Electrophysiological Biomarkers Research Articles

A phase 1b/2a multicenter study of the safety and preliminary pharmacodynamic effects of selective muscarinic M1 receptor agonist HTL0018318 in patients with mild-to-moderate Alzheimer's disease.

IntroductionThis study examined the safety and pharmacodynamic effects of selective muscarinic M1 receptor orthosteric agonist HTL0018318 in 60 patients with mild‐to‐moderate Alzheimer's disease (AD) on background donepezil 10 mg/day.MethodsA randomized, double‐blind, placebo‐controlled 4‐week safety study of HTL0018318 with up‐titration and maintenance phases, observing exploratory effects on electrophysiological biomarkers and cognition.ResultsTreatment‐emergent adverse events (TEAEs) were mild and less frequently reported during maintenance versus titration. Headache was most commonly reported (7–21%); 0 to 13% reported cholinergic TEAEs (abdominal pain, diarrhea, fatigue, nausea) and two patients discontinued due to TEAEs. At 1 to 2 hours post‐dose, HTL0018318‐related mean maximum elevations in systolic and diastolic blood pressure of 5 to 10 mmHg above placebo were observed during up‐titration but not maintenance. Postive effects of HTL0018318 were found on specific attention and memory endpoints.DiscussionHTL0018318 was well tolerated in mild‐to‐moderate AD patients and showed positive effects on attention and episodic memory on top of therapeutic doses of donepezil.

Attenuated alpha-gamma coupling in emotional dual pathways with right-Amygdala predicting ineffective antidepressant response.

AimsThe diversity of treatment outcomes for major depressive disorder (MDD) remains uncertain in neuropathology. The current study aimed at exploring electrophysiological biomarkers associated with treatment response.MethodsThe present study recruited 130 subjects including 100 MDD patients and 30 healthy controls. All subjects participated in a sad expression recognition task while their magnetoencephalography data were recorded. Patients who had a reduction of at least 50% in disorder severity at endpoint (>2 weeks) were considered as responders. Within‐frequency power and phase‐amplitude coupling were measured for the brain regions involved in the emotional visual information processing pathways.ResultsThe significant alpha–gamma decoupling from the right thalamus to the right amygdala in unconscious processing and from right orbital frontal cortices to the right amygdala in conscious processing was found in non‐responders relative to responders and healthy controls. These kinds of dysregulation could also predict the potential treatment response.ConclusionThe attenuated alpha–gamma coupling in dual pathways indicated increased sensitivity to the negative emotional information and reduced moderated effect of the amygdala, which might cause insensitivity to antidepressant treatment and could be regarded as potential neural mechanisms for treatment response prediction.

Impairment of recognition of facial expressions in different types of dementia

AbstractBackgroundImpairment of facial expression recognition in dementia is one of the cognitive deficits that could affect the social life of dementia patients. The severity of the facial expression recognition in different types of dementia has not been fully addressed. Previous studies showed that EEG event‐related oscillation (ERO) studies could reveal the brain dynamics during successful facial expression recognition. Furthermore, the impairment of facial recognition in Alzheimer's disease patients were represented with abnormal EROs (Güntekin et al., 2019). The present study aims to compare the impaired facial expression recognition between different types of dementia by analysis of event‐related delta responses. The role of delta responses in facial expression and emotional paradigms was reported previously by several researchers (Güntekin and Başar, 2014).Method25 Healthy elderly controls (HC), 25 Mild Cognitive Impairment patients (MCI), 25 patients with Alzheimer's disease (AD), 15 patients with Parkinson's disease with Mild Cognitive Impairment (PDMCI) and 16 patients with Parkinson's disease Dementia (PDD) were included in the study. The electroencephalographic activity was recorded during the perception of the facial expression recognition task (Angry, Happy, Neutral). Delta power and phase‐locking were computed for each facial recognition stimuli and compared among the groups (ANOVA, p<0.05).ResultBoth delta power and delta phase locking were lower in dementia groups than healthy controls being worst in PDD. Figure 1 shows the grand average of delta power in all groups for the right occipital location. PDD (p<0.0001) and AD (p<0.05) had lower delta power than the healthy controls, the reduction of delta responses in PDD and AD were especially found over occipital locations. MCI and PDMCI subjects were found less impaired facial expression recognition compared to AD and PDMCI.ConclusionThe present study showed that dementia patients had severe facial recognition deficits that increased cognitive impairment. Event‐related delta responses successfully showed the impaired recognition of facial expression in different types of dementia groups. Among all dementia subjects, PDD patients had the most reduced delta responses that could be an electrophysiological biomarker of impaired facial expression recognition. Acknowledgments: This work (grant number 218S314) was supported by TUBITAK.

