Brain Stimulation

Neural oscillations provide temporal frameworks for coordinating communication within and across distributed brain networks. In essential tremor (ET), pathological synchronization within the cerebello-thalamo-cortical circuit produces rhythmic activity that manifests as an involuntary action tremor. Although deep brain stimulation can effectively suppress tremor, its invasiveness and cost highlight the need for non-invasive interventions capable of selectively modulating pathological oscillations. Transcranial magnetic stimulation (TMS) offers a non-invasive means of engaging cortical circuits, yet conventional stimulation protocols are delivered independently of the ongoing neural dynamics. Such open-loop approaches ignore the temporal structure of tremor-related activity, potentially stimulating during both amplifying and suppressing phases of the oscillation. To address this, we compared two phase-targeted TMS paradigms: first-pulse phase-locked TMS (First-pulse-TMS), in which only the initial pulse of a stimulation train is aligned to the tremor phase, and cycle-by-cycle phase-locked TMS (Continuous-TMS), in which each pulse is continuously triggered based on real-time tremor phase. Ten patients with ET underwent stimulation guided by peripheral tremor recordings using an accelerometer, with tremor phase estimated in real time via the Oscilltrack algorithm. Sixty-four trains of TMS pulses were delivered at nine discrete phase bins of the tremor cycle, such that each phase bin was repeated approximately seven times. Continuous-TMS maintained accurate phase-locking across consecutive cycles (mean phase-locking value ~0.9), whereas First-pulse-TMS exhibited progressive drift over time and low phase consistency (mean phase-locking value <0.2). The circular concentration of stimulation phase was significantly greater for Continuous-TMS than First-pulse-TMS (Mann-Whitney U-test, p < 0.001), indicating a significant difference in overall phase-locking accuracy between the two protocols. Critically, Continuous-TMS, unlike First-pulse-TMS, induced bidirectional, phase-dependent modulation of tremor amplitude. Circular-linear modelling revealed a sinusoidal relationship between stimulation phase and changes in tremor amplitude, with tremor amplification and suppression occurring at opposite phases of the cycle. Covariates including baseline tremor amplitude and trial number were accounted for. In some people, tremor suppression outlasted the stimulation period, suggesting phase-locked TMS may be a potentially useful therapeutic tool. By enabling reliable, phase-specific stimulation of the tremor cycle, Continuous-TMS allows identification of the individual phase that produces maximal tremor suppression, supporting the development of personalized, phase-specific neuromodulation strategies. This proof-of-principle study demonstrates that temporally precise, closed-loop TMS can interact with pathological oscillations in real time, providing a mechanistic framework for probing oscillatory contributions to motor symptoms and a scalable therapeutic approach for ET and other oscillopathies.

Although transcranial magnetic stimulation (TMS) is widely used for brain stimulation, fundamental issues about its underlying mechanisms remain unresolved. We investigated some of these issues experimentally using an intact isolated turtle cerebellum in vitro, employing a novel chamber designed to deliver precisely calibrated induced electric fields along cortical depth. Our results show that single-pulse TMS can directly activate Purkinje cells and climbing fibers, and synaptically activate Purkinje cells via climbing fibers - all within the first 1.2 ms. Specifically, current source density analysis showed that TMS directly (non-synaptically) activated (1) climbing fibers near the bend with intracellular current directed toward the axonal terminals and (2) Purkinje cells directly near the axon initial segment with intracellular current directed toward the distal dendrites. The thresholds for direct activation of climbing fibers and Purkinje cells were found to be very similar, 25 {+/-} 1 V/m. The climbing fibers synaptically activated Purkinje cells, as expected, with intracellular current originating in the proximal dendritic trunk and directed toward the distal dendrites. At higher electric fields (> 58 {+/-} 17 V/m), TMS synaptically activated dendritic currents in Purkinje cells. These results provide new insight into how TMS may activate afferent fibers and cell bodies of cortical neurons.

