BMC Neurology

ObjectiveTo describe the design, feasibility, and baseline characteristics of the Migraine Impact on Neurocognitive Dynamics (MIND) study, a 30-day smartphone-based cohort for high-frequency assessment of cognition and symptoms in adults with migraine. BackgroundCognitive symptoms are an important component of migraine burden, but they are difficult to measure using single-visit testing or retrospective questionnaires. Repeated smartphone-based assessment may better capture real-world variability in cognition and symptoms. MethodsAdults meeting International Classification of Headache Disorders, 3rd edition, criteria for migraine were enrolled remotely and completed 30 days of once-daily ecological momentary assessments and mobile cognitive tasks delivered through the Mobile Monitoring of Cognitive Change platform. Baseline measures assessed demographics, migraine characteristics, disability, mood, stress, and treatment patterns. Feasibility was evaluated using enrollment, completion, and retention metrics. ResultsA total of 177 participants enrolled (mean age 38.8 {+/-} 11.9 years; 79.7% female), including 80/177 (45.2%) with chronic migraine. Across the 30-day protocol, 3688 daily assessments were completed, representing 70.8% of all possible study days, and 70.6% of participants completed at least 20 days of monitoring. Completion remained above 60% across study days. At baseline, chronic migraine was associated with greater burden than low-frequency and high-frequency episodic migraine, including higher MIDAS scores (98.6 vs. 38.7 and 70.3), more days with concentration difficulty (16.0 vs. 7.9 and 11.5), and more days with functional interference (18.5 vs. 7.6 and 13.0). ConclusionsThe MIND study demonstrates the feasibility of high-frequency smartphone-based assessment of cognition and symptoms in migraine and provides a methodological foundation for future analyses of within-person cognitive and symptom dynamics across the migraine cycle.

ImportanceMigraine attacks often occur unpredictably, limiting the ability of individuals to initiate timely preventive or preemptive treatment. Short-term probabilistic forecasting of migraine risk could enable more targeted management strategies. ObjectiveTo externally validate the previously developed Headache Prediction Model (HAPRED-I), evaluate an updated continuously learning model (HAPRED-II), and assess the feasibility and short-term safety of delivering individualized probabilistic migraine forecasts directly to patients. Design, Setting, and ParticipantsProspective 8-week cohort study conducted remotely at two academic medical centers in the United States (Massachusetts General Hospital and Wake Forest Health Sciences) between 2015 and 2019. Adults with recurrent migraine or tension-type headache completed twice-daily electronic diaries. A total of 230 participants contributed 23,335 diary entries across 11,862 participant-days of observation. Main Outcomes and MeasuresOccurrence of a headache attack within 24 hours following each evening diary entry. Model performance was evaluated using discrimination (area under the receiver operating characteristic curve [AUC]) and calibration. ResultsExternal validation of HAPRED-I demonstrated modest discrimination (AUC, 0.59; 95% CI, 0.57-0.61) and poor calibration, with predicted probabilities consistently exceeding observed headache risk. In contrast, the continuously updating HAPRED-II model demonstrated progressive improvement in predictive performance as participant-specific data accumulated. Discrimination increased from an AUC of 0.59 (95% CI, 0.57-0.61) during the first 14 days to 0.66 (95% CI, 0.63-0.70) after the first month, accompanied by improved calibration across predicted risk levels. Over the study period, 6999 individualized forecasts were delivered directly to participants. No evidence suggested that receipt of forecasts was associated with increasing headache frequency or worsening predicted headache risk trajectories. Conclusions and RelevanceA static migraine forecasting model demonstrated limited transportability to new individuals. In contrast, models that continuously update within individuals may improve predictive accuracy over time and enable real-time delivery of personalized migraine risk forecasts. Further work incorporating richer physiologic and contextual predictors will likely be necessary before such systems can reliably guide clinical treatment decisions.

