Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness.

BACKGROUND: In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations. METHODS: We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome. RESULTS: Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]). CONCLUSIONS: Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Lyme neuroborreliosis presenting with urinary retention and spastic paraparesis.

Progressive myelopathy, urinary retention and gait problems are rare presenting features of Lyme neuroborreliosis. A 30-year-old man had 11 months of urinary retention and 3 months of spastic paraparesis. MR scans of the brain and the spinal cord showed leptomeningeal thickening with contrast enhancement. Cerebrospinal fluid showed mononuclear pleocytosis, decreased glucose, increased protein and chemokine ligand 13, with intrathecal Borrelia-specific IgM and IgG antibodies. He received 14 days of intravenous ceftriaxone followed by 14 days of oral doxycycline. Despite improvement at 6 months, he still had severe gait problems. Urinary retention in otherwise healthy people needs investigation, and Lyme neuroborreliosis is a rare cause.

Out-of-hospital multimodal seizure detection: a pilot study.

Background: Out-of-hospital seizure detection aims to provide clinicians and patients with objective seizure documentation in efforts to improve the clinical management of epilepsy. In-patient studies have found that combining different modalities helps improve the seizure detection accuracy. In this study, the objective was to evaluate the viability of out-of-hospital seizure detection using wearable ECG, accelerometry and behind-the-ear electroencephalography (EEG). Furthermore, we examined the signal quality of out-of-hospital EEG recordings. Methods: Seventeen patients were monitored for up to 5 days. A support vector machine based seizure detection algorithm was applied using both in-patient seizures and out-of-hospital electrographic seizures in one patient. To assess the content of noise in the EEG signal, we compared the root-mean-square (RMS) of the recordings to a reference threshold derived from manually categorised segments of EEG recordings. Results: In total 1427 hours of continuous EEG was recorded. In one patient, we identified 15 electrographic focal impaired awareness seizures with a motor component. After training our algorithm on in-patient data, we found a sensitivity of 91% and a false alarm rate (FAR) of 18/24 hours for the detection of out-of-hospital seizures using a combination of EEG and ECG recordings. We estimated that 30.1% of the recorded EEG signal was physiological EEG, with an RMS value within the reference threshold. Conclusion: We found that detection of out-of-hospital focal impaired awareness seizures with a motor component is possible and that applying multiple modalities improves the diagnostic accuracy compared with unimodal EEG. However, significant challenges remain regarding a high FAR and that only 30.1% of the EEG data represented usable signal.

Plasma Aß biomarker for early diagnosis and prognosis of Alzheimer's disease - a systematic review.

INTRODUCTION: Alzheimer's disease (AD) is the most common cause of dementia worldwide and a cost-effective diagnostic biomarker is needed. This systematic review provides an overview of the current research on plasma amyloid beta (Aß) as a biomarker of AD and explores the clinical implications of this line of research. METHODS: PubMed was searched using the keywords plasma Aß and AD from 2017 to 2021. Only clinical studies involving amyloid PET (aPET) or cerebrospinal fluid (CSF) biomarker analysis (or both) were included. A meta-analysis of CSF Aß42/40 ratio, aPET and plasma Aß42/40 ratio was conducted when possible. RESULTS: A total of 17 articles were identified. Plasma Aß42/40 ratio was inversely correlated with aPET positivity r = -0.48 (95% confidence interval (CI): -0.65--0.31). In numerous studies, plasma Aß42/40 ratio was also found to be directly correlated with CSF Aß42 and CSF Aß42/40 ratio r = 0.50 (95% CI: 0.30-0.69). Three studies found plasma Aß42 to be positively associated with aPET positivity and CSF Aß42; however, four other studies found no significant association between these variables. Seven studies reported no significant association of plasma Aß40 with aPET or CSF Aß40. CONCLUSION: Plasma Aß42/40 ratio seems as a promising plasma biomarker as it significantly correlates inversely with aPET positivity and directly with CSF Aß42 and CSF Aß42/40 ratio. However, more research is warranted, including validation studies, longitudinally clinical studies, studies comparing measurement methods and studies of Aß kinetics.

Systematic Review of EEG Coherence in Alzheimer's Disease.

BACKGROUND: Magnitude-squared coherence (MSCOH) is an electroencephalography (EEG) measure of functional connectivity. MSCOH has been widely applied to investigate pathological changes in patients with Alzheimer's disease (AD). However, significant heterogeneity exists between the studies using MSOCH. OBJECTIVE: We systematically reviewed the literature on MSCOH changes in AD as compared to healthy controls to investigate the clinical utility of MSCOH as a marker of AD. METHODS: We searched PubMed, Embase, and Scopus to identify studies reporting EEG MSCOH used in patients with AD. The identified studies were independently screened by two researchers and the data was extracted, which included cognitive scores, preprocessing steps, and changes in MSCOH across frequency bands. RESULTS: A total of 35 studies investigating changes in MSCOH in patients with AD were included in the review. Alpha coherence was significantly decreased in patients with AD in 24 out of 34 studies. Differences in other frequency bands were less consistent. Some studies showed that MSCOH may serve as a diagnostic marker of AD. CONCLUSION: Reduced alpha MSCOH is present in patients with AD and MSCOH may serve as a diagnostic marker. However, studies validating MSCOH as a diagnostic marker are needed.

