Utilizing Entropy of Cadence to Optimize Cycling Rehabilitation in Individuals With Parkinson's Disease.

BACKGROUND: Previous studies have established that increased Sample Entropy (SampEn) of cadence, a measure of non-linear variability, during dynamic cycling leads to greater improvements in motor function for individuals with Parkinson's disease (PD). However, there is significant variability in responses among individuals with PD due to symptoms and disease progression. OBJECTIVES: The aim of this study was to develop and test a paradigm for adapting a cycling exercise intervention using SampEn of cadence and rider effort to improve motor function. METHODS: Twenty-two participants were randomized into either patient-specific adaptive dynamic cycling (PSADC) or non-adaptive (NA) group. SampEn of cadence was calculated after each of the 12 sessions, and motor function was evaluated using the Kinesia test. Pearson's correlation coefficient was used to analyze the relationship between SampEn of cadence and motor function improvement. Multiple linear regression (MLR) was used to identify the strongest predictors of motor function improvement. RESULTS: Pearson's correlation coefficient revealed a significant correlation between SampEn of cadence and motor function improvements (R2 = -.545, P = .009), suggesting that higher SampEn of cadence led to greater motor function improvement. MLR demonstrated that SampEn of cadence was the strongest predictor of motor function improvement (ß = -8.923, t = -2.632, P = .018) over the BMI, Levodopa equivalent daily dose, and effort. CONCLUSIONS: The findings show that PSADC paradigm promoted a greater improvement in motor function than NA dynamic cycling. These data will be used to develop a predictive model to optimize motor function improvement after cycling in individuals with PD.

The effect of early postnatal auditory stimulation on outcomes in preterm infants.

Preterm infants are deprived of in utero sensory stimulation during the third trimester, an important period of central nervous system development. As a result, maturational trajectories are often reduced in infants born preterm. One such system affected is the brain including the auditory and respiratory control pathways. During normal pregnancy the intrauterine environment attenuates external auditory stimuli while exposing the fetus to filtered maternal voice, intra-abdominal sounds, and external stimuli. In contrast, during the third trimester of development, preterm infants are exposed to a vastly different soundscape including non-attenuated auditory sounds and a lack of womb related stimuli, both of which may affect postnatal brain maturation. Therefore, fostering a nurturing postnatal auditory environment during hospitalization may have a significant impact on related outcomes of preterm infants. Studies using a range of postnatal auditory stimulations have suggested that exposure to sounds or lack thereof can have a significant impact on outcomes. However, studies are inconsistent with sound levels, duration of exposure to auditory stimuli, and the gestational age at which infants are exposed. IMPACT: Auditory stimulation can provide a low cost and low risk intervention to stabilize respiration, improve neuronal maturation and reduce long-term sequelae in preterm infants. The potential benefits of auditory stimulation are dependent on the type of sound, the duration of exposure and age at time of exposure. Future studies should focus on the optimal type and duration of sound exposure and postnatal developmental window to improve outcomes.

Interleaved Multi-Contact Peripheral Nerve Stimulation to Enhance Reproduction of Tactile Sensation: A Computational Modeling Study.

Peripheral nerve stimulation (PNS) is an effective means to elicit sensation for rehabilitation of people with loss of a limb or limb function. While most current PNS paradigms deliver current through single electrode contacts to elicit each tactile percept, multi-contact extraneural electrodes offer the opportunity to deliver PNS with groups of contacts individually or simultaneously. Multi-contact PNS strategies could be advantageous in developing biomimetic PNS paradigms to recreate the natural neural activity during touch, because they may be able to selectively recruit multiple distinct neural populations. We used computational models and optimization approaches to develop a novel biomimetic PNS paradigm that uses interleaved multi-contact (IMC) PNS to approximate the critical neural coding properties underlying touch. The IMC paradigm combines field shaping, in which two contacts are active simultaneously, with pulse-by-pulse contact and parameter variations throughout the touch stimulus. We show in simulation that IMC PNS results in better neural code mimicry than single contact PNS created with the same optimization techniques, and that field steering via two-contact IMC PNS results in better neural code mimicry than one-contact IMC PNS. We also show that IMC PNS results in better neural code mimicry than existing PNS paradigms, including prior biomimetic PNS. Future clinical studies will determine if the IMC paradigm can improve the naturalness and usefulness of sensory feedback for those with neurological disorders.