We are pleased to submit our Original article entitled "Assessing medication-related burden and medication adherence among older patients from Central Nepal: A machine learning approach" for consideration in your esteemed journal. In this paper, we assessed medication burden using validated Living with medicines Questionnaire (LMQ-3) and medication adherence using Adherence to Medication refills (ARMS) Scale. In this paper we analysed our result through machine learning approach in spite of traditional statistical approach to identify the complex factors influencing both. Six ML architectures (Ordinary Least Square, LightGBM, Random Forest, XGBoost, SVM, and Penalized linear regression) were employed to predict ARMS and LMQ scores using various socio-demographic, clinical and medication-related predictive features. Model explainability was provided through SHAP (Shapley Additive exPlanations). Our study identified the moderate medication burden with moderate non-adherence among older adults. Requiring assistance for medication and polypharmacy were the strongest drivers for the medication burden and non-adherence. The high predictive accuracy by ML suggests the appropriate clinical intervention like deprescribing to cope with the high prevalent medication burden and non-adherence among older adults in Nepal.

BackgroundCaring for people with dementia can impose a considerable psychological burden on caregivers, yet access to caregiver support in Malaysia remains limited. The World Health Organizations iSupport for Dementia program provides dementia education via textual, e-learning format. However, a culturally adapted Malaysian version has not been available. ObjectiveThis study aimed to develop and gather user feedback on a culturally adapted, multimedia version of iSupport tailored for Malaysia (iSupport-Malaysia). MethodsGuided by a four-phase cultural adaptation framework, the generic iSupport content was translated into Bahasa Malaysia, adapted to local customs, and transformed into multimedia lessons on an e-learning platform. A mixed-methods design was used to explore user perceptions and evaluate usability through four homogeneous focus group discussions and 15 individual usability test sessions with informal caregivers (FG: n=9; UT: n=9) and healthcare professionals (FG: n=11; UT: n=6). Focus groups examined aesthetics, ease of use, clarity, cultural relevance, comprehensiveness, and satisfaction. Usability testing involved Think Aloud tasks, post-test questionnaires, and brief interviews. Qualitative data was analysed thematically, and descriptive statistics summarised usability performance. ResultsiSupport-Malaysia demonstrated good usability (M=74.3{+/-}18.0), with most tasks completed without assistance. Strengths included interactive learning activities, peer discussion features, and flexible self-paced learning. Content was viewed as culturally appropriate, credible, and useful. Suggested improvements included enhancing visual aesthetics, shortening videos, refining quizzes, and increasing practical relevance. ConclusionUser insights indicate that iSupport-Malaysia is usable and culturally appropriate. These findings will inform refinement of the platform prior to the pilot feasibility study and provide recommendations for future multimedia-based caregiver interventions.

Introduction Large language models are increasingly being used in healthcare. In interventional pain medicine, clinical reasoning is essential for procedural planning. Prior studies show that simplified prompts reduce clinical detail in AI-generated responses. It remains unclear whether this reflects knowledge loss or simply prompt-driven suppression of information. Methods We performed a controlled comparative study using 15 standardized low back pain questions representing common interventional pain questions. Each question was submitted to ChatGPT under three conditions, professional-level prompt (DP), fourth-grade reading-level prompt (D4), and clinician-directed rewriting of the D4 response to a medical level (U4[->]MD). No follow-up prompting was allowed. Three physicians independently rated responses for accuracy using a 0-2 ordinal scale. Clinical completeness was determined by consensus. Word count and Flesch-Kincaid Grade Level (FKGL) were also measured. Paired t-tests compared conditions. Results Accuracy was highest with professional prompting (1.76). Accuracy declined with the fourth-grade prompt (1.33; p = 0.00086). When simplified responses were rewritten for clinicians, accuracy returned to baseline (1.76; p {approx} 1.00 vs DP). Clinical completeness followed the same pattern showing DP 80.0%, D4 6.7%, U4[->]MD 73.3%. Fourth-grade responses were shorter and less complex. Upscaled responses were more complex and similar in length to professional responses. Inter-rater reliability was low (Fleiss {kappa} = 0.17), but trends were consistent across conditions. Conclusions Reduced clinical detail under simplified prompts appears to reflect constrained output rather than loss of knowledge. Clinician-directed reframing restores omitted content. LLM performance in interventional pain depends strongly on prompt design and intended audience.

