Decoding visual information from electroencephalography (EEG) has recently achieved promising results, primarily focusing on reconstructing two-dimensional (2D) images from brain activity. However, the reconstruction of three-dimensional (3D) representations remains largely unexplored. This limits the geometric understanding and reduces the applicability of neural decoding in different contexts. To address this gap, we propose Brain3D, a multimodal architecture for EEG-to-3D reconstruction based on EEG-to-image decoding. It progressively transforms neural representations into the 3D domain using geometry-aware generative reasoning. Our pipeline first produces visually grounded images from EEG signals, then employs a multimodal large language model to extract structured 3D-aware descriptions, which guide a diffusion-based generation stage whose outputs are finally converted into coherent 3D meshes via a single-image-to-3D model. By decomposing the problem into structured stages, the proposed approach avoids direct EEG-to-3D mappings and enables scalable brain-driven 3D generation. We conduct a comprehensive evaluation comparing the reconstructed 3D outputs against the original visual stimuli, assessing both semantic alignment and geometric fidelity. Experimental results demonstrate strong performance of the proposed architecture, achieving up to 85.4

Brain networks coordinate distributed neuronal assemblies to support cognition. Spike-timing-dependent plasticity (STDP) and neuronal oscillations are key substractes for state-gated learning rules that shape network coupling and cognitive operations; nonetheless, how STDP mechanisms interact with neuronal oscillations is largely unexplored in humans. Corticocortical paired associative stimulation (ccPAS) provides a noninvasive system-level model of associative timing rules by pairing dual-site transcranial magnetic stimulation (TMS) across axonally connected regions with an interstimulus interval matched to pathway conduction. Here we (1) synthesize ccPAS applications and barriers to brain-state-coupled implementation in cognitive networks; (2) provide an actionable roadmap for real-time state estimation, targeting, and dual-site parameter selection; and (3) demonstrate a novel implementation of theta phase-locked frontoparietal (FP) ccPAS with concurrent EEG in adult human participants. We tested whether ccPAS delivered at the positive phase of ongoing theta (POS) induces distinct changes in evoked EEG activity and FP connectivity compared with phase-uncoupled ccPAS (RAND) and phase-locked single-site prefrontal (PREF) controls. At the evoked level, POS produced a frontocentral polarity reversal of the canonical N45 component and a right parietotemporal negativity relative to both controls. At the network level, POS induced frequency-specific reconfigurations in postintervention connectivity beyond either control ingredient alone. Together, these changes in evoked activity and rapid network reconfiguration provide the first empirical evidence consistent with phase-gated STDP in humans-whereby oscillatory phase gates cortical excitability and modulates STDP efficacy-emerging as short-term network-level expression. Future work will assess long-term plasticity by tracking connectivity at later time points and testing for concomitant behavioral effects.

Background Substantial variability in individual responses to intermittent theta-burst stimulation (iTBS) limits its clinical efficacy, yet neurophysiological mechanisms underlying this variability remain unclear. While most machine-learning studies have focused on modeling behavioral or clinical effects of repetitive transcranial magnetic stimulation (rTMS), the few studies examining neurophysiological outcomes utilized limited feature sets in single-visit settings, which captured only inter-subject variability and most importantly lacked independent validation sets.Methods To address these gaps, we employed supervised machine learning models that integrated baseline resting-state EEG (rsEEG) features and baseline transcranial magnetic stimulation (TMS)-evoked measures, including motor-evoked potentials (MEPs) and TMS-evoked potentials (TEPs), to predict neurophysiological responses to a single iTBS session applied over the primary motor cortex in two independent test-retest studies of healthy adults. We also employed statistical and reliability analysis to understand the statistical relationship between resting state EEG and responses to iTBS.Results Internal cross-validation within the training cohort yielded promising binary classification performance (accuracy: 81%), identifying coarse-grained multiscale distribution entropy of rsEEG as the most robust predictor of local cortical excitability changes indexed by the 100-131ms window of TEPs. However, predictive performance markedly declined upon external validation (accuracy: 69%), reflecting unstable relationships between predictors and outcomes likely driven by substantial intra- and inter-individual variability of iTBS-induced changes in neurophysiological outcomes.Conclusions These findings emphasize that while EEG complexity measures can capture baseline brain states relevant for neuromodulation to a certain degree, the inherent instability of single-session iTBS effects significantly constrains model generalizability and underscores the necessity of test-retest paradigm to avoid overly optimistic performance estimates. Future studies with multi-session and individualized stimulation protocols are urgently needed to better characterize neurophysiological mechanisms underlying rTMS effects and ultimately enhance its therapeutic potential.

