Objective: Real-time estimation of brain state is essential for efficient brain stimulation. Specifically, the electroencephalography (EEG) oscillation phase arose as a promising biomarker for instantaneous brain excitability, making it ideal for state-dependent brain stimulation. Current methods for real-time EEG phase extraction lose accuracy in the presence of non-stationary noise, motivating the development of a more robust and accurate algorithm. Here, we propose and validate Bayesian Temporal Prediction (BTP) as an effective method for EEG phase detection in real-time. Methods: BTP utilizes a short pre-session EEG recording and learning of the personalized prediction parameters, enabling subsequent high-precision real-time phase detection. We experimentally validate BTP in humans and compare its performance to a strong benchmark algorithm. Results: BTP demonstrates accurate EEG oscillation phase detection across a broad range of conditions and target oscillations, facilitating personalized brain stimulation. Conclusion: This study introduces BTP as a robust, computationally efficient, and accurate method for EEG state-dependent stimulation. Significance: The widespread adoption of BTP in research and clinical settings has the potential to enhance treatment efficacy and minimize inter- and intra-individual variability in brain stimulation interventions.

Background In Parkinson's disease, motor network electrophysiology frequently exhibits excessive beta oscillations. The cornerstone of therapeutic efficacy lies in the ability to modulate these pathological oscillations. Transcranial alternating current stimulation (tACS), a non-invasive method that applies oscillating electric fields to modulate ongoing brain activity, offers a promising approach. Objective The objective of the manuscript is to investigate the dose-dependent effects of tACS on motor network cortical neurons in a Parkinson's disease model. Methods We recorded neuronal spike activity in the motor cortex in parkinsonian non-human primates during tACS to determine how stimulation-induced electric fields affect spike timing. Results Strong electric fields entrained neural activity at the stimulation frequency but altered the preferred spiking phase. Conversely, weak fields disrupted beta-band synchronization by modulating spike timing and phase preference. Frequency-matched stimulation significantly enhanced entrainment when aligned with endogenous oscillatory activity. Conclusion Thus, with appropriately chosen stimulation parameters, tACS exhibits significant potential for controlling and modulating pathological oscillatory patterns that are characteristic of many neurological disorders.

The use of noninvasive transcranial brain stimulation methods, such as transcranial electrical stimulation (tES), transcranial magnetic stimulation (TMS), transcranial focused ultrasound stimulation (tFUS), and electroconvulsive therapy (ECT), has grown significantly over the past two decades. Evidence indicates that the dose-response relationship in brain stimulation is neither straightforward nor monotonic, with outcomes influenced by factors such as the brain state, anatomical variability, and neurophysiological mechanisms. Despite advancements in the field, there is still no consensus on standards for estimating and reporting delivered and received stimulation doses or defining dose-response relationships. This paper addresses these gaps by discussing four key areas: (1) factors influencing the delivered dose (stimulation parameters applied at the scalp), (2) quantification of the received dose (electric or acoustic fields delivered to brain tissue), (3) characterization of physiological, behavioral, and molecular responses to specific delivered/received doses, and (4) the dose-response relationship, which describes how variations in dose modulate brain function and behavior. Drawing on evidence from human and animal studies conducted in silico, in vitro, and in vivo, we outline challenges, propose solutions, and summarize current consensus standards. By promoting rigorous methodologies and transparent reporting, this paper aims to advance the reproducibility, safety, and efficacy of research on dose-response assessment in transcranial brain stimulation and its clinical applications.

Cortical traveling waves (TWs) are brain oscillation patterns that support the transfer of neural information across distinct brain regions, with their direction shaping cognitive function. However, direct evidence for their causal influence on brain dynamics and behavior remains lacking. Here, we establish such a causal link by externally applying TW-like electric field patterns. To achieve this, we develop a noninvasive brain stimulation protocol, traveling-wave transcranial alternating current stimulation (twtACS). twtACS can generate a precise directional electric field that propagates across the cortical surface, which we validate using human intracranial recordings. In monkey recordings, we show that neural spiking was directionally modulated, shifting systematically across space in line with the direction of twtACS. In humans, twtACS led to direction-dependent improvements in cognitive performance. Together, these findings demonstrate that externally imposed TWs can causally shape neural activity and cognition, highlighting the potential of twtACS as a neuromodulation technique for cognitive enhancement.