BackgroundAlthough neurogenic orthostatic hypotension (nOH) is a common and debilitating feature of multiple system atrophy (MSA), little is known about the burden of symptoms in the real world. ObjectivesTo design and conduct a cross-sectional community-based research survey targeting patients with MSA, with and without nOH. MethodsWe recruited patients with MSA to complete an anonymous online survey covering three core themes: 1) timely diagnosis, 2) nOH pharmacotherapy and refractory symptoms, and 3) confidence in physician knowledge. Responses were grouped by pre-specified diagnostic certainty levels. Relationships between symptoms, function, and pharmacotherapy were assessed using univariate and multivariate methods. ResultsWe analyzed 259 respondents with a self-reported diagnosis of MSA (age: M=64.38, SD=8.09 years; 44% female). In total, 42% also had a diagnosis nOH; 40% had symptoms highly suspicious of nOH, but no diagnosis; and 21% reported having never had their blood pressure measured in the standing position at a clinical visit. Treatment with a pressor agent was independently associated with the presence of other symptoms of autonomic failure. Each additional nOH symptom reported increased the odds of requiring pharmacotherapy by 18%. Yet, despite anti-hypotensive medication use, 97% of patients reported limitations in their ability to bathe, cook, or arise from a chair/bed with 76% needing caregiver support for refractory nOH symptoms. ConclusionsThis cross-sectional representative sample shows nOH is underrecognized and undertreated in MSA patients, leading to substantial functional limitations. It is our hope that these findings are leveraged for planning future trials and advocating for better treatments.

Background: Rett Syndrome (RTT) is a severe neurodevelopmental disorder affecting approximately 1 in 10,000 live female births worldwide. The Rett Syndrome Behaviour Questionnaire (RSBQ), remains one of the most widely used standardized behavioral assessment tools for RTT. However, the RSBQ was originally validated only in British English, limiting its applicability for Spanish-speaking caregivers and clinical centers across Latin America and Spain. Objective: The primary aim of this study was to develop and validate the comprehension of the Spanish translation of the RSBQ to ensure cultural and linguistic equivalence, enhance data reliability, and facilitate earlier, more accurate clinical assessments among Spanish-speaking RTT populations. Methods: Surveys were administered in two phases to Spanish-speaking caregivers between November 2023 and September 2025. Phase I consisted of 12 guided survey administrations with participants being able to ask clarifying questions and offer linguistic modifications of RSBQ questions. Phase II consisted of independent online administration of the refined Spanish RSBQ and a retest at least 7 days later. Participants were recruited through direct outreach and supported virtually during questionnaire completion. Results: Following data cleaning and quality control, a total of 51 caregivers successfully completed both surveys. The Spanish RSBQ demonstrated high caregiver comprehension and strong engagement across multiple Latin American countries, including Argentina, Mexico, and Peru. Responses were highly correlated between test and retest timepoints, and no question showed biased response distributions. A slight effect of response interval on test-retest correlation was observed, potentially indicating the impact of natural disease progression confounding retest evaluation for long (>80 day) intervals; however this effect did not impact the overall linguistic validation results as analysis of only <21 day test-retest responders confirmed the findings. Conclusions: This linguistic validation study represents the first formal step toward the clinical validation of the Spanish RSBQ, enabling broader inclusion of Spanish-speaking populations in RTT research. The collaborative, bilingual data collection strategy proved both feasible and effective, paving the way for multinational trials and expanding therapeutic accessibility through localized, patient-centered innovation.