Quantitative electroencephalographic evaluations of electrophysiological biomarkers and impaired functional networks of older adults diagnosed with dementia

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder attributable to neurofibrillary tangles, senile neuritic plaques, and a reduction of neurons and synapses. Because the etiopathogenesis of AD is complex and its associated risk factors are not well understood, clinical interventions to effectively treat AD are being developed. Thus, interventional strategies to delay disease progression have been the focus of numerous pharmacologically oriented trials. Recognition of near‐infrared photobiomodulation as having disease modifying properties in the treatment of age‐related dementia has been reported by Paul Chazot PhD, Senior Lecturer in Neuropharmacology at Durham University. The utilization of electrophysiological biomarkers and dysregulated functional networks may allow for neuromodulation applications to curtail symptomatic progression and therefore non‐invasive prophylactic strategies.Methods19‐channel electroencephalographic (EEG) assessment was employed across all participants (n=96) to evaluate responsivity to intensive (twice daily), self‐administered, near‐infrared treatment (INIRT) using active or placebo devices that mimicked the photobiomodulation intervention. The EEG recordings were evaluated using a normative database and standardized weight low resonance tomography (swLORETA) analysis software. Participants were administered a comprehensive neuropsychiatric battery before, during and after 60 days to evaluate cognitive performance while undergoing the PBM intervention or sham condition. Specifically, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which includes 12 inventories such as the Mini Mental State Exam (MMSE), clock drawing test (CDT), auditory verbal learning test‐ immediate (A.V.L.T.‐1), WAIS‐R digit symbol substitution test, the auditory verbal learning test‐ delayed (A.V.L.T.‐2) were employed to evaluate working memory, word recall, visuospatial recall, comprehension, logical memory, and number recall cognitive domains.ResultsPreliminary results indicate subjects in the active PBM condition demonstrated significant improvements in cognitive functioning and activities of daily living and alterations in neural connectivity and phase lag (inter‐electrode signal transmission) in both theta (4‐7hz) and delta (.5‐4hz) frequency bands and a normalization of alpha (8‐12hz) amplitude.ConclusionsExposure to PBM interventions indicate subjects in the active PBM condition demonstrated significant alterations in neural connectivity and phase lag (inter‐electrode signal transmission) in both theta (4‐7hz) and delta (.5‐4hz) frequency bands and a normalization of alpha (8‐12hz) amplitude. Normalization of the EEG correlated with symptomatic remission of cognitive impairment, alertness, attention, and improved sleep architecture. Participants exposed to the placebo condition did not report symptomatic remission and continued to exemplify cognitive decline associated with the progression of this neurodegenerative disorder.

Electrophysiological Biomarkers of Epileptogenicity in Alzheimer's Disease.

Cortical network hyperexcitability is an inextricable feature of Alzheimer’s disease (AD) that also might accelerate its progression. Seizures are reported in 10–22% of patients with AD, and subclinical epileptiform abnormalities have been identified in 21–42% of patients with AD without seizures. Accurate identification of hyperexcitability and appropriate intervention to slow the compromise of cognitive functions of AD might open up a new approach to treatment. Based on the results of several studies, epileptiform discharges, especially those with specific features (including high frequency, robust morphology, right temporal location, and occurrence during awake or rapid eye movement states), frequent small sharp spikes (SSSs), temporal intermittent rhythmic delta activities (TIRDAs), and paroxysmal slow wave events (PSWEs) recorded in long-term scalp electroencephalogram (EEG) provide sufficient sensitivity and specificity in detecting cortical network hyperexcitability and epileptogenicity of AD. In addition, magnetoencephalogram (MEG), foramen ovale (FO) electrodes, and computational approaches help to find subclinical seizures that are invisible on scalp EEGs. We performed a comprehensive analysis of the aforementioned electrophysiological biomarkers of AD-related seizures.

EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome.

