Search Results (76)

Background: The phase of electroencephalogram (EEG) signals critically influences cortical reactivity to external inputs. Phase-dependent effects and their sensitivity to stimulation intensity have been observed at suprathreshold levels, while subthreshold transcranial magnetic stimulation (TMS) cannot induce motor evoked potentials (MEPs), resulting in limited research on phase-dependent responses under subthreshold stimulation. In this study, we used a combined transcranial magnetic stimulation and electroencephalography (TMS–EEG) approach to examine how the ongoing EEG phase influences cortical responses at subthreshold intensity and to characterize these responses in terms of temporal, spatial, and spectral features. Methods: Thirty-four healthy adults received subthreshold single-pulse TMS at the motor hotspot during 64-channel EEG recording. The mu-phase at the time of TMS delivery was estimated using autoregression-based forward prediction and categorized into four bins (0°, 90°, 180°, and 270°). The cortical responses were assessed using inter-trial phase coherence (ITPC), TMS-evoked potentials (TEPs), global mean field power (GMFP), and event-related spectral perturbation (ERSP). Results: Phase estimation reliably distinguished four mu-phase bins. Subthreshold TMS–EEG responses showed clear phase dependence: early ITPC and several TEP components (N15, P30, N45, P60, and N100) differed significantly across phases, with 180° and 270° often eliciting stronger responses. GMFP revealed robust phase effects at mid-latency components, and TMS-induced mu-rhythms were the greatest at 180°. Conclusions: Our results showed that the EEG phase significantly modulates cortical reactivity at subthreshold stimulation levels, supporting mu-phase-based closed-loop TMS as a promising strategy for precise neuromodulation. Full article

Background/Objectives: Predicting individual response to depression therapy prior to treatment initiation remains a critical clinical challenge, as the response rate to both selective serotonin reuptake inhibitors (SSRIs) and repetitive transcranial magnetic stimulation (rTMS) is approximately 50%, leaving treatment selection largely trial-based. This study presents a computer-aided decision (CAD) framework that predicts depression therapy outcomes from pre-treatment electroencephalogram (EEG) signals using advanced time-frequency representations and pretrained convolutional neural networks (CNNs). Methods: EEG signals from 30 SSRI patients and 46 rTMS patients are transformed into time-frequency images using Continuous Wavelet Transform (CWT), Variational Mode Decomposition (VMD), and their pixel-level fusion. Four pretrained CNN architectures, including ResNet-18, MobileNet-V3, EfficientNet-B0, and TinyViT-Hybrid, are fine-tuned and evaluated under both image-independent and subject-independent 6-fold cross-validation (CV). Results: Results reveal a clear therapy-specific pattern: CWT-based representations yield superior discrimination for SSRI outcome prediction, with ResNet-18 achieving 99.43% image-level accuracy, while VMD-based representations are statistically superior for rTMS outcome prediction, with ResNet-18 reaching 98.77%. Pixel-level fusion of CWT and VMD does not consistently improve performance over the best individual representation in either therapy context. Pairwise Wilcoxon signed-rank tests confirm a two-tier architectural hierarchy in which ResNet-18 and TinyViT-Hybrid significantly outperform MobileNet-V3 and EfficientNet-B0 across all conditions, while remaining statistically indistinguishable from each other. At the subject level, the framework achieves 82.50% and 83.53% accuracy for SSRI and rTMS, respectively, under strict subject-independent evaluation. Per-channel analysis reveals occipital dominance for SSRI under CWT and frontotemporal dominance for rTMS under VMD, consistent with known neurophysiological mechanisms. Conclusions: These findings demonstrate that the choice of time-frequency representation is therapy-specific and at least as important as architectural complexity, and that competitive performance can be achieved without recurrent or attention layers by combining well-designed spectral images with a simple pretrained residual network. Full article

