Errors elicit frontoparietal theta-gamma coupling that is modulated by endogenous estradiol levels.

Cognitive control-related error monitoring is intimately involved in behavioral adaptation, learning, and individual differences in a variety of psychological traits and disorders. Accumulating evidence suggests that a focus on women's health and ovarian hormones is critical to the study of such cognitive brain functions. Here we sought to identify a novel index of error monitoring using a time-frequency based phase amplitude coupling (t-f PAC) measure and examine its modulation by endogenous levels of estradiol in females. Forty-three healthy, naturally cycling young adult females completed a flanker task while continuous electroencephalogram was recorded on four occasions across the menstrual cycle. Results revealed significant error-related t-f PAC between theta phase generated in fronto-central areas and gamma amplitude generated in parietal-occipital areas. Moreover, this error-related theta-gamma coupling was enhanced by endogenous levels of estradiol both within females across the cycle as well as between females with higher levels of average circulating estradiol. While the role of frontal midline theta in error processing is well documented, this paper extends the extant literature by illustrating that error monitoring involves the coordination between multiple distributed systems with the slow midline theta activity modulating the power of gamma-band oscillatory activity in parietal regions. They further show enhancement of inter-regional coupling by endogenous estradiol levels, consistent with research indicating modulation of cognitive control neural functions by the endocrine system in females. Together, this work identifies a novel neurophysiological marker of cognitive control-related error monitoring in females that has implications for neuroscience and women's health.

Perceptions of antibiotic stewardship programmes and determinants of antibiotic prescribing patterns among physicians in tertiary hospitals in Bangladesh: implications for future policy and practice.

BACKGROUND: The concerning growth of antimicrobial resistance (AMR) renders common infections life-threatening due to irrational antibiotic use and a lack of effective antimicrobial stewardship programmes (ASPs). AIM: To investigate the awareness, perceptions and practices of physicians regarding ASPs, AMR and antibiotic prescribing in tertiary care hospitals in Bangladesh. METHODS: This hospital-based cross-sectional survey was conducted in 11 tertiary care hospitals across Bangladesh between September 2020 and January 2021. A semi-structured questionnaire was administered through face-to-face interviews for data collection. Descriptive and multi-variate analyses were performed using STATA Version 13. RESULTS: In total, 559 physicians were enrolled in this survey. Overall, 40.6% [95% confidence interval (CI) 36.5-44.8] of physicians reported being aware of ASPs, and this figure was higher in public hospitals compared with private hospitals (43.8% vs 27.1%). None of the study hospitals had any ASP initiatives. More than half (55.1%) of the participants were willing to receive feedback from an ASP on their antibiotic selection. Only 30.9% of respondents stated that they wait for the findings of microbiological tests before prescribing antibiotics, although challenges included empiric use of broad-spectrum antibiotics, delayed laboratory results and the existence of drug-resistant patients. In contrast, physicians aware of ASPs were 33% (adjusted odds ratio 0.67, 95% CI 0.45-0.98; P=0.033) less likely to wait for laboratory results before prescribing antibiotics. However, 42.5% of physicians considered patient affordability of purchasing antibiotics when prescribing. CONCLUSIONS: Physicians' fundamental knowledge of ASPs and rational antibiotic prescription were found to fall short of the standard. Context-specific and integrated ASP activities, availability and use of guidelines, and improved laboratory facilities are required to battle AMR in Bangladesh.

Community detection in multi-frequency EEG networks.

Functional connectivity networks of the human brain are commonly studied using tools from complex network theory. Existing methods focus on functional connectivity within a single frequency band. However, it is well-known that higher order brain functions rely on the integration of information across oscillations at different frequencies. Therefore, there is a need to study these cross-frequency interactions. In this paper, we use multilayer networks to model functional connectivity across multiple frequencies, where each layer corresponds to a different frequency band. We then introduce the multilayer modularity metric to develop a multilayer community detection algorithm. The proposed approach is applied to electroencephalogram (EEG) data collected during a study of error monitoring in the human brain. The differences between the community structures within and across different frequency bands for two response types, i.e. error and correct, are studied. The results indicate that following an error response, the brain organizes itself to form communities across frequencies, in particular between theta and gamma bands while a similar cross-frequency community formation is not observed following the correct response.