Introduction: Fragile X syndrome (FXS) is a genetic disorder caused by a mutation of the fragile X mental retardation 1 gene (FMR1). FXS is associated with neurophysiological abnormalities, including cortical hyperexcitability. Alterations in electroencephalogram (EEG) resting-state power spectral density (PSD) are well-defined in FXS and were found to be linked to neurodevelopmental delays. Whether non-linear dynamics of the brain signal are also altered remains to be studied.Methods: In this study, resting-state EEG power, including alpha peak frequency (APF) and theta/beta ratio (TBR), as well as signal complexity using multi-scale entropy (MSE) were compared between 26 FXS participants (ages 5–28 years), and 77 neurotypical (NT) controls with a similar age distribution. Subsequently a replication study was carried out, comparing our cohort to 19 FXS participants independently recorded at a different site.Results: PSD results confirmed the increased gamma, decreased alpha power and APF in FXS participants compared to NT controls. No alterations in TBR were found. Importantly, results revealed reduced signal complexity in FXS participants, specifically in higher scales, suggesting that altered signal complexity is sensitive to brain alterations in this population. The replication study mostly confirmed these results and suggested critical points of stagnation in the neurodevelopmental curve of FXS.Conclusion: Signal complexity is a powerful feature that can be added to the electrophysiological biomarkers of brain maturation in FXS.

Controlling Clinical States Governed by Different Temporal Dynamics With Closed-Loop Deep Brain Stimulation: A Principled Framework.

Closed-loop strategies for deep brain stimulation (DBS) are paving the way for improving the efficacy of existing neuromodulation therapies across neurological disorders. Unlike continuous DBS, closed-loop DBS approaches (cl-DBS) optimize the delivery of stimulation in the temporal domain. However, clinical and neurophysiological manifestations exhibit highly diverse temporal properties and evolve over multiple time-constants. Moreover, throughout the day, patients are engaged in different activities such as walking, talking, or sleeping that may require specific therapeutic adjustments. This broad range of temporal properties, along with inter-dependencies affecting parallel manifestations, need to be integrated in the development of therapies to achieve a sustained, optimized control of multiple symptoms over time. This requires an extended view on future cl-DBS design. Here we propose a conceptual framework to guide the development of multi-objective therapies embedding parallel control loops. Its modular organization allows to optimize the personalization of cl-DBS therapies to heterogeneous patient profiles. We provide an overview of clinical states and symptoms, as well as putative electrophysiological biomarkers that may be integrated within this structure. This integrative framework may guide future developments and become an integral part of next-generation precision medicine instruments.

Sensing, computing and intervention for sleep health

<p indent=0mm>As an instinctive part of human life, sleep not only is of great significance to the individual’s physical and mental health, but also could be a natural means of regulating, restoring and enhancing body functions. Accordingly, sleep quality not only affects people’s quality of life, but also is closely related to the occurrence and development of many physiological and psychological diseases. Statistics show that about one-third of the population in China has sleep disorders, and a growing number of people have sleep health requirements. There is a prominent contradiction between this demand and the backward daily sleep health management. Therefore, it is urgent to set up theories and methods ranging from sleep structure conversion mechanism to sleep quality monitoring and intervention. For this reason and based on the discussion of the Y5th Xiangshan Scientific Conference (Smart Sensing and Computing for Sleep Health), this paper provides an in-depth analysis of sleep perception, intelligent sleep computing and sleep intervention methods in recent years. It foresees the key scientific and technical issues in the three stages of this closed-loop system. Combining wearable sensing and medical artificial intelligence technology, we should form a platform and develop algorithms from sleep quality monitoring to objective diagnosis of sleep disorders. Principles and methods of physical and behavioral interventions should be also explored to improve the physical and mental state of sleep regulation, so as to establish a new paradigm of “active sleep health” comprehensively. Considering the feasibility of the next few years from the perspective of principle, mechanisms, methods and measures, we propose the following recommendations: (1) Sleep-wake transformation mechanism. The study of sleep-related electrophysiology biomarkers could reveal the sleep-wake transformation and regulatory mechanisms to guide the development of sleep health standards for the Chinese population. Based on such a mechanism, an objective assessment of people’s overall health status could be achieved. It will be used for national sleep health screening and prevention of sleep disorders, and provide a theoretical basis for active sleep health promotion. (2) Sleep health promotion for specific populations. In order to adapt to the sleep health of children, youth, elderly and special types of personnel, research on the brain neural mechanism of biological rhythm should be carried out for different groups in order to build personalized multi-physiological parameter monitoring devices, intelligent analysis algorithms and active intervention paradigms. (3) Sleep health promotes physical and mental health. Sleep is closely related to the development of many physiological diseases. To reduce the incidence of chronic diseases, we should explore the mechanism of its association with the cardiovascular system, respiratory system, nervous system and endocrine system. A multimodality data analysis system could be developed with independent intellectual property rights. This will promote the advancement of sleep-related industries in China and propose relevant technical standards and guidelines to achieve active health management.