Transcranial magnetic stimulation (TMS) is a non-invasive technique to stimulate the brain, while electroencephalography (EEG) is a non-invasive technique to record its electrical activity. Their combined use (TMS-EEG) has been established only relatively recently, after successful development of TMS-compatible EEG amplifiers. TMS-EEG offers the unparalleled opportunity to directly perturb the brain with TMS and simultaneously record its response with EEG. This allows inferences on causal input-output relationships, therefore going critically beyond purely observational techniques, such as resting-state EEG or functional MRI, in the study of brain dynamics. This consensus review updates the work of Tremblay and coworkers [Clin Neurophysiol 2019; 130: 802-844]. Since then, substantial advances have been made in understanding contamination of TMS-EEG signals by physiological and non-physiological artifacts, as well as in developing strategies to avoid or control them. In parallel, new insights have emerged regarding the physiological mechanisms underlying TMS-EEG responses and their diagnostic and prognostic utility in a broad range of psychiatric and neurological disorders. As such, TMS-EEG is rapidly shaping a dynamic new field in clinical neurophysiology and neuroscience. This review provides a critical and comprehensive synthesis of current knowledge, including practical guidance for implementing TMS-EEG in the clinical setting.

Dystonia is increasingly recognized as a disorder of brain networks. This review integrates multimodal evidence from human studies to characterize the network-level pathophysiology of dystonia. Structural MRI studies using voxel-based morphometry and diffusion imaging reveal alterations in gray matter volume and white matter connectivity across the sensorimotor cortex, basal ganglia, cerebellum, and thalamus. Functional imaging modalities, including PET, fMRI, EEG, MEG, and fNIRS, demonstrate aberrant activity and connectivity in cortico-striato-pallido-thalamocortical and cerebello-thalamocortical loops. Invasive electrophysiological recordings from deep brain stimulation (DBS) provide high-resolution insights into abnormal oscillatory activity and effective connectivity within these circuits. Non-invasive brain stimulation (NIBS) techniques such as TMS, TES, and TUS provide a means of actively interrogating those networks through transient perturbation. They also provide an avenue for personalized neuromodulation. Computational models, including The Virtual Brain platform, enable integration of multimodal data to simulate dynamic network behavior. Across focal, generalized, and genetic forms of dystonia, shared patterns of network dysfunction are observed, though phenotypic and genotypic subtypes exhibit distinct topographies and circuit-level alterations. These findings underscore the importance of network dysfunction underlying dystonia. This network perspective informs the development of more targeted and individualized diagnostic and therapeutic approaches, including circuit-guided neuromodulation and closed-loop brain stimulation. Advancing multimodal and integrative methodologies will be essential to unraveling the complex dynamics underlying dystonia and translating mechanistic insights into precision interventions.