This roadmap provides a comprehensive and forward-looking perspective on the individualized application and safety of non-ionizing radiation (NIR) dosimetry in diagnostic and therapeutic medicine. Covering a wide range of frequencies, i.e., from low-frequency to terahertz, this document provides an overview of the current state of the art and anticipates future research needs in selected key topics of NIR-based medical applications. It also emphasizes the importance of personalized dosimetry, rigorous safety evaluation, and interdisciplinary collaboration to ensure safe and effective integration of NIR technologies in modern therapy and diagnosis.

BACKGROUND:Transcranial magnetic stimulation (TMS) is a promising treatment for substance use disorders (SUDs), although heterogeneous stimulation parameters hinder the identification of optimal strategies. Using meta-modeling, we linked treatment effect sizes (Hedges' g) to simulated electric field (E-field) distributions to identify brain regions associated with efficacy variability. METHODS:TMS trials in individuals with SUDs published through the end of 2025 were identified through a systematic PubMed search. Studies reporting craving or consumption outcomes with quantifiable effect sizes were included. Objectives were to (i) examine associations between study-level effect sizes and simulated local E-field strength in MNI space for craving and consumption outcomes; (ii) generate a combined E-field-effect size association map; and (iii) assess spatial overlap with fMRI drug cue-reactivity patterns in 60 individuals with SUDs. RESULTS:The analysis included 81 randomized controlled TMS studies, yielding 107 effect size estimates for craving and consumption (n = 75 and n = 32, respectively). Compared with sham stimulation, TMS produced small-to-moderate improvements in both outcomes. E-field modeling identified the pre-supplementary motor area (pre-SMA) and inferior frontal gyrus (IFG) as regions associated with variability in craving-related effect sizes, and the frontopolar cortex with variability in consumption-related effect sizes. Correlation maps were highly robust (mean leave-one-out similarity r = 0.996), and the frontopolar cluster showed significant spatial overlap with fMRI drug cue-reactivity patterns (Dice coefficient = 0.37). CONCLUSION:These findings identify frontopolar, pre-SMA, and IFG regions where local E-field strength is associated with SUD treatment effects, supporting more precise neuromodulation strategies.

Background Transcranial electrical stimulation (tES) is widely used to modulate brain activity in a safe and non-invasive manner. tES generates weak electric fields in cortical tissues, which can modulate membrane potential and alter the timing of neural spikes. These electric fields interact with cortical circuits in a layer-specific manner; however, the distribution of tES-generated electric fields across cortical layers remains poorly understood. Because cortical layers differ in cytoarchitecture, electric fields are likely not uniform and may differ across layers. However, direct in vivo evidence of layer-specific TES electric fields is still lacking. Methods We conducted laminar recordings in the visual cortex of two nonhuman primates (NHPs) during low-frequency transcranial alternating current stimulation to capture layer-specific electric fields. To estimate layer-specific effective electrical conductivity, we compared these in vivo measurements with electric fields from finite element method (FEM) simulations. We first constructed a simplified sandwich model matching the dimensions of the laminar setup. Building on this, we then created a realistic multi-layer FEM head model of the NHP and optimized the effective conductivity of individual cortical layers by minimizing the error between measured and simulated electric fields. Results Repeated laminar recordings showed inhomogeneous electric fields across cortical layers, with a peak in electric field strength in layers 2/3 followed by a gradual decrease toward the white matter in both NHPs. Accordingly, optimization produced non-uniform conductivity values across layers, with the lowest conductivity in layers 2/3 and relatively higher values in white matter compared to commonly used reference values. Conclusion Our findings provide direct in vivo evidence for layer-specific electric fields and effective electrical conductivity at the mesoscale in the primate cortex, emphasizing the importance of considering laminar cortical organization. This work advances the fundamental understanding of how externally applied currents interact with the brain and provides a basis for more accurate computational models and clinically relevant neuromodulation strategies.

