Neurophysiological Metrics Research Articles

The Neurophysiological Costs of Learning in a Noisy Classroom: An Ecological Virtual Reality Study.

Many real-life situations can be extremely noisy, which makes it difficult to understand what people say. Here, we introduce a novel audiovisual virtual reality experimental platform to study the behavioral and neurophysiological consequences of background noise on processing continuous speech in highly realistic environments. We focus on a context where the ability to understand speech is particularly important: the classroom. Participants (n = 32) experienced sitting in a virtual reality classroom and were told to pay attention to a virtual teacher giving a lecture. Trials were either quiet or contained background construction noise, emitted from outside the classroom window. Two realistic types of noise were used: continuous drilling and intermittent air hammers. Alongside behavioral outcomes, we measured several neurophysiological metrics, including neural activity (EEG), eye-gaze and skin conductance (galvanic skin response). Our results confirm the detrimental effect of background noise. Construction noise, and particularly intermittent noise, was associated with reduced behavioral performance, reduced neural tracking of the teacher's speech and an increase in skin conductance, although it did not have a significant effect on alpha-band oscillations or eye-gaze patterns. These results demonstrate the neurophysiological costs of learning in noisy environments and emphasize the role of temporal dynamics in speech-in-noise perception. The finding that intermittent noise was more disruptive than continuous noise supports a "habituation" rather than "glimpsing" hypothesis of speech-in-noise processing. These results also underscore the importance of increasing the ecologically relevance of neuroscientific research and considering acoustic, temporal, and semantic features of realistic stimuli as well as the cognitive demands of real-life environments.

Predicting consumer ad preferences: Leveraging a machine learning approach for EDA and FEA neurophysiological metrics

AbstractThis research unveils to predict consumer ad preferences by detecting seven basic emotions, attention and engagement triggered by advertising through the analysis of two specific physiological monitoring tools, electrodermal activity (EDA), and Facial Expression Analysis (FEA), applied to video advertising, offering a twofold contribution of significant value. First, to identify the most relevant physiological features for consumer preference prediction. We integrated a statistical module encompassing inferential and exploratory analysis tools, which identified emotions such as Joy, Disgust, and Surprise, enabling the statistical differentiation of preferences concerning various advertisements. Second, we present an artificial intelligence (AI) system founded on machine learning techniques, encompassing k‐Nearest Neighbors, Support Vector Machine, and Random Forest (RF). Our findings show that the RF technique emerged as the top performer, boasting an 81% Accuracy, 84% Precision, 79% Recall, and an F1‐score of 81% in predicting consumer preferences. In addition, our research proposes an eXplainable AI module based on feature importance, which discerned Attention, Engagement, Joy, and Disgust as the four most pivotal features influencing consumer ad preference prediction. The results indicate that computerized intelligent systems based on EDA and FEA data can be used to predict consumer ad preferences based on videos and effectively used as supporting tools for marketing specialists.

Study Protocol: Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project

BackgroundObjective and quantifiable markers are crucial for developing novel therapeutics for mental disorders by 1) stratifying clinically similar patients with different underlying neurobiological deficits and 2) objectively tracking disease trajectory and treatment response. Schizophrenia is often confounded with other psychiatric disorders, especially bipolar disorder, if based on cross-sectional symptoms. Awake and sleep EEG have shown promise in identifying neurophysiological differences as biomarkers for schizophrenia. However, most previous studies, while useful, were conducted in European and American populations, had small sample sizes, and utilized varying analytic methods, limiting comprehensive analyses or generalizability to diverse human populations. Furthermore, the extent to which wake and sleep neurophysiology metrics correlate with each other and with symptom severity or cognitive impairment remains unresolved. Moreover, how these neurophysiological markers compare across psychiatric conditions is not well characterized. The utility of biomarkers in clinical trials and practice would be significantly advanced by well-powered transdiagnostic studies. The Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project aims to address these questions through a large, multi-center cohort study involving East Asian populations. To promote transparency and reproducibility, we describe the protocol for the GRINS project.MethodsThe research procedure consists of an initial screening interview followed by three subsequent sessions: an introductory interview, an evaluation visit, and an overnight neurophysiological recording session. Data from multiple domains, including demographic and clinical characteristics, behavioral performance (cognitive tasks, motor sequence tasks), and neurophysiological metrics (both awake and sleep electroencephalography), are collected by research groups specialized in each domain.ConclusionPilot results from the GRINS project demonstrate the feasibility of this study protocol and highlight the importance of such research, as well as its potential to study a broader range of patients with psychiatric conditions. Through GRINS, we are generating a valuable dataset across multiple domains to identify neurophysiological markers of schizophrenia individually and in combination. By applying this protocol to related mental disorders often confounded with each other, we can gather information that offers insight into the neurophysiological characteristics and underlying mechanisms of these severe conditions, informing objective diagnosis, stratification for clinical research, and ultimately, the development of better-targeted treatment matching in the clinic.