Dynamics of ventilatory pattern variability and Cardioventilatory Coupling during systemic inflammation in rats.

Introduction: Biometrics of common physiologic signals can reflect health status. We have developed analytics to measure the predictability of ventilatory pattern variability (VPV, Nonlinear Complexity Index (NLCI) that quantifies the predictability of a continuous waveform associated with inhalation and exhalation) and the cardioventilatory coupling (CVC, the tendency of the last heartbeat in expiration to occur at preferred latency before the next inspiration). We hypothesized that measures of VPV and CVC are sensitive to the development of endotoxemia, which evoke neuroinflammation. Methods: We implanted Sprague Dawley male rats with BP transducers to monitor arterial blood pressure (BP) and recorded ventilatory waveforms and BP simultaneously using whole-body plethysmography in conjunction with BP transducer receivers. After baseline (BSLN) recordings, we injected lipopolysaccharide (LPS, n = 8) or phosphate buffered saline (PBS, n =3) intraperitoneally on 3 consecutive days. We recorded for 4-6 h after the injection, chose 3 epochs from each hour and analyzed VPV and CVC as well as heart rate variability (HRV). Results: First, the responses to sepsis varied across rats, but within rats the repeated measures of NLCI, CVC, as well as respiratory frequency (fR), HR, BP and HRV had a low coefficient of variation, (<0.2) at each time point. Second, HR, fR, and NLCI increased from BSLN on Days 1-3; whereas CVC decreased on Days 2 and 3. In contrast, changes in BP and the relative low-(LF) and high-frequency (HF) of HRV were not significant. The coefficient of variation decreased from BSLN to Day 3, except for CVC. Interestingly, NLCI increased before fR in LPS-treated rats. Finally, we histologically confirmed lung injury, systemic inflammation via ELISA and the presence of the proinflammatory cytokine, IL-1ß, with immunohistochemistry in the ponto-medullary respiratory nuclei. Discussion: Our findings support that NLCI reflects changes in the rat's health induced by systemic injection of LPS and reflected in increases in HR and fR. CVC decreased over the course to the experiment. We conclude that NLCI reflected the increase in predictability of the ventilatory waveform and (together with our previous work) may reflect action of inflammatory cytokines on the network generating respiration.

Association between postictal EEG suppression, postictal autonomic dysfunction, and sudden unexpected death in epilepsy: Evidence from intracranial EEG.

OBJECTIVE: The association between postictal electroencephalogram (EEG) suppression (PES), autonomic dysfunction, and Sudden Unexpected Death in Epilepsy (SUDEP) remains poorly understood. We compared PES on simultaneous intracranial and scalp-EEG and evaluated the association of PES with postictal heart rate variability (HRV) and SUDEP outcome. METHODS: Convulsive seizures were analyzed in patients with drug-resistant epilepsy at 5 centers. Intracranial PES was quantified using the Hilbert transform. HRV was quantified using root mean square of successive differences of interbeat intervals, low-frequency to high-frequency power ratio, and RR-intervals. RESULTS: There were 64 seizures from 63 patients without SUDEP and 11 seizures from 6 SUDEP patients. PES occurred in 99% and 87% of seizures on intracranial-EEG and scalp-EEG, respectively. Mean PES duration in intracranial and scalp-EEG was similar. Intracranial PES was regional (<90% of channels) in 46% of seizures; scalp PES was generalized in all seizures. Generalized PES showed greater decrease in postictal parasympathetic activity than regional PES. PES duration and extent were similar between patients with and without SUDEP. CONCLUSIONS: Regional intracranial PES can be present despite scalp-EEG demonstrating generalized or no PES. Postictal autonomic dysfunction correlates with the extent of PES. SIGNIFICANCE: Intracranial-EEG demonstrates changes in autonomic regulatory networks not seen on scalp-EEG.

A Predictive Biomarker Model Using Quantitative Electroencephalography in Adolescent Major Depressive Disorder.

