Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach.

A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocorticography recorded at high sampling frequency. Recent pilot studies suggest a possible utility of scalp electrodes generated electroencephalogram (EEG) for non-invasive SD detection. However, noise and attenuation of EEG signals makes this detection task extremely challenging. Previous methods focus on detecting temporal power change of EEG over a fixed high-density map of scalp electrodes, which is not always clinically feasible. Having a specialized spectrogram as an input to the automatic SD detection model, this study is the first to transform SD identification problem from a detection task on a 1-D time-series wave to a task on a sequential 2-D rendered imaging. This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning. Our proposed model has an ultra-fast processing speed (<0.3 sec). Compared to the conventional methods (2 hours), this is a huge advancement towards early SD detection and to facilitate instant brain injury prognosis. Seeing SDs with a new dimension - frequency on spectrograms, we demonstrated that such additional dimension could improve SD detection accuracy, providing preliminary evidence to support the hypothesis that SDs may show implicit features over the frequency profile.

Microdialysis in Abdominal Organ Transplantation and the Potential for Integration with Dynamic Preservation Platforms and Post Transplant Monitoring.

The perpetual organ shortage crisis worldwide has meant a paradigm shift in global thinking with subsequent expansion of the accepted criteria for an organ donor to meet the demand. Robust pre-transplant organ viability assessment is the next great challenge in the field of transplantation today. Organ preservation in the nature of static cold storage has reached its limits, and machine perfusion both cold and warm offers theoretically superior preservation and the potential to assess organs. Microdialysis is a novel technique with proven ability to allow remote assessment of tissue biochemistry and metabolism. It has been used in various pre-clinical and clinical models of abdominal organ preservation and transplantation. This review focuses on the use of microdialysis in the assessment of the kidney, liver, and pancreas, and where this novel technology is heading in the context of the assessing organ viability prior to and after transplantation.

Computational Modeling as a Tool to Drive the Development of a Novel, Chemical Device for Monitoring the Injured Brain and Body.

Real-time measurement of dynamic changes, occurring in the brain and other parts of the body, is useful for the detection and tracked progression of disease and injury. Chemical monitoring of such phenomena exists but is not commonplace, due to the penetrative nature of devices, the lack of continuous measurement, and the inflammatory responses that require pharmacological treatment to alleviate. Soft, flexible devices that more closely match the moduli and shape of monitored tissue and allow for surface microdialysis provide a viable alternative. Here, we show that computational modeling can be used to aid the development of such devices and highlight the considerations when developing a chemical monitoring probe in this way. These models pave the way for the development of a new class of chemical monitoring devices for monitoring neurotrauma, organs, and skin.

Dexamethasone-Enhanced Continuous Online Microdialysis for Neuromonitoring of O2 after Brain Injury.

Traumatic brain injury (TBI) is a major public health crisis in many regions of the world. Severe TBI may cause a primary brain lesion with a surrounding penumbra of tissue that is vulnerable to secondary injury. Secondary injury presents as progressive expansion of the lesion, possibly leading to severe disability, a persistent vegetive state, or death. Real time neuromonitoring to detect and monitor secondary injury is urgently needed. Dexamethasone-enhanced continuous online microdialysis (Dex-enhanced coMD) is an emerging paradigm for chronic neuromonitoring after brain injury. The present study employed Dex-enhanced coMD to monitor brain K+ and O2 during manually induced spreading depolarization in the cortex of anesthetized rats and after controlled cortical impact, a widely used rodent model of TBI, in behaving rats. Consistent with prior reports on glucose, O2 exhibited a variety of responses to spreading depolarization and a prolonged, essentially permanent decline in the days after controlled cortical impact. These findings confirm that Dex-enhanced coMD delivers valuable information regarding the impact of spreading depolarization and controlled cortical impact on O2 levels in the rat cortex.

A shortened surface electromyography recording is sufficient to facilitate home fasciculation assessment.