Directed physiological networks in the human prefrontal cortex at rest and post transcranial photobiomodulation

Cerebral infra-slow oscillation (ISO) is a source of vasomotion in endogenic (E; 0.005–0.02 Hz), neurogenic (N; 0.02–0.04 Hz), and myogenic (M; 0.04–0.2 Hz) frequency bands. In this study, we quantified changes in prefrontal concentrations of oxygenated hemoglobin (Δ[HbO]) and redox-state cytochrome c oxidase (Δ[CCO]) as hemodynamic and metabolic activity metrics, and electroencephalogram (EEG) powers as electrophysiological activity, using concurrent measurements of 2-channel broadband near-infrared spectroscopy and EEG on the forehead of 22 healthy participants at rest. After preprocessing, the multi-modality signals were analyzed using generalized partial directed coherence to construct unilateral neurophysiological networks among the three neurophysiological metrics (with simplified symbols of HbO, CCO, and EEG) in each E/N/M frequency band. The links in these networks represent neurovascular, neurometabolic, and metabolicvascular coupling (NVC, NMC, and MVC). The results illustrate that the demand for oxygen by neuronal activity and metabolism (EEG and CCO) drives the hemodynamic supply (HbO) in all E/N/M bands in the resting prefrontal cortex. Furthermore, to investigate the effect of transcranial photobiomodulation (tPBM), we performed a sham-controlled study by delivering an 800-nm laser beam to the left and right prefrontal cortex of the same participants. After performing the same data processing and statistical analysis, we obtained novel and important findings: tPBM delivered on either side of the prefrontal cortex triggered the alteration or reversal of directed network couplings among the three neurophysiological entities (i.e., HbO, CCO, and EEG frequency-specific powers) in the physiological network in the E and N bands, demonstrating that during the post-tPBM period, both metabolism and hemodynamic supply drive electrophysiological activity in directed network coupling of the prefrontal cortex (PFC). Overall, this study revealed that tPBM facilitates significant modulation of the directionality of neurophysiological networks in electrophysiological, metabolic, and hemodynamic activities.

Conscious and non-conscious responses to branded narrative advertising: Investigating narrativity level and device type

Narrative advertising enhances advertisement (ad) and brand evaluations from consumers. However, how the narrativity level of the ad impacts these evaluations is less clear. This study investigates affective and cognitive conscious and non-conscious responses to branded advertising in the form of two-dimensional videos that differ in narrativity level. In addition, it explores the effects of delivery modality (computer screen vs virtual reality). We collected self-reported, electroencephalography, and galvanic skin response data from participants watching real video ads with low and high narrativity levels. While self-reported data showed higher positive perceptions toward ads with a high (vs low) narrativity level, the neurophysiological metrics revealed no differences in arousal levels, cognitive load, and approach–avoidance behavior. Delivery modality had little to no effect on self-reported ad and brand metrics, but ads viewed through virtual reality evoked negatively valenced arousal. The findings suggest that narrativity level differently modulates conscious and unconscious cognitive and affective responses to video ads. JEL CLASSIFICATION: M31; M37

The contribution of neuroscience in evaluating human-robot collaboration: a multidimensional approach

Collaborative robots (cobots) are a recent introduction in the industrial sector and are designed to work on shared tasks with humans with the aim to provide physical and cognitive support. This has led to a growing interest in the study of factors affecting human-robot collaboration (HRC) with the idea of making cobots more responsive to the human psychophysiological state. Several studies have begun to investigate dimensions such as mental workload and stress of the individual interacting with a cobot using behavioural and neurophysiological metrics, leading to a fruitful convergence between the worlds of neuroscience and robotics. It is therefore discussed the utility of a multidisciplinary and multidimensional approach in the study of HRC. Relevant physiological, neurophysiological, behavioural, and subjective measures are presented, as well as the necessity of their integration in HRC research. It is also introduced the importance of considering in HRC individual differences in terms of cognitive and emotional functioning, and factors related to individual representations and interpersonal environment.