Reconstructing visual stimuli from non-invasive electroencephalography (EEG) remains challenging due to its low spatial resolution and high noise, particularly under realistic low-density electrode configurations. To address this, we present EEG2Vision, a modular, end-to-end EEG-to-image framework that systematically evaluates reconstruction performance across different EEG resolutions (128, 64, 32, and 24 channels) and enhances visual quality through a prompt-guided post-reconstruction boosting mechanism. Starting from EEG-conditioned diffusion reconstruction, the boosting stage uses a multimodal large language model to extract semantic descriptions and leverages image-to-image diffusion to refine geometry and perceptual coherence while preserving EEG-grounded structure. Our experiments show that semantic decoding accuracy degrades significantly with channel reduction (e.g., 50-way Top-1 Acc from 89

Posterior Cortical Atrophy (PCA) is a neurodegenerative syndrome most commonly associated with Alzheimer's disease, characterized by progressive visuospatial and visuoperceptual decline. Although voxel-based morphometry studies have described gray matter loss in PCA, a comprehensive and updated coordinate-based meta-analysis is still missing, and associated structural connectivity alterations remain unclear. We conducted a systematic review and meta-analysis of whole-brain voxel-based morphometry studies comparing patients with PCA and healthy controls (PROSPERO ID: CRD420251010673). Analyses were performed using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) with family-wise error correction, and meta-regressions assessed the impact of demographic and clinical variables. To investigate structural connectivity, deterministic tractography was carried out on a normative diffusion MRI template, using meta-analytic gray matter clusters as seeds. Eighteen studies were included (339 PCA; 577 healthy controls). The meta-analysis revealed consistent bilateral gray matter atrophy in the lateral occipital cortex, inferior parietal lobule, precuneus, and ventral occipitotemporal regions. Meta-regression highlighted an interaction between age and disease duration, associated with atrophy in the left superior temporal gyrus and right thalamus. Tractography demonstrated that affected clusters were embedded within major long-range pathways, including the superior and inferior longitudinal fasciculi, vertical occipital fasciculi, and parietal aslant tract. Regression-derived clusters additionally mapped onto the arcuate fasciculus, frontal aslant tract, and superior thalamic radiations. This is the first systematic review and voxel-based meta-analysis of PCA conducted after the establishment of consensus diagnostic criteria, providing a statistically robust characterization of gray and white matter alterations and identifying potential imaging biomarkers for diagnosis and treatment.

BACKGROUND:Life expectancy for cancer patients has improved due to new therapies, but many survivors experience long-lasting side effects, including cancer-related cognitive impairment (CRCI). CRCI symptoms-such as difficulties with attention, memory, processing speed, fatigue, and concentration-can persist for years post-treatment. While clinical evidence of CRCI exists, its underlying mechanisms and neural correlates remain unclear. This study aimed to identify functional brain networks associated with CRCI in breast cancer (BC) patients using structural and functional neuroimaging data. METHODS:We reviewed studies on CRCI-related brain changes assessed via Diffusion Tensor Imaging (DTI, n = 282), Voxel-Based Morphometry (VBM, n = 177), and functional MRI (fMRI, n = 328). Using the Yeo 7-network resting-state functional atlas, we evaluated the overlap between CRCI-related brain alterations and known functional networks. Overlapping percentages were calculated, and statistical independence tests assessed associations between CRCI and specific networks. RESULTS:Based on inclusion criteria, six DTI, six VBM, and ten fMRI studies were selected. Chi-square analyses revealed significant associations between CRCI-related changes and several networks: the Dorsal Attention Network (DAN, χ2 = 17.71, p = 0.0001), Default Mode Network (DMN, χ2 = 21.08, p = 0.0000002), Frontoparietal Network (FPN, χ2 = 110.63, p = 0.000002), and Ventral Attention Network (VAN, χ2 = 8.203, p = 0.01). CONCLUSIONS:Findings highlight network-specific alterations linked to CRCI symptoms in breast cancer survivors. These results enhance understanding of CRCI and may guide targeted therapeutic strategies.