INTRODUCTION/AIMS: Fasciculations are an early clinical hallmark of amyotrophic lateral sclerosis (ALS), amenable to detection by high-density surface electromyography (HDSEMG). In conjunction with the Surface Potential Quantification Engine (SPiQE), HDSEMG offers improved spatial resolution for the analysis of fasciculations. This study aims to establish an optimal recording duration to enable longitudinal remote monitoring in the home. METHODS: Twenty patients with ALS and five patients with benign fasciculation syndrome (BFS) underwent serial 30 min HDSEMG recordings from biceps brachii and gastrocnemii. SPiQE was independently applied to abbreviated epochs within each 30-min recording (0-5, 0-10, 0-15, 0-20, and 0-25 min), outputting fasciculation frequency, amplitude median and amplitude interquartile range. Bland-Altman plots and intraclass correlation coefficients (ICC) were used to assess agreement with the validated 30-min recording. RESULTS: In total, 506 full recordings were included. The 5 min recordings demonstrated diverse and relatively poor agreement with the 30 min baselines across all parameters, muscles and patient groups (ICC = 0.32-0.86). The 15-min recordings provided more acceptable and stable agreement (ICC = 0.78-0.98), which did not substantially improve in longer recordings. DISCUSSION: For the detection and quantification of fasciculations in patients with ALS and BFS, HDSEMG recordings can be halved from 30 to 15 min without significantly compromising the primary outputs. Reliance on a shorter recording duration should lead to improved tolerability and repeatability among patients, facilitating longitudinal remote monitoring in patients' homes.

A LoC Ion Imaging Platform for Spatio-Temporal Characterisation of Ion-Selective Membranes.

In this paper, a complete Lab-on-Chip (LoC) ion imaging platform for analysing Ion-Selective Membranes (ISM) using CMOS ISFET arrays is presented. An array of 128 × 128 ISFET pixels is employed with each pixel featuring 4 transistors to bias the ISFET to a common drain amplifier. Column-level 2-step readout circuits are designed to compensate for array offset variations in a range of up to ±1 V. The chemical signal associated with a change in ionic concentration is stored and fed back to a programmable gain instrumentation amplifier for compensation and signal amplification through a global system feedback loop. This column-parallel signal pipeline also integrates an 8-bit single slope ADC and an 8-bit R-2R DAC to quantise the processed pixel output. Designed and fabricated in the TSMC 180 nm BCD process, the System-on-Chip (SoC) operates in real time with a maximum frame rate of 1000 fps, whilst occupying a silicon area of 2.3 mm × 4.5 mm. The readout platform features a high-speed digital system to perform system-level feedback compensation with a USB 3.0 interface for data streaming. With this platform we show the first reported analysis and characterisation of ISMs using an ISFETs array through capturing real-time high-speed spatio-temporal information at a resolution of 16 µm in 1000 fps, extracting time-response and sensitivity. This work paves the way of understanding the electrochemical response of ISMs, which are widely used in various biomedical applications.

Validation of Dexamethasone-Enhanced Continuous-Online Microdialysis for Monitoring Glucose for 10 Days after Brain Injury.

Traumatic brain injury (TBI) induces a pathophysiologic state that can be worsened by secondary injury. Monitoring brain metabolism with intracranial microdialysis can provide clinical insights to limit secondary injury in the days following TBI. Recent enhancements to microdialysis include the implementation of continuously operating electrochemical biosensors for monitoring the dialysate sample stream in real time and dexamethasone retrodialysis to mitigate the tissue response to probe insertion. Dexamethasone-enhanced continuous-online microdialysis (Dex-enhanced coMD) records long-lasting declines of glucose after controlled cortical impact in rats and TBI in patients. The present study employed retrodialysis and fluorescence microscopy to investigate the mechanism responsible for the decline of dialysate glucose after injury of the rat cortex. Findings confirm the long-term functionality of Dex-enhanced coMD for monitoring brain glucose after injury, demonstrate that intracranial glucose microdialysis is coupled to glucose utilization in the tissues surrounding the probes, and validate the conclusion that aberrant glucose utilization drives the postinjury glucose decline.

Development and Evaluation of a Method for Automated Detection of Spreading Depolarizations in the Injured Human Brain.

BACKGROUND: Spreading depolarizations (SDs) occur in some 60% of patients receiving intensive care following severe traumatic brain injury and often occur at a higher incidence following serious subarachnoid hemorrhage and malignant hemisphere stroke (MHS); they are independently associated with worse clinical outcome. Detection of SDs to guide clinical management, as is now being advocated, currently requires continuous and skilled monitoring of the electrocorticogram (ECoG), frequently extending over many days. METHODS: We developed and evaluated in two clinical intensive care units (ICU) a software routine capable of detecting SDs both in real time at the bedside and retrospectively and also capable of displaying patterns of their occurrence with time. We tested this prototype software in 91 data files, each of approximately 24 h, from 18 patients, and the results were compared with those of manual assessment ("ground truth") by an experienced assessor blind to the software outputs. RESULTS: The software successfully detected SDs in real time at the bedside, including in patients with clusters of SDs. Counts of SDs by software (dependent variable) were compared with ground truth by the investigator (independent) using linear regression. The slope of the regression was 0.7855 (95% confidence interval 0.7149-0.8561); a slope value of 1.0 lies outside the 95% confidence interval of the slope, representing significant undersensitivity of 79%. R2 was 0.8415. CONCLUSIONS: Despite significant undersensitivity, there was no additional loss of sensitivity at high SD counts, thus ensuring that dense clusters of depolarizations of particular pathogenic potential can be detected by software and depicted to clinicians in real time and also be archived.

