive benefit from axonal opto-DBS exhibited time-dependent declines that were not observed with electrical stimulation. The improvement in response times declined within a session and was associated with attenuated magnitude of local midSTR evoked-response potentials. Across-testing days, we linked this decline to diminished mPFC responsiveness, evidenced by a reduction in the post-DBS functional strength of the circuit, suggesting a neuroplastic-like mechanism. These results demonstrate that mPFC-originating axons, rather than local neurons, are the primary drivers of cognitive control enhancements from electrical stimulation, providing insight into therapeutic mechanisms of VCVS DBS.

Deep Brain Stimulation (DBS) is an effective therapy for Parkinson's disease (PD), but clinical programming of stimulation parameters remains a time-consuming process largely guided by subjective symptom assessment. The increasing availability of sensing-enabled neurostimulators and wearable motion sensors provides an opportunity to introduce objective biomarkers into DBS titration. In this work, we present DBSgram, a multimodal framework designed to support data-driven DBS programming by integrating neurophysiological and kinematic measurements acquired during routine clinical titration. The proposed system combines subthalamic nucleus local field potential (STN-LFP) recordings from sensing-enabled neurostimulators with hand kinematic data acquired using wearable inertial measurement units (IMUs). A two-stage synchronization strategy aligns independent data streams from implanted and wearable devices, followed by automated signal processing pipelines for extracting electrophysiological and motor biomarkers. Patient-specific beta-band power is derived from LFP recordings, while tremor, rigidity, and bradykinesia metrics are computed from multi-axis IMU signals using symptom-specific processing algorithms. These synchronized features are then integrated into the DBSgram visualization framework, which maps stimulation amplitude to simultaneous changes in neural activity and objective motor performance. The framework was implemented in a standardized 40-minute clinical titration protocol conducted in a cohort of 18 PD patients implanted with sensing-enabled DBS systems. We present here the analysis of aligned multimodal datasets from different patients to demonstrate proof-of-concept feasibility. The resulting DBSgram visualizations capture stimulation-dependent suppression of pathological beta activity alongside quantitative motor improvements, enabling intuitive identification of patient-specific therapeutic windows. These results demonstrate the technical feasibility of integrating implanted neurophysiological recordings with wearable kinematic sensing during DBS programming. By providing synchronized physiological and motor biomarkers within a unified framework, the DBSgram approach may support more objective and data-driven DBS titration, and contribute to future closed-loop neuromodulation strategies.

Non-invasive brain stimulation (NiBS) studies frequently report exploratory correlations between individual-level changes in neurophysiological and behavioural measures. However, these analyses are typically underpowered and rely on ratio-based change scores with known statistical limitations. We addressed these limitations by pooling individual data from three independent studies (total N = 69), providing adequate power to detect small-to-medium effects. All studies applied 20 Hz transcranial alternating current stimulation (tACS) targeting the pre-supplementary motor area (preSMA) and right inferior frontal gyrus (rIFG), regions central to inhibitory control. Changes in preSMA-rIFG connectivity measured with EEG imaginary coherence (ImCoh) and response inhibition (stop-signal reaction time, SSRT) were quantified using reliable change indices (RCI), which were z-standardised within studies to enable pooled mixed-effects regression. No meaningful association was found between tACS-induced ImCoh change and SSRT change (r = .013, marginal R{superscript 2} = .004), with project-wise correlations that were small, non-significant, and inconsistent in direction. Sensitivity analysis using ratio-based change scores converged on the same null result (r = .014), though ratio scores showed severe distributional violations relative to the approximately normal RCI distributions, supporting the methodological case for RCI on statistical grounds. These results provide no support for a systematic individual-level brain-behaviour coupling between preSMA-rIFG connectivity and response inhibition following 20 Hz tACS, and suggest that any true effect is likely to be small. The present work offers a methodological benchmark for quantifying individual-level brain-behaviour coupling in NiBS research, and highlights the need for more sensitive neural markers and adequately powered design.