Background: Low-intensity focused ultrasound (LIFU) is an emerging noninvasive neuromodulation technique capable of targeting deep cortical and subcortical structures with high spatial precision. In healthy human volunteers, LIFU has demonstrated a favorable safety and tolerability profile across multiple studies. However, its safety and tolerability in clinical populations remains poorly characterized, representing a critical barrier to clinical translation. Here, we prospectively evaluate the safety and tolerability of LIFU targeting the left dorsal anterior insula (dAI) in patients with fibromyalgia (FM). Methods: In a single-blind, sham-controlled, within-subjects crossover design, 13 individuals with FM (43.1 +/- 13.2 years; 12 female) received 10 minutes of active LIFU (500 kHz, 1 kHz PRF, 36% duty cycle, 4.2 W/cm2 Isppa; 100 x 1-second pulse trains with a 5-second inter-train interval) targeting the left dorsal anterior insula (dAI) or sham on separate visits. Safety was evaluated through neuroradiological review of post vs. pre LIFU FLAIR MRI, quantitative voxel-wise FLAIR analysis, and patient report of symptoms (ROS). Tolerability was assessed using an experience assessment. Efficacy of the LIFU intervention was assessed using quantitative sensory testing (QST) including temporal summation of pain (TSP) and conditioned pain modulation (CPM). Results: Neuroradiological review identified no new evidence of edema, microhemorrhage, acute ischemia, or white matter injury on post-LIFU structural imaging. Quantitative FLAIR analysis using contralateral-mirror-referenced relative FLAIR (rFLAIR) showed no significant within-subject change in the stimulated beam volume (delta rFLAIR = 0.002 +/- 0.025, t(12) = 0.30, P = 0.769, Cohen's dz = 0.08). No serious adverse events were documented and ROS indicated no change due to LIFU sonication. Participants rated the procedure as comfortable and could not distinguish active from sham LIFU. LIFU did not result in statistically significant changes for TSP (p = 0.797) or CPM (p = 0.465). Conclusions: Ten minutes of LIFU targeting the left dAI was safe and well tolerated in individuals with FM, with no neuroradiological or quantitative MRI evidence of tissue effects and no serious adverse events. Blinding was preserved, and participants rated the procedure as comfortable. Although no significant changes were observed in experimental pain measures, these findings support the feasibility of targeting deep salience and pain amplification circuitry with LIFU in patients with FM and provide a foundation for adequately powered efficacy trials.

We employed a noninvasive high-density surface electromyography (HDsEMG) framework to track spinal motor neuron responses during repetitive transcranial magnetic stimulation (rTMS) and characterize corticospinal transmission of different stimulation frequencies and intensities to the alpha motor neuron pool. Eleven healthy individuals performed isometric thumb flexion at 10% of maximal voluntary contraction while rTMS was delivered over the motor cortex at five frequencies (5, 10, 20, 30, and 50 Hz) and three subthreshold intensities (50%, 60%, and 70% of resting motor threshold). Motor units were decomposed from HDsEMG signals before stimulation and tracked during rTMS. Input-output coupling was quantified using coherence between the rTMS train and individual motor unit spike trains or the cumulative spike train (CST), with shuffled spike trains used as surrogate controls. rTMS inputs were robustly transmitted to spinal motor neurons for all frequencies except 5 Hz, indicating widespread corticospinal coupling. Transmission behaved linearly, with CST output spectra reproducing input frequencies and scaling proportionally with stimulation intensity. The estimated transfer function revealed a bandpass-like profile, with maximal transmission between 10 and 60 Hz. Transmitted inputs also induced oscillatory components in the common synaptic input to motor neurons at stimulation frequency. Simulations indicated that this frequency selectivity emerges from balanced excitatory and inhibitory inputs to the motor neuron pool, with specific synaptic dynamics. These findings demonstrate that corticospinal transmission during rTMS acts as a frequency-selective linear system and provide a framework for assessing and modulating corticospinal pathways, with potential application as tool for tracking disease progression and neurorehabilitation. Highlights- HDsEMG decomposition tracks spinal motor neuron activity during rTMS. - Corticospinal transmission scales with rTMS stimulation intensity. - Corticospinal pathways act as a frequency-selective system. - rTMS transfer function shows maximal transmission at 10-60 Hz. - EPSP-IPSP interactions explain bandpass corticospinal transmission during rTMS.