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication, reciprocity, and adaptive behavior. Converging neurobiological evidence suggests that these clinical features arise from aberrant connectivity and dysregulated neuronal oscillations across distributed brain networks. In particular, dysfunction within the mirror neuron regions, concentrated in the inferior frontal gyrus (IFG) and inferior parietal lobule (IPL), has been implicated in deficits of imitation, empathy, and social cognition in ASD. Non-invasive neuromodulation using repetitive transcranial magnetic stimulation (rTMS) has shown modest behavioral benefits in ASD. However, most studies apply the conventional protocols targeting the dorsolateral prefrontal cortex. The effects of intermittent theta-burst stimulation (iTBS), a potent excitatory rTMS protocol targeting the mirror neuron regions, on the oscillatory dynamics in ASD remain largely unexplored. Objective: To investigate whether iTBS targeting the bilateral IFG and IPL modulates EEG-derived oscillatory activity in adolescents with ASD and to explore the relationship between oscillatory changes and social reciprocity. Methods: Six adolescents with Level I or II ASD (ages 13–18) underwent bilateral iTBS targeting the IFG and IPL using a figure-of-eight coil and standardized theta-burst parameters. Participants were randomized to receive either 18 active iTBS sessions or a waitlist-controlled crossover design (9 sham followed by 9 active sessions). Standard 21-channel EEG recordings were obtained during the first (EEG-1) and final (EEG-2) active stimulation sessions, including pre- and post-stimulation epochs. Power spectral analyses were conducted across frequency bands (delta through gamma). Behavioral outcomes were assessed using the Childhood Autism Rating Scale, Second Edition (CARS2), administered pre- and post-intervention. Results: All participants tolerated the intervention without adverse effects. Behavioral analysis demonstrated a significant reduction in CARS2 scores following iTBS and is reported in detail in our prior clinical outcomes manuscript, consistent with improved social reciprocity (p < 0.001). EEG analysis revealed an immediate post-stimulation increase in gamma-band power during EEG-1 in five of six participants, whereas lower-frequency bands exhibited variable responses. In contrast, EEG-2 showed no consistent post-stimulation gamma enhancement. Net comparisons between EEG-1 and EEG-2 demonstrated attenuation of the initial gamma response in the same five participants. At the group level, gamma percent change did not reach statistical significance at EEG-1 (p = 0.12) or EEG-2 (p = 0.66), and exploratory comparisons between the 9-active versus 18-active arms did not reach statistical significance. While ipsi-directional changes in gamma power and CARS2 scores were observed in four participants, correlation was not identified in this pilot sample. Conclusions: Bilateral iTBS targeting the IFG and IPL induces a transient enhancement of gamma oscillations in adolescents with ASD that attenuates with repeated stimulation. This pattern is consistent with adaptive homeostatic plasticity (metaplasticity) within excitatory–inhibitory circuits, potentially mediated by GABAergic interneurons. These findings support the feasibility of EEG as an objective biomarker of neuromodulatory engagement in ASD and highlight the importance of network-level and oscillatory mechanisms in interpreting therapeutic responses. Larger, sham-controlled studies incorporating multimodal biomarkers are warranted to clarify clinical relevance and optimize personalized neuromodulation strategies. Full article

