Brain Sciences

Objective: Published P300-speller fusion schemes fix prior trust regardless of trial reliability; we tested whether a reliability estimate improves on it. Methods: We reanalyzed 3,373 archived P300-speller selections from 47 people with ALS (BigP3BCI). A fair, matched-search-space comparison, tuning both a fixed weight and an adaptive policy out-of-fold, was evaluated across 22 evaluable language-model priors up to 46.7B parameters. Two representative priors, GPT-2 and a classical 5-gram, additionally received detailed naive and mechanistic analyses. Results: No prior's 95% CI favored adaptive fusion under the fair comparison, despite unexploited oracle headroom at every scale. Under GPT-2, the naive comparison was significantly worse for adaptive fusion; both anchors converged to a degenerate or near-degenerate fair-comparison solution. For the representative anchors, three further controllers failed to convert that headroom into benefit; the fixed-fused posterior's output probability outperformed the best controller for flagging errors (2.8- to 3.8-fold enrichment). Conclusion: A tuned fixed weight is a difficult-to-beat default across the tested scale range; reliability estimation gave no deployable adaptive advantage. Significance: Adaptive weighting should be validated against a fairly tuned baseline across model families and scales; in this dataset, the fused output's confidence identified high-risk selections better than the tested purpose-built ranker.

Purpose: There is a long-standing perception that individuals who stutter are less intelligent, with the disfluencies unique to stuttered speech often assumed to be the overt reflection of lower intelligence, despite no supporting evidence. The purpose of the present study was to explore the validity of this assumption by examining the cognitive abilities of adults who stutter compared to the general population using the NIH Toolbox (C) Cognition Battery (NIHTB-CB). Method: Sixty-three adults who stutter completed the NIHTB-CB, which includes seven standardized measures assessing crystallized cognition (Picture Vocabulary, Oral Reading) and fluid cognition (List Sorting Memory Test, Pattern Comparison Processing Speed Test, Flanker Inhibitory Control Test, Dimensional Change Card Sort Test, Picture Sequence Memory Test). The NIHTB-CB generates t-scores adjusted for demographic variables based on a large sample of neurotypical adults. Results: Composite scores of overall cognition for adults who stutter were not statistically equivalent, rather, their scores were higher than the general population. Higher scores were driven by crystallized cognition, with significantly higher scores on Oral Reading subscale. Conclusions: Present findings demonstrate that adults who stutter possess cognitive skills that are comparable to or potentially higher, than the general population. These results challenge the misconception that stuttering reflects diminished intelligence and offer evidence to mitigate stereotype threat.

BackgroundSelf-compassion has shown to be an effective emotion regulation strategy, but its neural mechanisms remain understudied in adolescents with depression who suffer from social exclusion. This study employed event-related potentials to investigate the psychophysiological mechanisms underlying impaired self-compassion in adolescents with depression during social exclusion. MethodsThirty-seven adolescents with major depressive disorder (age = 16.46 {+/-} 2.02) and thirty-two demographically matched healthy controls (age = 16.54 {+/-} 2.29) completed a social exclusion scenario imagination task. This task presented participants with social exclusion scenarios, asking them to imagine themselves as the excluded individual and subsequently rate their negative emotions. The regulation session required participants to use self-compassionate statements to regulate their emotional responses while imagining and rate how much self-compassion they have engaged in; the non-regulation required them not to use those statements and rate their negative emotions immediately following imagination. EEG signals were recorded during the task, and the late positive potential (LPP) was analyzed to examine the neural responses associated with self-compassion regulation following social exclusion. ResultsCompared with the non-regulation condition, both groups showed less negative emotion during the self-compassion regulation condition. Adolescents with depression exhibited significantly lower self-compassion ratings than healthy controls, and showed a significantly smaller decrease in negative emotions. Higher self-compassion ratings were correlated with greater emotion regulation effect, particularly in adolescents with depression. Furthermore, LPP amplitudes were significantly higher in adolescents with depression than in healthy controls during both regulation and non-regulation conditions. ConclusionsAdolescents with depression were characterized by impairment in using self-compassion to downregulate negative emotions when confronted with social exclusion. LPP amplitudes were consistently elevated in adolescents with depression, underscoring their potential as a critical focus for psychophysiologically-informed therapies.