Background Deep brain stimulation (DBS) targeting the ventral intermediate nucleus (Vim) of the thalamus is an established surgical therapy for medically refractory tremor, particularly essential tremor. Accurate localization of the Vim remains challenging because the nucleus is not directly visible on conventional MRI. Consequently, multiple targeting approaches have been developed, including atlas-based stereotactic coordinates, microelectrode recording (MER), advanced MRI visualization techniques, and diffusion-based tractography. This systematic review and meta-analysis evaluated current Vim targeting strategies and synthesized tremor outcomes following intervention. Methods This systematic review and meta-analysis was conducted according to PRISMA 2020 guidelines and registered in PROSPERO. PubMed/MEDLINE, Scopus, Web of Science, and Embase were searched from inception to January 29, 2026. Studies investigating Vim-targeted tremor surgery and reporting targeting strategies or tremor outcomes were eligible. Data extraction and risk of bias assessment were performed independently by two reviewers using JBI and QUADAS-2 tools. Random-effects meta-analysis using standardized mean differences (Hedges g) was performed to evaluate pre- to postoperative tremor improvement. Results A total of 2,398 records were identified, and 25 studies met inclusion criteria for the systematic review. Across these studies, 211 patients undergoing Vim-targeted tremor surgery were analyzed. Considerable heterogeneity was observed in study design, patient populations, imaging protocols, and targeting approaches, including atlas-based targeting, MER-guided localization, advanced MRI visualization, and diffusion tractography of tremor-related pathways such as the dentato-rubro-thalamic tract. Six studies comprising seven independent cohorts provided sufficient data for meta-analysis. Pooled analysis demonstrated substantial tremor improvement following intervention (SMD -3.91, 95% CI -4.81 to -3.01; p < 0.0001). Although between-study heterogeneity was moderate to substantial (Q = 18.12, p = 0.0059; I2 = 66.9%), all cohorts showed consistent reductions in tremor severity. Sensitivity analyses confirmed the stability of the pooled effect, and funnel plot and trim-and-fill analyses did not indicate significant publication bias. Conclusions Despite substantial heterogeneity in Vim targeting methodologies, surgical intervention consistently produces marked tremor reduction. Across anatomical, electrophysiological, and imaging-based targeting approaches, clinical outcomes remained robust. Future prospective studies with standardized outcome reporting and direct comparisons of targeting techniques are needed to determine whether emerging imaging-guided strategies provide measurable clinical advantages.

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique widely employed in basic and clinical research. Non-human primates (NHPs) represent translationally valuable models due to their close anatomical and functional similarity to humans. However, significant technical challenges remain in implementing human-like TMS protocols in awake NHPs. Here we developed a non-invasive head- and arm-fixation apparatus that enables reliable stimulation and electromyography recordings in awake NHPs without surgical intervention and validated the apparatus with two TMS protocols in rhesus macaques. First, we implemented an adaptive motor threshold (MT) determination method developed recently for humans, which converged successfully to valid MTs as defined by the International Federation of Clinical Neurophysiology. Second, we measured a robust short-interval intracortical inhibition effect for the first time in awake NHPs. Successful implementation of human TMS protocols in awake NHPs provides proof-of-concept validation of our apparatus, paving the way to bidirectionally translatable, clinically relevant neuromodulation protocols.

Patients with brain tumors involving language-critical regions face surgical limitations when balancing resection with preservation of function. Non-invasive neuromodulation-induced prehabilitation (NIP) aims to guide preoperative neuroplastic reorganization, potentially facilitating larger resections while preserving function. We investigated whether NIP selectively modulates the targeted language network compared with control networks, and whether such modulation is behaviorally safe. We enrolled 26 patients (mean age = 55.9) from the Prehabilita project (Clinical Trial: NCT05844605) with operable brain tumors affecting language or motor regions. Eleven received language-targeted NIP, combining transcranial magnetic stimulation and/or transcranial direct current stimulation with intensive language training. Fourteen patients with NIP targeting non-language networks, primarily motor networks, served controls. Assessments included task-based functional magnetic resonance imaging (tb-fMRI) and a neuropsychological battery assessing language and cognitive domains before and after prehabilitation. Results indicated a group-specific NIP effect on the language network. In the language-targeted group, tb-fMRI revealed reduced overlap between a region of interest centered on the stimulation target and fMRI-derived language activation maps, whereas no comparable changes were observed in controls. No significant modulation effects were detected in the motor network in either group. These findings indicate that NIP can selectively reorganize the language network, with modulation patterns differing in sensorimotor networks. Importantly, language network modulation occurred while preserving language and cognitive performance. These results support NIP targeting higher-order functions such as language as a safe preoperative strategy that may reduce functional constraints on surgery and enable larger and safer resections in patients with tumors involving language-critical regions.