Background: Following anterior cruciate ligament reconstruction (ACLR), ACLR patients often experience quadriceps dysfunction, potentially linked to increased corticospinal excitability. However, the role of motor cortex neuroadaptations in persistent quadriceps strength deficits remains unclear. Purpose: The purpose of this study is to investigate neural behavior during a force reproduction task using transcranial magnetic stimulation (TMS) in ACLR participants compared to healthy controls (CONT). Methods: Electrocortical activation of 16 ACLR (10F and 6M, 20.0 ± 1.2 years, 171.9 ± 7.2 cm, 75.8 ± 17.1 kg) and 16 CONT (10F and 6M, 20.6 ± 1.4 yrs, 168.0 ± 9.9 cm, 66.3 ± 11.0 kg) was measured using a 64-channel EEG system during an isometric force reproduction task. Sixty TMS pulses (≥120% active motor threshold) were delivered to the primary motor cortex while participants maintained 10% of quadriceps maximal voluntary isometric contraction (QMVIC10%). Motor-evoked torque (METnorm, %), normalized to 100% TMS intensity, was measured to assess neuroadaptation in the corticospinal tract. EEG data was processed to compute N100 (80–200 ms) and P200 (160–300 ms) TMS-evoked event-related potentials (TEPs, µV) at three regions of interest (ROI): the motor (ROI1), parietal (ROI2), and frontal (ROI3) cortices. MET and TEP comparisons were conducted using independent and unpaired two-sample permutation-based t-tests, respectively. Results: The ACLR group exhibited a significantly greater MET than CONT. Although exploratory, differences were found in P200 TEP at ROI1 with lower power in ACLR than CONT. Conclusions: Lower TEP amplitude at ROI1 implies neural inhibition in the motor cortex, while heightened MET in ACLR suggests greater corticospinal excitability. Neural adaptations in the corticospinal tract in ACLR patients may contribute to excessive quadriceps activation in response to unanticipated stimuli, potentially increasing the risk of re-injury. Full article

Background/Objectives: Swimming requires precise motor control, sustained attention, and optimal cognitive–motor integration, making it an ideal model for investigating neural efficiency—the phenomenon whereby expert performers achieve optimal outcomes with reduced neural resource expenditure, operationalized as lower activation, sparser connectivity, and enhanced functional integration. This systematic review examined cognitive performance and neural adaptations in swimming athletes, investigating neuroimaging and behavioral outcomes distinguishing swimmers from non-athletes across performance levels. Methods: Following PRISMA 2020 guidelines, seven databases were searched (1999–2024) for studies examining cognitive/neural outcomes in swimmers using neuroimaging or validated assessments. A total of 24 studies (neuroimaging: n = 9; behavioral: n = 15) met the inclusion criteria. Risk of bias assessment used adapted Cochrane RoB2 and Newcastle–Ottawa Scale criteria. Results: Neuroimaging modalities included EEG (n = 4), fMRI (n = 2), TMS (n = 1), and ERP (n = 2). Key associations identified included the following: (1) Neural Efficiency: elite swimmers showed sparser upper beta connectivity (35% fewer connections, d = 0.76, p = 0.040) and enhanced alpha rhythm intensity (p ≤ 0.01); (2) Cognitive Performance: superior attention, working memory, and executive control correlated with expertise (d = 0.69–1.31), with thalamo-sensorimotor functional connectivity explaining 41% of world ranking variance (r² = 0.41, p < 0.001); (3) Attention: external focus strategies improved performance in intermediate swimmers but showed inconsistent effects in experts; (4) Mental Fatigue: impaired performance in young adult swimmers (1.2% decrement, d = 0.13) but not master swimmers (p = 0.49); (5) Genetics: COMT Val158Met polymorphism associated with performance differences (p = 0.026). Effect sizes ranged from small to large, with Cohen’s d = 0.13–1.31. Conclusions: Swimming expertise is associated with specific neural and cognitive characteristics, including efficient brain connectivity and enhanced cognitive control. However, cross-sectional designs (88% of studies) and small samples (median n = 36; all studies underpowered) preclude causal inference. The lack of spatially quantitative synthesis and visualization of neuroimaging findings represents a methodological limitation of this review and the field. The findings suggest potential applications for talent identification, training optimization, and mental health promotion through swimming but require longitudinal validation and development of standardized swimmer brain atlases before definitive recommendations. Full article