Postdiction is a perceptual phenomenon where the perception of an earlier stimulus is influenced by a later one. This effect is commonly studied using the ‘rabbit illusion’, in which temporally regular, but spatially irregular, stimuli are perceived as equidistant. While previous research has focused on short inter-stimulus intervals (100–200 ms), the role of longer intervals, which may engage late attentional processes, remains unexplored. This study investigates whether postdiction is purely perceptual or also involves attentional mechanisms by using visual stimuli separated by extended intervals. 33 participants (17 females) were assigned to two experimental groups with two different temporal inter-flash intervals (IFI) between stimuli (250 ms: 250-IFI group; 500 ms: 500-IFI). Two stimulation protocols of active transcranial electrical stimulation (tES) and one control condition were tested on the left precuneus/inferior parietal gyrus: (i) transcranial alternating current stimulation (tACS) at the individual alpha frequency (IAF) (IAF-tACS); (ii) transcranial random noise stimulation across the whole alpha band (i.e., 8–12 Hz, Alpha-tRNS) and (iii) a placebo (Sham) stimulation. The postdiction phenomenon was observable in both experimental groups. The participants in the 500-IFI group demonstrated enhanced performance in detecting the illusion during the rabbit illusion task when IAF-tACS was applied. The behavioral results suggest that attentional functions, beyond perceptual ones, play a key role in the postdiction phenomenon.

Spaceflight and ground-based models cause fluidshift to the upper part of the body, especially the head. Intracranial compliance (ICC) and cardiovascular autonomic modulation (CAM) can be impacted, impairing cerebral blood flow. ICC is poorly explored but important for astronauts' health. Also, microgravity can reduce brain activity and affect cerebral functions. Fluidshift models are relevant for understanding its effects and the most used one is head-down tilt (HDT). Thus, this study aimed to investigate the immediate effects of HDT at -6º and - 15º on ICC, CAM and brain oscillations in healthy individuals. Sixty-one subjects (22 females) participated in the study (age 32.7 ± 6.2 years). The 30-minute HDT protocol was performed at -6º and - 15º. ICC was assessed non-invasively through the strain gauge sensor, from brain4care system, along with CAM and cortical activity during cognitive and motor tests. Participants had an increase in the ICP P2/P1 ratio when comparing pre and HDT at -6º (p = 0.004) and a trend toward elevation at -15º (p = 0.058). The sympathetic component of CAM was predominant in both HDT, and brain oscillations were reduced in most tests. The study suggests that acute HDT can reduce ICC, altering sympathovagal balance and causing cortical inhibition.

Accurate prediction of glioblastoma patient survival can significantly aid in personalized treatment planning. While pre-operative multimodal magnetic resonance imaging (MRI) offers complementary information, current methods are constrained by relatively limited data and largely rely on hand-crafted features extracted from segmentation results. To address these issues, in this work, we propose a data-efficient multi-task framework to take advantage of hierarchical segmentation features within advanced Swin UNETR for survival prediction. By integrating multi-scale features, we are able to capture detailed spatial information and global context, while employing the shifted window mechanism to maintain computational efficiency and scalability for 3D volumes. We further alleviate survival data scarcity through segmentation pre-training, while the features are fine-tuned to align with the survival prediction task and refined by statistical F-values. In addition, age information is incorporated alongside the extracted features to enhance survival prediction performance. Through comprehensive evaluations on the BraTS dataset, we demonstrate that our model achieves superior segmentation accuracy and state-of-the-art survival prediction performance, offering a robust solution for clinical prognosis in glioblastoma patients.

