Search Results (505)

Background: Acute transverse myelitis (ATM) is an inflammatory disorder of the spinal cord with heterogeneous etiologies, including autoimmune, infectious, paraneoplastic, and drug-induced causes. Tumor necrosis factor-alpha (TNF-α) inhibitors have been infrequently associated with inflammatory central nervous system events, including transverse myelitis. TNF-inhibitor-associated myelitis typically presents with short-segment lesions, a normal brain MRI, and partial responsiveness to corticosteroids. Longitudinally extensive transverse myelitis (LETM) and steroid-refractory cases are uncommon. Case Presentation: A 39-year-old woman with psoriatic arthritis treated with etanercept for two years presented with subacute progressive bilateral lower-extremity sensory loss and weakness. MRI revealed a T2 hyperintense spinal cord lesion extending from T11 to L1 with gadolinium enhancement, consistent with transverse myelitis, while brain MRI was normal. Cerebrospinal fluid analysis showed lymphocytic pleocytosis, elevated protein, oligoclonal bands, and increased kappa free light chains. Extensive infectious, metabolic, paraneoplastic, and autoimmune testing, including aquaporin-4 and MOG antibodies, was negative. Despite high-dose intravenous corticosteroids and the discontinuation of etanercept, the patient experienced clinical worsening with lesion expansion, meeting criteria for LETM, and developed urinary retention. She subsequently underwent plasma exchange, resulting in radiologic improvement and moderate clinical recovery. Conclusions: This case highlights an atypical presentation of TNF-inhibitor-associated myelitis characterized by a biphasic course, longitudinally extensive spinal cord involvement, steroid refractoriness, and responsiveness to plasma exchange. These features suggest either an unusually severe TNF-inhibitor-related inflammatory phenotype or a TNF-inhibitor-triggered antibody-mediated demyelinating process. Reports of TNF-inhibitor-associated myelitis evolving into longitudinally extensive, steroid-refractory disease remain limited, and this presentation may broaden the recognized clinical spectrum of TNF-α-related CNS inflammatory events. Close neurologic follow-up and heightened awareness of severe CNS complications associated with TNF-α inhibitors are warranted. Full article

To explore the specificity of processing verbal emotional information by individuals with depressive tendencies, individuals with depressive tendencies and healthy individuals were asked to make valence judgments on words, and the changes in brain potential induced by words were recorded. The behavioral results show that compared with healthy individuals, individuals with depressive tendencies had a lower accuracy rate in judging the valence of words and showed a longer time to judge neutral words. The electroencephalogram (EEG) results revealed that when processing negative words, individuals with depressive tendencies induced smaller N400 amplitudes in the frontal region, front–central region, and central region than healthy individuals, and also induced a larger LPP with weakened energy in the alpha band. In the front–central region, central region and central–parietal region, individuals with depressive tendencies showed a greater induction of LPP amplitude when processing neutral words than healthy individuals. The above results suggest that individuals with depressive tendencies have specificity in processing verbal emotional information and slower and less accurate judgment of lexical valence, accompanied by specific changes in brain potential. Full article

Breathing-controlled electrical stimulation (BreEStim) is an innovative neuromodulation intervention that synchronizes deep voluntary breathing with peripheral electrical stimulation. Prior studies have shown its analgesic effects in healthy adults and spinal cord injury patients with neuropathic pain. The present study used EEG to examine BreEStim’s neural effects on sensory, affective, and cognitive components of pain. Fourteen healthy participants (7 M, 7 F) completed 30 min of BreEStim and conventional electrical stimulation (EStim) interventions in a randomized, crossover within-subject design. Electrical pain thresholds (EPT) and EEG were recorded pre- and post-intervention. Event-related potentials (ERPs) at pre-EPT-level stimuli before and immediately after each intervention were analyzed for early sensory (P30) and affective (P250) processing, while resting-state EEG assessed spectral power across delta, theta, alpha, and beta bands for cognitive processing. Both BreEStim and EStim increased EPT, indicating short-term habituation. There was no change in early ERP responses (P30) after each intervention, suggesting preserved sensory perception. BreEStim selectively reduced P250, reflective of the affective component of pain. BreEStim significantly increased delta and theta band power and reduced alpha band power on resting-state EEG analyses, whereas no significant changes after EStim were observed. Collectively, BreEStim preserves sensory encoding while selectively modulating affective and cognitive dimensions of pain, supporting its potential as a targeted, non-pharmacological neuromodulation strategy. Full article