Intraoperative neural signals predict rapid antidepressant effects of deep brain stimulation

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is a promising intervention for treatment-resistant depression (TRD). Despite the failure of a clinical trial, multiple case series have described encouraging results, especially with the introduction of improved surgical protocols. Recent evidence further suggests that tractography targeting and intraoperative exposure to stimulation enhances early antidepressant effects that further evolve with ongoing chronic DBS. Accelerating treatment gains is critical to the care of this at-risk population, and identification of intraoperative electrophysiological biomarkers of early antidepressant effects will help guide future treatment protocols. Eight patients underwent intraoperative electrophysiological recording when bilateral DBS leads were implanted in the SCC using a connectomic approach at the site previously shown to optimize 6-month treatment outcomes. A machine learning classification method was used to discriminate between intracranial local field potentials (LFPs) recorded at baseline (stimulation-naïve) and after the first exposure to SCC DBS during surgical procedures. Spectral inputs (theta, 4–8 Hz; alpha, 9–12 Hz; beta, 13–30 Hz) to the model were then evaluated for importance to classifier success and tested as predictors of the antidepressant response. A decline in depression scores by 45.6% was observed after 1 week and this early antidepressant response correlated with a decrease in SCC LFP beta power, which most contributed to classifier success. Intraoperative exposure to therapeutic stimulation may result in an acute decrease in symptoms of depression following SCC DBS surgery. The correlation of symptom improvement with an intraoperative reduction in SCC beta power suggests this electrophysiological finding as a biomarker for treatment optimization.

A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders.

Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.

Identifying translational electrophysiological biomarkers of compulsivity for deep brain stimulation

Identifying translational electrophysiological biomarkers of compulsivity for deep brain stimulation

Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms

There has been a fundamental failure to translate preclinically supported research into clinically efficacious treatments for psychiatric disorders. One of the greatest impediments toward improving this species gap has been the difficulty of identifying translatable neurophysiological signals that are related to specific behavioral constructs. Here, we present evidence from three paradigms that were completed by humans and mice using analogous procedures, with each task eliciting candidate a priori defined electrophysiological signals underlying effortful motivation, reinforcement learning, and cognitive control. The effortful motivation was assessed using a progressive ratio breakpoint task, yielding a similar decrease in alpha-band activity over time in both species. Reinforcement learning was assessed via feedback in a probabilistic learning task with delta power significantly modulated by reward surprise in both species. Additionally, cognitive control was assessed in the five-choice continuous performance task, yielding response-locked theta power seen across species, and modulated by difficulty in humans. Together, these successes, and also the teachings from these failures, provide a roadmap towards the use of electrophysiology as a method for translating findings from the preclinical assays to the clinical settings.

Motor unit number index: A potential electrophysiological biomarker for pediatric spinal muscular atrophy.

In adult spinal muscular atrophy (SMA), the motor unit number index (MUNIX) has been shown to be an useful electrophysiological biomarker. This study evaluated the feasibility and the clinical relevance of using the MUNIX technique for patients with pediatric SMA (Ped-SMA) and correlated MUNIX results with clinical scores. Fourteen patients with type II Ped-SMA (11 females; median age 11 y [interquartile range (IQR), 4.8-17 y]) and 14 controls (nine females; median age 10.75 y [IQR, 6.5-13.4y]) were enrolled and matched by sex, age, height, weight, and body mass index. Clinical examination included manual muscle testing, dynamometry (grasp and pinch), and motor function measure (MFM). The MUNIX technique was evaluated in the abductor digiti minimi (ADM) and abductor pollicis brevis (APB) on two sides when possible. In the patients with Ped-SMA, the MUNIX and compound muscle action potential (CMAP) amplitudes were significantly decreased and the motor size unit index (MUSIX) was significantly increased in the ADM and APB when compared to controls. The intraclass correlation coefficient was good for the intrarater variability of the CMAP amplitude, MUNIX, and MUSIX in the ADM (0.95, 0.83, and 0.89, respectively) and the APB (0.98, 0.96, and 0.94, respectively). The total CMAP amplitude correlated with the grasp and pinch scores (P< .05), and the MUNIX measurements correlated with the MFM scores. The MUNIX technique, which accurately estimated lower motor neuron loss and the number of remaining functional motor units, was shown to be a useful electrophysiological biomarker for disease progression and a potential biomarker for treatment response.