Current closed-loop TMS-EEG systems rely on phase prediction algorithms that require highly periodic signals, limiting their ability to target brief, event-related activity. We developed a real-time closed-loop (RT-CL) TMS-EEG system that directly detects oscillatory phase without prediction, enabling phase-locked stimulation within microseconds. We validated the system against a prediction-based approach using simulated sine waves and human EEG data (N=18), without active TMS delivery. Across frequency-modulated sweeps and spontaneous occipital alpha oscillations (eyes-open vs. closed), the RT-CL system achieved higher triggering probability (16-24%) and reduced the phase error variability (2-10{degrees}). Importantly, when targeting event-related theta oscillations during two spatial navigation tasks, RT-CL produced [~]18% higher triggering probabilities and [~]19{degrees} lower phase error variability than phase prediction. These findings validate the RT-CL system for probing phase-dependent mechanisms during active cognition and the development of precision TMS interventions targeting pathological brain states.

BackgroundIntrinsic functional coupling at multiple temporal scales is a hallmark of human brain dynamics. Among these coupling modes, slow co-fluctuations of oscillatory amplitudes, termed amplitude coupling, are thought to represent a key organizing principle of the large-scale functional architecture, constraining and gating network activity. Yet, despite extensive correlational evidence, direct causal access to amplitude coupling remains limited, restricting insight into its functional relevance. ObjectivesHere, we investigated whether dual-site amplitude-modulated transcranial alternating current stimulation (AM-tACS) can selectively modulate interhemispheric amplitude coupling in human resting-state networks. MethodsTwenty-eight participants received AM-tACS with a carrier frequency in the beta-band whose amplitude was modulated by low-frequency, scale-free dynamics. By applying dual-site AM-tACS either coherently or incoherently across bilateral parieto-occipital cortices, we tested whether stimulation could systematically enhance or disrupt amplitude co-fluctuations in the electrophysiological aftereffect. ResultsIncoherent AM-tACS significantly reduced interhemispheric amplitude coupling between targeted parieto-occipital cortices, with the strongest effects observed in the stimulated beta-band carrier frequency range. This modulation occurred independently of changes in local power or inter-areal phase coupling, indicating a selective effect of AM-tACS on amplitude-based connectivity. Moreover, reductions in amplitude coupling were correlated with the induced electric field strength, suggesting a dose-dependent relationship between stimulation intensity and coupling modulation. ConclusionsOur findings demonstrate that dual-site AM-tACS can causally and selectively modulate amplitude coupling in the human brain. By establishing causal control over lasting amplitude coupling dynamics, this work provides a methodological foundation for future investigations into the functional and behavioral relevance of amplitude coupling in both healthy and pathological brain states. HighlightsO_LIDual-site AM-tACS selectively modulates amplitude coupling in humans C_LIO_LIAM-tACS was designed to mimic natural, scale-free amplitude fluctuations C_LIO_LIStimulation effects are spatially confined to interactions between target regions C_LIO_LIE-field strength predicts the change in amplitude coupling, suggesting a dose-response relationship C_LIO_LIAmplitude coupling modulations are not mediated by band-limited power changes C_LI

Subcallosal cingulate cortex (SCC) deep brain stimulation (DBS) can provide relief for individuals with Treatment Resistant Depression (TRD), but ongoing clinical management remains challenging due to nonspecific symptom fluctuations that can obscure core depression recovery on standard rating scales. Objective, stable biomarkers that selectively track the therapeutic effects of SCC DBS are therefore essential for developing principled decision support systems to guide stimulation adjustments. Recent bidirectional DBS systems enable chronic recording of local field potentials (LFPs) and prior work using the Activa PC+S device identified an electrophysiological signature of stable clinical recovery. However, translation to practical clinical deployment requires demonstrating that this biomarker is robustly generalizable, specific to the impact of the DBS therapy, and deployable in real-world recording contexts. To address this need, we developed an at-home SCC LFP data collection platform (built on the Medtronic Summit RC+S system) enabling at home data collection for a new cohort of ten SCC DBS participants with TRD (ClinicalTrials.gov identifier NCT04106466). Using longitudinal LFP recordings collected from this system, we report findings demonstrating that the previously reported biomarker of stable recovery generalizes across subject cohorts and devices, is robust to common potential confounds (including time of day and stimulation status), and shows symptom specificity, sensitivity and stability necessary to support clinical decision making. Across both cohorts, biomarker changes show relationships to pre-DBS white matter structure and network function measured using diffusion MRI and resting-state functional MRI (rsFMRI). These findings replicating and extending previous findings support the biomarkers utility as a foundation for scalable, electrophysiology-informed decision support in SCC DBS.