Background: Neuropathic pain (NP) remains a therapeutic challenge, with conventional repetitive transcranial magnetic stimulation (rTMS) of the primary motor cortex (M1) yielding a response rate of approximately 40%. Personalised targeting based on dysfunctional neurocircuitry offers a promising strategy to enhance efficacy, yet its application in NP is unexplored. This open-label trial investigated a novel targeting approach guided by the recently described cingulo-opercular and somato-cognitive action (CON-SCAN) network, a circuit integrating cognitive and affective dimensions of pain. Methods: Twenty patients with NP received 10 sessions of M1-rTMS over two weeks, with the stimulation site individually localised based on maximal functional connectivity to a CON template. Results: Increased CON-SCAN connectivity from baseline to post-treatment was associated with reduction in pain interference, anxiety and depression scores. The response rate was 50% post-treatment, which was maintained at the 1-month follow-up. Improvements were also observed in neuropathic pain symptoms, negative affect, and overall health. Conclusions: As the first connectivity-guided rTMS trial for NP, this study provides preliminary evidence that personalised targeting of the CON-SCAN network is feasible and associated with the analgesic effects of M1-rTMS, supporting further investigation in randomised controlled trials. Trial registration: Chinese Clinical Trial Registry, ChiCTR2500104679. Registered 20 June 2025, http://www.chictr.org.cn. Chinese Clinical Trial Registry, ChiCTR2400094568. Registered 24 December 2024, http://www.chictr.org.cn. Keywords: Personalised TMS; Pain; M1; CON; SCAN

Aims: To evaluate whether Cue-Induced Retrieval and Reconsolidation with Episodic Future Thinking (CIREF), which combines personalized drug-cue retrieval with structured future-oriented processing, produces greater changes in craving and delay discounting than a recent-past episodic active control in individuals with opioid use disorder receiving methadone maintenance treatment. Design: Multicentre, two-arm, parallel-group randomized controlled pilot trial with per-protocol analyses. Setting: Two outpatient addiction treatment and rehabilitation centres in Tehran, Iran. Participants: Thirty participants with opioid use disorder receiving methadone maintenance treatment were randomized to CIREF or Episodic Recent Thinking (ERT; n = 15 per group). Twenty-eight completed the intervention and were included in the analyses (n = 14 per group). Intervention and comparator: Participants completed one screening, baseline, and personalized cue-development session followed by three 75-minute intervention sessions. CIREF combined personalized drug-cue retrieval with future-oriented simulation, prediction, intention, and planning. ERT was structurally matched but anchored episodic processing to the recent past. Measurements: Primary craving outcomes comprised the three Desire for Drug Questionnaire (DDQ) subscales assessing session-level phasic/current craving immediately before and after each intervention session and the four Obsessive-Compulsive Drug Use Scale (OCDUS) subscales assessing tonic craving before and after the intervention period. The secondary outcome was Monetary Choice Questionnaire (MCQ) log(k), with more negative values indicating less steep delay discounting. Findings: After Holm correction across the three DDQ subscales, Group x Occasion interactions indicated greater reductions with CIREF for Desire and Intention to Drug Use, FGG(1.23, 32.09) = 24.37, pHolm < .001, partial eta-squared = .484, and Negative Reinforcement, FGG(1.35, 35.23) = 17.67, pHolm < .001, partial eta-squared = .405, but not Drug Abuse Control (pHolm = .172). After Holm correction across the four OCDUS subscales, only Desire and Mental Preoccupation with Drugs showed a significant Group x Time interaction, F(1, 26) = 12.35, pHolm = .007, partial eta-squared = .322; the remaining subscales were not significant (adjusted ps >= .177). MCQ log(k) showed a Group x Time interaction, F(1, 26) = 7.18, p = .013, partial eta-squared = .217; mean log(k) changed from -1.22 (0.36) to -1.80 (0.55) in CIREF and from -1.39 (0.32) to -1.44 (0.44) in ERT. Conclusions: In this small pilot sample, the future-oriented retrieval-based intervention produced greater changes than the recent-past active control in two dimensions of session-level phasic craving, one dimension of tonic craving, and monetary delay discounting. The results are preliminary and do not establish memory reconsolidation or effects on relapse or longer-term clinical outcomes.

