Transcranial stimulation of alpha oscillations modulates brain state dynamics in sustained attention.

The brain operates an advanced complex system to support mental activities. Cognition is thought to emerge from dynamic states of the complex brain system, which are organized spatially through large-scale neural networks and temporally via neural synchrony. However, specific mechanisms underlying these processes remain obscure. Applying high-definition alpha-frequency transcranial alternating-current stimulation (HD α-tACS) in a continuous performance task (CPT) during functional resonance imaging (fMRI), we causally elucidate these major organizational architectures in a key cognitive operation-sustained attention. We demonstrated that α-tACS enhanced both electroencephalogram (EEG) alpha power and sustained attention, in a correlated fashion. Akin to temporal fluctuations inherent in sustained attention, our hidden Markov modeling (HMM) of fMRI timeseries uncovered several recurrent, dynamic brain states, which were organized through a few major neural networks and regulated by the alpha oscillation. Specifically, during sustain attention, α-tACS regulated the temporal dynamics of the brain states by suppressing a Task-Negative state (characterized by activation of the default mode network/DMN) and Distraction state (with activation of the ventral attention and visual networks). These findings thus linked dynamic states of major neural networks and alpha oscillations, providing important insights into systems-level mechanisms of attention. They also highlight the efficacy of non-invasive oscillatory neuromodulation in probing the functioning of the complex brain system and encourage future clinical applications to improve neural systems health and cognitive performance.

Development of brain state dynamics involved in working memory.

Human functional brain networks are dynamically organized to enable cognitive and behavioral flexibility to meet ever-changing environmental demands. Frontal-parietal network (FPN) and default mode network (DMN) are recognized to play an essential role in executive functions such as working memory. However, little is known about the developmental differences in the brain-state dynamics of these two networks involved in working memory from childhood to adulthood. Here, we implemented Bayesian switching dynamical systems approach to identify brain states of the FPN and DMN during working memory in 69 school-age children and 51 adults. We identified five brain states with rapid transitions, which are characterized by dynamic configurations among FPN and DMN nodes with active and inactive engagement in different task demands. Compared with adults, children exhibited less frequent brain states with the highest activity in FPN nodes dominant to high demand, and its occupancy rate increased with age. Children preferred to attain inactive brain states with low activity in both FPN and DMN nodes. Moreover, children exhibited lower transition probability from low-to-high demand states and such a transition was positively correlated with working memory performance. Notably, higher transition probability from low-to-high demand states was associated with a stronger structural connectivity across FPN and DMN, but with weaker structure-function coupling of these two networks. These findings extend our understanding of how FPN and DMN nodes are dynamically organized into a set of transient brain states to support moment-to-moment information updating during working memory and suggest immature organization of these functional brain networks in childhood, which is constrained by the structural connectivity.

Emotional learning retroactively promotes memory integration through rapid neural reactivation and reorganization.

Neutral events preceding emotional experiences can be better remembered, likely by assigning them as significant to guide possible use in future. Yet, the neurobiological mechanisms of how emotional learning enhances memory for past mundane events remain unclear. By two behavioral studies and one functional magnetic resonance imaging study with an adapted sensory preconditioning paradigm, we show rapid neural reactivation and connectivity changes underlying emotion-charged retroactive memory enhancement. Behaviorally, emotional learning retroactively enhanced initial memory for neutral associations across the three studies. Neurally, emotional learning potentiated trial-specific reactivation of overlapping neural traces in the hippocampus and stimulus-relevant neocortex. It further induced rapid hippocampal-neocortical functional reorganization supporting such retroactive memory benefit, as characterized by enhanced hippocampal-neocortical coupling modulated by the amygdala during emotional learning, and a shift of hippocampal connectivity from stimulus-relevant neocortex to distributed transmodal prefrontal-parietal areas at post-learning rests. Together, emotional learning retroactively promotes memory integration for past neutral events through stimulating trial-specific reactivation of overlapping representations and reorganization of associated memories into an integrated network to foster its priority for future use.

Oscillatory properties of class C notifiable infectious diseases in China from 2009 to 2021.