Urban residents require space-efficient interventions to mitigate chronic stress. While indoor digital nature shows promise, the precise impact of interactive design parameters remains unclear. This study investigated how interactive feedback intensity (none, slow, fast) and color hue (neutral, warm, cool) influence psychological and physiological restoration. Following negative emotion induction, healthy participants engaged in within-subject conditions evaluated via multimodal assessments, including EEG, HRV, and subjective scales (PANAS, PRS, SAM/PAD). Results identified interactive feedback intensity as the primary driver of restoration. Specifically, fast feedback improved positive affect by up to 20.4% and reduced negative affect by 20.8% compared to passive self-restoration. Neurologically, interactive engagement was associated with elevated EEG alpha-band activity by up to 97.8% relative to standing controls, a pattern consistent with cortical relaxation. Furthermore, while physical interaction was uniformly associated with physiological indices broadly consistent with recovery, color hue significantly moderated subjective outcomes. Neutral and warm hues generated significantly higher overall perceived restorativeness (M = 73.18 and M = 70.14, respectively) than the self-restoration control (M = 61.26). Notably, neutral tones were uniquely associated with modest changes in HRV time-domain indices suggestive of parasympathetic autonomic modulation. These findings provide actionable, empirically validated guidelines for deploying responsive digital interventions to support mental well-being in dense urban interiors. Full article

Background/Objectives: Goal-directed praxis actions (GOPAs) integrate perception, motor planning, and executive control. While neural correlates of single actions are known, less is understood about how complexity conditions and their hierarchical organization into elementary tasks shape neural dynamics during ecologically manual assembly tasks. This study tested how electrophysiological (EEG) activity reflects global complexity and selective engagement of executive and sensorimotor systems across GOPAs. Methods: 38 healthy young adults completed two assembly conditions differing in complexity (basic and advanced) decomposed into four elementary tasks: identification, handling, alignment, and joining. EEG was recorded across five frequency bands (delta, theta, alpha, beta, and gamma) and four regions of interest (ROI): frontal, fronto-central, temporo-central, and parieto-occipital. Results: Neural activity varied significantly depending on different complexity, elementary task, and ROI. The advanced-complexity condition elicited stronger neural responses compared to the basic-complexity condition, reflecting greater cognitive, and sensorimotor demands. A task-related gradient emerged, with joining showing the highest activity, followed by alignment, while identification and handling showed lower activation. Frontal regions, particularly in theta activity, were more involved under higher complexity, suggesting increased executive control. In contrast, beta and gamma activity predominated in temporo-central and parieto-occipital regions, supporting visuomotor and sensorimotor integration. Conclusions: EEG oscillatory dynamics during ecological GOPAs are selectively modulated by complexity condition and hierarchical task organization. Neural activity tracks functional demands of specific action phases rather than general arousal, highlighting dynamic coordination between executive and sensorimotor systems during complex manual behavior. Full article

Aiming to clarify the spatial distribution characteristics of soil microbial assemblages and the environmental factors shaping them across a narrow altitudinal transect, this investigation concentrated on the surface soil layer within naturally occurring mixed forests of Picea crassifolia and Betula platyphylla, situated in the elevation band from 2400 to 2800 m along the southern flank of the Qilian Mountains. Leveraging the Illumina NextSeq 2000 high-throughput sequencing platform, integrated with α- and β-diversity analyses and redundancy analysis (RDA), we systematically characterized the composition and diversity traits of soil bacterial and fungal communities, as well as their associations with environmental factors. Notably, the bacterial communities were dominated by Pseudomonadota, Actinomycetota, and Acidobacteria with the abundance of Pseudomonadota decreasing with increasing altitude and that of Acidobacteria increasing with increasing altitude. Furthermore, Ascomycota and Basidiomycota were the dominant phyla in the fungal community. In contrast, bacterial α-diversity—as estimated by the Ace index—showed no significant variation across altitudes. Yet, the fungal alpha diversity metrics—namely Ace and Chao1—were markedly elevated at the 2800 m elevation relative to those observed at both intermediate and lower-altitude locations. Importantly, fungal diversity and community composition showed stronger altitudinal differentiation than bacterial communities in this dataset. Moreover, soil pH, total phosphorus, organic carbon, litter C:N:P stoichiometric ratios, and microbial biomass C:N:P stoichiometric ratios were strongly associated with soil microbial community variation along the altitude gradient, suggesting that they may act as important environmental filters. In conclusion, altitude-driven variations in litter characteristics and soil physicochemical properties jointly shape the assembly processes and spatial distribution patterns of soil microbial communities in this region. Full article