First-recruited motor units adopt a faster phenotype in amyotrophic lateral sclerosis.

KEY POINTS: Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disorder of motor neurons, carrying a short survival. High-density motor unit recordings permit analysis of motor unit size (amplitude) and firing behaviour (afterhyperpolarization duration and muscle fibre conduction velocity). Serial recordings from biceps brachii indicated that motor units fired faster and with greater amplitude as disease progressed. First-recruited motor units in the latter stages of ALS developed characteristics akin to fast-twitch motor units, possibly as a compensatory mechanism for the selective loss of this motor unit subset. This process may become maladaptive, highlighting a novel therapeutic target to reduce motor unit vulnerability. ABSTRACT: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with a median survival of 3 years. We employed serial high-density surface electromyography (HDSEMG) to characterize voluntary and ectopic patterns of motor unit (MU) firing at different stages of disease. By distinguishing MU subtypes with variable vulnerability to disease, we aimed to evaluate compensatory neuronal adaptations that accompany disease progression. Twenty patients with ALS and five patients with benign fasciculation syndrome (BFS) underwent 1-7 assessments each. HDSEMG measurements comprised 30 min of resting muscle and 1 min of light voluntary activity from biceps brachii bilaterally. MU decomposition was performed by the progressive FastICA peel-off technique. Inter-spike interval, firing pattern, MU potential area, afterhyperpolarization duration and muscle fibre conduction velocity were determined. In total, 373 MUs (ALS = 287; BFS = 86) were identified from 182 recordings. Weak ALS muscles demonstrated a lower mean inter-spike interval (82.7 ms) than strong ALS muscles (96.0 ms; P = 0.00919) and BFS muscles (95.3 ms; P = 0.0039). Mean MU potential area (area under the curve: 487.5 vs. 98.7 µV ms; P < 0.0001) and muscle fibre conduction velocity (6.2 vs. 5.1 m/s; P = 0.0292) were greater in weak ALS muscles than in BFS muscles. Purely fasciculating MUs had a greater mean MU potential area than MUs also under voluntary command (area under the curve: 679.6 vs. 232.4 µV ms; P = 0.00144). These results suggest that first-recruited MUs develop a faster phenotype in the latter stages of ALS, likely driven by the preferential loss of vulnerable fast-twitch MUs. Inhibition of this potentially maladaptive phenotypic drift may protect the longevity of the MU pool, stimulating a novel therapeutic avenue.

Fiber-Based Electrochemical Biosensors for Monitoring pH and Transient Neurometabolic Lactate.

Developing tools that are able to monitor transient neurochemical dynamics is important to decipher brain chemistry and function. Multifunctional polymer-based fibers have been recently applied to monitor and modulate neural activity. Here, we explore the potential of polymer fibers comprising six graphite-doped electrodes and two microfluidic channels within a flexible polycarbonate body as a platform for sensing pH and neurometabolic lactate. Electrodes were made into potentiometric sensors (responsive to pH) or amperometric sensors (lactate biosensors). The growth of an iridium oxide layer made the fiber electrodes responsive to pH in a physiologically relevant range. Lactate biosensors were fabricated via platinum black growth on the fiber electrode, followed by an enzyme layer, making them responsive to lactate concentration. Lactate fiber biosensors detected transient neurometabolic lactate changes in an in vivo mouse model. Lactate concentration changes were associated with spreading depolarizations, known to be detrimental to the injured brain. Induced waves were identified by a signature lactate concentration change profile and measured as having a speed of ∼2.7 mm/min (n = 4 waves). Our work highlights the potential applications of fiber-based biosensors for direct monitoring of brain metabolites in the context of injury.

Randomized blinded trial of automated REBOA during CPR in a porcine model of cardiac arrest.