ObjectiveThis study evaluates the impact of systemic GLP-1 receptor agonist (GLP-1RA) use on surgical wound healing in high-risk surgical populations, including patients with diabetes, and implications for perioperative planning and healing outcomes. ApproachThis pilot retrospective cohort study compared adult surgery patients with non-healing postoperative wounds by their GLP-1RA use. Outcomes included healing status, time to wound closure, and number of surgical interventions. ResultsThe cohort included 35 non-GLP-1RA users and 16 GLP-1RA users with comparable baseline characteristics, except for significant higher prevalence of venous insufficiency among users. Though median time to closure was similar for all patients, users required fewer surgical interventions and their wounds reached closure in significant difference from non-users. Among patients with diabetes, all GLP-1RA users healed significantly compared to non-users. InnovationThe impact of GLP-1RA therapy on wound healing in high-risk reconstructive and soft-tissue surgery remains poorly defined. This pilot cohort addresses that gap, offering an early signal that GLP-1RA use is associated with improved wound healing and fewer postoperative interventions. These findings may inform perioperative practice by identifying a systemic pharmacologic factor that optimizes surgical outcomes in high-risk populations. ConclusionGLP-1RA use was associated with higher healing rates and fewer interventions, particularly among patients with diabetes. These findings support a beneficial role in surgical wound healing and warrant larger multi-site studies.

Parkinson's Disease (PD) is a complex neurodegenerative disorder that manifests through systemic, large-scale physiological reorganizations. While research often focuses on region-specific neural changes, there is a growing need for multidomain approaches to capture the complexity of the disease and its clinical heterogeneity. This study proposes an analytical pipeline to evaluate Brain-Heart Interplay (BHI) as a novel systemic biomarker for neurodegeneration and healthy ageing. In this study we assessed BHI across three open-source datasets (EEG and ECG signals). We compared Healthy Young, Healthy Elderly, and PD patients in resting state to investigate the effects of ageing and cognitive performance. Additionally, we studied BHI trends in PD patients in the moment of freezing of gait (FOG). Methodologically, brain network organization was quantified using coherence-based EEG connectivity and graph theory, while heart activity was analyzed through Poincare plot-derived measures of cardiac autonomic activity. The coupling between these two systems was measured using the Maximal Information Coefficient to capture linear and non-linear dependencies between global cortical organization and cardiac autonomic outflow. The results demonstrate that BHI is a sensitive biomarker for detecting early multisystem dysfunction in both neurodegeneration and ageing. Furthermore, the identification of specific BHI trends during FOG onset suggests new opportunities for understanding the physiological mechanisms driving motor complications in PD. Our proposed pipeline provides a guiding tool for large-scale physiological assessment in clinical research.

Background: Current medical large language model (LLM) evaluations largely rely on small collections of cases, whereas rigorous safety testing requires large-scale, diverse, and complex cases with verifiable ground truth. Multiple Sclerosis (MS) provides an ideal evaluation model, with validated diagnostic criteria and numerous paraclinical tests informing differential diagnosis, investigation, and management. Methods: We generated synthetic MS cases with ground-truth labels for diagnosis, localisation, and management. Four frontier LLMs (Gemini 3 Pro/Flash, GPT 5.2/5 mini) were instructed to analyse cases to provide anatomical localisation, differential diagnoses, investigations, and management plans. An automated evaluator compared these outputs to the ground-truth labels. Blinded subspecialty experts validated 70 cases for realism and automated evaluator accuracy. We then evaluated LLM decision-making across 1,000 cases and scaled to 10,000 to characterise rare, catastrophic failures. Results: Subspecialist expert review confirmed 100% synthetic case realism and 99.8% (95% CI 95.5 to 100) automated evaluation accuracy. Across 1,000 generated MS cases, all LLMs successfully included MS in the differential diagnoses for more than 91% cases. However, diagnostic competence did not associate with treatment safety. Gemini 3 models had low rates of clinically appropriate steroid recommendations (Flash: 7.2% 95% CI 5.6 to 8.8; Pro: 15.8% 95% CI 13.6 to 18.1) compared to GPT 5 mini (23.5% 95% CI 20.8 to 26.1), frequently overlooking contraindications like active infection. OpenAI models inappropriately recommended acute intravenous thrombolysis for MS cases (9.6% GPT 5.2; 6.4% GPT 5 mini) compared to below 1% for Gemini models. Expanded evaluation (to 10,000 cases) probed these errors in detail. Thrombolysis was recommended in 10.1% of cases lacking symptom timing information and paradoxically persisted (2.9%) even when symptoms were explicitly documented as more than 14 days old. Conclusion: Automated expert-level evaluation across 10,000 cases characterised artificial intelligence clinical blind spots hitherto invisible to small-scale testing. Massive-scale simulation and automated interrogation should become standard for uncovering serious failures and implementing safety guardrails before clinical deployment exposes patients to risk.