Background: There are currently no therapies proven to accelerate recovery of consciousness for patient with acute severe traumatic brain injury (TBI) in the intensive care unit (ICU). Methods: We performed an open-label, Phase 1 safety and dose-finding study of intravenous methylphenidate (IV MPH) in ICU patients with acute disorders of consciousness (DoC) caused by severe TBI. IV MPH was administered in daily doses of 0.5, 1.0, and 2.0 mg/kg. The primary outcome measure was the number of adverse events (AEs) at each dose. IV MPH pharmacokinetics were measured for 24 hours after each dose. The effect of IV MPH on brain networks was measured using EEG and resting-state functional MRI (rs-fMRI). A pharmacodynamic response was defined by change-point analysis of EEG and rs-fMRI time-series data. Behavioral responses were assessed using the Coma Recovery Scale-Revised (CRS-R). Findings: Between August 24, 2020, and April 1, 2024, we screened 488 ICU patients with TBI and enrolled 9 males (age 23-79 years) with acute traumatic DoC: coma (n=3), vegetative state/unresponsive wakefulness syndrome (n=3), and minimally conscious state (n=3). There were no serious AEs at any dose. Mild-moderate AEs observed at 1.0 mg/kg or 2.0 mg/kg included insomnia, emesis, paroxysmal sympathetic hyperactivity, and transaminitis. Maximum plasma MPH concentration ranged from mean (SD) 312.7 (100.6) ng/mL to 1319.5 (433.8) ng/mL and occurred within a median of 7-14 minutes across doses. Pharmacodynamic responses were observed via EEG in 7/8 participants who received 0.5 mg/kg (1/9 did not undergo EEG), 6/9 who received 1.0 mg/kg, and 4/6 who received 2.0 mg/kg. One of two patients who completed rs-fMRI showed a pharmacodynamic response. CRS-R level of arousal increased within 15 min of the IV MPH bolus for 6/9 participants at 0.5 mg/kg, 5/9 at 1.0 mg/kg, and 0/6 at 2.0 mg/kg. Interpretation: For patients with acute severe TBI, IV MPH may be safe at doses of 0.5-2.0 mg/kg. Pharmacodynamic and behavioral responses suggest that IV MPH promotes recovery of arousal, a prerequisite of consciousness, in the ICU.

Sleep is proposed to have a beneficial effect on the consolidation of memories and gist abstraction in adults. For children, gist abstraction is of special relevance to transform new information from social and emotional contexts into stable representations. This study investigated the effect of sleep on emotional and social recognition and gist abstraction on N=34 typically developing children assessed in a sleep and a wake condition. In an emotional memory task, reward-associated stimuli were presented, while a social memory task used face-stimuli to implement social acceptance or rejection from peers. Both paradigms relied on a hidden rule to be abstracted. In general, children were able to remember emotional and social stimuli and to abstract gist information. With respect to sleep, we found a beneficial effect of sleep on the recognition of emotional stimuli but no sleep-dependent enhancement for either social recognition or emotional or social gist abstraction. Overall, our results indicate, that sleep-dependent recognition might depend on the type of memory task. Furthermore, nighttime sleep as compared to daytime wakefulness has no differential influence on gist abstraction in children as assessed in our paradigms, contrasting previous results found in adults.

Background. Post-stroke anxiety and depression frequently co-occur, and whether each is independently associated with cognitive impairment remains unclear because most studies model one without adjusting for the other variable. In a five-year cohort, we tested whether anxiety and depression have distinct cognitive correlates and whether early affective status predicted subsequent cognitive recovery. Methods. Patients from the STROKDEM cohort were assessed at 6, 12, 36, and 60 months post-stroke for anxiety, depression, and five cognitive domains (memory, executive functioning, attention, visuospatial functioning, and language). Two symmetric random-intercept linear mixed models regressed each affective score on the cognitive domains while adjusting for other scores. Repeated-measures correlations, multiple imputations for attrition, and exploratory trajectory and prognostic models were also used. Results. After mutual adjustment and correction, depression was independently associated with executive and visuospatial function, whereas anxiety showed no independent cognitive correlation. Repeated-measures correlation confirmed this dissociation. The anxiety findings were stable across the sensitivity analyses and multiple imputations. Attrition was selective for baseline cognition, and exploratory associations between early affect and cognitive recovery did not survive multiple imputations and were inconclusive. Limitations. Attrition was substantial and selective on baseline cognition; the persistent anxiety subgroup was small, limiting the power for trajectory analyses; and psychiatric history, psychotropic medication, and cognitive reserve beyond education were unavailable. Conclusions. The cognitive burden of post-stroke affective disorders is carried by depression, rather than anxiety. Because anxiety-related cognitive impairment largely reflects comorbid depression, screening for depression rather than anxiety alone may better identify stroke survivors at risk of cognitive impairment.