Background: Recent studies have investigated methods for improving the acquisition of complex visuomotor skills in virtual reality (VR) settings, but the results have been inconclusive. Objective/Hypothesis: This study aims to examine whether transcranial random noise stimulation (tRNS), a non-invasive brain stimulation technique, can accelerate the learning process of a VR first-person shooter (VR-FPS) training and its impact on gaming abilities and on cognitive functions. Methods: After exclusion of 9 subjects due to VR-cybersickness, twenty-two healthy young volunteers (6 females, 16 males; mean age 26.5 +/- 4.9 years) participated in a five-day VR-FPS training. The participants were randomly assigned to either the Active (real)-tRNS (n=11) or the Sham (placebo)-tRNS group (n=11). Each day, tRNS targeting an ad-hoc visuo-motor functional brain network was administered for the first two rounds (tRNS ON), but not in the last two rounds out of four (tRNS OFF). The difficulty of the round was adjusted according to the ratio of overwhelmed enemies (O) to the player's defeats (D): (O/D). The participants' shooting skills and cognitive abilities were evaluated before, immediately after and one week after the training (T0, T1, T2). Results: The Active-tRNS group showed significantly higher O/D performance compared to the Sham-tRNS group (p < .05), particularly during tRNS OFF rounds (p < .05). Additionally, at T2, the Active-tRNS group exhibited significantly better performance in a long-range shooting task than the Sham-tRNS group. Both groups showed improved cognitive abilities at T1 and at T2. Conclusions: tRNS of an hybrid visuo-motor network can enhance the learning curve of VR-FPS training, with persistent and strong after-effects. This finding has potential applications for both performance training and treatment of clinical conditions.

Background: Transcranial electric stimulation (TES) is a non-invasive neuromodulation technique with therapeutic potential for diverse neurological disorders including Alzheimer's disease. Conventional TES montages with stimulation electrodes in standardized positions suffer from highly varying electric fields across subjects due to variable anatomy. Biophysical modelling using individual's brain imaging has thus become popular for montage planning but may be limited by fixed scalp electrode locations. Objective: Here, we explore the potential benefits of flexible electrode positioning with 3D-printed neurostimulator caps. Methods: We modeled 10 healthy subjects and simulated montages targeting the left angular gyrus, which is relevant for restoring memory functions impaired by Alzheimer's disease. Using quantitative metrics and visual inspection, we benchmark montages with flexible electrode placement against well-established montage selection approaches. Results: Personalized montages optimized with flexible electrode positioning provided tunable intensity and control over the focality-intensity trade-off, outperforming conventional montages across the range of achievable target intensities. Compared to montages optimized on a reference model, personalized optimization significantly reduced variance of the stimulation intensity in the target. Finally, increasing available electrode positions from 32 to around 86 significantly increased target engagement across a range of target intensities and current limits. Conclusions: In summary, we provide an in silico proof-of-concept that digitally designed and 3D-printed TES caps with flexible electrode positioning can increase target engagement with precise and tunable control of applied dose to a cortical target. This is of interest for stimulation of brain networks such as the default mode network with spatially proximate correlated and anti-correlated cortical nodes.

BACKGROUND:Personalized repetitive transcranial magnetic stimulation (rTMS) of the precuneus (PC) is emerging as a new non-invasive therapeutic approach in treating Alzheimer's disease (AD). Here we sought to investigate the effects of 52 weeks of rTMS applied over the PC on cognitive functions in patients with mild-to-moderate dementia due to AD. METHODS:Forty-eight patients with mild-to-moderate dementia due to AD were enrolled for the study. Of those 31 patients were extended to 52 weeks after being included in a 24-week trial (NCT03778151) with the same experimental design. The trial included a 52-week treatment with a 2-week intensive course where rTMS (or sham) was applied over the PC daily (5 times per week, Monday to Friday), followed by a 50-week maintenance phase in which the same stimulation was applied once weekly. Personalization of rTMS treatment was established using neuronavigated TMS in combination with electroencephalography (TMS-EEG). The primary outcome measure was change from baseline to week 52 of the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB). Secondary outcomes included score changes in the Alzheimer's Disease Assessment Scale- Cognitive Subscale (ADAS-Cog)11, Mini Mental State Examination (MMSE), Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL) and Neuropsychiatric Inventory (NPI). Changes in cortical activity and connectivity were monitored by TMS-EEG. RESULTS:Among 48 patients randomized (mean age 72.8 years; 56% women), 32 (68%) completed the study. Repetitive TMS of the PC (PC-rTMS) had a significant effect on the primary outcome measure. The estimated mean change in CDR-SB after 52 week was 1.36 for PC-rTMS (95% confidence interval (CI) [0.68, 2.04]) and 2.45 for sham-rTMS group (95%CI [1.85, 3.05]). There were also significant effects for the secondary outcomes ADAS-Cog11, ADCS-ADL and NPI scores. Stronger DMN connectivity at baseline was associated with favorable response to rTMS treatment. CONCLUSIONS:Fifty-two weeks of PC-rTMS may slow down the impairment of cognitive functions, activities of daily living and behavioral disturbances in patients with mild-to-moderate AD. Further multicenter studies are needed to confirm the clinical potential of DMN personalized rTMS. TRIAL REGISTRATION:The study was registered on the clinicaltrial.gov website on 07-07-2022 (NCT05454540).