Introduction Despite the proven benefits of reperfusion therapies in acute ischemic stroke, treatment decisions in the hyperacute phase remain complex and are rarely supported by individualized outcome predictions. Artificial intelligence (AI)-based clinical decision support systems (CDSS) offer potential real-time prognostic estimates, but prospective evidence of their feasibility and performance in routine clinical workflows is limited. Our aim is to prospectively evaluate real-time feasibility, usability, and predictive performance of an AI-based CDSS (VALIDATE-CDSS) for individualized outcome prediction in acute stroke care. Methods and analysis Prospective, multicenter, observational study enrolling consecutive patients with acute ischemic stroke presenting to three tertiary stroke centers. Clinical management will follow standard practice at the discretion of treating physicians. In parallel, a dedicated researcher will collect patient data in real time and input them into the VALIDATE-CDSS using a mobile application, operating in shadow mode without influencing clinical decisions. The system will generate individualized predictions of 3-month functional outcome (modified Rankin Scale) for four treatment strategies (intravenous thrombolysis, endovascular thrombectomy, combined therapy, or no reperfusion) at three sequential time points: baseline clinical data, non-contrast CT, and CT angiography. The primary outcome is the real-world feasibility and usability of the VALIDATE-CDSS in the hyperacute stroke workflow. Secondary outcomes include predictive performance, agreement between model-suggested and actual treatments, incremental value with increasing data availability, and assessment of potential bias across predefined subgroups. This study will provide prospective real-world evidence on the implementation and clinical potential of AI-based decision support for personalized treatment selection in acute ischemic stroke Ethics and dissemination Patient enrollment began after approval from the ethics committees of all participating centers. Results will be disseminated through peer-reviewed open-access journals and conference presentations. Following open science principles, anonymized data and metadata will be made publicly available in the Zenodo repository upon study completion. Trial registration: ClinicalTrials.gov (NCT05622539).

This analysis evaluates the feasibility and psychometric properties of daily digital cognitive assessments (DCAs) delivered on smartphones using data from the large, international Identifying Digital Endpoints to Assess FAtigue, Sleep and acTivities of daily living in Neurodegenerative disorders and Immune-mediated inflammatory diseases (IDEA-FAST) study. The data we analyse were collected from patients with neurodegenerative diseases (NDDs) and immune-mediated inflammatory diseases (IMIDs), and healthy controls (a subset who participated in all phases of the study, total N=977) in their own homes. These data were obtained alongside data from other devices that monitored physiology, kinematics, and sleep quality. Following a baseline visit, participants were remotely monitored via three scheduled daily sessions for 6-7 days in each of 4 active assessment phases (APs). APs were separated by 6-week intervals. Daily schedules comprised a morning psychomotor vigilance task (PVT) with eDiary, afternoon session (eDiary only), and an evening digit symbol substitution task (DSST) with eDiary. We evaluated session coverage using logistic mixed effects, test-retest reliability using ICCs, disease impacts on performance using linear mixed effect ANCOVA, and familiarisation using linear mixed effects. Overall coverage was 67.5% for the PVT and 77.0% for the DSST, with no significant differences between the healthy volunteers and disease cohorts. Coverage varied significantly by time-of-day (Evening > Morning > Afternoon), and improved with age, with an interaction revealing session time-of-day affected older participants less, all p < .001. Coverage was highest in AP 1 and reduced in subsequent APs. AP-day effects on coverage interacted significantly with AP, with a modest decline over AP 1, and the pattern reversed in APs 2-4. Baseline reliability was good (> .70) for both PVT mean reaction time and DSST total correct across all cohorts, and the movement-based measure from the DSST ranged [.55, .75], with lower values in the Parkinson's Disease and Primary Sjogren's Syndrome cohorts. Both tasks showed significant cohort effects, with performance in IMID cohorts intermediate between healthy controls and NDD. Longitudinal analysis revealed significant familiarisation effects in DSST. This was greatest in healthy controls, with significant attenuation of these effects in disease cohorts. No effect of familiarisation was seen in the PVT. Collectively, these results support the usefulness of at-home cognitive assessment on smartphones. Brief measures of cognition can be captured remotely in disease as well as controls with good adherence and sensitivity to distinguish known patient groups from healthy controls.