Chronic pain is a pervasive and debilitating condition that affects millions of individuals worldwide. Unlike acute pain, which serves a protective physiological role, chronic pain persists beyond routine tissue healing and often arises without a discernible peripheral cause. Accumulating evidence indicates that chronic pain is not merely a symptom but a disorder of the central nervous system, underpinned by interacting molecular, neurochemical, and network-level alterations. Molecular neuroimaging using PET and MR spectroscopy has revealed dysregulated excitatory–inhibitory balance (glutamate/GABA), altered monoaminergic and opioidergic signaling, and neuroimmune activation (e.g., TSPO-indexed glial activation) in key pain-related regions such as the insula, anterior cingulate cortex, thalamus, and prefrontal cortex. Converging multimodal imaging—including functional MRI, diffusion MRI, and EEG/MEG—demonstrates aberrant activity and connectivity across the default mode, salience, and sensorimotor networks, alongside structural remodeling in cortical and subcortical circuits. Parallel advances in neuromodulation, including transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), deep brain stimulation (DBS), and emerging biomarker-guided closed-loop approaches, provide tools to perturb these maladaptive circuits and to test mechanistic hypotheses in vivo. This review integrates neuroimaging findings with molecular and systems-level mechanistic insights into chronic pain and its modulation, highlighting how imaging markers can link biochemical signatures to neural dynamics and guide precision pain management and individualized therapeutic strategies. Full article

Understanding consciousness requires bridging theoretical models and clinically measurable brain dynamics. This review integrates three complementary frameworks that converge on a dynamical view of conscious processing: continuous formulations of Integrated Information Theory (IIT), attractor-landscape modeling of brain-state transitions, and perturbational complexity metrics from transcranial magnetic stimulation combined with electroencephalography (TMS-EEG). Continuous-time IIT formalizes how integrated information evolves across temporal hierarchies, while dynamical-systems approaches show that consciousness emerges near criticality, where metastable attractors enable flexible transitions between partially synchronized states. Perturbational-complexity indices capture these properties empirically, quantifying the brain’s capacity for integration and differentiation even without behavioral responsiveness. Across anesthesia, disorders of consciousness, epilepsy, and neurodegeneration, TMS-EEG biomarkers reveal reduced complexity and altered synchronization consistent with structural and functional disconnection. Integrating multimodal data—diffusion MRI, fMRI, EEG, and causal perturbations—is consistent with individualized modeling of consciousness-related dynamics. Standardized protocols, mechanistically interpretable machine learning, and longitudinal validation are essential for clinical translation. By uniting information-theoretic, dynamical, and empirical perspectives, this framework offers a reproducible foundation for consciousness biomarkers that mechanistically link brain dynamics to subjective experience, paving the way for precision applications in neurology, psychiatry, and anesthesia. Full article

Background/Objectives: Chronic psychomotor and cognitive slowing after stroke can persist despite standard rehabilitation, especially in young adults with subcortical injuries. Innovative, integrated interventions are crucial for patients who have reached a plateau in their rehabilitation. We present a case of a 41-year-old male with chronic psychomotor and cognitive slowing following a left lenticulostriate infarction (NIHSS score = 5 at onset), who had plateaued after conventional rehabilitation. Methods: Over 4 weeks the patient underwent 20 sessions of a multimodal approach including high-frequency repetitive transcranial magnetic resonance stimulation over the supplementary motor area and bilateral temporo-parietal junctions and simultaneous computerized cognitive training targeting attention and executive function. Both motor and cognitive assessments, along with quantitative EEG (qEEG) evaluations, were conducted before and after the treatment. Results: At the end of treatment, the patient showed significant clinical improvement: speed and coordination in upper extremities (Finger Tapping Test) increased by 66% (dominant hand) and 74% (non-dominant hand), while finger dexterity (Nine-Hole Peg Test) increased by 25% (dominant hand) and 19% (non-dominant hand). Cognitive scores improved in alertness (58%), visual exploration (25%), and flexibility (24%), while divided attention remained stable. qEEG investigation showed increases in alpha (79%), gamma (33%), and beta (10%) power, with topographic shifts in the stimulated regions. Conclusions: These findings highlight the feasibility of combining targeted rTMS and cognitive training to enhance neuroplasticity in the chronic phase of stroke. Clinical recovery was accompanied by normalized cortical rhythms, suggesting qEEG biomarkers may be useful for tracking treatment response. Multimodal precision neurorehabilitation may offer a path forward for patients with persistent cognitive–motor deficits post-stroke. Full article