BACKGROUND: Resuscitative endovascular balloon occlusion of the aorta (REBOA) reportedly elevates arterial blood pressure (ABP) during non-traumatic cardiac arrest. OBJECTIVES: This randomized, blinded trial of cardiac arrest in pigs evaluated the effect of automated REBOA two minutes after balloon inflation on ABP (primary endpoint) as well as arterial blood gas values and markers of cerebral haemodynamics and metabolism. METHODS: Twenty anesthetized pigs were randomized to REBOA inflation or sham-inflation (n = 10 in each group) followed by insertion of invasive monitoring and a novel, automated REBOA catheter (NEURESCUE® Catheter & NEURESCUE® Assistant). Cardiac arrest was induced by ventricular pacing. Cardiopulmonary resuscitation was initiated three min after cardiac arrest, and the automated REBOA was inflated or sham-inflated (blinded to the investigators) five min after cardiac arrest. RESULTS: In the inflation compared to the sham group, mean ABP above the REBOA balloon after inflation was higher (inflation: 54 (95%CI: 43-65) mmHg; sham: 44 (33-55) mmHg; P = 0.06), and diastolic ABP was higher (inflation: 38 (29-47) mmHg; sham: 26 (20-33) mmHg; P = 0.02), and the arterial to jugular oxygen content difference was lower (P = 0.04). After return of spontaneous circulation, mean ABP (inflation: 111 (95%CI: 94-128) mmHg; sham: 94 (95%CI: 65-123) mmHg; P = 0.04), diastolic ABP (inflation: 95 (95%CI: 78-113) mmHg; sham: 78 (95%CI: 50-105) mmHg; P = 0.02), CPP (P = 0.01), and brain tissue oxygen tension (inflation: 315 (95%CI: 139-491)% of baseline; sham: 204 (95%CI: 75-333)%; P = 0.04) were higher in the inflation compared to the sham group. CONCLUSION: Inflation of REBOA in a porcine model of non-traumatic cardiac arrest improves central diastolic arterial pressure as a surrogate marker of coronary artery pressure, and cerebral perfusion. INSTITUTIONAL PROTOCOL NUMBER: 2017-15-0201-01371.

Fasciculation analysis reveals a novel parameter that correlates with predicted survival in amyotrophic lateral sclerosis.

INTRODUCTION: Prognostic uncertainty in amyotrophic lateral sclerosis (ALS) confounds clinical management planning, patient counseling, and trial stratification. Fasciculations are an early clinical hallmark of disease and can be quantified noninvasively. Using an innovative analytical method, we correlated novel fasciculation parameters with a predictive survival model. METHODS: Using high-density surface electromyography, we collected biceps recordings from ALS patients on their first research visit. By accessing an online survival prediction tool, we provided eight clinical and genetic parameters to estimate individual patient survival. Fasciculation analysis was performed using an automated algorithm (Surface Potential Quantification Engine), with a Cox proportional hazards model to calculate hazard ratios. RESULTS: The median predicted survival for 31 patients was 41 (interquartile range, 31.5-57) months. Univariate hazard ratios were 1.09 (95% confidence interval [CI], 1.03-1.16) for the rate of change of fasciculation frequency (RoCoFF) and 1.10 (95% CI, 1.01-1.19) for the amplitude dispersion rate. Only the RoCoFF remained significant (P = .04) in a multivariate model. DISCUSSION: Noninvasive measurement of fasciculations at a single time-point could enhance prognostic models in ALS, where higher RoCoFF values indicate shorter survival.