Altered Connectivity of the Frontoparietal Network During Attention Processing in Prolactinomas.

Prolactinomas have been reported for the failure of cognitive functions. However, the electrophysiological mechanisms of attention processing in prolactinomas remain unclear. In a visual mission, we monitored the scalp electroencephalography (EEG) of the participants. Compared with the healthy controls (HCs), larger frontoparietal theta and alpha coherence were found in the patients, especially in the right-lateralized hemisphere, which indicated a deficit in attention processing. Moreover, the frontoparietal coherence was positively correlated with altered prolactin (PRL) levels, implying the significance of PRL for adaptive brain compensation in prolactinomas. Taken together, this research showed the variations in attention processing between the HCs and prolactinomas. The coherence between frontal and parietal regions may be one of the possible electrophysiological biomarkers for detecting deficient attention processing in prolactinomas.

Neuronal biomarkers of Parkinson's disease are present in healthy aging

The prevalence of Parkinson's disease (PD) increases with aging and both processes share similar cellular mechanisms and alterations in the dopaminergic system. Yet it remains to be investigated whether aging can also demonstrate electrophysiological neuronal signatures typically associated with PD. Previous work has shown that phase-amplitude coupling (PAC) between the phase of beta oscillations and the amplitude of gamma oscillations as well as beta bursts features can serve as electrophysiological biomarkers for PD. Here we hypothesize that these metrics are also present in apparently healthy elderly subjects. Using resting state multichannel EEG measurements, we show that PAC between beta oscillation and broadband gamma activity (50–150 Hz) is elevated in a group of elderly (59–77 years) compared to young volunteers (20–35 years) without PD. Importantly, the increase of PAC is statistically significant even after ruling out confounds relating to changes in spectral power and non-sinusoidal shape of beta oscillation. Moreover, a trend for a higher percentage of longer beta bursts (> 0.2 s) along with the increase in their incidence rate is also observed for elderly subjects. Using inverse modeling, we further show that elevated PAC and longer beta bursts are most pronounced in the sensorimotor areas. Moreover, we show that PAC and longer beta bursts might reflect distinct mechanisms, since their spatial patterns only partially overlap and the correlation between them is weak. Taken together, our findings provide novel evidence that electrophysiological biomarkers of PD may already occur in apparently healthy elderly subjects. We hypothesize that PAC and beta bursts characteristics in aging might reflect a pre-clinical state of PD and suggest their predictive value to be tested in prospective longitudinal studies.

EEG Biomarkers of Repository Corticotropin Injection Treatment.

Hypsarrhythmia is one of the major diagnostic and treatment response criteria in infantile spasms (IS). The clinical and electrophysiological effect of repository corticotropin injection treatment on IS was evaluated using electrophysiological biomarkers. Consecutive infants (<24 months) treated with repository corticotropin injection for IS were included in this retrospective descriptive study. Inclusion criteria were (1) clinical IS diagnosis, (2) repository corticotropin injection treatment, and (3) consecutive EEG recordings before and after repository corticotropin injection treatment. Patients with tuberous sclerosis complex were excluded. Response to treatment was defined as freedom from IS for at least 7 consecutive days during the treatment and resolution of hypsarrhythmia. The authors defined "relapse" as the recurrence of seizures after an initial response. Electrophysiological biomarker assessment included evaluation of semiautomatic spike counting algorithm, delta power, and delta coherence calculation during non-REM sleep EEG. One hundred fifty patients (83 males; 55%; median age of IS onset: 5.9 months) with complete data were included, including 101 responders (67%, 71 with sustained response, and 30 relapses). Fifty patients (33%) with complete EEG data also underwent advanced EEG analysis. Baseline delta coherence was higher in sustained responders than in nonresponders or patients who relapsed. Greater decreases in semiautomatic spike counting algorithm, delta power, and delta coherence were found in sustained responders compared with nonresponders or patients who relapsed. Repository corticotropin injection treatment was associated with a 67% response rate in patients with IS. Computational biomarkers beyond hypsarrhythmia may provide additional information during IS treatment, such as early determination of treatment response and outcome assessment.