Transcranial electrical stimulation (tES) studies in substance use disorders (SUDs) have shown promise in reducing drug craving and consumption. Effect sizes reported in previous studies provide a quantitative measure of the efficacy of this intervention. However, the specific brain regions where electric field strength is most strongly associated with therapeutic effect sizes remain unclear. To address this gap, we quantified the efficacy of tES in reducing craving and consumption using Hedges' g and employed a combined meta-analysis and electric field modeling (meta-modeling) framework to identify brain regions whose modeled E-field exposure was most strongly associated with these outcomes. Studies published up to April 2026 were identified through a systematic PubMed search. Trials reporting craving or consumption outcomes with measurable effect sizes (Hedges' g) were included. A total of 71 randomized clinical trials with 3840 total participants that received at least one session of active or sham tES were analyzed. We applied computational head modeling to simulate tES-induced E-fields across 360 anatomically realistic head models and 31 electrode montages. This produced 11,160 E-field maps across six SUD categories, which served as the basis for constructing brain-wide E-field-behavior correlation maps. The primary outcomes were correlations between cortical E-field strength and changes in drug craving and consumption, quantified as Hedges' g. tES significantly reduced craving (g = 0.34, 95% CI [0.23, 0.45], p < 0.001) and consumption (g = 0.22, 95% CI [0.09, 0.36], p < 0.001) compared to sham stimulation. Correlation meta-modeling identified the left dorsolateral prefrontal cortex (DLPFC) as the primary region associated with reductions in craving and consumption, with E-field strength in this region explaining 29% of the variance in treatment effects. In subgroup analyses, the dorsomedial prefrontal cortex accounted for an additional 7.8% of the variance. Decomposition of the normal E-field component further revealed that inward-directed current flow in these regions was positively associated with clinical improvement, whereas outward-directed current showed a negative association. These findings highlight the critical role of the left DLPFC and bilateral dorsomedial PFC, as well as E-field directionality, in modulating craving and consumption in SUDs. Integrating computational modeling into tES protocols may enable more personalized and effective neuromodulation strategies for addiction treatment.

Transcranial magnetic stimulation (TMS) is a powerful non-invasive tool for safely modulating neural activity in humans. In particular, the left dorsolateral prefrontal cortex (DLPFC) is a common target site for clinical interventions in disorders such as treatment-resistant depression. Yet, clinical trials investigating the efficacy of TMS often lack neural markers of target engagement of the DLPFC. Local field potentials (LFPs), such as prefrontal theta oscillations, have been implicated in the clinical symptoms of these disorders. However, non-invasive electroencephalography (EEG) recordings in humans are limited by their spatial resolution and challenges of interpreting EEG signals. In this study, we investigate the effects of single-pulse TMS applied to the left prefrontal cortex in non-human primates on LFPs recorded through intracranial EEG. Compared to sham TMS, the intensity of active TMS pulses scaled with LFP power changes in a 1-13 Hz range at contacts close to the stimulation site in the prefrontal cortex (e.g., caudate nucleus, anterior cingulate cortex, insular cortex) as well as contacts that were more distal (e.g., posterior cingulate cortex, temporal lobe). To test how TMS modulates connectivity between these regions, we conducted a phase-based connectivity analysis. TMS pulses initially enhanced and then disrupted connectivity at 1-13 Hz between the stimulation site and other contacts. Connectivity rebounded approximately 1500 ms post-stimulation. Only the initial enhancement in connectivity scaled with TMS intensity. Our results demonstrate a dose-dependent power modulation of low frequency LFPs across prefrontal, parietal and temporal cortical regions by single pulses. Furthermore, they show that TMS applied over the left prefrontal cortex can enhance and interrupt short- and long-range connectivity. Our study advances the understanding of the effects of TMS on brain oscillations and connectivity with direct relevance for clinical applications in neuromodulation therapies.