Visual wayfinding is essential for safe navigation but remains poorly characterized in people with ultra-low vision (ULV). Because assessing complex environments in the real world carries safety risks, this study utilized a calibrated virtual reality (VR) platform to safely quantify navigation. Participants with ULV, normal vision (NV), and simulated ULV (sULV) completed tasks across three environments (street crossing, cafeteria, and metro station) of increasing complexity to determine which metrics best capture task difficulty. Navigation metrics included motion onset latency, walking speed, path efficiency, and turn deviation derived from head position data. Participants with ULV showed longer onset latency, slower walking speed, reduced path efficiency, and greater turn deviation compared with NV, while sULV showed intermediate performance. These metrics successfully reflected increasing task difficulty across environments, with the metro station posing the greatest challenge. Path efficiency consistently detected differences between environments across groups, whereas turn deviation provided insight into complex tasks. Findings indicate that diverse virtual environments capture distinct aspects of navigation that cannot be safely studied in the real world, and trajectory-based metrics capture navigation behavior more effectively than conventional measures. VR-based assessment offers a useful approach for evaluating functional navigation and guiding rehabilitation strategies in profound vision loss.

Physiological mirror activity (pMA) is the increase in involuntary muscle activity observed on the contralateral side during unilateral voluntary movement in neurologically healthy participants. This cross-sectional study aimed to explore the relationship between pMA and corticomuscular coherence (CMC) during finger dexterity tasks in young and older adults. Thirty-one right-handed young adults and 24 older adults performed a left-hand finger dexterity task. Electroencephalogram (EEG) signals were recorded from C3 and C4, and electromyogram (EMG) signals were collected from bilateral finger flexors and extensors. pMA was quantified as the change in right-hand EMG from rest to task. Gamma-band CMC was calculated from task-related EEG-EMG pairs, and its association with pMA was analyzed. In young adults, greater pMA was associated with lower CMC (C3- and C4-right flexors), whereas in older adults, greater pMA was associated with higher CMC (C3-left flexor). Young adults may suppress pMA emergence by appropriately monitoring and inhibiting activity, in the hand not performing the task. Conversely, in older adults, the mobilization of the ipsilateral motor cortex may have contributed to pMA emergence. This study suggests that the neuromuscular mechanisms involved in pMA during finger dexterity tasks differ between young and older adults.

Although non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has demonstrated a therapeutic-relevant potential by enhancing mood recovery and fear extinction, its influence on neural dynamics during naturalistic, sustained fear processing remains unclear. In this study, we employed a randomized, sham-controlled, parallel-group design involving 63 participants (taVNS: n = 33; sham: n = 30) who provided continuous subjective fear ratings (1170 timepoints) while watching a 10-minute fear-inducing video, with simultaneous fNIRS recordings. We employed: (1) a convolutional neural network (CNN) to decode fear ratings from frontal activations, (2) validation of stimulus-evoked activity comparing fNIRS with fMRI signal, (3) dynamic conditional correlation analysis to assess taVNS-induced connectivity changes, and (4) moderation analysis to examine anxiety state effects. Behaviorally, taVNS significantly attenuated fear responses during four threat phases by content analysis: T1 (ghost appearance), T2 (escape sequence), T3 (sudden threat emergence) and T4 (suicide scene). Neurally, taVNS suppressed medial prefrontal cortex (mPFC) activation during escape (T2) and disrupted the typical fear coupling between fear experience and brain activity. Furthermore, taVNS enhanced intra-mPFC functional connectivity, suggesting a potential neural basis for modulating subjective threat appraisal. Additionally, state anxiety significantly moderated brain-behavior relationships. These findings demonstrate that taVNS attenuates fear responses through modulation of mPFC engagement and strengthening frontal network integration. Our results highlight taVNS as a promising neuromodulatory intervention for fear-related disorders (e.g., anxiety disorder), particularly as an early adjunct to exposure-based therapies, warranting further clinical validation.