Quercus pyrenaica (Willd.), a sub-Mediterranean oak, is expected to experience substantial distribution shifts under climate change, with some populations in Portugal at risk. Beyond climate-driven pressures, long-standing anthropogenic pressures have likely contributed to the species’ current vulnerability. This work aims to characterize the current status of closed-canopy Q. pyrenaica forests by providing a spatio-temporal assessment of forest fragmentation and its recent evolution. Using multispectral bands from Sentinel-2 time-series data, vegetation indices, embedding vectors generated by Google’s AlphaEarth foundational model, and topographic variables, we applied a machine learning Random Forest classifier to map Q. pyrenaica forests in 2019 and 2024 and to analyze their spatial configuration patterns. The findings indicate robust predictive performance (spatial cross-validation OA of 95.1%, Kappa of 83.7%, and F1 of 86.9%) and reveal the prominent role of AlphaEarth embedding features in the RF classifier, suggesting that these features are well-suited for classifying forest habitats of conservation importance. Quercus pyrenaica occurs predominantly at mid-elevations (~820 m a.s.l.), on gentle slopes (~9°), topographically neutral terrain, and northwestern-facing aspects, consistently across both years. Between 2019 and 2024, the Q. pyrenaica forest area showed an increasing signal. However, the results point to a landscape in an initial phase of forest recovery, constrained by land-use legacies, with cover increasing predominantly through the sprawl of small, geometrically complex, and poorly connected patches. Together, these results provide a baseline to track recent changes in Q. pyrenaica distribution and fragmentation, highlighting a contrast between apparent area expansion and declining overall structural integrity. In the future, patch connectivity and full recovery of secondary succession should be a priority for policymakers and forest owners. Full article

Background: Hippotherapy, a sensorimotor-rich intervention proposed for children with Autism Spectrum Disorder (ASD), is suggested to influence executive function (EF). However, the underlying electrophysiological mechanisms, particularly changes observed in resting-state Electroencephalography (EEG), remain underexplored. Methods: A total of forty-eight children with ASD, aged 9–12 years, participated in this quasi-experimental, non-randomized pre-test–post-test study. Participants were assigned to either a standardized 12-session hippotherapy program (n = 24) or a waitlist Control group (n = 24). EF was evaluated pre- and post-intervention using validated measures: the Wisconsin Card Sorting Test, Stroop Color–Word Test, Corsi Block-Tapping Task, and Tower of London. Resting-state EEG data (19 channels, 250 Hz) were recorded before and after the intervention and analyzed for spectral power, pairwise Pearson correlation, phase-based functional connectivity using the Phase Lag Index (PLI), and directed effective connectivity using Phase Transfer Entropy (PTE). EEG effects were tested with linear mixed models in MATLAB (fitlme), with the measured values in each ROI as the dependent variable, group and time as fixed effects, and SubjectID included as a random intercept; EF outcomes were analyzed with ANCOVA/MANCOVA, adjusting post-test scores for baseline. The assumptions of homogeneity of slopes, Levene’s test, and the Shapiro–Wilk test were examined, and the Holm–Bonferroni correction together with partial η² effect sizes were reported. Results: Following baseline adjustment, the hippotherapy group showed substantial and statistically significant improvements across all EF measures compared with controls partial η² range = 0.473–0.855; all adjusted p < 0.001; e.g., Stroop Incongruent Reaction Time (F(1,45) = 265.80, p < 0.001, ηp² = 0.855). EEG analyses revealed localized Group × Time interaction effects involving frontal delta power as well as selected alpha-, theta-, and beta-band connectivity measures within frontally anchored networks. In addition to these focal interaction effects, the hippotherapy group exhibited a narrower distribution of pre–post EEG changes across spectral power and connectivity metrics compared with controls, indicating greater temporal consistency in resting-state electrophysiological dynamics across sessions. Because group allocation was non-random (based on scheduling feasibility and parental preference), results should be interpreted as associations rather than causal effects. While the hippotherapy group exhibited significant EF improvements and relative stabilization in EEG spectral and connectivity metrics, particularly in frontal delta/theta/alpha/beta bands, a direct mapping between individual EEG changes and behavioral gains was not observed. Conclusions: A standardized 12-session hippotherapy program was associated with substantial improvements in EF and with relative stabilization of resting-state electrophysiological dynamics in children with ASD. However, the direct mechanistic link between these EEG and behavioral changes warrants further investigation. Larger randomized trials employing active control conditions, task-evoked electrophysiological measures, and extended longitudinal follow-up are needed to confirm efficacy, clarify mechanisms, and establish the durability of effects. Full article

