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. Excitatory (E) and inhibitory (I) systems are critical for regulating the flow of information in the brain. Without narrow control over the E/I ratio, runaway excitation or quiescence would occur, impeding adequate information processing 1,2. In clinical terms, disruption of E/I balance has become a dominant theory on the pathogenesis of various neurodevelopmental disorders, and perhaps most explicitly in autism spectrum disorder (ASD) 3-6. The nature, however, of implicated E/I imbalances on ASD is diverse, ranging from molecular changes to altered neuronal circuits 3,7,8 and has been hypothesized to explain some of the variability in treatment responses 7. E/I ratio may be regulated at the level of synaptic currents and network connectivity 2,4,9,10 and both levels affect the dynamics of ongoing network activity 11-16. Thus, it may be possible to derive a quantitative measure of E/I ratio from ongoing brain activity, e.g., as measured with electroencephalography (EEG). Such a measure would allow testing hypotheses about the functional role of E/I ratio 8 , could enable physiological stratification within neurodevelopmental disorders and facilitate personalized application of E/I-modulating therapies 3,7,17 .

Neuronal oscillations exhibit complex amplitude fluctuations with autocorrelations that persist over thousands of oscillatory cycles. Such long-range temporal correlations (LRTC) are thought to reflect neuronal systems poised near a critical state, which would render them capable of quick reorganization and responsive to changing processing demands. When we concentrate, however, the influence of internal and external sources of distraction is better reduced, suggesting that neuronal systems involved with sustained attention could benefit from a shift toward the less volatile sub-critical state. To test these ideas, we recorded electroencephalography (EEG) from healthy volunteers during eyes-closed rest and during a sustained attention task requiring a speeded response to images deviating in their presentation duration. We show that for oscillations recorded during rest, high levels of alpha-band LRTC in the sensorimotor region predicted good reaction-time performance in the attention task. During task execution, however, fast reaction times were associated with high-amplitude beta and gamma oscillations with low LRTC. Finally, we show that reduced LRTC during the attention task compared to the rest condition correlates with better performance, while increased LRTC of oscillations from rest to attention is associated with reduced performance. To our knowledge, this is the first empirical evidence that 'resting-state criticality' of neuronal networks predicts swift behavioral responses in a sensorimotor task, and that steady attentive processing of visual stimuli requires brain dynamics with suppressed temporal complexity.

The diuretic agent bumetanide has recently been put forward as a novel, promising treatment of behavioral symptoms in autism spectrum disorder (ASD) and related conditions. Bumetanide can decrease neuronal chloride concentrations and may thereby reinstate g-aminobutyric acid (GABA)-ergic inhibition in patients with neurodevelopmental disorders. However, strategies to select appropriate candidates for bumetanide treatment are lacking. We hypothesized that a paradoxical response to GABA-enforcing agents such as benzodiazepines may predict the efficacy of bumetanide treatment in neurodevelopmental disorders. We describe a case of a 10-year-old girl with ASD, epilepsy, cortical dysplasia, and a 15q11.2 duplication who had exhibited marked behavioral arousal after previous treatment with clobazam, a benzodiazepine. We hypothesized that this response indicated the presence of depolarizing excitatory GABA and started bumetanide treatment with monitoring of behavior, cognition, and EEG. The treatment resulted in a marked clinical improvement in sensory behaviors, rigidity, and memory performance, which was substantiated by questionnaires and cognitive assessments. At baseline, the girl's EEG showed a depression in absolute a power, an electrographic sign previously related to ASD, which was normalized with bumetanide treatment. The effects of bumetanide on cognition and EEG seemed to mirror the "nonparadoxical" responses to benzodiazepines in healthy subjects. In addition, temporal lobe epilepsy and cortical dysplasia have both been linked to disturbed chloride homeostasis and seem to support our assumption that the observed paradoxical response was due to GABA-mediated excitation. This case highlights that a paradoxical behavioral response to GABA-enforcing drugs may constitute a framework for targeted treatment with bumetanide. PATIENT PRESENTATIONThe patient, a 10-year-old girl, was the fourth child of nonconsanguineous healthy parents. The family history was unremarkable, and the pregnancy and birth were uneventful. The child displayed an apparently normal development up to 4 years of age, when she began experiencing epileptic seizures. She was diagnosed with focal epilepsy with right temporoparietal localization, with focal seizures occurring in clusters.An MRI revealed a focal cortical dysplasia of the right temporal lobe. Coinciding with the onset of epilepsy, learning difficulty and behavioral rigidity emerged but were not evaluated at that time by a child psychiatrist. At the age of 8 years, she was referred to our clinic for diagnostic evaluation of increasing learning disability, behavioral inflexibility, and emotional disturbance. We observed a delay in language development, rigidity, and repetitive behaviors with peculiar

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.