BackgroundAccelerated long-term forgetting (ALF), defined as an increased rate of memory loss over extended intervals, has so far been detected in a pilot study of patients with mild multiple sclerosis (MS). This study aimed to (I) confirm the presence of ALF in a larger, heterogeneous MS sample, (II) explore associations with patient-reported outcomes, and (III) assess the diagnostic performance of ALF tests for subjective memory impairment. MethodsThis study compared 62 MS patients and 65 age-, sex-, and education-matched healthy controls using standardized memory tests (RAVLT, WMS-IV Logical Memory subtest). Recall was assessed immediately, after 30 minutes, and after 7 days. Seven-day/30-minute recall ratios (QRAVLT, QWMS) served as primary outcomes. Self-report measures included memory complaints, fatigue, depression, and sleep disturbances. Linear regression and Receiver operating characteristic (ROC) analyses assessed predictors and diagnostic accuracy. ResultsALF was observed in multiple sclerosis since QRAVLT was lower in patients than in controls (0.64 [95% CI 0.59-0.69] vs. 0.78 [0.73-0.82], p < 0.001), as was QWMS (0.79 [95% CI 0.74-0.84] vs. 0.95 [0.90-1.00], p < 0.001), despite comparable initial learning. Greater fatigue, higher memory complaints, longer disease duration, older age, and greater disability were associated with lower ALF scores. The combined ALF score moderately discriminated subjective memory impairment (AUC 0.74; sensitivity 0.73; specificity 0.73). ConclusionMS patients showed ALF despite normal initial learning, indicating a specific memory deficit undetected by standard tests. Long-delay recall using RAVLT and WMS-IV Logical Memory subtest may improve cognitive impairment detection in MS.

Background: Prognostication after moderate-to-severe traumatic brain injury (TBI) rarely captures long-term functional recovery, despite its importance to patients, families, and clinicians. Large trauma registries such as the Trauma Quality Improvement Program (TQIP) dataset contain detailed clinical data but lack systematic follow-up, limiting their ability to study longer-term functional outcomes. Methods: We developed and externally validated a machine learning model to predict favorable six-month functional outcome (GOS MD/GR or GOSE >=5) using harmonized data from two randomized clinical trials: CRASH (training) and ROC-TBI (validation). Five candidate classifiers (random forest [RF], linear discriminant analysis, k-nearest neighbors, naive Bayes, and support vector machine) were trained using seven shared clinical predictors. Models were evaluated using ROC-AUC, calibration metrics, and performance at the Youden optimal threshold and a high-sensitivity secondary threshold. The final model was applied to patients with moderate-to-severe TBI in the national TQIP registry (2017-2022) to estimate population-level recovery patterns. Results: The RF model demonstrated the highest overall performance after recalibration, achieving strong discrimination (AUC internal and external, 0.887 and 0.784), good calibration, and high sensitivity (0.890) and negative predictive value (0.909). Applied to 63,289 patients from TQIP, the model estimated that 45% would achieve favorable six-month outcomes at the Youden optimal threshold and 57% at the high-sensitivity threshold, with predicted recovery aligning with established clinical correlates such as younger age, higher admission GCS, and lower rates of penetrating or brainstem injuries. Conclusion: A machine learning model trained on high-quality trial data can generate clinically plausible estimates of long-term functional recovery when applied at scale to national trauma registries that lack systematic follow-up. This approach enables imputation of functional outcomes in datasets lacking follow-up, supports benchmarking and quality improvement across trauma systems, and provides a foundation for future models incorporating physiologic time-series, imaging, and biomarker data.