Music therapy (MT) is increasingly being integrated into intensive care unit (ICU) settings to modulate pain, stress, and emotional dysregulation. Although clinically promising, objective biomarkers for quantifying its neurophysiological effects are still missing. In this context, the electroencephalogram (EEG) represents a valid tool to assess cortical dynamics associated with cognitive–affective engagement elicited by MT. Our study aims to evaluate the role of electroencephalography as an objective tool for monitoring cortical responses to MT in the ICU. EEGs acquired from nine burn patients undergoing MT in the ICU were considered. Signals were preprocessed to improve the signal-to-noise ratio. Then, six frequency bands (delta, theta, alpha, beta, gamma, and sensorimotor rhythm) were extracted to compute band powers and derive 37 involvement indexes, which were statistically compared across three experimental phases: before, during, and after MT. Results demonstrate that involvement indexes effectively capture neurophysiological shifts induced by MT. Significant differences were observed in 22 indexes when comparing During-MT and Post-MT phases, with 2 indexes being statistically different also when comparing During-MT and Pre-MT phases; 5 indexes differed statistically when comparing Pre-MT and Post-MT phases. These results suggest a transient cortical engagement elicited during MT in ICU settings. Our findings align with previous research reporting EEG (and certain EEG-derived involvement indexes) sensitivity to capture music-induced cognitive and emotional modulation. This confirms electroencephalography potential to objectively reflect MT effects and support its integration in multidisciplinary burn care; however, analysis on larger cohorts is necessary to validate EEG as a clinical tool in MT. Full article

Objectives: This study aimed to investigated the role of Giardia extracellular vesicles (EVs) in intercellular communication and to evaluated their potential as vaccine candidates. Methods: The immunomodulatory effects of Giardia EVs were assessed in mouse macrophages and human monocyte-derived dendritic cells (Mo-DCs), with a particular focus on key inflammatory signaling pathways. In vivo immunogenicity was evaluated following EV administration, and the antigenic composition of EV cargo was characterized by proteomic analysis. Results: Giardia EVs activated pro-inflammatory signaling pathways in mouse macrphages, including SAPK/JNK, ERK1/2, and NF-κB. This activation was associated with IκB-α degradation and nuclear translocation of p65. Furthermore, EV stimulation significantly upregulated the expression of pro-inflammatory genes, including Il1β, Il6, Il4, Ptgs2, Nos2, and Tnf, with log₂ fold changes ranging from 3.9 to 15.8. Consistently, EVs increased iNOS protein expression (28–45%) and nitrite production (9.6–12.3-fold). In human Mo-DCs, Giardia EVs promoted cellular maturation, as evidenced by increased expression of MHC-II, CD80, and CD86, and enhanced T-cell proliferation with a Th1-skewed profile. In vivo immunization induced antigen-specific antibody responses, with IgG subclass distribution indicative of a balanced Th1/Th2 response. Proteomic analysis identified immunoreactive EV-associated proteins, including elongation factor 1-alpha, α-7.3 giardin, tubulin, and variant surface proteins (VSPs), which are well-established antigens in Giardia infection, with prominent bands observed at approximately 22 kDa and 50 kDa. Conclusions: Collectively, these findings demonstrate that Giardia EVs modulate innate immune responses in vitro, elicit antigen-specific humoral immunity in vivo, and contain conserved immunogenic proteins. These properties support their potential as a promising cell-free vaccine platform against giardiasis. Full article

This study examined temporal scalp electroencephalography (EEG) absolute power and brief self-reported psychological state measures before and after participation in a Life Story Program (LSP) in older adults. Five older women participated in the study. For each participant, pre- and post-assessments were scheduled at approximately the same time of day and included a brief four-item questionnaire and biosignal acquisition in a controlled seated environment. EEG was recorded at 500 Hz from T5 and T6 during an eyes-closed resting condition. For EEG analysis, only non-speaking segments were used; the initial 3–5 min stabilization period was excluded, and the subsequent 10 min of data were analyzed. One participant was excluded after outlier screening, resulting in a final EEG sample of four participants. EEG preprocessing included linear detrending, 60 Hz notch filtering, 0.5–50 Hz band-pass filtering, artifact rejection, and Welch-based estimation of absolute power in the delta, theta, alpha, beta, and gamma bands. Given the small sample size, all analyses were treated as exploratory. Questionnaire responses remained generally stable across assessments. No statistically significant pre–post differences were observed after false discovery rate correction, although small reductions, particularly in the gamma band, were observed. These findings should be interpreted as preliminary observations requiring confirmation in larger controlled studies with broader multichannel EEG coverage and more robust recording configurations. Full article