Transcranial Magnetic Stimulation (TMS) is a non-invasive method to modulate neural activity by inducing an electric field in the human brain. Computational models are an important tool for informing TMS targeting and dosing. State-of-the-art modeling techniques use numerical methods, such as the finite element method (FEM), to produce highly accurate simulation results. However, these methods operate at a high computational cost, limiting real-time integration and high throughput applications. Deep learning (DL) methods, particularly U-Nets, are being investigated for TMS electric field estimations. However, their performance across large datasets and whole-head stimulation conditions has not been systematically evaluated. Here, we develop a DL framework to estimate TMS-induced electric fields directly from an anatomical magnetic resonance image (MRI) and TMS coil parameters. We perform a comprehensive evaluation of the performance of our U-Net approach compared to the FEM gold standard. We selected a dataset of 100 MRI scans from a diverse population demographic (ethnic, gender, age) made available by the Human Connectome Project. For each MRI, we generated a FEM head model and simulated the electric fields for 13 TMS coil orientations and 1206 positions (a total of 15,678 coil configurations per participant). We trained a modified U-Net architecture to predict individual TMS-induced electric fields in the brain based on an input T1-weighted MRI scan and stimulation parameters. We characterized the model's performance according to computational efficiency and simulation accuracy compared to FEM using an independent testing dataset. The U-Net results demonstrated an accelerated electric field modeling speed at 0.8 s per simulation (×97,000 times acceleration over the FEM-based approach). Sampling stimulation conditions across the whole brain yielded an average DICE coefficient of 0.71 ± 0.06 mm and an average center of gravity deviation of 7.52 ± 4.06 mm from the FEM-based approach. Our findings indicate that while deep learning has the potential to significantly accelerate electric field predictions, the precision it achieves needs to be evaluated for the specific TMS application.

Non-invasive Brain Stimulation (NIBS) technologies, including transcranial electrical (tES) and magnetic (TMS) stimulation, have emerged as promising interventions for various psychiatric disorders. FDA-approved TMS protocols in depression, OCD and nicotine use disorder provide a meaningful improvement. Treatment efficacy however remains inconsistent across individuals, and one relevant reason is intervention effect variability based on individual factors. There is a growing effort to develop individualized interventions, reinforced recently by FDA approval of a new TMS protocol that includes individualized fMRI-based targeting along with other modifications with higher reported effect size than previous “one size fits all” protocols. This paper discusses the dimensions for individualizing tES/TMS protocols to enhance therapeutic efficacy. We propose a multifaceted approach to personalizing NIBS, considering four levels: (1) context, (2) target, (3) dose, and (4) timing. By addressing inter- and intra-individual variability, we highlight a path toward precision medicine using individualized Brain Stimulation to treat psychiatric diseases. Despite challenges and limitations, this approach encourages broader and more systematic adoption of personalized Brain Stimulation techniques to improve clinical outcomes.

Flash sintering is a novel technology, which enables densification of ceramics in seconds to minutes at moderate furnace temperatures. To date, it has mostly been demonstrated on samples with simple geometries like dog bones, bars, or cylinders, which are quite far from real applications. In the present work, we extend flash sintering to gadolinium‐doped ceria (GDC) thin ceramic layers (∼15 × 8 × 0.008 mm 3 ) screen printed onto rigid alumina substrates. Building on our previous work with GDC dog bones, we selected the same material due to its relevance for solid oxide cell applications. All experiments were performed isothermally under voltage‐to‐current control mode. Flash sintering was triggered under relatively high electric fields (> 500 V cm −1 ), current densities (> 600 mA mm −2 ), and furnace temperatures (> 1100°C), as indicated by the characteristic abrupt increase in the specimen's conductivity and bright light emission. However, significant effects of the electric current were observed at a furnace temperature of 1200°C, with current densities above 800 mA mm −2 , and a dwell time of 180 min, leading to relative densities above 90%, compared to only 75% for conventional sintering under the same temperature and time. The harsher conditions needed to flash sinter these specimens are explained by the very high aspect ratio (surface area‐to‐volume) compared to other usual geometries in flash sintering experiments. In addition, the heat dissipation in the special experimental setup plays an important role in terms of energy balance.