IntroductionFamily Constellation Therapy (FCT) has been widely disseminated in clinical, public health, and judicial settings despite persistent concerns regarding its theoretical basis, safety, and the limited availability of rigorous randomised evidence supporting its clinical use. ObjectiveThe aim of this systematic review is to assess the effects of FCT across all clinical conditions, explicitly considering both benefits and harms; and summarise the characteristics of studies and intervention settings used in randomised controlled trials of FCT. MethodsFollowing a prospectively registered protocol (CRD420251136190), we conducted a systematic search of seven databases (PubMed, EMBASE, APA PsycInfo, CENTRAL, BVS, Web of Science, and CINAHL) and grey literature (ICTRP and ProQuest database) without language or date restrictions to identify published and unpublished randomised controlled trials of FCT. Study selection, data extraction, risk of bias (RoB 2), and certainty of evidence (GRADE) were performed in duplicate. Statistical analyses followed a prospectively registered analysis plan with prespecified criteria for data pooling and for handling analytical limitations. ResultsNo reliable evidence was found to support the use of FCT for any condition across both clinical and non-clinical samples. All trials included were judged to be at high risk of bias and all comparisons were rated as very low-certainty evidence. Concerns regarding potential adverse effects were identified, and the available data was insufficient to establish the effectiveness of the intervention, precluding any clinical recommendation. ConclusionClinicians, policymakers, and consumers should reconsider adopting FCT while reliable evidence is not available.

Background: Cognitive impairment poses a significant challenge to healthcare systems worldwide, impacting patient autonomy, social participation, and quality of life, while placing a considerable burden on caregivers. Non pharmacological interventions, particularly cognitive training and non invasive brain stimulation, have emerged as promising therapeutic strategies. Objective: This study aims to quantify the synergistic effects of transcranial direct current stimulation (tDCS) with cognitive training on cognitive function across a spectrum of pathologies that induce cognitive impairment. Methods: We conducted a systematic review and metaanalysis following PRISMA guidelines. We searched PubMed for randomized controlled trials that investigated the effect of combined tDCS and cognitive training compared with cognitive training alone. The analysis was based on the GRADE framework for systematic reviews and metaanalyses. Results: Across 27 studies including 1,012 participants, tDCS combined with cognitive training showed a small effect compared with cognitive training alone (SMD = 0.36, 95% CI: 0.15 0.56). The effect was found only immediately after the intervention and declined during follow-up. Conclusion: tDCS combined with cognitive training may provide a small, short term benefit for cognitive function, but high heterogeneity across studies and loss of effect at follow up underscore the need for larger, better standardized trials to clarify its clinical value.

Objective: To assess ethnic disparities in time to hospital presentation, use of acute reperfusion therapies, and in-hospital treatment times among patients presenting with stroke in a Dutch emergency department. Methods: In this single-centre observational cohort study, we included patients with a first-ever ischemic stroke between September 2020 and September 2021. Patients were categorized by ethnicity (with or without migration background). Demographic and stroke characteristics were compared between groups. Outcomes included: rates of presentation outside therapeutic time window, acute reperfusion therapy (intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT)), and, when applicable, door-to-treatment time (DTTT), with a door-to-needle time (DTNT) and door-to-groin time (DTGT) for IVT and EVT respectively. Univariable and multivariable linear and logistic regression analyses were performed, adjusted for age, sex, and NIHSS at presentation, where appropriate. Results: A total of 232 patients were included, of whom 62 (26.7%) had a migration background. These patients were younger (66.6 vs 71.2 years) and more frequently had diabetes (27.4% vs 15.9%). Sex distribution was similar (59.7% vs 60.6% male). Stroke etiology differed between groups with less cardio-embolism (4.8% vs 15.3%) and more small vessel disease (69.4% vs 48.2%) among patients with a migration background. These latter patients presented more often outside the therapeutic time window (53.2% vs 37.1%; OR 1.90; 95% CI 1.05-3.45). EVT was less frequently performed in patients with a migration background compared to those without (8.1% vs 22.4%; OR 0.28; 95% CI 0.10-0.75). There were no significant differences in treatment times (DTTT 38min vs 30min, DTNT 35min vs 26min, DTGT 64min vs 54min). Conclusion: Patients with a migration background were more likely to present outside the therapeutic time window and had a lower rate of EVT. In order to improve access for these patients, more insight into prehospital and within hospital barriers and facilitators for appropriate management are needed.