BackgroundElectrophysiological recordings from chronically implanted Deep Brain Stimulation (DBS) electrodes can greatly advance understanding of disease and treatment mechanisms of motor and psychiatric disorders. The Medtronic Percept system allows for chronic recordings of local field potentials (LFP) from DBS target regions. However, these systems lack an inbuilt synchronization option to align LFP recordings to other recording modalities and consequently events in computerized tasks. ObjectiveWe propose and evaluate a synchronization method based on Transcutaneous Electrical Stimulation (TES) with low amplitudes to precisely align recorded LFP signals from the DBS electrodes to EEG recordings. MethodsThe TES-based synchronization approach was implemented and tested in 11 participants implanted with the Medtronic Percept for treatment of Parkinsons disease. ResultsThe proposed method provides high reliability, precise alignment and usability across all Medtronic Percept recording modes. Notably, the method enables recordings during adaptive DBS and with stimulation turned off. In this recording mode, LFP signals can be acquired from all recording contact pairs simultaneously, with a high signal-to-noise ratio. We provide detailed setup plans and share Python and Matlab scripts for signal alignment to enable easy application of our approach. ConclusionBy enabling reliable, well-aligned LFP recordings from all DBS contacts, our method provides a robust tool for studying neural dynamics and refining therapeutic interventions in diverse neurological conditions.

Transcranial direct or alternating current stimulation (tDCS or tACS) are used for the treatment of several cognitive disorders, many of which are due to imbalances in excitatory-inhibitory (E-I) interactions, but how stimulation affects the underlying cortical network remains an open question. In the primary visual cortex (V1), E-I interactions due to presentation of large gratings induce slow (20 Hz-35 Hz) and fast gamma (40 Hz-70 Hz) oscillations, which weaken with ageing and neurodegeneration and have been associated with different subtypes of interneurons. However, the effect of tDCS/tACS on stimulus-induced gamma is unknown. To investigate the impact of sustained stimulation on cortical E-I networks, we applied tDCS and tACS to two alert non-human primates while presenting full-screen gratings. We analyzed local field potentials before, during, and post-stimulation, focusing on gamma power and field-field coherency (FFC) as a measure of phase consistency. We found that tDCS significantly increased post-stimulation slow and fast gamma power, as well as FFC (60 Hz-100 Hz), with this effect lasting for approximately 1.5 hours. In contrast, tACS at 20 Hz consistently reduced slow gamma power, along with FFC (40 Hz-60 Hz). Our experimental observations were replicated in a physiologically realistic computational model of gamma generation by introducing targeted modifications to the synaptic weights within the simulated E-I network. Because long-lasting changes in local power and coherency strongly influence and modify cortical network, our findings reiterate the therapeutic and experimental potential of transcranial electrical stimulation to induce sustained modulation of cortical networks. SIGNIFICANCE STATEMENTNon-invasive transcranial current stimulation is used as a treatment for neural disorders, but how it modifies cortical dynamics to produce lasting changes is still debated. We recorded stimulus-induced gamma oscillations in macaque visual cortex as a readout of network interaction to study the effect of cortical stimulation. After stimulation over clinically relevant durations ([~]20 minutes), direct and alternating current increased and suppressed gamma power and connectivity, respectively, and this effect persisted for more than an hour. We also found that modifying the synaptic weights informed by earlier research in a realistic gamma-generating model could replicate our experimental results. Our results establish gamma rhythm as a useful indicator to study the effect of neurostimulation on neural circuitry.

Adult hippocampal neurogenesis plays a central role in learning, memory formation, and adaptive neural plasticity, making it an attractive target for noninvasive neuromodulation strategies. Low-intensity focused ultrasound (LIFU) has emerged as a promising modality for modulating brain function, yet its effects on adult neurogenesis and the role of stimulation frequency remain incompletely understood. In this study, we evaluated whether transcranial LIFU applied to the dentate gyrus influences neurogenic and cognitive outcomes in a frequency-dependent manner. Adult rats received twice-weekly ultrasound stimulation for four weeks at 0.5, 1, or 5 MHz. Neurogenesis was assessed through BrdU incorporation and neuronal differentiation by BrdU/NeuN co-labeling, while expression of neurogenesis-associated markers (BDNF, FGF-2, and Sox-2) was quantified using qRT-PCR. Behavioral effects were examined using the novel object recognition task. Among the tested conditions, 0.5 MHz stimulation produced the most pronounced neurogenic response, with increased cellular proliferation in the dentate gyrus, elevated expression of neurogenic markers, and improved recognition memory relative to sham-treated animals. Higher stimulation frequencies yielded comparatively weaker effects. These findings identify stimulation frequency as a critical determinant of LIFU-driven neuroplastic responses and support the potential of focused ultrasound as a noninvasive approach for promoting hippocampal regeneration and functional recovery.