Medial Prefrontal Cortex Serotonin Input Regulates Cognitive Flexibility in Mice.

The medial prefrontal cortex (mPFC) regulates cognitive flexibility and emotional behavior. Neurons that release serotonin project to the mPFC, and serotonergic drugs influence emotion and cognition. Yet, the specific roles of endogenous serotonin release in the mPFC on neurophysiology and behavior are unknown. We show that axonal serotonin release in the mPFC directly inhibits the major mPFC output neurons. In serotonergic neurons projecting from the dorsal raphe to the mPFC, we find endogenous activity signatures pre-reward retrieval and at reward retrieval during a cognitive flexibility task. In vivo optogenetic activation of this pathway during pre-reward retrieval selectively improved extradimensional rule shift performance while inhibition impaired it, demonstrating sufficiency and necessity for mPFC serotonin release in cognitive flexibility. Locomotor activity and anxiety-like behavior were not affected by either optogenetic manipulation. Collectively, our data reveal a powerful and specific modulatory role of endogenous serotonin release from dorsal raphe-to-mPFC projecting neurons in cognitive flexibility.

Assessment of dynamic phase amplitude coupling using matching pursuit.

BACKGROUND: Neuronal transmission and communication are enabled by the interactions across multiple oscillatory frequencies. Phase amplitude coupling (PAC) quantifies these interactions during cognitive brain functions. PAC is defined as the modulation of the amplitude of the high frequency rhythm by the phase of the low frequency rhythm. Existing PAC measures are limited to quantifying the average coupling within a time window of interest. However, as PAC is dynamic, it is necessary to quantify time-varying PAC. Existing time-varying PAC approaches are based on using a sliding window approach. These approaches do not adapt to the signal dynamics, and thus the arbitrary selection of the window length substantially hampers PAC estimation. NEW METHOD: To address the limitations of sliding window PAC estimation approaches, in this paper, we introduce a dynamic PAC measure that relies on matching pursuit (MP). This approach decomposes the signal into time and frequency localized atoms that best describe the signal. Dynamic PAC is quantified by computing the coupling between these time and frequency localized atoms. As such, the proposed approach is data-driven and tracks the change of PAC with time. We evaluate the proposed method on both synthesized and real electroencephalogram (EEG) data. RESULTS: The results from synthesized data show that the proposed method detects the coupled frequencies and the time variation of the coupling correctly with high time and frequency resolution. The analysis of EEG data revealed theta-gamma and alpha-gamma PAC during response and post-response time intervals. COMPARISON WITH EXISTING METHOD(S): Compared to the existing sliding window based approach, the proposed MP based dynamic PAC measure is more effective at capturing PAC within a short time window and is more robust to noise. This is because this method quantifies the low frequency phase and high frequency amplitude components from the time and frequency localized MP atoms and, as such, can capture the signal dynamics. CONCLUSIONS: We posit that the proposed MP based data-driven approach offers a more robust and possibly more sensitive method to effectively quantify and track dynamic PAC.

Graph Theoretic Analysis of Multilayer EEG Connectivity Networks.

Over the past twenty years, functional connectivity of the human brain has been studied in detail using tools from complex network theory. These methods include graph theoretic metrics ranging from the micro-scale such as the degree of a node to the macro-scale such as the small worldness of the brain network. However, most of these network models focus on average activity within a time window of interest and given frequency band. Therefore, they cannot capture the changes in network connectivity across time and different frequency bands. Recently, multilayer brain networks have attracted a lot of attention as they can capture the full view of neuronal connectivity. In this paper, we introduce a multilayer view of the functional connectivity network of the brain, where each layer corresponds to a different frequency band. We construct multi-frequency connectivity networks from electroencephalogram data where the intra-layer edges are quantified by phase synchrony while the inter-layer edges are quantified by phase-amplitude coupling. We then introduce multilayer degree, participation coefficient and clustering coefficient to quantify the centrality of nodes across frequency layers and to identify the importance of different frequency bands. The proposed framework is applied to electroencephalogram data collected during a study of error monitoring in the human brain.

Multivariate Analysis of Bivariate Phase-Amplitude Coupling in EEG Data Using Tensor Robust PCA.