Background: With evolving understanding of psychiatric diagnosis and treatment, demand for biomarkers for psychiatric disorders in children and adolescents has grown dramatically. This study utilized quantitative electroencephalography (qEEG) to develop a predictive model for adolescent major depressive disorder (MDD). We hypothesized that youth with MDD compared to healthy controls (HCs) could be differentiated using a singular logistic regression model that utilized qEEG data alone. Methods: qEEG data and psychometric measures were obtained in adolescents aged 14-17 years with MDD (n = 35) and age- and gender-matched HCs (n = 14). qEEG in four frequency bands (alpha, beta, theta, and delta) was collected and coherence, cross-correlation, and power data streams obtained. A two-stage analytical framework was then used to develop the final logistic regression model, which was then evaluated using a receiver-operating characteristic curve (ROC) analysis. Results: Within the initial analysis, six qEEG dyads (all coherence) had significant predictive values. Within the final biomarkers, just four predictors, including F3-C3 (R frontal) alpha coherence, P3-O1 (R parietal) theta coherence, CZ-PZ (central) beta coherence, and P8-O2 (L parietal occipital) theta power were used in the final model, which yielded an ROC area of 0.8226. Conclusions: We replicated our previous findings of qEEG differences between adolescents and HCs and successfully developed a single-value predictive model with a robust ROC area. Furthermore, the brain areas involved in behavioral disinhibition and resting state/default mode networks were again shown to be involved in the observed differences. Thus, qEEG appears to be a potential low-cost and effective intermediate biomarker for MDD in youth.

An enhanced EEG microstate recognition framework based on deep neural networks: an application to Parkinson's disease.

Variations in brain activity patterns reveal impairments of motor and cognitive functions in the human brain. Electroencephalogram (EEG) microstates embody brain activity patterns at a microscopic time scale. However, current microstate analysis method can only recognize less than 90% of EEG signals per subject, which severely limits the characterization of dynamic brain activity. As an application to early Parkinson's disease (PD), we propose an enhanced EEG microstate recognition framework based on deep neural networks, which yields recognition rates from 90% to 99%, as accompanied by a strong anti-artifact property. Additionally, gradient-weighted class activation mapping, as a visualization technique, is employed to locate the activated functional brain regions of each microstate class. We find that each microstate class corresponds to a particular activated brain region. Finally, based on the improved identification of microstate sequences, we explore the EEG microstate characteristics and their clinical associations. We show that the decreased occurrences of a particular microstate class reflect the degree of cognitive decline in early PD, and reduced transitions between certain microstates suggest injury in motor-related brain regions. The novel EEG microstate recognition framework paves the way to revealing more effective biomarkers for early PD.

BiCoSS: Toward Large-Scale Cognition Brain With Multigranular Neuromorphic Architecture.

The further exploration of the neural mechanisms underlying the biological activities of the human brain depends on the development of large-scale spiking neural networks (SNNs) with different categories at different levels, as well as the corresponding computing platforms. Neuromorphic engineering provides approaches to high-performance biologically plausible computational paradigms inspired by neural systems. In this article, we present a biological-inspired cognitive supercomputing system (BiCoSS) that integrates multiple granules (GRs) of SNNs to realize a hybrid compatible neuromorphic platform. A scalable hierarchical heterogeneous multicore architecture is presented, and a synergistic routing scheme for hybrid neural information is proposed. The BiCoSS system can accommodate different levels of GRs and biological plausibility of SNN models in an efficient and scalable manner. Over four million neurons can be realized on BiCoSS with a power efficiency of 2.8k larger than the GPU platform, and the average latency of BiCoSS is 3.62 and 2.49 times higher than conventional architectures of digital neuromorphic systems. For the verification, BiCoSS is used to replicate various biological cognitive activities, including motor learning, action selection, context-dependent learning, and movement disorders. Comprehensively considering the programmability, biological plausibility, learning capability, computational power, and scalability, BiCoSS is shown to outperform the alternative state-of-the-art works for large-scale SNN, while its real-time computational capability enables a wide range of potential applications.

Closing the loop of DBS using the beta oscillations in cortex.

Cortical information has great importance to reflect the deep brain stimulation (DBS) effects for Parkinson's disease patients. Using cortical activities to feedback is an available closed-loop idea for DBS. Previous studies have demonstrated the pathological beta (12-35 Hz) cortical oscillations can be suppressed by appropriate DBS settings. Thus, here we propose to close the loop of DBS based on the beta oscillations in cortex. By modify the cortico-basal ganglia-thalamic neural loop model, more biologically realistic underlying the Parkinsonian phenomenon is approached. Stimulation results show the proposed closed-loop DBS strategy using cortical beta oscillation as feedback information has more profound roles in alleviating the pathological neural abnormality than the traditional open-loop DBS. Additionally, we compare the stimulation effects with subthalamic nucleus feedback strategy. It is shown that using cortical beta information as the feedback signals can further enlarge the control parameter space based on proportional-integral control structure with a lower energy expenditure. This work may pave the way to optimizing the DBS effects in a closed-loop arrangement.