Stimulation-induced motor contractions are among the most common dose-limiting side effects in subthalamic nucleus deep brain stimulation for Parkinsons disease, yet no quantitative models exist to predict their occurrence from imaging data. Here, we combine pathway activation modeling of the corticospinal and corticobulbar tracts within the posterior limb of the internal capsule with a data-driven prediction framework. Evaluated by leave-one-patient-out cross-validation across an intraoperative (42 patients, 352 sites) and postoperative sub-cohort (11 patients, 176 contacts), the model explained 31-35% of the variance in observed motor contraction thresholds. The model reliably identified contacts with the lowest and highest motor contraction thresholds within individual electrodes, with strongest performance for distinguishing directional segments at the same electrode level (84% and 64% accuracy; p < 0.001), supporting its potential value for postoperative DBS programming. Back-projection of model coefficients provided an anatomically interpretable mapping consistent with known capsular anatomy. The framework is openly available and may inform computational tools for surgical planning, intraoperative validation, and postoperative programming in DBS.

BackgroundTranscranial magnetic stimulation (TMS) over the primary motor cortex (M1) elicits motor-evoked potentials (MEPs), a neurophysiological marker of corticospinal excitability. Ongoing brain activity at the time of stimulation, such as the phase and power of the sensorimotor mu rhythm (8-13 Hz), has a significant impact on MEP amplitudes. However, it remains unclear whether these endogenous excitability states also influence the consistency of MEP amplitudes across repeated trials. ObjectivesWe investigated whether instantaneous mu dynamics modulate not only the magnitude but also the consistency of corticospinal responses to TMS. MethodsTwenty-nine healthy participants received 1200 single TMS pulses over the left M1 during simultaneous EEG recording. Trials were stratified based on pre-stimulus mu power, phase, and interhemispheric M1-M1 functional connectivity. Brain-state-resolved MEP variability was quantified using the coefficient of variation (CV) within subsets of trials defined by similar pre-stimulus mu dynamics. ResultsTrial subsets characterized by high mu power or high M1-M1 functional connectivity were associated with reduced MEP variability, indicating more consistent corticospinal output. In contrast, the mu phase did not significantly influence response consistency. Brain-state-resolved MEP variability showed greater stability across sessions compared to MEP variability estimated from random trial subsampling. ConclusionsPre-stimulus mu dynamics shape not only magnitude but also consistency of corticospinal responses to TMS. We show that corticospinal response consistency reflects a structured, brain-state-dependent property of the sensorimotor network. These findings contribute to our mechanistic understanding of brain-state-dependent neuromodulation and may be leveraged to reduce variability and improve efficacy to TMS. HighlightsO_LIOngoing sensorimotor mu dynamics shape both magnitude and consistency of MEPs. C_LIO_LITrial subsets characterized by high mu power were associated with reduced MEP variability. C_LIO_LIMu phase modulated MEP amplitude but did not influence MEP consistency. C_LIO_LIBrain-state-resolved estimates of MEP variability were more reliable across sessions. C_LIO_LIFuture TMS protocols may reduce effect variability by targeting stable excitability states. C_LI