Pitfalls in EEG Analysis in Patients With Nonconvulsive Status Epilepticus: A Preliminary Study.

Objective: Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. Methods: We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals' clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Results: Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. Conclusion: A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study.

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Towards a wearable multi-modal seizure detection system in epilepsy: A pilot study.

OBJECTIVE: To explore the possibilities of wearable multi-modal monitoring in epilepsy and to identify effective strategies for seizure-detection. METHODS: Thirty patients with suspected epilepsy admitted to video electroencephalography (EEG) monitoring were equipped with a wearable multi-modal setup capable of continuous recording of electrocardiography (ECG), accelerometry (ACM) and behind-the-ear EEG. A support vector machine (SVM) algorithm was trained for cross-modal automated seizure detection. Visualizations of multi-modal time series data were used to generate ideas for seizure detection strategies. RESULTS: Three patients had more than five seizures and were eligible for SVM classification. Classification of 47 focal tonic seizures in one patient found a sensitivity of 84% with a false alarm rate (FAR) of 8/24 h. In two patients each with nine focal nonmotor seizures it yielded a sensitivity of 100% and a FAR of 13/24 h and 5/24. Visual comparisons of features were used to identify strategies for seizure detection in future research. CONCLUSIONS: Multi-modal monitoring in epilepsy using wearables is feasible and automatic seizure detection may benefit from multiple modalities when compared to uni-modal EEG. SIGNIFICANCE: This study is unique in exploring a combination of wearable EEG, ECG and ACM and can help inform future research on monitoring of epilepsy.

Fully automatic peak frequency estimation of the posterior dominant rhythm in a large retrospective hospital EEG cohort.

OBJECTIVE: To develop and test a fully automated method for estimation of the peak frequency of the posterior dominant rhythm (PDR) in a large retrospective EEG cohort. METHODS: Thresholding was used to select suitable EEG data segments for spectral estimation for electrode O1 and O2. A random sample of 100 peak frequency estimates were blindly rated by two independent raters to validate the results of the automatic PDR peak frequency estimates. We investigated the relationship with age, sex and binary EEG classification. RESULTS: There were 9197 eligible EEGs which resulted in a total of 6104 PDR peak frequency estimates. The relationship between automatic estimates and age was found to be consistent with the literature. The correlation between human ratings and automatic scoring was very high, rho = 0.94-0.95. There was a sex difference of d = 0.33 emerging at puberty with females having a faster PDR peak frequency than males. CONCLUSIONS: Fully automatic PDR peak frequency estimation not dependent on annotated EEG produced results that are very close to human ratings. SIGNIFICANCE: PDR peak frequency can be automatically estimated. A compiled version of the algorithm is included as an app for independent use.

A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings.

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.

Readiness among people with epilepsy to carry body-worn monitor devices in everyday life: A qualitative study.

OBJECTIVE: There have been intensive efforts to design and develop new wearable technology for epileptic seizure detection. Several studies have focused on the technical aspects, but the readiness of patients with epilepsy (PWEs) to use wearables in everyday life, which is crucial, remains relatively unexplored. METHODS: We conducted a qualitative interview study involving eight PWEs. The study was designed to provide insights into patient readiness to use wearables for home monitoring of epilepsy. RESULTS: Three themes were identified: 1) making invisible situations visible, 2) having companionship within a troubled everyday life, and 3) sharing ownership of no recognizable moments. The analysis and interpretation revealed that the expectations of the participants for wearables were rooted in aspects that had a significant impact on their lives and self-image. CONCLUSION: Patients with epilepsy disclosed that their readiness to use technology, specifically wearables, in everyday life relied on the assumption that they would provide an existential and comforting experience, in which the voids of their individual needs would be addressed in a more patient-friendly manner. Wearable design should consider the valuable insight that technology should be more than just technical tools that monitor symptoms; wearables are expected to be existential and esthetic artifacts that provide PWEs with meaningful experience.

Gamma Entrainment in a Large Retrospective Cohort: Implications for Photic Stimulation Therapy for Alzheimer's Disease.