Background: Aging is traditionally characterized by progressive structural and cognitive decline; however, increasing evidence shows that the aging brain retains a remarkable capacity for reorganization. This adaptive neuroplasticity supports cognitive resilience—defined as the ability to maintain efficient cognitive performance despite age-related neural vulnerability. Objective: To synthesize current molecular, cellular, neuroimaging, and electrophysiological neuromarkers that characterize adaptive neuroplasticity and to examine how these mechanisms contribute to cognitive resilience across aging. Methods: This narrative review integrates findings from molecular neuroscience, multimodal neuroimaging (fMRI, DTI, PET), electrophysiology (EEG, MEG, TMS), and behavioral research to outline multiscale biomarkers associated with compensatory and efficient neural reorganization in older adults. Results: Adaptive neuroplasticity emerges from the coordinated interaction of neurotrophic signaling (BDNF, CREB, IGF-1), glial modulation (astrocytic lactate metabolism, regulated microglial activity), synaptic remodeling, and neurovascular support (VEGF, nitric oxide). Multimodal neuromarkers—including preserved frontoparietal connectivity, DMN–FPCN coupling, synaptic density (SV2A-PET), theta–gamma coherence, and LTP-like excitability—consistently correlate with resilience in executive functions, memory, and processing speed. Behavioral enrichment, physical activity, and cognitive training further enhance these biomarkers, creating a bidirectional loop between experience and neural adaptability. Conclusions: Adaptive neuroplasticity represents a fundamental mechanism through which older adults maintain cognitive function despite biological aging. Integrating molecular, imaging, electrophysiological, and behavioral neuromarkers provides a comprehensive framework to identify resilience trajectories and to guide personalized interventions aimed at preserving cognition. Understanding these multilevel adaptive mechanisms reframes aging not as passive decline but as a dynamic continuum of biological compensation and cognitive preservation. Full article

Background/Objectives: This systematic review examines neuroplasticity-informed approaches to learning under cognitive load, synthesizing evidence from functional imaging, brain stimulation, and educational technology research. As digital learning environments increasingly challenge learners with complex cognitive demands, understanding how neuroplasticity principles can inform adaptive educational design becomes critical. This review examines how neural mechanisms underlying learning under cognitive load can inform the development of evidence-based educational technologies that optimize neuroplastic potential while mitigating cognitive overload. Methods: Following PRISMA guidelines, we synthesized 94 empirical studies published between 2005 and 2025 across PubMed, Scopus, Web of Science, and PsycINFO. Studies were selected based on rigorous inclusion criteria that emphasized functional neuroimaging (fMRI, EEG), non-invasive brain stimulation (tDCS, TMS), and educational technology applications, which examined learning outcomes under varying cognitive load conditions. Priority was given to research with translational implications for adaptive learning systems and personalized educational interventions. Results: Functional imaging studies reveal an inverted-U relationship between cognitive load and neuroplasticity, with a moderate challenge in optimizing prefrontal-parietal network activation and learning-related neural adaptations. Brain stimulation research demonstrates that tDCS and TMS can enhance neuroplastic responses under cognitive load, particularly benefiting learners with lower baseline abilities. Educational technology applications demonstrate that neuroplasticity-informed adaptive systems, which incorporate real-time cognitive load monitoring and dynamic difficulty adjustment, significantly enhance learning outcomes compared to traditional approaches. Individual differences in cognitive capacity, neurodiversity, and baseline brain states substantially moderate these effects, necessitating the development of personalized intervention strategies. Conclusions: Neuroplasticity-informed learning approaches offer a robust framework for educational technology design that respects cognitive load limitations while maximizing adaptive neural changes. Integration of functional imaging insights, brain stimulation protocols, and adaptive algorithms enables the development of inclusive educational technologies that support diverse learners under cognitive stress. Future research should focus on scalable implementations of real-time neuroplasticity monitoring in authentic educational settings, as well as on developing ethical frameworks for deploying neurotechnology-enhanced learning systems across diverse populations. Full article