Brain stimulation is increasingly recognized as an effective and important therapeutic intervention for many brain diseases. Distance between the scalp and other brain regions is a pivotal variable for neurostimulation planning and the development of new techniques, but alterations in the distance between the scalp and other regions in brain diseases are largely unknown. In this study, we developed an automatic pipeline to calculate scalp-to-region distance (SRD) values from T1 MR images and applied it to a total of 1382 participants, including patients with autism spectrum disorder (ASD), Parkinsons disease (PD), Alzheimers disease (AD), and cognitively normal controls (CNs). Cloud points were uniformly sampled on the automatically extracted scalp surface and cortex surface, on which the point-wise distance maps were generated. The brain was then coregistered with the BCI-DNI atlas, and SRD value for each brain region was extracted. Analysis of covariance (ANCOVA) was performed for SRD in each brain region, with age and sex as covariates. Compared with CNs, ASD patients showed widespread SRD decreases across the brain with prominent involvement of the frontal lobe, especially the orbitofrontal cortex and adjacent regions. In contrast, in AD patients, significantly increased SRD values were observed in various regions of the frontal gyrus. No significant SRD alteration was found in PD patients after correction. The automatic SRD calculation pipeline and the different patterns of SRD alterations in these diseases might be helpful for future neurostimulation planning in clinical practice. HighlightsO_LIAutomatic pipeline enables scalp-to-region distance (SRD) measurement, facilitates brain stimulation planning. C_LIO_LIASD patients show widespread SRD decreases, especially in the orbitofrontal cortex and adjacent regions. C_LIO_LIAD patients present increased SRD in the frontal gyrus and decreased SRD in the parahippocampal gyrus. C_LI

IntroductionStimulation-evoked potentials (SEPs), recorded both during and after deep brain stimulation (DBS) surgery, have shown promise for guiding DBS targeting and programming. However, filtering protocols applied to stimulation trains produce an artifact we call a filter-induced oscillation (FIO) which closely mimics physiological SEPs. Hence, we outline the mechanistic origins of this distortion and describe a means of differentiating it from valid SEP activity. MethodsWe recorded in 18 patients undergoing DBS surgery targeting the subthalamic nucleus or globus pallidus internus. We stimulated target nuclei with cathode-first (CF) and anode-first (AF) pulses to record native SEPs, and in white matter tracts (null condition). Recordings were subsequently filtered to illustrate FIO. Next, we filtered harmonic frequencies of an artificial stimulation train to demonstrate FIO origins. Finally, FIO was deliberately generated in white matter recordings with a notch filter, and its behaviour contrasted with SEPs during AF and CF stimulation. ResultsFiltering stimulation trains produced FIOs that depended on filter order and corner frequency. We also showed that FIO emerges from filter-induced attenuations of harmonic frequencies which compose stimulation trains, producing oscillations of like frequency around pulses. Finally, FIOs reverse in polarity depending on AF or CF stimulation, whereas SEPs do not. ConclusionsGiven the potential for widespread adoption of SEPs in DBS targeting and programming, safe analytical protocols are imperative to avoid the induction of processing-related artifacts which can be misinterpreted as biological signals. Here we establish the necessary theory for identifying FIOs and tuning analytical pipelines to avoid their generation.

Transcranial temporal interference stimulation (tTIS) is a promising non-invasive brain stimulation technique that has the potential to selectively modulate deep brain regions by delivering two high-frequency alternating currents that interfere to produce a low-frequency amplitude-modulated envelope at the target. A key challenge in deploying tTIS is finding electrode current patterns that are simultaneously effective, focal, and safe. This is a fundamentally non-convex optimization problem for which a number of methods have recently been proposed. However, no systematic comparison of these methods across a large and diverse set of brain targets has been performed, leaving practitioners without clear guidance on how best to optimize for a particular experimental setting. In this work, we present a comprehensive benchmarking study comparing seven tTIS optimization methods that have appeared in the literature in recent years: exhaustive search, genetic algorithm, multi-objective evolutionary algorithm (MOVEA), min-max optimization, convex TI (CVXTI), non-convex optimization with convex relaxations, and an unsupervised neural network. All methods were evaluated across 250 brain targets spanning cortical and subcortical gray matter and white matter regions in five finite element head models. Each method was evaluated on two key metrics: mean electric field strength within the target region of interest, and off-target stimulated brain volume. Results were stratified by tissue type and target depth to identify systematic performance differences. Based on these results, we provide practical evidence-based recommendations for optimization method selection among these seven methods depending on target location, tissue type, and available computation time. Moreover we provide the code base that will allow other investigators to use these methods for their own applications. Our goal is to provide researchers and clinicians with a clear, evidence-based framework for choosing a tTIS optimization method suited to their specific target and application.

