PubMed دسترسی آزاد

EEG functional connectivity measures for differentiating 6-month consciousness-related outcome after brain injury.

استودیوی صوتی مقاله

پخش حرفه‌ای فارسی و انگلیسی

در حال بررسی نسخه‌های صوتی ذخیره‌شده…

صوت تولیدشده با هوش مصنوعی است. برای کاربرد علمی یا درمانی، متن و منبع اصلی را بررسی کنید.
خواندن هوشمند فارسی و انگلیسی در حال آماده‌سازی صداهای مرورگر…
تنظیم صدای طبیعی و سرعت

صداهایی که در نامشان «Natural»، «Neural» یا «Online» دیده می‌شود معمولاً طبیعی‌ترند. انتخاب صدا به صداهای نصب‌شده در ویندوز و مرورگر شما بستگی دارد.

چکیده اصلی

BACKGROUND: Prognostication in patients with impaired consciousness after brain injury remains challenging because clinical assessments such as the Glasgow Coma Scale (GCS) may not fully capture underlying neurophysiological function. Electroencephalography (EEG) provides an accessible approach for assessing functional brain connectivity at the bedside. This study explored the association between EEG connectivity, clinical variables, and 6-month consciousness-related outcomes after brain injury. METHODS: EEG was recorded from 111 hospitalized patients with impaired consciousness after brain injury. Baseline clinical status was assessed using the GCS. Patients were followed for 6 months and categorized according to command-following ability into good outcome and bad outcome groups. Functional connectivity was computed using weighted phase lag index (wPLI), debiased squared wPLI (wPLI2), and coherence. Clinical and EEG variables were evaluated using group comparisons, multivariable regression, receiver operating characteristic analysis, leave-one-out cross-validation, and sensitivity analyses. RESULTS: Patients in the good outcome group were younger and had higher baseline GCS scores than those in the bad outcome group. Among EEG measures, alpha-band wPLI during the moving condition showed the strongest nominal group difference, with higher values in the good outcome group (nominal p = 0.006). This result did not survive false discovery rate correction across EEG connectivity variables (pFDR = 0.169). Alpha-band wPLI showed a nominal adjusted association with the 6-month consciousness-related outcome after adjustment for age and GCS, although the estimate was imprecise and attenuated in sensitivity analyses. In leave-one-out cross-validation, the clinical single-modality analysis using age and GCS achieved an AUC of 0.778, whereas the multimodality analysis combining age, GCS, and mean wPLI achieved an AUC of 0.733. The incremental improvement from adding EEG connectivity to clinical variables was not statistically significant. CONCLUSIONS: Alpha-band wPLI during task-based EEG showed an exploratory association with 6-month consciousness-related outcome after brain injury. However, this finding did not survive correction for multiple EEG comparisons and did not significantly improve discrimination beyond age and GCS in this cohort. Task-related EEG connectivity may serve as a candidate complementary marker, but larger multicenter studies with external validation are needed.

متن کامل اصلی

نسخه دارای مجوز در منبع علمی در دسترس است.

لینک مستقیم از metadata منبع گرفته شده و در تب جدید باز می‌شود.

باز کردن متن کامل

کلیدواژه‌ها

Brain injuryConsciousnessEEGFunctional connectivityGlasgow coma scaleMultimodalityPrognosisWeighted phase lag index
در همین زیرشاخه

مقاله‌های مرتبط

PubMed2026

Clinical effectiveness and safety of metadoxine in the management of acute alcohol intoxication: A single-center retrospective cohort study.

BACKGROUND: Acute alcohol intoxication (AAI) is a common emergency with no specific antidote. Metadoxine has shown potential but lacks sufficient real-world evidence, particularly in Chinese populations. OBJECTIVES: To evaluate the clinical efficacy and safety of metadoxine in patients with acute alcohol intoxication. METHODS: This single-center retrospective cohort study included 124 patients with AAI admitted to an emergency departme…

PubMed2026

D3MI: an efficient and powerful federated imputation method for bias reduction in the analysis of distributed incomplete data by accounting for within-site correlation and between-site heterogeneity.

BACKGROUND: Electronic health records (EHRs) collected from diverse healthcare institutions offer a rich and representative data source for clinical research. Federated learning enables analysis of these distributed data without sharing sensitive patient-level information, preserving privacy. However, missing data remain a major challenge and can introduce substantial bias if not properly addressed. Very few distributed imputation meth…

PubMed2026

Extraction of Pain Severity and Functional Interference From Clinical Narratives Using Domain-Informed Large Language Models: Protocol for a Development and Validation Study.

BACKGROUND: Chronic pain is a leading cause of disability and requires multidimensional assessment of pain intensity and functioning, yet electronic health records rarely capture these measures systematically. By contrast, surveys collecting patient-reported outcomes can assess pain over multiple dimensions but remain resource-intensive and difficult to scale for continuous population-level monitoring. OBJECTIVE: The objective of this …

PubMed2026

From data entry to digital transformation: Allied health perspectives on standardised electronic medical records data.

BACKGROUND: Electronic medical records (EMRs) currently rely on standardised data fields to support secondary data use for clinical care, performance monitoring, and system-level reporting. However, utilisation of standardised data capture and reporting within allied health remains underdeveloped in practice. Greater understanding of how allied health clinicians and managers perceive the purpose, value, and impact of standardised data …