Unilateral Mitochondrial-Hemodynamic Coupling and Bilateral Connectivity in the Prefrontal Cortices of Young and Older Healthy Adults.

A recent study demonstrated that noninvasive measurements of cortical hemodynamics and metabolism in the resting human prefrontal cortex can facilitate quantitative metrics of unilateral mitochondrial-hemodynamic coupling and bilateral connectivity in infraslow oscillation frequencies in young adults. The infraslow oscillation includes three distinct vasomotions with endogenic (E), neurogenic (N), and myogenic (M) frequency bands. The goal of this study was to prove the hypothesis that there are significant differences between young and older adults in the unilateral coupling (uCOP) and bilateral connectivity (bCON) in the prefrontal cortex. Accordingly, we performed measurements from 24 older adults (67.2 ± 5.9 years of age) using the same two-channel broadband near-infrared spectroscopy (bbNIRS) setup and resting-state experimental protocol as those in the recent study. After quantification of uCOP and bCON in three E/N/M frequencies and statistical analysis, we demonstrated that older adults had significantly weaker bilateral hemodynamic connectivity but significantly stronger bilateral metabolic connectivity than young adults in the M band. Furthermore, older adults exhibited significantly stronger unilateral coupling on both prefrontal sides in all E/N/M bands, particularly with a very large effect size in the M band (>1.9). These age-related results clearly support our hypothesis and were well interpreted following neurophysiological principles. The key finding of this paper is that the neurophysiological metrics of uCOP and bCON are highly associated with age and may have the potential to become meaningful features for human brain health and be translatable for future clinical applications, such as the early detection of Alzheimer's disease.

This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving.

This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement. Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect "edge cases" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time. Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions-with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded. DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload-though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation. Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level. These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation.

Effects of attention bias modification for anxiety: Neurophysiological indices and moderation by symptom severity

ObjectiveAttention bias modification (ABM) aims to decrease anxiety symptom severity through the reduction of threat-related attention bias (AB). Individual differences in treatment response and poor measurement reliability of AB have called its clinical promise into question. The current study examined whether individual differences in anxiety severity at baseline moderated treatment response, and employed both behavioral and neurophysiological metrics of AB. MethodsParticipants (N = 99) were randomly assigned to four weeks of ABM or placebo control training (PT). Self-reported anxiety symptom severity, and AB metrics and ERPs generated during the dot probe task were collected at baseline (Time 1), one-week post-intervention (Time 5), and at a three-month follow-up (Time 6). ResultsABM, relative to PT, reduced ERPs indexing attention discrimination (N170) and increased ERPs indexing salience tracking (P3). Increases in P3 were associated with ABM-related reductions in anxiety. Anxiety severity was reduced following ABM, but only among those with higher baseline anxiety symptom severity. ConclusionsABM effectively reduced symptom severity among those with higher levels of anxiety, and modulated neurophysiological indices of AB. SignificanceResults provide evidence for attention-relevant ERPs as outcomes of ABM treatment responsivity and suggest that ABM may be most beneficial for those with more severe anxiety symptoms.

Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness.

Neural somatosensory dysfunction is masked by variable executive declines across the Alzheimer’s disease spectrum

AbstractBackgroundDespite decades of research suggesting that primary somatosensory cortical function is spared until relatively late in the progression of Alzheimer’s disease (AD), very few of these studies have considered the role of domain‐specific cognitive function. This is essential, as recent literature has established a robust neurocognitive relationship between attention and executive functions and somatosensory neurophysiology, and patients with AD exhibit highly variable inter‐individual declines in these cognitive domains.MethodIn this study, we combine the high spatio‐temporal resolution of source imaged magnetoencephalography (MEG) with an extensive battery of neuropsychological tests to examine the potential mediating role of domain‐specific cognitive declines on somatosensory neural dysfunction in AD. In a sample of 38 biomarker‐confirmed patients on the AD spectrum and a group of 18 biomarker‐negative older adults, we model mediation/suppression effects of five distinct cognitive composite domains on group differences in two neurophysiological metrics of somatosensory function using a nonparametric bootstrapping approach.ResultInitial models that did not consider cognitive abilities provided weak or null support for somatosensory dysfunction in patients on the AD spectrum. However, the addition of domain‐specific cognitive scores to these models significantly increased their predictive capacity, and revealed robust differences in somatosensory function between these patients and their cognitively‐normal counterparts. The indirect effects of these cognitive variables on somatosensory group differences were significant as well, indicating a suppression effect. Specifically, across‐participant variability in processing speed declines was found to mask groupwise differences in mean somatosensory response amplitude, and similar variability in both processing speed and attention function was found to mask differences in the functional gating of redundant somatosensory information.ConclusionBy considering an important potential confounder in our models of somatosensory dysfunction in patients on the AD spectrum, we show that both general somatosensory responses and the functional gating of incoming somatosensory information exhibit profound alterations in these patients. These findings hold important implications for both our understanding of functional somatosensory pathology in AD, as well as for the importance of considering the role of domain‐specific cognitive variability in future AD‐neuroimaging research.