Background/Objectives: Closed-loop phase-locked TMS aims to deliver stimulation at targeted EEG phases, but real-time phase prediction remains a practical bottleneck. Timing errors are especially harmful near peaks and troughs, where small offsets can substantially degrade phase targeting. We benchmark representative predictors and develop models that improve phase consistency while reducing peak/trough lag. Methods: Using the publicly available Monash University TEPs–MEPs dataset, we benchmark classical predictors (AR- and FFT-based) and recurrent baselines (LSTM, GRU). To quantify extremum-specific behavior critical for closed-loop triggering, we propose Mean Lag Time (MLT), defined as the average temporal offset between predicted and ground-truth extrema, alongside PLV, APE, MAE, and RMSE. We further propose a parallel DSC-Attention-GRU architecture combining depthwise separable convolutions for efficient multi-channel spatio-temporal feature extraction with self-attention for spatial reweighting and dependency modeling, followed by a GRU phase predictor. A lightweight SqueezeNet-Attention-GRU variant is also designed for real-time constraints. Results: LSTM/GRU outperform AR/FFT in capturing temporal dynamics but retain residual peak/trough lag. Across stimulation intensities and frequency bands, DSC-Attention-GRU consistently improves phase consistency and prediction accuracy and reduces extremum lag, lowering MLT from ~7.77–7.79 ms to ~7.50–7.56 ms. The lightweight variant maintains stable performance with an average 3.7% inference speedup. Conclusions: Explicitly optimizing extremum timing via MLT and enhancing multi-channel modeling with DSC and attention reduces peak/trough lag and improves phase-consistent prediction, supporting low-latency closed-loop phase-locked TMS. Full article

Background: Repetitive transcranial magnetic stimulation (rTMS) is an established neuromodulatory method, yet its multiscale neurophysiological effects in autism spectrum disorder (ASD) remain insufficiently characterized. Recent EEG analytic advances—such as spectral parameterization, long-range temporal correlation (LRTC) assessment, and connectivity modeling—enable quantitative evaluation of excitation–inhibition (E/I) balance and network organization. Objective: This study aimed to examine whether an eight-session, EEG-guided mixed-frequency rTMS protocol—combining inhibitory 1 Hz and excitatory 10 Hz trains individualized to quantitative EEG (qEEG) abnormalities—produces measurable changes in spectral dynamics, temporal correlations, and functional connectivity in a pediatric ASD case. Methods: An 11-year-old right-handed female with ASD (DSM-5-TR, ADOS-2) underwent resting-state EEG one week before and four months after intervention. Preprocessing used a validated automated pipeline, followed by spectral parameterization (FOOOF), detrended fluctuation analysis (DFA), and connectivity analyses (phase-lag index and Granger causality) in MATLAB (2023b). No inferential statistics were applied due to the single-case design. The study was conducted at Cosmos Healthcare (London, UK) with in-kind institutional support and approved by the Atlantic International University IRB (AIU-IRB-22-101). Results: Post-rTMS EEG showed (i) increased delta and reduced theta/alpha/beta power over central regions; (ii) steeper aperiodic slope and higher offset, maximal at Cz, suggesting increased inhibitory tone; (iii) reduced Hurst exponents (1–10 Hz) at Fz, Cz, and Pz, indicating decreased long-range temporal correlations; (iv) reorganization of hubs away from midline with marked Cz decoupling; and (v) strengthened parietal-to-central directional connectivity (Pz→Cz) with reduced Cz→Pz influence. Conclusions: Mixed-frequency, EEG-guided rTMS produced convergent changes across spectral, aperiodic, temporal, and connectivity measures consistent with modulation of cortical E/I balance and network organization. Findings are preliminary and hypothesis-generating. The study was supported by in-kind resources from Cosmos Healthcare, whose authors participated as investigators but had no influence on analysis or interpretation. Controlled trials are warranted to validate these exploratory results. Full article