Deep brain stimulation (DBS) modeling relies heavily on biophysical neuron models to estimate neural activation thresholds and predict stimulation spread. In this study, we systematically compared a widely adopted axon model, the McIntyre-Richardson-Grill (MRG) model (Model I), with a more detailed biophysical model, the Cohen model (Model II), to assess how structural and electrophysiological differences affect predicted DBS outcomes. Electric field distributions generated by 2202 DBS lead were applied to the neuron models as extracellular input stimuli. Both models were simulated under biphasic pulse stimulation across varying axon-electrode distances, pulse widths, and stimulation frequencies. Activation distances ranged from approximately 2 to 10 mm depending on stimulation parameters and contact location. At 2 mA, Model I achieved an activation distance of 6 mm, whereas Model II reached 10 mm, indicating greater excitability. Across matched fiber tracts, threshold differences ranged from -1.40 mA to 0.27 mA, with Model II requiring lower thresholds in 97.7% of cases. Both models showed a strong inverse relationship between pulse width and activation threshold. However, frequency responses differed: Model II exhibited increasing thresholds at higher frequencies, while Model I showed a slight decrease. Machine learning regressors trained on distance, pulse width, and frequency achieved high predictive accuracy, with Gradient Boosting performing best. Model II demonstrated superior prediction metrics (R^2 = 0.986; RMSE = 0.045 mA; MAE = 0.034 mA) compared to Model I (R^2 = 0.977; RMSE = 0.089 mA; MAE = 0.068 mA). Overall, both models reliably estimate DBS-induced activation, but structural differences significantly affect excitability and frequency-dependent behavior. With appropriate awareness of their respective strengths and limitations, either model can be used to derive activation distances for estimating electric field isolevels and the volume of tissue activated in patient-specific DBS simulations.

Objective: Disruption of the excitation/inhibition balance may contribute to the pathophysiology of bipolar disorder, with post-mortem studies reporting abnormalities in GABA-receptors, interneurons and inhibitory signaling in prefrontal areas. Transcranial magnetic stimulation with electroencephalography (TMS-EEG) enables in vivo assessment of cortical excitability/inhibition. This study examined short-latency intracortical inhibition (SICI) after left- and right-dorsolateral prefrontal cortex (DLPFC) stimulation in bipolar depression (BDep, n=10) and healthy controls (HC, n=22). Methods: SICI (paired-pulse TMS) and excitability (single-pulse TMS) were quantified using local- and global-mean-field-power. Associations with lithium use and between-group differences in TMS-evoked potential amplitudes were also explored. Results: For left-DLPFC stimulation, BDep showed weaker SICI than HC (local: 3.7%{+/-}12.5 vs 9.0%{+/-}20.3, p=0.05; global: 1.5%{+/-}11.7 vs 8.8%{+/-}20.6, p=0.10), driven by larger ppTMS responses (weaker inhibition). For right-DLPFC stimulation, BDep showed stronger SICI than HC (local: 17.3%{+/-}16.1 vs 0.75%{+/-}27.1, p=0.36; global: 16.2%{+/-}14.6 vs -1.0%{+/-}21.8, p=0.03), driven by a larger spTMS response (enlarged excitability). Stronger right-hemispheric global-SICI was most pronounced in BDep patients not using lithium (17.9%{+/-}7.0) vs lithium users (3.9%{+/-}11.9) and HC (pFDR=0.03). Conclusions: BDep is characterized by reduced cortical inhibition after left DLPFC stimulation and enlarged cortical excitability after right DLPFC stimulation; the latter partly normalized by lithium. Significance: To our knowledge, this is the first study to apply TMS-EEG to the bilateral DLPFC in BDep, revealing distinct patterns of hemispheric dysfunction. These findings warrant replication in larger samples, to further elucidate the underlying pathophysiology and inform the mechanisms of action of neuromodulation treatments, such as rTMS.