Implementation of biohybrid olfactory bulb on a high-density CMOS-chip to reveal large-scale spatiotemporal circuit information

Large-scale multi-site biosensors are essential to probe the olfactory bulb (OB) circuitry for understanding the spatiotemporal dynamics of simultaneous discharge patterns. Current ex-vivo biosensing techniques are limited to recording a small set of neurons and cannot provide an adequate resolution, which hinders revealing the fast dynamic underlying the information coding mechanisms in the OB circuit. Here, we demonstrate a novel biohybrid OB-CMOS biosensing platform to decipher the cross-scale dynamics of the OB electrogenesis and quantify the distinct neuronal coding properties. The approach with 4096-microelectrodes offers a non-invasive, label-free, bioelectrical imaging to decode simultaneous firing patterns from thousands of connected neuronal ensembles in acute OB slices. The platform can measure spontaneous and drug-induced extracellular field potential activity with substantially improved spatiotemporal resolution over conventional OB-based biosensors. Also, we employ our OB-CMOS recordings to perform multidimensional analysis to instantiate specific neurophysiological metrics underlying the olfactory spatiotemporal coding that emerged from the OB interconnected layers. Our results delineate the computational implications of large-scale activity patterns in functional olfactory processing. The systematic interplay of the experimental CMOS-base platform architecture and the high-content characterization of the olfactory circuit with various computational analyses endow significant functional interrogations of the OB information processing, high-spatiotemporal connectivity mapping, and global circuit dynamics. Thus, our study can inspire the design of advanced biomimetic olfactory-based biosensors and neuromorphic approaches for diagnostic biomarkers and drug discovery applications.

Explorers of the cells: Toward cross-platform knowledge integration to evaluate neuronal function

In this issue of Neuron, Petersen etal. (2021) introduce CellExplorer, an open-source tool to integrate neurophysiological metrics of neuronal activity from circuits to behavior. Together with other neuroinformatic resources, it may facilitate community-based multidisciplinary characterization of brain cell types.

Improving Psychological Resilience with Cognitive Retraining Methods Using EEG Brain Network Biomarkers: Example from UND/NASA Lunar/Martian Habitat

The brain is the only organ that does not heal itself once injured, but it does adapt and relearn quickly once injured. Whether the brain is cognitively optimized or is dysfunctional, the same brain networks and brain systems are at play to optimize or regulate and repair. That is why studying brain function of optimized brains from astronaut candidates, or individuals within TBI and depression populations can help both ends of the cognitive spectrum to achieve repair for dysfunctional populations or maintain optimal performance. Opportunities to increase coping capabilities neurophysiologically that impact psychological resilience are appealing both clinically and when applied to space travel. The subject of this paper is reporting the results of one such method that is currently being employed in an ongoing UND/NASA Inflatable Lunar Mars Analog Habitat (ILMAH) simulator study. Our Habitat study’s primary goals are many fold: 1) to develop a predictive profile, based on real-time measurable neurophysiological metrics that model cognitive health and resulting task/behavioral health performance; 2) demonstrate the viability of developing a wearable dry sensor device that produces a profile that can be used in extreme environments such as long duration space missions; 3) demonstrate the viability to provide crew countermeasures that mediate negative reduced resilient stress effects on an on-going and as needed basis. Our study method employs the NeuroCoach® Training System that focuses on developing targeted resilient flexible adaptability neural circuit responses through the application of brain training exercises to support psychological resilience. The training program assumption is that if key neural circuits and network systems that support resilient, adaptive behaviors are coupled with proper problem-solving skills, resilient adaptive behaviors emerge. The NeuroCoach® program is based on modern Restorative Cognitive Rehabilitation Training Methods (rCRT). The program provides in-the-moment neural network performance metrics to monitor and adjust the training difficulty level using a Brain Computer Interface. Experimental and clinical results demonstrate the program success at increasing and maintaining optimal cognitive and brain performance quantitatively (by the numbers) and qualitatively (social reintegration). We have found that by studying astronaut crew needs to remain at optimized performance during long duration space travel as well as our studies with various clinical populations with acquired brain dysfunctions presents us with a unique opportunity to compare. They are opposite ends of the spectrum, but both are instructive in what a damaged brain can potentially achieve vs what an optimized brain might suffer during deep space travel.