Brain–computer interfaces (BCIs) are essential in assistive technologies to restore mobility in individuals with motor impairments. Although electroencephalography (EEG)-based brain-controlled wheelchairs have been extensively studied, most tongue-controlled systems rely on physical tongue movements, intraoral devices, or limited offline commands, which reduces the usability and comfort. This study introduces an EEG-based tongue motor imagery (MI) BCI for intuitive and entirely mental wheelchair control. By leveraging preserved motor function and the cortical representation of the tongue, the system enables natural four-directional control through imagined tongue movements. Six imagined tongue actions—touching the left and right mouth corners, the upper and lower lips, and producing left and right cheek bulges—were designed to elicit alpha-band event-related desynchronization (ERD) patterns over the tongue motor cortex. EEG data were collected from 15 healthy participants using a 14-channel consumer-grade EMOTIV EPOC X headset. Alpha-band ERD features were extracted and classified using linear discriminant analysis, support vector machine, naïve Bayes, and artificial neural networks (ANNs). Simpler command sets yielded the highest accuracy: two-class tasks achieved 76.19%, while the performance decreased with increasing task complexity. The ANN achieved superior results in multi-class scenarios. The proposed tongue MI method offers initial support for developing a BCI control strategy for assistive technology; however, further improvements in classification techniques, user training, and real-time validation are needed to improve the robustness and practical usability. Full article

Accurate irrigated cropland mapping is critical for agricultural water management and food security. Existing image-based irrigation mapping workflows primarily rely on vegetation indices and synthetic aperture radar (SAR) backscatter features, which have limited capacity to characterize the temporal evolution of irrigation processes and crop growth conditions. The AlphaEarth Foundation (AEF) model developed by Google DeepMind provides compact embeddings with temporal semantic information learned via self-supervision, yet their utility for irrigation mapping has not been systematically assessed. In this study, a comprehensive assessment of AEF embeddings for irrigated cropland mapping was performed in terms of feature separability, classification performance, and spatiotemporal transferability. Experiments were conducted in two representative irrigated regions: the Guanzhong Plain in China and Kansas in the USA. Class separability of the 64 embedding dimensions was quantified using the Jeffries–Matusita (JM) distance. Then, the AEF embeddings were compared with the Sentinel feature set (Sentinel-2 bands, normalized difference vegetation index(NDVI), enhanced vegetation index(EVI), normalized difference water index(NDWI) and Sentinel-1 vertical transmit vertical receive(VV), vertical transmit horizontal receive(VH)) using K-means clustering and supervised classifiers, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Finally, transfer experiments across 2022 and 2024 in the Guanzhong Plain and Kansas were conducted to examine cross-year and cross-region performance. The results showed that AEF embeddings consistently provide stronger class separability in both study areas, with a maximum JM distance of 1.58 (A29). Using AEF embeddings, RF achieved overall accuracies (OA) of 0.95 in the Guanzhong Plain and 0.93 in Kansas, outperforming models based on Sentinel-1/2 bands and indices. Notably, unsupervised K-means clustering on AEF embeddings yielded OA > 0.85, indicating high intrinsic separability between irrigated and rainfed croplands. Transfer experiments further demonstrate stable temporal transfer (cross-year OA > 0.87), whereas cross-region transfer is constrained by differences in irrigation regimes, crop phenology and management practices, resulting in limited spatial generalization (OA~0.3). Overall, this study demonstrates the potential of high-information-density representations from geospatial foundation models for irrigated cropland mapping and provides methodological and technical insights to support transfer learning and operational mapping over large areas. Full article