Motor memory retention is impaired in Parkinson's disease (PD), affecting long-term rehabilitation outcomes. It appears that NREM sleep could be beneficial for consolidation processes in PD, and could be leveraged with non-invasive sleep interventions. This study examined the effect of auditory targeted memory reactivation (TMR) during NREM sleep on the retention of a motor sequence learning finger tapping task in 20 PD and 20 healthy older adults (HOA). TMR was applied during a 2-hour nap and its effect on motor retention was post-nap, after 24-hours and with a dual-task. The impact of TMR on sleep electrophysiology was also evaluated. Results showed no effect of TMR on motor retention or dual-tasking, with no difference between the groups. However, the TMR intervention did increase slow-wave density and decreased spindle density in both groups, and slow-wave amplitude during the presentation of the auditory cues was positively associated with performance in HOA. In conclusion, TMR applied during a 2 hour nap did not enhance motor retention, but the changes in sleep physiological features could be linked to a possible underlying effect on memory processing that warrants further investigation.

BackgroundTheory of Mind (ToM) deficits are well-documented in right-hemisphere stroke but remain understudied in post-stroke aphasia. Prior work suggests that performance on tasks assessing ToM may be relatively preserved in aphasia and dissociable from language impairment, but these findings are based largely on small studies. This study examined performance on nonverbal false-belief tasks in post-stroke aphasia, its relationship with aphasia severity, and whether vascular brain health, operationalized using cerebral small vessel disease (CSVD) markers, contributed to variability in performance. MethodsForty-four individuals with aphasia completed two nonverbal belief-reasoning tasks assessing spontaneous perspective-taking and self-perspective inhibition. Task accuracy served as the primary outcome. Linear regression models examined associations between task performance, aphasia severity (Western Aphasia Battery-Revised Aphasia Quotient), and CSVD markers, including white matter hyperintensities, cerebral microbleeds, lacunes and enlarged perivascular spaces in the basal ganglia and centrum semiovale. ResultsPerformance was heterogeneous across tasks, with reduced performance observed in 23% of participants on the Reality-Unknown task and 36% on the Reality-Known task. Aphasia severity was not associated with task accuracy. Greater cerebral microbleed count was associated with lower accuracy on both tasks, while greater basal ganglia enlarged perivascular spaces burden showed a more selective association with lower performance. ConclusionsPerformance on nonverbal false-belief tasks in aphasia is variable and not explained by aphasia severity alone. These findings suggest that apparent ToM-related difficulties in aphasia may be shaped by broader vascular brain health, supporting a more multidimensional framework for interpreting social-cognitive task performance after stroke.

Abstract Background: Acute ischemic stroke (AIS) remains a significant cause of disability worldwide. Current treatments, primarily intravenous thrombolysis (IVT), are limited by narrow time windows and reperfusion injury, leading to suboptimal outcomes for many patients. Chuanzhi Tongluo (CZTL), a traditional Chinese medicine, has been preliminarily recognized as a novel cerebral protection agent in animal models. Objectives: This trial investigates the efficacy and safety of CZTL capsule in patients with AIS who are not eligible for IVT or who experience early neurological deterioration after IVT. Methods and design: The CONCERN trial is an investigator-initiated, prospective, multicenter, double-blind, parallel-control, randomized clinical study in China. An estimated 1,208 eligible participants will be consecutively randomized to receive CZTL capsule therapy or placebo in 1:1 ratio across approximately 70 stroke centers in China. All enrolled patients are orally administered 2 capsules of CZTL or placebo 3 times a day together with antiplatelet agents for 3 months. Outcomes: The primary endpoint is an excellent functional outcome, defined as a score of 0 or 1 on the mRS at 90 days. Lead safety endpoints included 90-day mortality and symptomatic intracranial hemorrhage within 48 hours. Conclusions: Results of CONCERN trial will determine the clinical efficacy and safety of the traditional Chinese medicine CZTL capsule in the treatment of AIS patients. Trial registry number: ChiCTR2300074147 (www.chictr.org.cn).