Abstract: Objective: Post-traumatic epilepsy (PTE) is a common sequela of traumatic brain injury (TBI). Research indicates that individuals with PTE tend to experience greater cognitive difficulties compared to those with TBI alone. However, it is plausible that a distinct cognitive profile exists that distinguishes between TBI cases with and without PTE. We aimed to identify longitudinal changes in cognitive measures among TBI patients to better assess the changes associated with developing PTE. Setting: Outpatient. Participants: Prospective subjects who had suffered TBI within 6 months post-injury (TBI-6M, n=32), retrospective subjects with pre-existing PTE diagnoses (PTE, n=20), and healthy control subjects (HC, n=41). Design: We examined cognitive performance for TBI patients within 6 months post-injury, then again within 12 months (TBI-12M, n=26), and within 18-months (TBI-18M, n=25), and compared this with cognitive performance among HC and PTE. Main Measures: Cognitive tests administered yielded 15 test components for analysis. We utilized linear mixed effects modeling to examine cohort-level differences cognitive function. Results: 11/15 tests showed a significant performance deficit in the PTE subjects compared to HC. TBI-6M was not significantly different from the PTE subjects; with time, 9/15 tests showed some degree of recovery in TBI subjects. Tests for information processing speed/working memory and executive function showed strong recovery (TBI-6M vs. TBI-18M, SDMT written: p<0.0001, SDMT oral and COWAT: p<0.001). Tests for visual attention/working memory also showed a smaller but significant recovery (TBI-18M vs. PTE, p<0.05). By contrast, tests for verbal memory [HVLT-R Delayed Recall] showed chronic impairment in TBI (TBI-18M vs HC, p<0.0001). TBI subjects generally trend towards recovery in cognitive performance post-TBI. Conclusions: Information processing speed/working memory are strong indicators for TBI recovery, while auditory learning/memory shows chronic impairment. The stagnation of recovery in cognitive domains typically characterized by robust recovery may correlate with an elevated risk of developing PTE.

Background: Multiple sclerosis (MS) is a chronic, immune-mediated disease of the central nervous system marked by demyelination and neurodegeneration. Beyond physical symptoms, MS is often linked to clinically relevant sleep disturbances. The variability and unpredictability of symptoms and disease progression can also fuel fear of relapse (FoR), undermining well-being and potentially increasing morbidity through inflammatory processes. Understanding biopsychosocial risk factors, including childhood maltreatment (CM) and sleep, in relation to FoR remains an important gap in MS management and research. Methods: Data from N = 48 participants were collected via an online survey. We used the Pittsburgh Sleep Quality Index (PSQI), the Fear-of-Relapse Scale (FoR), and the Childhood Trauma Questionnaire (CTQ) to assess the variables of interest. In addition, time points of exposure to different CM subtypes were assessed. Linear regression analyses were conducted to examine associations within the proposed negative triad. Results: A significant negative association between overall sleep quality and FoR was observed. In the total cohort, the interaction between CM and sleep was not a significant predictor of FoR. However, exploratory analysis revealed a significant interaction between CM and sleep among male participants, whereas the same interaction was not significant among female participants. Conclusion: A history of CM and impaired sleep quality introduce new stressors in managing one's own illness that have received little attention to date. However, the present study found that these factors were at least partly influential on the FoR. The results underscore the translational need for additional support services to enhance prevention and personalized care.

Objectives: Non-invasive brain stimulation (NIBS) holds significant promise for treating neurological and neuropsychiatric conditions, yet translation into routine clinical practice remains limited. We aimed to explore stakeholder perceptions of NIBS and barriers to its clinical adoption. Methods: We conducted focus-group interviews with 33 participants across three key stakeholder groups in the UK: (1) people with lived experience of brain injury, depression, or dementia; (2) healthcare professionals; and (3) researchers. Reflexive thematic analysis was used to identify themes in the data. Findings: Seven key themes emerged spanning preferences, hope and disappointment, communication, accessibility, infrastructure, ethical/regulatory uncertainty, and the evidence base. Across groups, NIBS was viewed positively and with cautious optimism, but substantial barriers were highlighted, including limited public and clinical awareness, challenges in demonstrating cost-effectiveness, infrastructure constraints, and difficulties navigating regulatory and translational pathways. Participants emphasised the importance of clear communication, improved education, and stronger interdisciplinary collaboration to support adoption. Notably, stakeholders prioritised evidence of clinical efficacy and usability over detailed mechanistic understanding. Conclusions: These findings provide actionable insights into the translational gap in NIBS and highlight priorities for facilitating its integration into clinical care.

Introduction: Individuals diagnosed with mild cognitive impairment (MCI) have an increased risk of developing dementia. Since there are currently no curative pharmacological therapies for MCI, nonpharmacological and exercise-related medical therapies represent a promising approach. This study aims to evaluate the effects of nonpharmacological interventions on cognitive performance in individuals with MCI. Methods and analysis: The study will be a prospective, randomized, sham-controlled, single-centre superiority trial with a parallel-group design. A total of 100 patients aged 60 years with MCI will be randomly assigned to one of the three intervention groups or the control group. The three intervention arms comprise high-intensity interval training (HIIT), yoga, and intermittent hypoxia-hyperoxia exposure (IHHE), whereas participants in the control group will receive a sham application of IHHE (IHHE-S). The primary outcome is the cognitive function after 3-month intervention period assessed by Montreal Cognitive Assessment. The secondary outcomes and the evaluation of modifiable risk factors for dementia include quality of life, laboratory data, physical activity, anthropometric data, and cardiorespiratory fitness. All harms and (serious) adverse events will be assessed systematically at each study visit. Ethics and dissemination: This study has been approved by the Ethics committee of the Medical Faculty of the University of Tuebingen (494/2025BO1). Research findings will be published in peer-reviewed journals and presented to stakeholders and at scientific conferences.