The efficacy of transcranial magnetic stimulation (TMS) is influenced by the brain’s real-time activity state. This study aimed to investigate the correlation between cortical excitability states and EEG features, specifically the phase and power of the sensorimotor μ rhythm. We developed a high-precision real-time phase prediction algorithm based on a Long Short-Term Memory (LSTM) network and constructed a closed-loop TMS system dependent on EEG phase and power. Thirty healthy subjects were recruited for single-pulse TMS experiments. Motor evoked potentials (MEPs) and TMS-evoked potentials (TEPs) were recorded simultaneously to assess cortical excitability states triggered in real time based on different EEG phase and power features. The results demonstrated no significant correlation between the μ rhythm phase and the amplitudes of MEPs or most TEP components. In contrast, pre-stimulus μ rhythm power showed a significant positive correlation with MEP amplitude. Under high-power conditions, the amplitude of the late P180 component in the sensorimotor cortex was significantly enhanced. The early-to-mid components (N15-N100) of the global mean field potential (GMFP) also exhibited significantly increased amplitudes. This study found that, compared to phase, EEG μ rhythm power exhibits a more significant correlation with TMS-assessed cortical excitability states. This finding provides a key basis for developing EEG power-dependent closed-loop TMS methods to enhance the efficacy of TMS modulation. Full article

Ischemic stroke remains a leading cause of mortality and long-term disability worldwide, with prognosis influenced by heterogeneous biological and neuroanatomical factors. In the past decade, numerous possible biomarkers—molecular, imaging, and electrophysiological—have been investigated to improve outcome prediction and guide rehabilitation strategies and main objectives. Among them, neurofilament light chain (NFL), a cytoskeletal protein released during neuroaxonal injury, has become an effective marker of the severity of the neurological condition and the integrity of the neurons. Additional circulating biomarkers, including thioredoxin, netrin-1, omentin-1, bilirubin, and others, have been linked to oxidative stress, angiogenesis, neuroprotection, and regenerative processes. Meanwhile, innovations in electrophysiology (EEG and TMS-based predictions) and neuroimaging (diffusion tensor imaging, corticospinal tract lesion load, and functional connectivity) add some additional perspectives on the possibility for brain recovery. This work is a narrative synthesizing evidence from PubMed, Scopus, and Web of Science between 2015 and 2025, including both clinical and experimental studies addressing stroke biomarkers and outcome prediction. The review outlines a framework for the integration of multimodal biomarkers to support precision medicine and individualized rehabilitation in stroke. Full article

Background/Objectives: As the global population ages, the prevalence of disorders associated with memory dysfunction (e.g., Alzheimer’s disease) continues to increase. There is a need for novel interventions that can enhance memory and support affected individuals. Non-invasive brain stimulation provides a promising approach to engage circuits within the hippocampal network, a group of brain regions critical for episodic memory, and thereby improve cognition. Methods: Twenty healthy participants completed a single-blind, within-subject crossover study over four sessions. In each session, they received one of four interventions whilst viewing pictures of real-world objects: 40 Hz synchronised audiovisual stimulation (AVS), theta burst stimulation (TBS), a combination of synchronised 5 Hz repetitive transcranial magnetic stimulation with AVS (rTMS + AVS), or sham rTMS. Electroencephalography (EEG) was recorded to measure associated brain activity changes. Following each intervention, participants completed a recognition memory task. Results: Mixed-effect repeated measure models (MRMMs) revealed no significant differences in recognition memory performance or theta (5 Hz) activity across conditions. However, both TBS and rTMS + AVS significantly increased gamma (40 Hz) activity compared to sham rTMS, and TBS induced a widespread increase in theta-gamma phase-amplitude coupling during picture viewing. Conclusions: While the neuromodulatory interventions did not enhance memory performance, the observed increase in gamma activity, particularly following rTMS-based stimulation, suggests potential engagement of neural processes associated with memory. These findings warrant further investigation into the role of gamma oscillations in memory and cognitive enhancement. Full article