BACKGROUND: Studies on mice models of Alzheimer's disease (AD) have suggested potential therapeutic benefits of intermittent photic stimulation at 40 Hz. OBJECTIVE: We examined the physiological response of 40 Hz intermittent photic stimulation (IPS) on routine EEG in a large retrospective cohort to investigate the effects of age on induced gamma oscillations by intermittent photic stimulation. Since most AD patients are elderly, it is important for future research to know if age affects photic stimulation. METHODS: Retrospective data from 1,464 subjects aged 0- 91. We performed frequency analysis and automatic peak detection and used regression analysis to investigate the effects of age and sex on peak frequencies and amplitude changes. To investigate the spread of the induced gamma oscillations, we assessed averaged topographies of 40 Hz band power. RESULTS: There was a statistically significant but very minor effect of age on amplitude change (- 0.002 normalized power per year, p < 0.0001) but not for sex (p = 0.728). Detection probability of induced peaks was significantly predicted by both age (OR = 0.988, CI 95 % [0.984, 0.993], p < 0.00001) and sex (OR = 0.625, CI 95 % [0.496, 0.787>], p < 0.0001). The induced 40 Hz gamma entrainment is spatially confined to the occipital area. CONCLUSION: There is a significant effect of age on induced gamma activity, but advanced age does not fundamentally change the behavior of the response in either magnitude or spatial distribution. This fact is important regarding future research into the possible therapeutic effects of photic stimulation in patients with AD.

Quantitative characteristics of spike-wave paroxysms in genetic generalized epilepsy.

OBJECTIVE: To characterize generalized spike-wave paroxysms (GSW) in children with generalized genetic epilepsy (GGE). METHODS: We annotated 15-19 channel scalp EEGs from a retrospective cohort from patients with a variety of GGE syndromes. Connectivity, entropy, frequency, power, spike-amplitudes were compared with a normal baseline activity and analyzed for the effect of age and sex. Cluster analysis was used to group spike-topographies between patients. RESULTS: In total, 864 GSWs from 100 patients aged 2-18 were analyzed. Age had a significant effect on peak frequency, entropy and connectivity. Female sex was associated with significantly higher probability of positive responsiveness to photic stimulation (OR 4.28, CI [1.65, 11.73], p = 0.0036). Entropy decreases significantly during GSW (D = -0.29, CI [-0.31, -0.27], p ≪ 0.0001) and connectivity significantly increases (D = 0.39, CI [0.36, 0.40], p ≪ 0.0001). Within patient spike-voltage maps exhibit remarkable consistency between spikes. Spike-topographies cluster together to predict age, connectivity and entropy. CONCLUSIONS: A quantitative characterization is possible and reveals significant relationships between age, sex and spike characteristics and multidimensional EEG features. SIGNIFICANCE: Quantitative GSW characterization can capture aspects from traditional qualitative GSW analysis while being unaffected by intra- and interrater variation and this may be useful for multidimensional predictors of patient outcomes in GGE in the future.

Subcutaneous EEG Monitoring Reveals AED Response and Breakthrough Seizures.

Unrecognized seizures are a common problem in temporal lobe epilepsy potentially leading to undertreatment. Objective seizure counting using EEG home monitoring for prolonged periods with a minimally invasive device has not been feasible until now. We present a case in which a novel, subcutaneous EEG device was utilized to provide an objective seizure count. This information revealed unrecognized breakthrough seizures and informed treatment response, prompting treatment adjustment. The case illustrates how objective seizure counting in epilepsy using new devices can completely change diagnosis and management.

Event marker compliance in actigraphy.

Actigraphy is a versatile tool for evaluating sleep-wake cycles over time in the home-environment. Patients using the Phillips Actiwatch place an event marker when going to sleep and upon awakening. We investigate compliance in pressing the Actiwatch event marker button for patients referred for insomnia, hypersomnia and disorders of circadian rhythm. We retrospectively analysed event markers from 150 patients undergoing actigraphy for 2,117 nights combined. Compliance was evaluated from inspection of actigraphy records, and coded as full or partial. From patient records, a construct called the C-factor, designed to describe poor social resources and chronic unemployment, was used together with age and sex to predict compliance. We found a mean compliance between 54.0% and 76.3% for a median monitoring duration of 14 days. There was an overall insignificant effect of age (p = .081), but when analysed only for females there was a significant effect of 0.56% pr. year (p = .0038). Compliance was higher for women, Cohen's d = 0.65 (p = .01). The C-factor predicts 18.3% (confidence interval 9%-27.5%) lower compliance. Morning and evening compliance are correlated at r = .65. In conclusion, actigraphy event marker compliance is generally moderate or high, with older women exhibiting the highest compliance. C-factor predicts lower compliance, and this pattern may further translate to other circumstances. If compliance is important, clinicians may want to consider the effects of age, sex and C-factor.

Ear-EEG-based sleep scoring in epilepsy: A comparison with scalp-EEG.