Cross-frequency coupling is emerging as a crucial mechanism that coordinates the integration of spectrally and spatially distributed neuronal oscillations. Recently, phase-amplitude coupling, a form of cross-frequency coupling, where the phase of a slow oscillation modulates the amplitude of a fast oscillation, has gained attention. Existing phase-amplitude coupling measures are mostly confined to either coupling within a region or between pairs of brain regions. Given the availability of multi-channel electroencephalography recordings, a multivariate analysis of phase amplitude coupling is needed to accurately quantify the coupling across multiple frequencies and brain regions. In the present work, we propose a tensor based approach, i.e., higher order robust principal component analysis, to identify response-evoked phase-amplitude coupling across multiple frequency bands and brain regions. Our experiments on both simulated and electroencephalography data demonstrate that the proposed multivariate phase-amplitude coupling method can capture the spatial and spectral dynamics of phase-amplitude coupling more accurately compared to existing methods. Accordingly, we posit that the proposed higher order robust principal component analysis based approach filters out the background phase-amplitude coupling activity and predominantly captures the event-related phase-amplitude coupling dynamics to provide insight into the spatially distributed brain networks across different frequency bands.

Time-Frequency Based Phase-Amplitude Coupling Measure For Neuronal Oscillations.

Oscillatory activity in the brain has been associated with a wide variety of cognitive processes including decision making, feedback processing, and working memory. The high temporal resolution provided by electroencephalography (EEG) enables the study of variation of oscillatory power and coupling across time. Various forms of neural synchrony across frequency bands have been suggested as the mechanism underlying neural binding. Recently, a considerable amount of work has focused on phase-amplitude coupling (PAC)- a form of cross-frequency coupling where the amplitude of a high frequency signal is modulated by the phase of low frequency oscillations. The existing methods for assessing PAC have some limitations including limited frequency resolution and sensitivity to noise, data length and sampling rate due to the inherent dependence on bandpass filtering. In this paper, we propose a new time-frequency based PAC (t-f PAC) measure that can address these issues. The proposed method relies on a complex time-frequency distribution, known as the Reduced Interference Distribution (RID)-Rihaczek distribution, to estimate both the phase and the envelope of low and high frequency oscillations, respectively. As such, it does not rely on bandpass filtering and possesses some of the desirable properties of time-frequency distributions such as high frequency resolution. The proposed technique is first evaluated for simulated data and then applied to an EEG speeded reaction task dataset. The results illustrate that the proposed time-frequency based PAC is more robust to varying signal parameters and provides a more accurate measure of coupling strength.

A Novel EEG Based Spectral Analysis of Persistent Brain Function Alteration in Athletes with Concussion History.

The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Detecting deficits are vital in making a decision about the treatment plan as it can persist one year or more following a brain injury. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits combining EEG analysis with three standard post-concussive assessment tools. Data were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency (Hjorth Parameters) and nonlinear features (approximate entropy and Hurst exponent) for the first time to explore post-concussive deficits. Besides traditional frequency-band analysis, we also presented a new individual frequency-based approach for EEG assessment. While EEG analysis exhibited significant discrepancies between the groups, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlights that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management.

Preliminary results of residual deficits observed in athletes with concussion history: combined EEG and cognitive study.

Assessment, treatment, and management of sport-related concussions are a widely recognized public health issue. Although several neuropsychological and motor assessment tools have been developed and implemented for sports teams at various levels and ages, the sensitivity of these tests has yet to be validated with more objective measures to make return-to-play (RTP) decisions more confidently. The present study sought to analyze the residual effect of concussions on a sample of adolescent athletes who sustained one or more previous concussions compared to those who had no concussion history. For this purpose, a wide variety of assessment tools containing both neurocognitive and electroencephalogram (EEG) elements were used. All clinical testing and EEG were repeated at 8 months, 10 months, and 12 months post-injury for both healthy and concussed athletes. The concussed athletes performed poorer than healthy athletes on processing speed and impulse control subtest of neurocognitive test on month 8, but no alterations were marked in terms of visual and postural stability. EEG analysis revealed significant differences in brain activities of concussed athletes through all three intervals. These long-term neurocognitive and EEG deficits found from this ongoing sport-related concussion study suggest that the post-concussion physiological deficits may last longer than the observed clinical recovery.