ObjectiveNeurogenic lower urinary tract dysfunction (NLUTD) impairs bladder control and remains difficult to treat. We aim to define how electrical stimulation (ES) parameters of the external urethral sphincter (EUS) affect urinary leakage thresholds to guide neuromodulation strategies for NLUTD. MethodsWe performed direct EUS stimulation in anesthetized rats using charge-balanced biphasic pulses while systematically varying current amplitude (0.5-3.0 mA), frequency (20-100 Hz), and pulse duration (0.5-3 ms). Urine leakage thresholds were mapped across the multidimensional parameter space. ResultsStimulation parameters exhibited strong nonlinear interdependence in determining leakage onset. At a fixed pulse duration, higher current amplitudes required lower stimulation frequencies to evoke leakage. Increasing pulse duration substantially reduced both current and frequency thresholds. Age and sex caused modest shifts in absolute thresholds but did not alter the fundamental parameter-response relationships. ConclusionPulse duration, current amplitude, and frequency jointly govern urinary leakage thresholds, with pulse duration serving as the dominant modulator of stimulation efficiency. SignificanceThis work establishes a quantitative framework for charge-efficient stimulation parameter selection, enabling the design of energy-aware, precision neuromodulation protocols and implantable systems for NLUTD rehabilitation.

Background: Deep brain stimulation (DBS) of the ventral intermediate nucleus and posterior subthalamic area (VIM/PSA) in Essential Tremor (ET) and the subthalamic nucleus (STN) in Parkinson's disease (PD) are established treatment for tremor. To achieve maximum tremor control, increasing stimulation frequency beyond 130 Hz is part of clinical practice, but lacks scientific evidence. Objective: To compare tremor suppression under total electrical energy delivered (TEED)-equivalent stimulation at 130 Hz versus 185 Hz in STN-DBS for PD and VIM/PSA-DBS for ET. Methods: In this prospective, double-blind study, acute DBS effects were assessed in 18 people with ET (n = 29 hemispheres), and 25 people with PD (n = 30 hemispheres). Tremor-suppressive effects, evaluated by accelerometry, were compared with TEED-equivalent stimulation at 130 Hz and 185 Hz using linear mixed-effects models, explorative pairwise comparisons, and equivalency testing. Results: Linear mixed-effects models revealed no significant effect of stimulation frequency on tremor improvement in both cohorts. Pairwise comparisons showed no consistent differences in total tremor improvement with TEED-equivalent 185 Hz vs 130 Hz DBS. Post-hoc equivalence testing confirmed equivalence of stimulation frequencies under TEED-equivalent conditions within a +/- 20% margin of relative tremor improvement. Conclusion: This study provides Level II evidence that a higher stimulation frequency of 185 Hz does not offer additional benefit in deep brain stimulation for tremor and supports 130 Hz as the standard stimulation frequency for tremor suppression in ET and PD.

Subthalamic local field potential (STN-LFP) activity within patient-specific beta frequency ranges is an established biomarker for adaptive deep brain stimulation (aDBS) in Parkinsons disease (PD). While beta power correlates with akinetic-rigid symptoms, it is also modulated by physiological states such as sleep, highlighting the importance of understanding state-dependent spectral dynamics for adaptive stimulation. We continuously recorded STN-LFP spectra in ten PD patients over two weeks in both conventional and adaptive DBS. We show that sleep-related reductions in beta power occur independently of stimulation mode and are primarily associated with broadband aperiodic spectral changes across both low and high beta bands rather than periodic beta oscillations. These findings support the integration of aperiodic spectral features into future adaptive neuromodulation algorithms to improve biomarker specificity and optimize aDBS in PD.

