Prediction of Behavioral Improvement Through Resting-State Electroencephalography and Clinical Severity in a Randomized Controlled Trial Testing Bumetanide in Autism Spectrum Disorder.

BACKGROUND: Mechanism-based treatments such as bumetanide are being repurposed for autism spectrum disorder. We recently reported beneficial effects on repetitive behavioral symptoms that might be related to regulating excitation-inhibition (E/I) balance in the brain. Here, we tested the neurophysiological effects of bumetanide and the relationship to clinical outcome variability and investigated the potential for machine learning-based predictions of meaningful clinical improvement. METHODS: Using modified linear mixed models applied to intention-to-treat population, we analyzed E/I-sensitive electroencephalography (EEG) measures before and after 91 days of treatment in the double-blind, randomized, placebo-controlled Bumetanide in Autism Medication and Biomarker study. Resting-state EEG of 82 subjects out of 92 participants (7-15 years) were available. Alpha frequency band absolute and relative power, central frequency, long-range temporal correlations, and functional E/I ratio treatment effects were related to the Repetitive Behavior Scale-Revised (RBS-R) and the Social Responsiveness Scale 2 as clinical outcomes. RESULTS: We observed superior bumetanide effects on EEG, reflected in increased absolute and relative alpha power and functional E/I ratio and in decreased central frequency. Associations between EEG and clinical outcome change were restricted to subgroups with medium to high RBS-R improvement. Using machine learning, medium and high RBS-R improvement could be predicted by baseline RBS-R score and EEG measures with 80% and 92% accuracy, respectively. CONCLUSIONS: Bumetanide exerts neurophysiological effects related to clinical changes in more responsive subsets, in whom prediction of improvement was feasible through EEG and clinical measures.

Adults with autism spectrum disorder show atypical patterns of thoughts and feelings during rest.

LAY ABSTRACT: Everyone knows the feeling of letting one's mind wander freely in a quiet moment. The thoughts and feelings experienced in those moments have been shown to influence our well-being-and vice versa. In this study, we looked at which thoughts and feelings are being experienced by adults with autism spectrum disorder and compared them to adults without autism spectrum disorder. In total, 88 adults with autism spectrum disorder and 90 adults without autism spectrum disorder were asked to rest for 5 min with their eyes closed and let their mind wander. Directly after, they filled in the Amsterdam Resting-State Questionnaire, which probes what participants were feeling and thinking during the period of rest. We found that adults with autism spectrum disorder tend to think less about others, felt less comfortable, and had more disrupted thoughts during the rest compared to adults without autism spectrum disorder. Interestingly, autism spectrum disorder participants reporting lower levels of comfort during the rest also reported more autism spectrum disorder symptoms, specifically in social behaviors and skills, attention switching, and imagination. We propose to use the eyes-closed rest condition in combination with the Amsterdam Resting-State Questionnaire more widely to shed light on aberrant thoughts and feelings in brain disorders and to study the effect of therapeutic interventions.

Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics.

Balance between excitation (E) and inhibition (I) is a key principle for neuronal network organization and information processing. Consistent with this notion, excitation-inhibition imbalances are considered a pathophysiological mechanism in many brain disorders including autism spectrum disorder (ASD). However, methods to measure E/I ratios in human brain networks are lacking. Here, we present a method to quantify a functional E/I ratio (fE/I) from neuronal oscillations, and validate it in healthy subjects and children with ASD. We define structural E/I ratio in an in silico neuronal network, investigate how it relates to power and long-range temporal correlations (LRTC) of the network's activity, and use these relationships to design the fE/I algorithm. Application of this algorithm to the EEGs of healthy adults showed that fE/I is balanced at the population level and is decreased through GABAergic enforcement. In children with ASD, we observed larger fE/I variability and stronger LRTC compared to typically developing children (TDC). Interestingly, visual grading for EEG abnormalities that are thought to reflect E/I imbalances revealed elevated fE/I and LRTC in ASD children with normal EEG compared to TDC or ASD with abnormal EEG. We speculate that our approach will help understand physiological heterogeneity also in other brain disorders.