Ear-EEG is a wearable electroencephalogram-recording device. It relies on recording electrodes that are nested within a custom-fitted earpiece in the external ear canal. The concept has previously been tested for seizure detection in epileptic patients and for sleep recordings in a healthy population. This study is the first to examine the use of ear-EEG recordings for sleep staging in patients with epilepsy, comparing it with standard recordings from scalp-EEG. We use individuals with epilepsy because of their multiple sleep disturbances, and their complex relationship between seizures and sleep, which make this group very likely to benefit from wearable electroencephalogram devices for sleep if it were introduced in the clinic. The accuracy of the ear-EEG against that of the scalp-EEG is compared for sleep staging, and we evaluate features of sleep architecture in individuals with epilepsy. A mean kappa value of 0.74 is found for the agreement between hypnograms derived from ear-EEG and scalp-EEG. Furthermore, it was discovered that sleep stage transition frequency could be contributing to the kappa variation. These findings are related to other ear-recording systems in the literature, and the potentials and future obstacles of the device are discussed.

Postictal EEG changes following focal seizures: Interrater agreement and comparison to frequency analysis.

OBJECTIVES: To compare frequency analysis to human raters and determine the interrater agreement of postictal EEG changes after focal seizures. METHODS: 24 focal seizures with and without impaired awareness recorded with scalp-EEG in the epilepsy monitoring unit were selected. Five board-certified neurophysiologists annotated seizure termination and end of postictal changes for all seizures. We assessed agreement using intraclass correlation, described the band-power changes by time-frequency analysis, and correlated these measures with the rater annotations. RESULTS: Interrater agreement on the duration of the postictal changes was moderate (0.64, 95% confidence interval: 0.36-0.82). The interrater agreement for seizure termination was excellent (1.00). Median duration of the postictal interval of seizures with impaired awareness was significantly shorter than for seizures with retained awareness (p = 0.0004). Mean postictal duration was 16.4 min. Seizure duration did not predict duration of the postictal changes. We found a strong correlation of 0.8 between the median human rater and the duration of the decrease in spectral edge frequency. CONCLUSIONS: The agreement of neurophysiologists is moderate for duration of postictal changes and high for seizure termination. Rater determination of postictal duration is correlated with measures of EEG slowing. SIGNIFICANCE: Disagreement between neurophysiologists on postictal duration need to be considered.

Modulation of task-related cortical connectivity in the acute and subacute phase after stroke.

The functional relevance of cortical reorganization post-stroke is still not well understood. In this study, we investigated task-specific modulation of cortical connectivity between neural oscillations in key motor regions during the early phase after stroke. EEG and EMG recordings were examined from 15 patients and 18 controls during a precision grip task using the affected hand. Each patient attended two sessions in the acute and subacute phase (median of 3 and 34 days) post-stroke. Dynamic causal modelling (DCM) for induced responses was used to investigate task-specific modulations of oscillatory couplings in a bilateral network comprising supplementary motor area (SMA), dorsal premotor cortex (PMd) and primary motor cortex (M1). Fourteen models were constructed for each subject, and the input induced by the experimental manipulation (task) was set to inferior parietal lobule (IPL). Bayesian model selection favoured a fully connected model. A reduced coupling from SMA and intact M1 in the γ-band (31-48 Hz) to lesioned M1 in the ß-band (15-30 Hz) was observed in patients in the acute phase compared to controls. Behavioural performance improved significantly in the subacute phase, while an increased positive coupling from intact PMd to lesioned M1 and a less negative modulation from lesioned M1 to intact M1 were observed for patients compared to controls both from the γ-band to the ß-band. We infer that the observed differences in cross-frequency cortical interactions are important for functional recovery.

Corticomuscular coherence in the acute and subacute phase after stroke.

OBJECTIVE: Stroke is one of the leading causes of physical disability due to damage of the motor cortex or the corticospinal tract. In the present study we set out to investigate the role of adaptations in the corticospinal pathway for motor recovery during the subacute phase after stroke. METHODS: We examined 19 patients with clinically diagnosed stroke and 18 controls. The patients had unilateral mild to moderate weakness of the hand. Each patient attended two sessions at approximately 3days (acute) and 38days post stroke (subacute). Task-related changes in the communication between motor cortex and muscles were evaluated from coupling in the frequency domain between EEG and EMG during movement of the paretic hand. RESULTS: Corticomuscular coherence (CMC) and intermuscular coherence (IMC) were reduced in patients as compared to controls. Paretic hand motor performance improved within 4-6weeks after stroke, but no change was observed in CMC or IMC. CONCLUSIONS: CMC and IMC were reduced in patients in the early phase after stroke. However, changes in coherence do not appear to be an efficient marker for early recovery of hand function following stroke. SIGNIFICANCE: This is the first study to demonstrate sustained reduced coherence in acute and subacute stroke.