Background: Thalamic low-intensity transcranial focused ultrasound (tFUS) has shown promise for increasing behavioral responsiveness in disorders of consciousness (DOC), but no study has examined whether it can causally modulate the well-validated behavioral, electrophysiological, and metabolic biomarkers of DOC impairment. Methods: Sixteen adult patients (44% Female; Age, M=37.81, SD=15.97) with a chronic DOC (Time Since Injury, M=3.39, SD=1.94 years) secondary to severe brain injury (TBI 44%, non-TBI 56%) underwent a 10-day inpatient, longitudinal, single-arm, open-label protocol. tFUS was delivered in a single session targeting the left central thalamus. Well-known behavioral (CRS-R), electrophysiological (EEG {delta}/{beta} ratio), metabolic (18F-FDG PET), and polysomnographic outcomes were assessed at baseline and after sonication. Results: The maximum CRS-R total score increased significantly following tFUS compared to baseline (M=13.27 vs. M=10.33; t(14)=7.407, p<0.001, d=1.913), as did the global EEG {delta}/{beta} ratio (N=14; W=17, p=0.025, r=0.68), with the degree of frontal slowing positively predicting behavioral gains ({tau}b=0.51, p=0.016). Glucose metabolism decreased bilaterally in thalamus and frontal, temporal, and parietal cortices at both post-tFUS timepoints compared to baseline. Finally, N2 sleep increased by 33% following tFUS (N=11; t(10)=2.386, p=0.038, d=0.72), though this did not survive correction. No severe adverse events were observed. Conclusion: Thalamic tFUS can causally modulate well-validated behavioral, electrophysiological, and metabolic biomarkers of DOC. The convergent inhibitory signature across these measures suggests a thalamocortical reset mechanism, complementing existing excitatory neuromodulation approaches and providing the mechanistic foundation for a large, randomized sham-controlled trial.

Background: Post-stroke apathy (PSA) is a common, disabling syndrome with few evidence-based treatment options. We evaluated the safety, feasibility, acceptability, and evidence of effects of a three-day accelerated intermittent theta burst stimulation-repetitive transcranial magnetic stimulation (iTBS-rTMS) protocol targeting the left dorsomedial prefrontal cortex (dmPFC) in chronic stroke survivors with apathy. Methods: Stroke survivors with symptomatic apathy received open-label iTBS-rTMS at the left dmPFC (21,600 pulses across 36 sessions; 3 treatment days; 12 sessions/day within one week). Safety endpoints included adverse events, neuroradiological findings, and objective cognitive performance. Secondary outcomes included measures of apathy and other neuropsychiatric symptoms as well as psychosocial functioning, including quality of life and caregiver burden. Participants were followed up for one month. Results: Fourteen participants (mean age = 61.8 {+/-} 14.0 years; mean time since stroke = 55.6 {+/-} 31.6 months) completed the iTBS-rTMS treatment course. No serious adverse events occurred. Participants rated the treatment as highly acceptable, and cognitive performance was stable from pre- to post-rTMS with no treatment-related changes on structural MRI. Regarding apathy, participants had significant improvements with moderate to large effect sizes on the Lille Apathy Rating Scale (LARS), on both self (d = 0.78) and caregiver-rated versions (d = 1.28), p<0.05 pretreatment-to-one-month follow-up. In addition, secondary measures of psychosocial function also showed improvement with moderate to large effect sizes (Stroke Specific Quality of Life Scale: d = 0.62; Zarit Burden Interview: d = 0.72), and the Brief Inventory of Psychosocial Function: d = 0.89). Conclusions: In chronic stroke survivors with PSA, accelerated iTBS-rTMS targeting the left dmPFC appears to be safe, feasible, tolerable, and highly acceptable, with preliminary evidence suggesting a potential role in reducing apathy and secondarily promoting improvements in quality of life, caregiver burden, and broader psychosocial function.

Machine-learning surrogate models are positioned to help optimize deep brain stimulation (DBS) usage by predicting neural activation in response to electrical stimulation, while minimizing tradeoffs between computational expense and accuracy. Previous work has developed high accuracy artificial neural network (ANN) and convolutional neural network (CNN) surrogate models that predict activation of individual, myelinated axons, to extracellular electrical stimulation for subsets of DBS programming configurations. Moreover, more traditional machine learning methods including extreme gradient boosting (XGBoost) have been used effectively for peripheral-nerve single-fiber activation predictions. We build upon the previous work and compare ANN, CNN and XGBoost methods to a much expanded set of electrode programming configurations including: monopolar, bipolar, tripolar, quadrupolar, multiple monopolar, and multiple cases of directional leads. Training used datasets generated from a finite-element model of an implanted DBS lead together with multi-compartment cable models of synthetically generated axons. We evaluated the machine learning predictors using white matter pathways derived from group-averaged connectome data within a patient-specific tissue conductivity field, comparing both predicted stimulus activation thresholds and pathway recruitment across a clinically relevant range of stimulus amplitudes and pulse widths. Our ANN and CNN models successfully predicted neural fiber activation for almost all electrode configurations with low error, expanding the scope of our previous predictor model. Results also showed key limitations of XGBoost models and superior performance of CNNs for more complex electrostatic fields of the directional leads.