An EEG nicotinic acetylcholine index to assess the efficacy of pro-cognitive compounds.

OBJECTIVES: Cognitive impairment models are used in clinical studies aimed at proving pharmacology of drugs being developed for Alzheimer's disease and other cognitive disorders. Due to rising interest in nicotinic agonists, we aimed to establish a method to monitor neurophysiological effects of modulating the nicotinic cholinergic system. METHODS: In a four-way cross-over study, eyes-closed rest EEG was recorded in 28 healthy subjects receiving mecamylamine-a nicotinic acetylcholine receptor (nAChR) antagonist, which induces temporary cognitive dysfunction in healthy subjects-with co-administration of placebo, nicotine or galantamine. RESULTS: Using machine learning to optimally contrast the effects of 30 mg of mecamylamine and placebo on the brain, we developed a nAChR index that consists of 10 EEG biomarkers and shows high classification accuracy (∼95% non-cross-validated, ∼70% cross-validated). Importantly, using the nAChR index, we demonstrate reversal of mecamylamine-induced neurophysiological effects due to 16 mg of galantamine as well as administering 21 mg of nicotine transdermally. CONCLUSIONS: Our findings indicate that the mecamylamine challenge model jointly with the nAChR index-a measure of the nicotinic EEG profile-could aid future proof-of-pharmacology studies to demonstrate effects of nicotinic cholinergic compounds. SIGNIFICANCE: This novel measure for quantifying nicotinic cholinergic effects on the EEG could serve as a useful tool in drug development of pro-cognitive compounds.

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electroencephalography (EEG) recordings to capture the brain's multi-faceted signature of disease or pharmacological intervention and use machine learning to improve classification performance. Using data from healthy subjects receiving scopolamine we developed an index of the muscarinic acetylcholine receptor antagonist (mAChR) consisting of 14 EEG biomarkers. This mAChR index yielded higher classification performance than any single EEG biomarker with cross-validated accuracy, sensitivity, specificity and precision ranging from 88-92%. The mAChR index also discriminated healthy elderly from patients with Alzheimer's disease (AD); however, an index optimized for AD pathophysiology provided a better classification. We conclude that integrating multiple EEG biomarkers can enhance the accuracy of identifying disease or drug interventions, which is essential for clinical trials.

Multiple phenotypes of resting-state cognition are altered in insomnia disorder.

BACKGROUND: Research has supported the role of cognitive processes in the development and maintenance of insomnia, yet a standardized characterization of mind-wandering experiences in insomniacs is lacking. OBJECTIVES: The aim was to understand the quantitative nature of thoughts and feelings during mind wandering in insomniacs and healthy controls and their relationship with sleep-related parameters. METHODS: We used the 5-minute eyes-closed wakeful rest as an experimental model condition of mind wandering. Forty-seven individuals with insomnia disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (48.66±15.62 years; 31 women) and 29 healthy controls (50.66±15.14 years; 17 women) participated in the experiments and completed the Amsterdam Resting-State Questionnaire (ARSQ) immediately after the resting session. Participants also completed the Insomnia Severity Index (ISI), the Pittsburgh Sleep Quality Index (PSQI), the Dysfunctional Beliefs and Attitudes About Sleep Scale (DBAS). Statistical analyses included multiple regression to elucidate the independent determinants of ARSQ phenotypes. RESULTS: Participants with insomnia presented higher ISI, PSQI, and DBAS scores than did healthy controls. Insomniacs had strikingly different scores on most dimensions of the ARSQ, in particular Discontinuity of Mind, Self, Sleepiness, and Health Concern, that correlated positively with ISI and DBAS. Multiple regressions highlighted that for insomniacs, ISI was the best predictor of both Discontinuity of Mind and Health Concern. CONCLUSIONS: Resting-state activity in insomnia is altered and it seems to be related to unhelpful beliefs and insomnia severity. Resting-state neuroimaging in combination with the ARSQ could reveal important associations between these aberrant cognitive scores and their underlying systems-level brain mechanisms.