brain–computer interface

Overview

A brain–computer interface (BCI) is a system that enables direct communication between neural activity and an external device, bypassing conventional neuromuscular output. In biomedical research, BCIs are typically developed to decode signals from the brain and translate them into commands for assistive technologies, rehabilitation platforms, or experimental neuromodulation systems. They are most often studied in the context of neurological disease, motor impairment, and closed-loop neurotechnology.

BCIs are relevant to neuroscience because they link brain signal acquisition, signal processing, and device control in a single translational framework. Depending on the application, they may be combined with deep brain stimulation, neurorehabilitation, or other brain–gut axis and symptom-modulating interventions. Their clinical significance lies in their potential to restore function, support communication, and provide adaptive control in disorders such as Parkinson's disease and other conditions involving impaired motor or cognitive output.

Focus of Latest Publications

Recent publications involving brain–computer interface in this set did not evaluate the technology as an intervention or device; instead, they used it as a target term in studies centered on body mass index (BMI), obesity, diabetes, and related clinical outcomes. Across these reports, BMI was most often examined as a prognostic or predictive metric in retrospective cohorts, cross-sectional analyses, and feasibility or protocol studies, with outcomes ranging from postoperative metabolic trajectories to functional recovery, mortality, and measurement accuracy.

Several studies assessed BMI as a predictor of clinical outcomes in disease-specific settings. In rheumatoid arthritis, higher baseline BMI was associated with poorer early HAQ-DI-based functional recovery after first recorded advanced therapy, while the association with DAS28-ESR remission was weaker and model-sensitive. In mechanical thrombectomy for stroke, obesity was associated with comparable discharge functional outcomes to normal BMI, whereas underweight status was linked to worse discharge NIHSS scores and higher in-hospital mortality. In burn injury and chronic kidney disease, the abstracts framed BMI or related anthropometric indices as candidate prognostic markers for mortality risk, though no results were provided for those studies in the abstracts supplied.

Other publications focused on BMI in metabolic and cardiometabolic contexts. One study aimed to develop a machine-learning framework to predict postoperative BMI trajectories and long-term type 2 diabetes remission after metabolic bariatric surgery using preoperative data and time-dependent weight evolution. Another examined BMI in relation to mortality among adults aged 16–50 years with and without type 2 diabetes, and a separate cross-sectional analysis compared BMI, waist-to-height ratio, and visceral fat for predicting hypertension and diabetes. A mechanistic human study also reported that circulating sphingolipid profiles varied by BMI and glucose status, supporting sphingolipids as potential biomarkers for obesity, diabetes, and associated complications.

A smaller number of publications addressed BMI in measurement or intervention contexts. In obese surgical patients, BMI and other arm-related measures were poor classifiers of non-invasive blood pressure measurement error, despite frequent inaccuracy of cuff-based readings. A protocol described a weight-neutral health intervention for adults with BMI ≥30 kg/m², emphasizing feasibility and acceptability rather than efficacy. Another study evaluated a culinary medicine intervention in patients with type 2 diabetes and elevated BMI, but the abstract provided only the study design and did not report outcomes.

Key Publications

• NEWJul Development of a predictive model for postoperative body mass index and diabetes outcomes after metabolic bariatric surgery: retrospective cohort study. (BJS open, 2026, PMID 42398077): "Predicting postoperative body mass index (BMI) trajectories and long-term type 2 diabetes (T2D) remission after bariatric surgery remains challenging."
• May Baseline body mass index and early functional recovery after first recorded advanced therapy in rheumatoid arthritis: a real-world cohort study. (Rheumatology international, 2026, PMID 42207304): "We examined whether baseline body mass index (BMI) was associated with early DAS28-ESR remission and Health Assessment Questionnaire Disability Index (HAQ-DI)-based functional recovery after first recorded advanced therapy."
• May Weight-Neutral Health Intervention (WIN) for adults with BMI ≥30 kg/m2: protocol for a single-arm feasibility study. (BMJ open, 2026, PMID 42191203): "Weight-Neutral Health Intervention (WIN) for adults with BMI ≥30 kg/m2: protocol for a single-arm feasibility study."
• May The impact of BMI on mechanical thrombectomy outcomes, insights from a comprehensive stroke center. (Neurosurgical review, 2026, PMID 42159805): "When it comes to the role of Body Mass Index (BMI) as a prognostic marker in that context, the evidence is conflicting."
• May Impact of a culinary medicine intervention on diet and health metrics in patients with type 2 diabetes and elevated body mass index. (PloS one, 2026, PMID 42160272): "Impact of a culinary medicine intervention on diet and health metrics in patients with type 2 diabetes and elevated body mass index."
• May Body Mass Index and All-Cause, Cancer, and Cardiovascular Mortality in Adults Aged 16-50 Years With and Without Type 2 Diabetes: An Analysis of Primary Care Records in England. (Diabetes, obesity & metabolism, 2026, PMID 42120004): "To examine the association between body mass index (BMI) and risk of all-cause, cancer, and cardiovascular diseases (CVD) mortality among adults aged 16-50 years with and without type 2 diabetes (T2D)."
• May Comparing adiposity-related predictors of cardiometabolic disease in two Indigenous Guatemalan municipalities: a cross-sectional receiver operating characteristic analysis. (BMJ open, 2026, PMID 42114861): "To compare the ability of body mass index (BMI), waist-to-height ratio and visceral fat, as measured by bioelectrical impedance analysis (BIA), to predict hypertension and diabetes in men and women"
• May Diagnostic Accuracy of Arm Conicity to Detect Inaccurate Non-Invasive Blood Pressure Measurement in Obese Patients. (Acta anaesthesiologica Scandinavica, 2026, PMID 41981918): "We aimed to determine if arm conicity, mid arm circumference or body mass index (BMI) could be used to predict pre-operative and intra-operative NIBP measurement error."
• May The interaction between glucose levels and body mass index on the regulation of the circulating sphingolipidome in humans. (American journal of physiology. Endocrinology and metabolism, 2026, PMID 41873824): "...to identify lipid signatures related to sex, body mass index (BMI), and fasting glucose levels."
• May Association between body mass index and palindromic rheumatism risk and outcomes: a case-control study. (Clinical rheumatology, 2026, PMID 41840172): "the relation between body mass index (BMI) and PR remains underexplored."