The rise and fall of fasciculations in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis is a devastating neurodegenerative disease with a median survival of 3 years from symptom onset. Accessible and reliable biomarkers of motor neuron decline are urgently needed to quicken the pace of drug discovery. Fasciculations represent an early pathophysiological hallmark of amyotrophic lateral sclerosis and can be reliably detected by high-density surface electromyography. We set out to quantify fasciculation potentials prospectively over 14 months, seeking comparisons with established markers of disease progression. Twenty patients with amyotrophic lateral sclerosis and five patients with benign fasciculation syndrome underwent up to seven assessments each. At each assessment, we performed the amyotrophic lateral sclerosis-functional rating scale, sum power score, slow vital capacity, 30-min high-density surface electromyography recordings from biceps and gastrocnemius and the motor unit number index. We employed the Surface Potential Quantification Engine, which is an automated analytical tool to detect and characterize fasciculations. Linear mixed-effect models were employed to account for the pseudoreplication of serial measurements. The amyotrophic lateral sclerosis-functional rating scale declined by 0.65 points per month (P < 0.0001), 35% slower than average. A total of 526 recordings were analysed. Compared with benign fasciculation syndrome, biceps fasciculation frequency in amyotrophic lateral sclerosis was 10 times greater in strong muscles and 40 times greater in weak muscles. This was coupled with a decline in fasciculation frequency among weak muscles of -7.6/min per month (P = 0.003), demonstrating the rise and fall of fasciculation frequency in biceps muscles. Gastrocnemius behaved differently, whereby strong muscles in amyotrophic lateral sclerosis had fasciculation frequencies five times greater than patients with benign fasciculation syndrome while weak muscles were increased by only 1.5 times. Gastrocnemius demonstrated a significant decline in fasciculation frequency in strong muscles (2.4/min per month, P < 0.0001), which levelled off in weak muscles. Fasciculation amplitude, an easily quantifiable surrogate of the reinnervation process, was highest in the biceps muscles that transitioned from strong to weak during the study. Pooled analysis of >900 000 fasciculations revealed inter-fasciculation intervals <100 ms in the biceps of patients with amyotrophic lateral sclerosis, particularly in strong muscles, consistent with the occurrence of doublets. We hereby present the most comprehensive longitudinal quantification of fasciculation parameters in amyotrophic lateral sclerosis, proposing a unifying model of the interactions between motor unit loss, muscle power and fasciculation frequency. The latter showed promise as a disease biomarker with linear rates of decline in strong gastrocnemius and weak biceps muscles, reflecting the motor unit loss that drives clinical progression.

Traumatic brain injury neuroelectrochemical monitoring: behind-the-ear micro-instrument and cloud application.

BACKGROUND: Traumatic Brain Injury (TBI) is a leading cause of fatality and disability worldwide, partly due to the occurrence of secondary injury and late interventions. Correct diagnosis and timely monitoring ensure effective medical intervention aimed at improving clinical outcome. However, due to the limitations in size and cost of current ambulatory bioinstruments, they cannot be used to monitor patients who may still be at risk of secondary injury outside the ICU. METHODS: We propose a complete system consisting of a wearable wireless bioinstrument and a cloud-based application for real-time TBI monitoring. The bioinstrument can simultaneously record up to ten channels including both ECoG biopotential and neurochemicals (e.g. potassium, glucose and lactate), and supports various electrochemical methods including potentiometry, amperometry and cyclic voltammetry. All channels support variable gain programming to automatically tune the input dynamic range and address biosensors' falling sensitivity. The instrument is flexible and can be folded to occupy a small space behind the ear. A Bluetooth Low-Energy (BLE) receiver is used to wirelessly connect the instrument to a cloud application where the recorded data is stored, processed and visualised in real-time. Bench testing has been used to validate device performance. RESULTS: The instrument successfully monitored spreading depolarisations (SDs) - reproduced using a signal generator - with an SNR of 29.07 dB and NF of 0.26 dB. The potentiostat generates a wide voltage range from -1.65V to +1.65V with a resolution of 0.8mV and the sensitivity of the amperometric AFE was verified by recording 5 pA currents. Different potassium, glucose and lactate concentrations prepared in lab were accurately measured and their respective working curves were constructed. Finally,the instrument achieved a maximum sampling rate of 1.25 ksps/channel with a throughput of 105 kbps. All measurements were successfully received at the cloud. CONCLUSION: The proposed instrument uniquely positions itself by presenting an aggressive optimisation of size and cost while maintaining high measurement accuracy. The system can effectively extend neuroelectrochemical monitoring to all TBI patients including those who are mobile and those who are outside the ICU. Finally, data recorded in the cloud application could be used to help diagnosis and guide rehabilitation.

Complementary Metal-Oxide-Semiconductor Potentiometric Field-Effect Transistor Array Platform Using Sensor Learning for Multi-ion Imaging.

This work describes an array of 1024 ion-sensitive field-effect transistors (ISFETs) using sensor-learning techniques to perform multi-ion imaging for concurrent detection of potassium, sodium, calcium, and hydrogen. Analyte-specific ionophore membranes are deposited on the surface of the ISFET array chip, yielding pixels with quasi-Nernstian sensitivity to K+, Na+, or Ca2+. Uncoated pixels display pH sensitivity from the standard Si3N4 passivation layer. The platform is then trained by inducing a change in single-ion concentration and measuring the responses of all pixels. Sensor learning relies on offline training algorithms including k-means clustering and density-based spatial clustering of applications with noise to yield membrane mapping and sensitivity of each pixel to target electrolytes. We demonstrate multi-ion imaging with an average error of 3.7% (K+), 4.6% (Na+), and 1.8% (pH) for each ion, respectively, while Ca2+ incurs a larger error of 24.2% and hence is included to demonstrate versatility. We validate the platform with a brain dialysate fluid sample and demonstrate reading by comparing with a gold-standard spectrometry technique.