Delayed Feedback-Based Suppression of Pathological Oscillations in a Neural Mass Model.

Suppression of excessively synchronous beta frequency (12-35 Hz) oscillatory activity in the basal ganglia is believed to correlate with the alleviation of hypokinetic motor symptoms of the Parkinson's disease. Delayed feedback is an effective strategy to interrupt the synchronization and has been used in the design of closed-loop neuromodulation methods computationally. Although tremendous efforts in this are being made by optimizing delayed feedback algorithm and stimulation waveforms, there are still remaining problems in the selection of effective parameters in the delayed feedback control schemes. In most delayed feedback neuromodulation strategies, the stimulation signal is obtained from the local field potential (LFP) of the excitatory subthalamic nucleus (STN) neurons and then is administered back to STN itself only. The inhibitory external globus pallidus (GPe) nucleus in the excitatory-inhibitory STN-GPe reciprocal network has not been involved in the design of the delayed feedback control strategies. Thus, considering the role of GPe, this paper proposes three schemes involving GPe in the design of the delayed feedback strategies and compared their effectiveness to the traditional paradigm using STN only. Based on a neural mass model of STN-GPe network having capability of simulating the LFP directly, the proposed stimulation strategies are tested and compared. Our simulation results show that the four types of delayed feedback control schemes are all effective, even if with a simple linear delayed feedback algorithm. But the three new control strategies we propose here further improve the control performance by enlarging the oscillatory suppression space and reducing the energy expenditure, suggesting that they may be more effective in applications. This paper may guide a new approach to optimize the closed-loop deep brain stimulation treatment to alleviate the Parkinsonian state by retargeting the measurement and stimulation nucleus.

Hyperventilation Increases the Randomness of Ocular Palatal Tremor Waveforms.

Hyperventilation changes the extracellular pH modulating many central pathologies, such as tremor. The questions that remain unanswered are the following: (1) Hyperventilation modulates which aspects of the oscillations? (2) Whether the effects of hyperventilation are instantaneous and the recovery is rapid and complete? Here we study the effects of hyperventilation on eye oscillations in the syndrome of oculopalatal tremor (OPT), a disease model affecting the inferior olive and cerebellar system. These regions are commonly involved in the pathogenesis of many movement disorders. The focus on the ocular motor system also allows access to the well-known physiology and precise measurement techniques. We found that hyperventilation causes modest but insignificant changes in the intensity of oscillation displacement (i.e., how large the eye excursions are) and velocity (i.e., how fast do the eyes move during oscillations). We found the robust increase in the randomness of the oscillatory waveform during hyperventilation and it instantaneously reverts to the baseline after hyperventilation. The subsequent analysis classified the oscillations according to their waveform shape and randomness into different clusters. The hyperventilation substantially changed the cluster type in 60% of the subjects, but it reverted to the pre-hyperventilation cluster at the conclusion of the hyperventilation. In summary, hyperventilation instantaneously affects the randomness of the oscillatory waveforms but there are less substantial effects on the intensity. The deficits reverse immediately at the end of the hyperventilation.

Gabapentin and memantine increases randomness of oscillatory waveform in ocular palatal tremor.

Syndrome of oculopalatal tremor (OPT) causes pendular nystagmus of the eyes and its disabling consequence on the visual system. Classic pharmacotherapeutic studies revealed reduction in the eye velocity of the oscillatory waveforms. Subjective improvement in vision, however, remains out of proportionately low. Elegant models depicting quasi-sinusoidal coarse oscillations of the eyes highlighted two distinct oscillators; one at the inferior olive causing primary 2 Hz oscillations, while the second, independent oscillator, at the cerebellum adding the randomness to the waveform. Here we examined whether pharmacotherapy affects the randomness of the oscillatory waveform. Horizontal, vertical, and torsional angular eye positions were measured independently from both eyes as six subjects with OPT directed gaze toward a straight-ahead target. The measurements were performed before administration of alpha-2-delta calcium channel blocker (gabapentin) or NMDA receptor antagonist (memantine) and after the subjects were treated with each of these drugs for at least 8 days. Amplitude and velocity of eye oscillations were reduced by gabapentin and memantine, but there was an increase in the waveform randomness. We found that the increase in randomness was proportionate to the amount of reduction in the waveform velocity or amplitude. Hierarchical clustering revealed distinct patterns of oscillatory waveforms, with each subject belonging to a specific cluster group. The pharmacotherapy changed the waveform clustering pattern of the waveform in each subject. We conclude that in addition to incomplete resolution of the oscillation intensity, increased randomness could be one of the reasons why there is not enough clinical difference in the patients' visual quality.