The influence of classroom width on attention and memory: virtual-reality-based task performance and neurophysiological effects

ABSTRACT Classroom design influences the cognitive processes that determine learning. However, the effects of classroom geometry have been little studied, in part due to the difficulty of modifying physical spaces for experimental purposes. Today, virtual reality allows researchers to very closely control many environmental conditions while collecting psychological and neurophysiological metrics of the user experience. The objective of the present study is to analyse the influence of classroom width on the attention and memory performance of university students. The performance of 90 subjects in three classroom width settings (8.80, 8.20, and 7.60 m), implemented in virtual reality, was evaluated through measures of their attention- and memory-related psychological and neurophysiological responses. The results showed that wider classrooms are associated with poorer performance and lower emotional arousal. This demonstrates a link between the geometric variables of classrooms and the cognitive and physiological responses of students. In general, the present study and its methodology can help architects and researchers develop design guidelines that can improve students’ cognitive processes.

Cold and warm coloured classrooms. Effects on students’ attention and memory measured through psychological and neurophysiological responses

Mounting evidence indicates that the colour hue of classroom walls influences student performance. However, the effect of this design parameter has not hitherto been simultaneously assessed for two key cognitive learning functions of attention and memory. The objective of the present study is to analyse the impact that warm and cold hue coloured classroom walls have on the cognitive attention and memory functions of university students. To this end, the attention and memory performance of 160 participants was evaluated in 12 warm and 12 cold hue colour settings in a virtual classroom. Their performance was quantified through psychological (attention and memory tasks) and neurophysiological (heart rate variability and electroencephalogram) metrics related to the cognitive functions analysed. The results showed that cold hue colours increase arousal and improve performance in attention and memory tasks; and design guidelines can be established. Furthermore, correlations were observed between the psychological and neurophysiological metrics, which represents an important advance in the neuroarchitecture discipline. The variety of implications of the results makes this work useful for architectural design professionals, researchers, and policymakers working on improving learning spaces.

Neurophysiological aspects of the completely locked-in syndrome in patients with advanced amyotrophic lateral sclerosis

ObjectiveAmyotrophic lateral sclerosis (ALS) patients in completely locked-in syndrome (CLIS) are incapable of expressing themselves, and their state of consciousness and awareness is difficult to evaluate. Due to the complete paralysis included paralysis of eye muscles, any assessment of the perceptual and psychophysiological state can only be implemented in passive experimental paradigms with neurophysiological recordings. MethodsFour patients in CLIS were investigated in several experiments including resting state, visual stimulation (eyes open vs eyes closed), auditory stimulation (modified local-global paradigm), somatosensory stimulation (electrical stimulation of the median nerve), and during sleep. ResultsAll patients showed altered neurophysiological metrics, but a unique and common pattern could not be found between patients. However, slowing of the electroencephalography (EEG) and attenuation or absence of alpha wave activity was common in all patients. In two of the four patients, a slow dominant frequency emerged at 4 Hz with synchronized EEG at all channels. In the other two patients slowing of EEG appears less synchronized. EEGs between eyes open and eyes closed were significantly different in all patients. The dominant slow frequency during the day changes during slow-wave sleep (supposedly sleep stage 3) to even slower frequencies below 2 Hz. Somatosensory evoked potentials (SEPs) were absent or significantly altered in comparison to healthy subjects, similarly for auditory evoked potentials (AEPs). ConclusionsThe heterogeneity of the results underscores the fact that no single neurophysiological index is available to assess psychophysiological states in unresponsive ALS patients in CLIS. This caveat may also be valid for the assessment of cognitive processes; a functioning BCI can be the solution. SignificanceMost of the studies of the neurophysiology of ALS patients focused on the early stage of the disease, and there are very few studies on the late stage when patients are completely paralyzed with no means of communication (i.e., CLIS). This study provides quantitative metrics of different neurophysiological aspects of these patients.