Fasciculations demonstrate daytime consistency in amyotrophic lateral sclerosis.

INTRODUCTION: Fasciculations represent early neuronal hyperexcitability in amyotrophic lateral sclerosis (ALS). To aid calibration as a disease biomarker, we set out to characterize the daytime variability of fasciculation firing. METHODS: Fasciculation awareness scores were compiled from 19 ALS patients. In addition, 10 ALS patients prospectively underwent high-density surface electromyographic (HDSEMG) recordings from biceps and gastrocnemius at three time-points during a single day. RESULTS: Daytime fasciculation awareness scores were low (mean: 0.28 muscle groups), demonstrating significant variability (coefficient of variation: 303%). Biceps HDSEMG recordings were highly consistent for fasciculation potential frequency (intraclass correlation coefficient [ICC] = 95%, n = 19) and the interquartile range of fasciculation potential amplitude (ICC = 95%, n = 19). These parameters exhibited robustness to observed fluctuations in data quality parameters. Gastrocnemius demonstrated more modest levels of consistency overall (44% to 62%, n = 20). DISCUSSION: There was remarkable daytime consistency of fasciculation firing in the biceps of ALS patients, despite sparse and intermittent awareness among patients' accounts.

Real-time neurochemical measurement of dynamic metabolic events during cardiac arrest and resuscitation in a porcine model.

This work describes a fully-integrated portable microfluidic analysis system for real-time monitoring of dynamic changes in glucose and lactate occurring in the brain as a result of cardiac arrest and resuscitation. Brain metabolites are sampled using FDA-approved microdialysis probes and coupled to a high-temporal resolution 3D printed microfluidic chip housing glucose and lactate biosensors. The microfluidic biosensors are integrated with a wireless 2-channel potentiostat forming a compact analysis system that is ideal for use in a crowded operating theatre. Data are transmitted to a custom-written app running on a tablet for real-time visualisation of metabolic trends. In a proof-of-concept porcine model of cardiac arrest, the integrated analysis system proved reliable in a challenging environment resembling a clinical setting; noise levels were found to be comparable with those seen in the lab and were not affected by major clinical interventions such as defibrillation of the heart. Using this system, we were able, for the first time, to measure changes in brain glucose and lactate levels caused by cardiac arrest and resuscitation; the system was sensitive to clinical interventions such as infusion of adrenaline. Trends suggest that cardiopulmonary resuscitation alone does not meet the high energy demands of the brain as metabolite levels only return to their values preceding cardiac arrest upon return of spontaneous circulation.

Non-invasive measurement of fasciculation frequency demonstrates diagnostic accuracy in amyotrophic lateral sclerosis.

Delayed diagnosis of amyotrophic lateral sclerosis prevents early entry into clinical trials at a time when neuroprotective therapies would be most effective. Fasciculations are an early hallmark of amyotrophic lateral sclerosis, preceding muscle weakness and atrophy. To assess the potential diagnostic utility of fasciculations measured by high-density surface electromyography, we carried out 30-min biceps brachii recordings in 39 patients with amyotrophic lateral sclerosis, 7 patients with benign fasciculation syndrome, 1 patient with multifocal motor neuropathy and 17 healthy individuals. We employed the surface potential quantification engine to compute fasciculation frequency, fasciculation amplitude and inter-fasciculation interval. Inter-group comparison was assessed by Welch's analysis of variance. Logistic regression, receiver operating characteristic curves and decision trees discerned the diagnostic performance of these measures. Fasciculation frequency, median fasciculation amplitude and proportion of inter-fasciculation intervals <100 ms showed significant differences between the groups. In the best-fit regression model, increasing fasciculation frequency and median fasciculation amplitude were independently associated with the diagnosis of amyotrophic lateral sclerosis. Fasciculation frequency was the single best measure predictive of the disease, with an area under the curve of 0.89 (95% confidence interval 0.81-0.98). The cut-off of more than 14 fasciculation potentials per minute achieved 80% sensitivity (95% confidence interval 63-90%) and 96% specificity (95% confidence interval 78-100%). In conclusion, non-invasive measurement of fasciculation frequency at a single time-point reliably distinguished amyotrophic lateral sclerosis from its mimicking conditions and healthy individuals, warranting further research into its diagnostic applications.