Alzheimer’s disease (AD) affects over 55 million people worldwide and is characterized by abnormal deposition of amyloid-β and tau in the brain causing neuronal damage and disrupting transmission within brain circuits. Episodic memory loss, executive deficits, and depression are common symptoms arising from altered function in spatially distinct brain circuits that greatly contribute to disability. Transcranial electrical stimulation (tES) can target these circuits and has shown promise to relieve specific symptoms. However, previous trials focused on a single symptom and have been limited by poor quantification of induced electric fields (E-field) in the intended cortical target(s). The studies aim to provide multi-symptom relief to older adults with AD by combining two types of tES. Fourteen participants diagnosed with mild cognitive impairment (MCI) or early dementia due to AD were recruited as part of two studies (Table 1). Optimization of tES was performed by modeling the normal component of the induced E-field (En) to target the left dorsolateral prefrontal cortex (DLPFC, Brodmann area 46) with transcranial direct current stimulation (tDCS) and to target the left angular gyrus (AG, Brodmann areas 39/40) with transcranial alternating current stimulation (tACS – 40 Hz) (Figure 1). Participants received daily stimulation sessions for four weeks at home, with baseline, post-intervention, and 3-month follow-up assessments. E-field modeling using MRIs evaluates behavioral effects' dependency on E-field induced by tDCS and tACS in DLPFC and AG. Both studies showed excellent adherence to a home-based, multi-symptom tDCS/tACS intervention. To date, out of 280 scheduled sessions across 14 participants, 278 were completed, with a 99% adherence rate. The most common side effects were mild and transient (Table 2). Structural MRI scans were used to quantify E-field modeling in target brain regions. The studies demonstrate the safety, feasibility, and adherence of a remote-supervised, caregiver-led home-based intervention combining tDCS and tACS to target two distinct brain networks and thus induce a more meaningful clinical impact by reducing distinct disabilities in AD. Quantifying the induced E-field will provide data to assess the mediating effects of E-field on treatment outcomes, yielding critical insights to enable future larger-scale trials.

This guideline summarizes updated safety data (2017-2025) and provides expert recommendations on the use of low intensity transcranial electrical stimulation (tES) in humans. tES encompasses several techniques including transcranial direct current stimulation (tDCS), oscillatory transcranial direct current stimulation (otDCS), transcranial alternating current stimulation (tACS), transcranial random noise stimulation (tRNS), transcranial temporal interference stimulation (tTIS), and their combinations or variations. Across over 300,000 sessions involving healthy individuals, patients with neuropsychiatric conditions, and other clinical populations, no tES-related serious adverse events (AEs) have been reported. Moderate AEs are rare and limited to a small range of specific applications. Mild AEs are common and include transient symptoms such as localized sensations (e.g., tingling or burning), headaches, and fatigue. Similar mild AEs are also reported by individuals receiving placebo stimulation. The frequency, magnitude, and type of AEs are comparable across healthy, clinical, and vulnerable groups, including children, elderly, or pregnant women. Combined interventions (e.g., co-application with EEG, TMS, or neuroimaging) have not shown increased safety risks. Safety is well-established for both bipolar and multichannel tES when applied up to 4 mA and up to 60 min per day. Higher intensities and longer stimulation durations may also be safe. Nevertheless, the number of studies using intensities above 4 mA or stimulating longer than 60 min is low. Home-based use of treatments is growing rapidly, leveraging remote supervision to provide patients with greater access and enable repeated, sustained dosing paradigms. We recommend using screening and AE questionnaires in future controlled studies, in particular when planning to extend the stimulation parameters applied. We discuss recent regulatory and ethical issues.

BackgroundRepetitive transcranial magnetic stimulation (rTMS) induces long-term changes in synapses, but the mechanisms behind these modifications are not fully understood. Although there has been progress in the development of multi-scale modeling tools, no comprehensive module for simulating rTMS-induced synaptic plasticity in biophysically realistic neurons exists.ObjectiveWe developed a modelling framework that allows the replication and detailed prediction of long-term changes of excitatory synapses in neurons stimulated by rTMS.MethodsWe implemented a voltage-dependent plasticity model that has been previously established for simulating frequency-, time-, and compartment-dependent spatio-temporal changes of excitatory synapses in neuronal dendrites. The plasticity model can be incorporated into biophysical neuronal models and coupled to electrical field simulations.ResultsWe show that the plasticity modelling framework replicates long-term potentiation (LTP)-like plasticity in hippocampal CA1 pyramidal cells evoked by 10-Hz repetitive magnetic stimulation (rMS). In line with previous experimental studies, this plasticity was strongly distance dependent and localised to the proximal synapses of the neuron. We predicted a decrease in the plasticity amplitude for 5 Hz and 1 Hz protocols with decreasing frequency. Finally, we successfully modelled plasticity in distal synapses upon local electrical theta-burst stimulation (TBS) and predicted proximal and distal plasticity for rMS TBS. Notably, the rMS TBS-evoked synaptic plasticity exhibited robust facilitation by dendritic spikes and low sensitivity to inhibitory suppression.ConclusionThe plasticity modelling framework enables precise simulations of LTP-like cellular effects with high spatio-temporal resolution, enhancing the efficiency of parameter screening and the development of plasticity-inducing rTMS protocols.