Effect of Ezogabine on Cortical and Spinal Motor Neuron Excitability in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease of the motor nervous system. Clinical studies have demonstrated cortical and spinal motor neuron hyperexcitability using transcranial magnetic stimulation and threshold tracking nerve conduction studies, respectively, although metrics of excitability have not been used as pharmacodynamic biomarkers in multi-site clinical trials.To ascertain whether ezogabine decreases cortical and spinal motor neuron excitability in ALS.This double-blind, placebo-controlled phase 2 randomized clinical trial sought consent from eligible participants from November 3, 2015, to November 9, 2017, and was conducted at 12 US sites within the Northeast ALS Consortium. Participants were randomized in equal numbers to a higher or lower dose of ezogabine or to an identical matched placebo, and they completed in-person visits at screening, baseline, week 6, and week 8 for clinical assessment and neurophysiological measurements.Participants were randomized to receive 600 mg/d or 900 mg/d of ezogabine or a matched placebo for 10 weeks.The primary outcome was change in short-interval intracortical inhibition (SICI; SICI-1 was used in analysis to reflect stronger inhibition from an increase in amplitude) from pretreatment mean at screening and baseline to the full-dose treatment mean at weeks 6 and 8. The secondary outcomes included levels of cortical motor neuron excitability (including resting motor threshold) measured by transcranial magnetic stimulation and spinal motor neuron excitability (including strength-duration time constant) measured by threshold tracking nerve conduction studies.A total of 65 participants were randomized to placebo (23), 600 mg/d of ezogabine (23), and 900 mg/d of ezogabine (19 participants); 45 were men (69.2%) and the mean (SD) age was 58.3 (8.8) years. The SICI-1 increased by 53% (mean ratio, 1.53; 95% CI, 1.12-2.09; P = .009) in the 900-mg/d ezogabine group vs placebo group. The SICI-1 did not change in the 600-mg/d ezogabine group vs placebo group (mean ratio, 1.15; 95% CI, 0.87-1.52; P = .31). The resting motor threshold increased in the 600-mg/d ezogabine group vs placebo group (mean ratio, 4.61; 95% CI, 0.21-9.01; P = .04) but not in the 900-mg/d ezogabine group vs placebo group (mean ratio, 1.95; 95% CI, -2.64 to 6.54; P = .40). Ezogabine caused a dose-dependent decrease in excitability by several other metrics, including strength-duration time constant in the 900-mg/d ezogabine group vs placebo group (mean ratio, 0.73; 95% CI, 0.60 to 0.87; P < .001).Ezogabine decreased cortical and spinal motor neuron excitability in participants with ALS, suggesting that such neurophysiological metrics may be used as pharmacodynamic biomarkers in multisite clinical trials.ClinicalTrials.gov Identifier: NCT02450552.

S57. Does the Acquisition of Social Cognitive Skills Over Adolescence Coincide With Neurophysiological Metric of Brain Maturation? A Cross-Sectional Analysis of the NAPLS-II Consortium Sample

Schizophrenia is conceptualized as a neurodevelopmental disorder, including a prodromal period during which biological vulnerability is expressed before the emergence of formal symptoms. Individuals at clinical high-risk for psychosis (CHR) are characterized by prodromal features resembling attenuated symptoms of psychosis and were previously found to show a blunted developmental trajectory for attaining theory of mind (ToM). Analysis of the NAPLS-II consortium sample was undertaken to determine whether ToM ability corresponds to neurophysiological metrics of brain maturation, and whether this relationship is altered in CHR.

Cortical distance, not cancellation, dominates inter-subject EEG gamma rhythm amplitude

The neurophysiological response to visual stimulation in both humans and animals is characterized by an increase in high frequency amplitude peaking in the gamma range (40–100Hz) and a suppression of low frequency amplitude peaking in the alpha range (10–16Hz). Due to the large number of studies linking amplitude and peak frequency to perception and neurological disorders, there is great interest in understanding the basis of inter-subject variability in gamma and alpha responses. To address this, we measured gamma and alpha amplitude and peak frequency of response to visual stimulation in 42 healthy humans. Using FMRI to delineate active cortical tissue in the same subjects, we correlated these neurophysiological metrics with two structural metrics: distance from active cortex to electrode, and dipole cancellation over active cortex. We find that distance strongly predicted inter-subject gamma amplitude, but had little effect on alpha amplitude, while cancellation had little effect on gamma or alpha amplitude. Neither alpha peak frequency nor gamma peak frequency correlated with our structural metrics. These results suggest that inter-subject variability in gamma amplitude may reflect gross morphology rather than neurophysiological variability, and should be interpreted with caution, while peak frequency may serve as a more sensitive metric of differences in neuronal activity across subjects.