Background: Substance use disorders (SUDs) remain common worldwide and inadequately treated. Neuromodulation targets neural circuits involved in reward, craving, and cognitive control. However, the primary research literature has not been systematically mapped regarding the relative contributions of clinical and preclinical studies. Methods: We conducted a multi-database bibliometric analysis of primary studies on neuromodulation for SUD. Web of Science, Scopus, and PubMed were searched covering 2000-2025. After scope classification and exclusion of secondary literature, 810 primary research documents remained. Performance analysis and science mapping were performed with bibliometrix and VOSviewer. Results: Scientific output grew at a compound annual growth rate of 14.16% (2001-2025), accelerating after 2015. Of 810 studies, 82.8% were clinical, 12.5% preclinical, and 4.7% mixed/translational. Alcohol (31.5%) and nicotine/tobacco (25.7%) dominated the literature and were overwhelmingly clinical (>92%), whereas cocaine and opioids retained larger preclinical shares (24-27%). Repetitive transcranial magnetic stimulation (rTMS) was the leading modality (31.6%), followed by deep brain stimulation (24.9%) and transcranial direct current stimulation (24.2%). Keyword co-occurrence revealed three clusters: a clinical neuromodulation core, a nicotine/tobacco axis, and a preclinical reward-circuitry module. The United States and China led in output. Conclusions: Neuromodulation research for SUD is expanding rapidly and is heavily skewed toward clinical investigations. A persistent clinical-preclinical asymmetry and limited explicitly translational work constitute structural features of the field. Greater integration between mechanistic and clinical research is needed to advance definitive trials.

EEG microstates are a distinct number of quasi-stable spatial distributions of brain activity. Microstate trajectories are strongly suspected to reflect the underlying neural mechanisms during information processing and are therefore also called the "building blocks" of human thought. In this study, we examined, if EEG microstate sequences can serve as potential triggers for a Brain-Computer Interface (BCI). To this end, a semi-supervised deep learning model architecture consisting of an LSTM-based autoencoder and a dense neural network was utilized to classify between left- and right-hand motor imagery EEG data, with the resulting classification output serving as the BCI trigger. On the one hand, this was done in a 2-step approach, in which the autoencoder and classifer have been trained separately. On the other hand, an end-to-end approach was employed, where training was performed by combining reconstruction and classification losses. Results show that the proposed model architecture was able to extract relevant features from microstate sequences and exploit them for within subjects and sessions classification. Applying transfer learning to session-to-session or across-subject transfer resulted in peak classification accuracies around 89%. We also investigated to what extent transfer learning has to be applied to reach considerable classification accuracies serving as the calibration time representative. We found that on average around 400s are needed for BCI calibration when emplyoing our approach to reach 80% classification accuracy. The present study signifies that the investigation of EEG microstate trajectories can be a promising approach for extracting BCI triggers, as it reduces the dimensionality of multi-channel recorded EEG signals to a distinct number of brain states over time. Deep learning methods, especially transfer learning, applied to EEG microstate trajectories seem promising regarding user-convenient and calibration-free BCIs in real-world applications.

Large language models are integrated into brain-computer interfaces for communication, but accuracy does not show whether an emitted character depended on neural evidence or on the language-model prior. We retrospectively re-decoded 3,373 P300-speller selections from 47 people with amyotrophic lateral sclerosis, reconstructing a neural posterior and combining it with 25 language priors ranging from 5-grams to 46.7-billion-parameter models. Under held-out, per-source calibration and equal fusion weighting, the prior accounted for a participant-weighted mean 8.6% of posterior displacement (median across selections, 2.9%); the corresponding neural contribution fraction was 0.914 (95% CI, 0.896-0.934). In 4.4% of selections (95% CI, 3.5-5.3), the fused system emitted the intended character although neural evidence alone would not have selected it. Results were similar across 21 neural language models. Accuracy alone does not reveal how strongly a fused brain-computer interface decision depends on its language prior.