ObjectiveMagnetophosphenes are visual percepts induced by extremely low-frequency magnetic fields (ELF-MF; <300 Hz), yet their EEG correlates remain poorly characterized and are not reliably captured by classical low-frequency markers. We tested whether magnetophosphene perception is associated with broadband high-frequency EEG changes rather than focal oscillatory effects. ApproachEEG was recorded in N=13 healthy volunteers during 20 Hz sinusoidal magnetic-field exposure delivered using transcranial alternating magnetic stimulation (tAMS) in a global-head configuration. Three conditions were analyzed: no exposure (0 mT), subthreshold (5 mT), and suprathreshold (50 mT). Gamma-band activity (30-80 Hz) was quantified using complementary spectral approaches, including aperiodic-adjusted measures. Main resultsPerception reports sharply dissociated the three conditions, with frequent perception at 50 mT only. Suprathreshold stimulation was associated with spatially distributed increases in gamma-band activity over frontal and occipital electrodes. These effects persisted after aperiodic correction using two independent parameterization methods and did not exhibit a consistent narrowband peak, indicating broadband high-frequency changes. SignificanceMagnetophosphene perception is not reliably captured by focal low-frequency EEG markers but is instead associated with distributed broadband high-frequency activity. These findings challenge standard assumptions derived from classical visual paradigms and suggest that perception under magnetic stimulation reflects large-scale, state-dependent neural dynamics.

BackgroundRespiration is a key central nervous system rhythm that modulates sensorimotor function in healthy individuals, but the neurophysiological mechanisms of volitional breathing-mediated sensorimotor modulation and its preservation in stroke patients remain unclear. This study aimed to characterize the effects of volitional fast inspiration on sensorimotor pathway excitability in healthy and stroke populations, and provide a mechanistic basis for respiratory-integrated post-stroke rehabilitation. MethodsA multimodal case-control neurophysiology study was conducted in 52 healthy volunteers (26 {+/-} 3 years, 30 males) and 44 first-ever subacute stroke patients (66 {+/-} 10 years, 30 males). Three complementary experiments assessed transcranial magnetic stimulation-induced motor-evoked potentials (MEPs), peripheral nerve stimulation-induced somatosensory-evoked potentials (SEPs), and functional electrical stimulation -evoked muscle force under three breathing conditions: volitional fast inspiration (IN), fast expiration (EX), and spontaneous breathing (CON). Two-way and one-way repeated measures ANOVA with Bonferroni post hoc tests were used for statistical analysis. ResultsVolitional fast inspiration significantly enhanced sensorimotor pathway excitability and muscle force generation in both groups. Volitional fast inspiration increased MEP amplitudes relative to spontaneous breathing and fast expiration (p {inverted exclamation} 0.05), with further amplification during active muscle contraction (p {inverted exclamation} 0.05). It also elevated SEP amplitudes in healthy parietal/frontal cortical regions and the stroke parietal cortex (p {inverted exclamation} 0.05). Synchronizing volitional fast inspiration with voluntary finger contraction increased muscle force evoked by functional electrical stimulation by 16-18% relative to spontaneous breathing (p {inverted exclamation} 0.05), with non-significant force gains at rest. ConclusionsVolitional fast inspiration bidirectionally enhances corticospinal transmission, somatosensory integration, and functional force generation in both healthy individuals and stroke patients, with preserved respiratory modulation in stroke-damaged neuropathways. By demonstrating preserved respiratory modulation in stroke-damaged neuropathways, our results provide mechanistic support for integrating controlled breathing into low-cost, non-invasive post-stroke rehabilitation paradigms.

The present study aimed at evaluating the impact of high-definition transcranial random noise stimulation (HD-tRNS) applied to the right dorsolateral prefrontal cortex (DLPFC) on direct learning in computer-based complex tasks, and potential far transfer effects to a flight simulator task. Thirty young pilots in general aviation participated in a double-blind 11-week protocol that included a two-hour baseline session (week 1), 10 one-hour training sessions (weeks 2 to 6), a short-term (week 7) and a long-term (week 11) evaluations. Both stimulated, and sham groups exhibited improvements in trained (MATB and Space Fortress video game) and untrained (Flight Simulator) tasks from baseline to the first and last evaluation sessions. No significant differences between groups have been found either in terms of direct (trained tasks) or transfer (flight simulator and associated mental workload) effects. These findings contribute to the ongoing debate on the efficacy of transcranial brain stimulation for enhancing learning in healthy participants. Specifically, the present study demonstrates that the applied stimulation protocol yields no measurable benefit to learning processes, underscoring the need to explore alternative stimulation parameters and methodological approaches.