α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated from heterogeneous sources (e.g., disease related data, demographic, mobility and social media data), in this work, we propose and develop an AI-driven system (named α-Satellite), as an initial offering, to provide dynamic COVID-19 risk assessment in the United States. More specifically, given a point of interest (POI), the system will automatically provide risk indices associated with it in a hierarchical manner (e.g., state, county, POI) to enable people to select appropriate actions for protection while minimizing disruptions to daily life. To comprehensively evaluate our system for dynamic COVID-19 risk assessment, we first conduct a set of empirical studies; and then we validate it based on a real-world dataset consisting of 5,060 annotated POIs, which achieves the area of under curve (AUC) of 0.9202. As of June 18, 2020, α-Satellite has had 56,980 users. Based on the feedback from its large-scale users, we perform further analysis and have three key findings: i) people from more severe regions (i.e., with larger numbers of COVID-19 cases) have stronger interests using our system to assist with actionable information; ii) users are more concerned about their nearby areas in terms of COVID-19 risks; iii) the user feedback about their perceptions towards COVID-19 risks of their query POIs indicate the challenge of public concerns about the safety versus its negative effects on society and the economy. Our system and generated datasets have been made publicly accessible via our website.

INSMA: An integrated system for multimodal data acquisition and analysis in the intensive care unit.

Modern intensive care units (ICU) are equipped with a variety of different medical devices to monitor the physiological status of patients. These devices can generate large amounts of multimodal data daily that include physiological waveform signals (arterial blood pressure, electrocardiogram, respiration), patient alarm messages, numeric vitals data, etc. In order to provide opportunities for increasingly improved patient care, it is necessary to develop an effective data acquisition and analysis system that can assist clinicians and provide decision support at the patient bedside. Previous research has discussed various data collection methods, but a comprehensive solution for bedside data acquisition to analysis has not been achieved. In this paper, we proposed a multimodal data acquisition and analysis system called INSMA, with the ability to acquire, store, process, and visualize multiple types of data from the Philips IntelliVue patient monitor. We also discuss how the acquired data can be used for patient state tracking. INSMA is being tested in the ICU at University Hospitals Cleveland Medical Center.

Comments and Corrections Corrections to "Application of Reinforcement Learning to Deep Brain Stimulation in a Computational Model of Parkinson's Disease".

In the above article [1], financial support was incorrectly published. The correct information is as follows: This work was supported in part by the National Natural Science Foundation of China under Grant 61501330 and Grant 61771330, and in part by the Tianjin Municipal Special Program of Talents Development for Excellent Youth Scholars under Grant TJTZJH-QNBJRC-2-2.

The role of coupling connections in a model of the cortico-basal ganglia-thalamocortical neural loop for the generation of beta oscillations.

Excessive neural synchronization in the cortico-basal ganglia-thalamocortical circuits in the beta (ß) frequency range (12-35 Hz) is closely associated with dopamine depletion in Parkinson's disease (PD) and correlated with movement impairments, but the neural basis remains unclear. In this work, we establish a double-oscillator neural mass model for the cortico-basal ganglia-thalamocortical closed-loop system and explore the impacts of dopamine depletion induced changes in coupling connections within or between the two oscillators on neural activities within the loop. Spectral analysis of the neural mass activities revealed that the power and frequency of their principal components are greatly dependent on the coupling strengths between nuclei. We found that the increased intra-coupling in the basal ganglia-thalamic (BG-Th) oscillator contributes to increased oscillations in the lower ß frequency band (12-25 Hz), while increased intra-coupling in the cortical oscillator mainly contributes to increased oscillations in the upper ß frequency band (26-35 Hz). Interestingly, pathological upper ß oscillations in the cortical oscillator may be another origin of the lower ß oscillations in the BG-Th oscillator, in addition to increased intra-coupling strength within the BG-Th network. Lower ß oscillations in the BG-Th oscillator can also change the dominant oscillation frequency of a cortical nucleus from the upper to the lower ß band. Thus, this work may pave the way towards revealing a possible neural basis underlying the Parkinsonian state.