Background: The global burden of mental disorders continues to escalate, necessitating scalable, evidence-based interventions. Artificial intelligence (AI)-delivered health promotion programs represent a promising approach to addressing treatment gaps by targeting the neuropsychological mechanisms that underlie mental health outcomes. This meta-analysis synthesizes evidence on the effectiveness of AI-delivered interventions in improving executive function, emotion regulation, and clinical outcomes across diverse populations. Methods: A systematic search identified 186 studies (n = 22,755 participants) published between 2020 and 2025. Random-effects meta-analyses estimated pooled effect sizes (Hedges’ g, calculated as between-group standardized mean differences with small-sample correction [J = 1 − 3/(4df − 1)]) for primary outcomes. Between-study heterogeneity was quantified using I² and τ² statistics. To address dependency among effect sizes from studies reporting multiple outcomes, robust variance estimation (RVE) was employed. Subgroup analyses examined intervention modalities, delivery formats, and clinical populations. Moderator analyses explored sources of heterogeneity, including publication year, sample size, intervention duration, control condition type, risk-of-bias rating, geographic region, and AI sophistication tier, and mediational models tested putative therapeutic mechanisms. Results: AI-delivered interventions demonstrated a significant overall effect on health outcomes (g = 0.68, 95% CI [0.58, 0.78]; τ² = 0.12; I² = 73.4%). Executive function outcomes showed moderate effects (g = 0.61, τ² = 0.08), with working memory improvements being strongest (g = 0.72). Emotion regulation outcomes demonstrated moderate-to-large effects (g = 0.61, 95% CI [0.51, 0.70], τ² = 0.006); formal subgroup pooled estimates by emotion regulation strategy were not calculated due to insufficient studies per strategy (k < 3 per category); individual study effect sizes ranged from g = 0.27 to g = 1.11. Among 41 studies examining neuropsychological mechanisms, convergent patterns suggested involvement of prefrontal neural circuits (DLPFC), enhanced alpha-band activity, and improved heart rate variability; however, formal mediation was tested in only 18 studies (9.7%). Among clinical populations, interventions for cognitive impairment yielded the largest effects (g = 1.02; this finding should be interpreted cautiously given modest cumulative sample size [n = 482], potential small-study effects [Egger’s p = 0.08], and trim-and-fill adjusted estimate of g = 0.85), followed by mental health conditions (g = 0.72), while other clinical populations showed smaller but significant improvements (g = 0.19). Mobile applications (g = 0.78) and chatbot-based interventions (g = 0.74) demonstrated the strongest effects among delivery formats. Among studies testing formal mediation, analyses suggested mindfulness (β = 0.42), decentering (β = 0.38), and cognitive reappraisal (β = 0.45) as processes associated with therapeutic outcomes. Conclusions: AI-delivered health promotion programs demonstrate significant effectiveness across executive function, emotion regulation, and clinical outcomes, though substantial heterogeneity (I² = 45–82%) indicates meaningful variability warranting attention to subgroup-specific effects. Given the diversity of intervention types included (chatbots, mobile apps, VR systems, neuromodulation), pooled estimates should be interpreted as characterizing the average effect across this heterogeneous landscape; subgroup-specific estimates provide more precise guidance for clinical decision-making regarding specific modalities. Effects are associated with convergent patterns of neuropsychological mechanisms, though mechanistic conclusions remain preliminary given that only 22% of studies (41/186) examined neuropsychological mechanisms, and formal mediation analyses were conducted in only 18 studies (9.7%); most of the mechanistic evidence is correlational rather than causal. Future research should establish standardized AI taxonomies, optimize adaptive algorithms, conduct adequately powered replication studies in populations with cognitive impairment, prioritize experimental mediation designs to establish causal pathways, and evaluate long-term maintenance effects with a minimum of 6–12-month follow-up periods. Full article

Low-field magnetic stimulation (LFMS) has been proposed as a non-invasive approach for modulating cortical oscillations through electromagnetic coupling. Frequency-aligned enhancement of alpha-band activity is of interest due to its association with cortical inhibitory balance and relaxed wakefulness. This study investigates whether a 10 Hz LFMS applied to the occipital area can induce measurable alpha-band modulation. Electromagnetic simulations were performed to determine magnetic flux distributions within a simplified spherical head model with magnetic susceptibility, which was approximating the brain’s parameters. The 10 Hz stimulation waveform—a positive ramp sawtooth—was analyzed in both time and frequency domains. Electroencephalographic (EEG) recordings were obtained before and after stimulation, and spectral analyses of relevant occipital channels were used to quantify the power redistributions. Simulations indicated localized magnetic field gradients in the occipital region. Post-stimulation EEG recordings showed a redistribution of spectral power toward the alpha-band, representing approximately 50% of total occipital spectral power, with relative increases exceeding 140% across the analyzed channels. These combined modeling and electrophysiological findings provide preliminary proof-of-concept evidence that frequency-aligned LFMS is associated with a redistribution of spectral power toward the alpha-band. Full article