Alzheimer disease (AD) is characterized by dysregulated gamma brain oscillations. Transcranial alternating current stimulation (tACS) is a novel, noninvasive brain stimulation technique capable of entraining cerebral oscillations at targeted frequencies. To assess the safety, feasibility, and efficacy of home-based gamma tACS applied over the precuneus in patients with prodromal and mild AD. This double-blind, randomized, sham-controlled clinical trial with an open-label extension phase was conducted at a tertiary AD research clinic in Italy from December 10, 2022, to October 15, 2024. Patients with a diagnosis of AD were eligible to participate. Participants were randomized to receive either home-based gamma tACS (5 sessions/wk, 60 minutes each) or sham stimulation for 8 weeks (double-blind phase). All participants subsequently received gamma tACS for an additional 8 weeks (open-label phase) and an 8-week follow-up. The primary end points were safety, feasibility, and clinical efficacy. Secondary end points included measures of biological efficacy, including gamma band power via electroencephalography, cholinergic neurotransmission, AD plasma biomarker levels, and brain connectivity as assessed via magnetic resonance imaging. Sixty consecutive patients with prodromal or mild AD were screened; 50 were randomized to gamma or sham tACS (mean [SD] age, 67.3 [7.8] years; 25 [50.0%] female and 25 [50.0%] male). Home-based gamma tACS was safe and well-tolerated. A significant enhancement in global cognitive functions, activities of daily living, and associative memory performances was observed. Marginal mean differences between the sham vs gamma tACS groups were significant for the Clinical Dementia Rating sum of boxes (0.35; 95% CI, 0.10-0.61; P = .007), Alzheimer Disease Assessment Scale–cognitive subscale (0.93; 95% CI, 0.50-1.36; P = .001), Alzheimer Disease Cooperative Study–Activities of Daily Living (−0.55; 95% CI, −0.89 to −0.21; P = .02), and Face-Name Association Test (−1.14; 95% CI, −1.66 to −0.61; P ≤ .001). During the open-label phase, a significant marginal mean difference was observed for Alzheimer Disease Assessment Scale–cognitive subscale (−0.59; 95% CI, −1.02 to −0.16; P = .007), Alzheimer Disease Cooperative Study–Activities of Daily Living (0.41; 95% CI, 0.04-0.08; P = .02), and Face-Name Association Test (1.04; 95% CI, 0.50-1.57; P = .003). Neurophysiological measures showed an increase in cholinergic transmission, coinciding with an increase in gamma power following gamma tACS, effects not seen with sham stimulation. No changes of plasma biomarkers were observed. No add-on effect was observed after 2 repeated treatments with gamma tACS, suggesting that 8 rather than 16 weeks of treatment represents the ideal duration. In this randomized clinical trial, home-based gamma tACS was feasible and improved clinical outcomes in AD, with neurophysiological evidence of brain engagement. These findings support further investigation of gamma tACS as a potential therapeutic intervention for AD. ClinicalTrials.gov Identifier: NCT05643326