Application of Reinforcement Learning to Deep Brain Stimulation in a Computational Model of Parkinson's Disease.

Deep brain stimulation (DBS) has been proven to be an effective treatment to deal with the symptoms of Parkinson's disease (PD). Currently, the DBS is in an open-loop pattern with which the stimulation parameters remain constant regardless of fluctuations in the disease state, and adjustments of parameters rely mostly on trial and error of experienced clinicians. This could bring adverse effects to patients due to possible overstimulation. Thus closed-loop DBS of which stimulation parameters are automatically adjusted based on variations in the ongoing neurophysiological signals is desired. In this paper, we present a closed-loop DBS method based on reinforcement learning (RL) to regulate stimulation parameters based on a computational model. The network model consists of interconnected biophysically-based spiking neurons, and the PD state is described as distorted relay reliability of thalamus (TH). Results show that the RL-based closed-loop control strategy can effectively restore the distorted relay reliability of the TH but with less DBS energy expenditure.

Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons.

Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons. In this paper, we present a hardware efficient, scalable, and real-time computing strategy for the implementation of large-scale biologically meaningful neural networks with one million multi-compartment neurons (CMNs). The hardware platform uses four Altera Stratix III field-programmable gate arrays, and both the cellular and the network levels are considered, which provides an efficient implementation of a large-scale spiking neural network with biophysically plausible dynamics. At the cellular level, a cost-efficient multi-CMN model is presented, which can reproduce the detailed neuronal dynamics with representative neuronal morphology. A set of efficient neuromorphic techniques for single-CMN implementation are presented with all the hardware cost of memory and multiplier resources removed and with hardware performance of computational speed enhanced by 56.59% in comparison with the classical digital implementation method. At the network level, a scalable network-on-chip (NoC) architecture is proposed with a novel routing algorithm to enhance the NoC performance including throughput and computational latency, leading to higher computational efficiency and capability in comparison with state-of-the-art projects. The experimental results demonstrate that the proposed work can provide an efficient model and architecture for large-scale biologically meaningful networks, while the hardware synthesis results demonstrate low area utilization and high computational speed that supports the scalability of the approach.

Collection and Analysis of Multimodal Data for SUDEP Biomarker Discovery.

This paper discusses the acquisition and processing of multimodal physiological data from patients with epilepsy in Epilepsy Monitoring Units for the discovery of risk factors for Sudden Expected Death in Epilepsy (SUDEP) that can be combined through integrative analysis for biomarker discovery.

Resting-State Quantitative Electroencephalography Demonstrates Differential Connectivity in Adolescents with Major Depressive Disorder.

Background: Biomarkers for psychiatric disorders in children and adolescents are urgently needed. This cross-sectional pilot study investigated quantitative electroencephalogram (qEEG), a promising intermediate biomarker, in pediatric patients with major depressive disorder (MDD) compared with healthy controls (HCs). We hypothesized that youth with MDD would have increased coherence (connectivity) and absolute alpha power in the frontal cortex compared with HC. Methods: qEEG was obtained in adolescents aged 14-17 years with MDD (n = 25) and age- and gender-matched HCs (n = 14). The primary outcome was overall coherence on qEEG in the four frequency bands (alpha, beta, theta, and delta). Other outcomes included frontal-only coherence, overall and frontal-only qEEG power, and clinician-rated measures of anhedonia and anxiety. Results: Average coherence in the theta band was significantly lower in MDD patients versus HCs, and also lower in frontal cortex among MDD patients. Seven node pairs were significantly different or trending toward significance between MDD and HC; all had lower coherence in MDD patients. Average frontal delta power was significantly higher in MDD versus HCs. Conclusions: Brain connectivity measured by qEEG differs significantly between adolescents with MDD and HCs. Compared with HCs, youth with MDD showed decreased connectivity, yet no differences in power in any frequency bands. In the frontal cortex, youth with MDD showed decreased resting connectivity in the alpha and theta frequency bands. Impaired development of a resting-state brain network (e.g., default mode network) in adolescents with MDD may represent an intermediate phenotype that can be assessed with qEEG.