IntroductionFunctional magnetic resonance imaging (fMRI) is widely used to study neural processes of behavior, but evaluations of test-retest reliability (TRR) of task-related blood-oxygen-level-dependent (BOLD) responses are scarce for many cognitive tasks. Such information is particularly important for longitudinal and intervention research. The ability to learn associations between choices and outcomes across decision stages is crucial for daily behavior. We assessed the measurement reliability of behavioral performance and fMRI BOLD signals during value-based sequential decision making to evaluate the TRR of task-relevant regions for future research on non-invasive brain stimulations. MethodsTwenty healthy adults (22 to 40 years) completed two task-fMRI sessions that were at least 2 weeks apart. During scanning, participants performed two variants of a three-stage Markov decision task with conditions varied in temporal contingency (immediate vs. delayed) and magnitude of choice outcomes (high vs. low). Both sessions were conducted under sham tDCS via a focal 3 x 1 montage targeting left dorsolateral prefrontal cortex (DLPFC). The TRR was assessed using intraclass correlation coefficients (ICC) with a two-way mixed-effects consistency model for decision performance and task-related fMRI signals at voxel-wise level and summarized in key regions defined by the extended Human Connectome Project atlas (HCPex). ResultsDecision performance was lower with delayed than immediate outcomes (p < 0.001). Higher outcome magnitude improved performance (p < 0.001). Decision performance increased across learning bins (p < 0.001). The behavioral TRR was in the moderate to good level (ICC(3,1) = 0.742 for accuracy; ICC(3,1) = 0.801 for reaction time). At the whole-brain level, contrasting brain activities in delayed with immediate condition revealed suprathreshold cluster peaks in several frontal-parietal (e.g., bilateral orbitofrontal, bilateral dorsolateral prefrontal, and medial parietal cortices) and striatal regions (e.g., bilateral putamen). Voxel-wise ICCs revealed variable but partly good-to-excellent TRR across task-relevant regions, with stronger reliability in several striatal, orbitofrontal, and left dorsolateral prefrontal parcels, and more variable reliability across anterior cingulate and medial prefrontal parcels. ConclusionThese results from a 2-session tDCS sham-sham stimulation study establish the validity of using the three-stage Markov decision task in future studies about intervention effects on the frontal-parietal-striatal network.

Closed-loop deep brain stimulation (cDBS) for Parkinsons disease requires control strategies that tolerate noisy sensing, patient-specific stimulation responses, medication-related fluctuations, and embedded hardware constraints. We developed a patient-calibrated minute-scale dynamical model of subthalamic beta activity and an embedded explicit trend-zone predictive controller, eTZPC. The model combined a basal-ganglia mechanistic prior with stimulation-amplitude and medication-cycle recordings from five patients, and incorporated individualized stimulation-{beta}STN maps, fast- and slow-timescale stimulation responses, levodopa-related modulation, background drift, and observation noise. eTZPC was designed to maintain {beta}STN activity within a patient-specific target zone under stimulation-amplitude, step-size, and quantization constraints. Compared with dual-threshold (DT) and proportional-integral-derivative (PID) controllers across four disturbance scenarios, eTZPC achieved target-zone regulation close to PID while reducing stimulation-switching burden toward the low-switching profile of DT. Ablation analyses identified distinct contributions of smoothing, trend prediction, patient-specific action modeling, and embedded explicit implementation. Parameter-mismatch tests showed that eTZPC was relatively robust to dynamic and disturbance-parameter deviations, but remained sensitive to errors in the steady-state stimulation-{beta}STN map. Patient-in-the-loop recordings in five patients further confirmed execution consistency and compliance with stimulation-boundary and step-size constraints. These findings support patient-calibrated dynamical modeling combined with low-complexity explicit control as a feasible framework for further embedded cDBS evaluation.