Gamma oscillations predict treatment response to aripiprazole in bipolar disorder

ObjectiveTreatment of Bipolar Disorder (BD) is a challenging issue. Aripiprazole monotherapy is a recommended option for the treatment of mania in BD. The electrophysiological markers of treatment response to aripiprazole could be potentially identified by quantitative Electroencephalography (qEEG). MethodsTwenty-four patients with BD were analysed retrospectively. Based on the percentage reduction in Young Mania Rating Scale, they were classified as responders (N = 14) and non-responders (N = 10) to aripiprazole monotherapy. Their resting-state qEEG recordings were examined. Spectral power across all frequency bands were calculated. Absolute powers for all frequency bands were compared between these groups. ResultsIndependent sample Mann-Whitney U test revealed that patients who did not respond to aripiprazole had greater gamma power than aripiprazole treatment responders. ConclusionsBased on the present findings, it can be proposed that excess in gamma power could be the electrophysiological biomarkers of unresponsiveness to aripiprazole treatment in BD.

O-MN003. Decremental response to repetitive nerve stimulation occurs in early stage of amyotrophic lateral sclerosis

Introduction . This study aimed to clarify the features and clinical significance of decremental response during slow-rate repetitive nerve stimulation (RNS) in amyotrophic lateral sclerosis (ALS) patients of early stage. Methods . 3-Hz RNS tests were performed in 175 nerve/muscle pairs from 22 ALS patients, whose duration of illness was less than 1 year, and lower motor neuron signs were limited in upper limbs. Both proximal and distal muscles were included. All patients were given a follow- up evaluation by telephone every 3 months. 15 patients had an electromyography (EMG) reexamination in 6 months. Besides, 81 nerve/muscle pairs in 37 normal controls were collected. Results . In ALS patients, compound muscle action potentials (CMAP) decrement percentages to RNS in all tested nerve/muscle pairs were greater than in controls (p < 0.01). Muscles without EMG spontaneous activity (including fibrillation potentials and positive sharp waves) showed higher decrement percentage than normal controls (p < 0.05), but were significantly lower compared to muscles with spontaneous activity (p < 0.05). Additionally, in muscles without EMG spontaneous activity, 14/58 pairs had RNS decrements over 5%, mainly in the deltoid (38.5%) and trapezius (35.3%). Furthermore, nerve/muscle pairs with higher RNS decrement percentage had greater drops in CMAP and higher increases in EMG spontaneous activity 6 months later, but had no correlation with the changes of ALSFRS-R scores. Conclusion . RNS decrements changed in the early stage of ALS, even before the denervation of corresponding muscles. In ALS patients within 12 months, higher RNS decrement percentage indicated faster low motor neuron injury and denervation during the progression of the disease. RNS could be a possible electrophysiological biomarker to evaluate prognosis.

Electrophysiological biomarkers for deep brain stimulation outcomes in movement disorders: state of the art and future challenges

Several neurological diseases are accompanied by rhythmic oscillatory dysfunctions in various frequency ranges and disturbed cross-frequency relationships on regional, interregional, and whole brain levels. Knowledge of these disease-specific oscillopathies is important mainly in the context of deep brain stimulation (DBS) therapy. Electrophysiological biomarkers have been used as input signals for adaptive DBS (aDBS) as well as preoperative outcome predictors. As movement disorders, particularly Parkinson's disease (PD), are among the most frequent DBS indications, the current research of DBS is the most advanced in the movement disorders field. We reviewed the literature published mainly between 2010 and 2020 to identify the most important findings concerning the current evolution of electrophysiological biomarkers in DBS and to address future challenges for prospective research.

Electrophysiological Biomarkers in Genetic Epilepsies.

Precision treatments for epilepsy targeting the underlying genetic diagnoses are becoming a reality. Historically, the goal of epilepsy treatments was to reduce seizure frequency. In the era of precision medicine, however, outcomes such as prevention of epilepsy progression or even improvements in cognitive functions are both aspirational targets for any intervention. Developing methods, both in clinical trial design and in novel endpoints, will be necessary for measuring, not only seizures, but also the other neurodevelopmental outcomes that are predicted to be targeted by precision treatments. Biomarkers that quantitatively measure disease progression or network level changes are needed to allow for unbiased measurements of the effects of any gene-level treatments. Here, we discuss some of the promising electrophysiological biomarkers that may be of use in clinical trials of precision therapies, as well as the difficulties in implementing them.