Background: Epidemics of infectious diseases have a great negative impact on people's daily life. How it changes over time and what kind of laws it obeys are important questions that researchers are always interested in. Among the characteristics of infectious diseases, the phenomenon of recrudescence is undoubtedly of great concern. Understanding the mechanisms of the outbreak cycle of infectious diseases could be conducive for public health policies to the government. Method: In this study, we collected time-series data for nine class C notifiable infectious diseases from 2009 to 2021 using public datasets from the National Health Commission of China. Oscillatory power of each infectious disease was captured using the method of the power spectrum analysis. Results: We found that all the nine class C diseases have strong oscillations, which could be divided into three categories according to their oscillatory frequencies each year. Then, we calculated the oscillation power and the average number of infected cases of all nine diseases in the first 6 years (2009-2015) and the next 6 years (2015-2021) since the update of the surveillance system. The change of oscillation power is positively correlated to the change in the number of infected cases. Moreover, the diseases that break out in summer are more selective than those in winter. Conclusion: Our results enable us to better understand the oscillation characteristics of class C infectious diseases and provide guidance and suggestions for the government's prevention and control policies.

Changes in Temporal Properties of Notifiable Infectious Disease Epidemics in China During the COVID-19 Pandemic: Population-Based Surveillance Study.

BACKGROUND: COVID-19 was first reported in 2019, and the Chinese government immediately carried out stringent and effective control measures in response to the epidemic. OBJECTIVE: Nonpharmaceutical interventions (NPIs) may have impacted incidences of other infectious diseases as well. Potential explanations underlying this reduction, however, are not clear. Hence, in this study, we aim to study the influence of the COVID-19 prevention policies on other infectious diseases (mainly class B infectious diseases) in China. METHODS: Time series data sets between 2017 and 2021 for 23 notifiable infectious diseases were extracted from public data sets from the National Health Commission of the People's Republic of China. Several indices (peak and trough amplitudes, infection selectivity, preferred time to outbreak, oscillatory strength) of each infectious disease were calculated before and after the COVID-19 outbreak. RESULTS: We found that the prevention and control policies for COVID-19 had a strong, significant reduction effect on outbreaks of other infectious diseases. A clear event-related trough (ERT) was observed after the outbreak of COVID-19 under the strict control policies, and its decreasing amplitude is related to the infection selectivity and preferred outbreak time of the disease before COVID-19. We also calculated the oscillatory strength before and after the COVID-19 outbreak and found that it was significantly stronger before the COVID-19 outbreak and does not correlate with the trough amplitude. CONCLUSIONS: Our results directly demonstrate that prevention policies for COVID-19 have immediate additional benefits for controlling most class B infectious diseases, and several factors (infection selectivity, preferred outbreak time) may have contributed to the reduction in outbreaks. This study may guide the implementation of nonpharmaceutical interventions to control a wider range of infectious diseases.

Dynamic integration and segregation of amygdala subregional functional circuits linking to physiological arousal.

The dynamical organization of brain networks is essential to support human cognition and emotion for rapid adaption to ever-changing environment. As the core nodes of emotion-related brain circuitry, the basolateral amygdala (BLA) and centromedial amygdala (CMA) as two major amygdalar nuclei, are recognized to play distinct roles in affective functions and internal states, via their unique connections with cortical and subcortical structures in rodents. However, little is known how the dynamical organization of emotion-related brain circuitry reflects internal autonomic responses in humans. Using resting-state functional magnetic resonance imaging (fMRI) with K-means clustering approach in a total of 79 young healthy individuals (cohort 1: 42; cohort 2: 37), we identified two distinct states of BLA- and CMA-based intrinsic connectivity patterns, with one state (integration) showing generally stronger BLA- and CMA-based intrinsic connectivity with multiple brain networks, while the other (segregation) exhibiting weaker yet dissociable connectivity patterns. In an independent cohort 2 of fMRI data with concurrent recording of skin conductance, we replicated two similar dynamic states and further found higher skin conductance level in the integration than segregation state. Moreover, machine learning-based Elastic-net regression analyses revealed that time-varying BLA and CMA intrinsic connectivity with distinct network configurations yield higher predictive values for spontaneous fluctuations of skin conductance level in the integration than segregation state. Our findings highlight dynamic functional organization of emotion-related amygdala nuclei circuits and networks and its links to spontaneous autonomic arousal in humans.

Hysteretic Genetic Circuit for Detection of Proteasomal Degradation in Mammalian Cells.

Synthetic hysteretic mammalian gene circuits generating sustained cellular responses to transient perturbations provide important tools to investigate complex cellular behaviors and reprogram cells for a variety of applications, ranging from protein production to cell fate decisions. The design rules of synthetic gene circuits with controlled hysteretic behaviors, however, remain uncharacterized. To identify the criteria for achieving predictable control of hysteresis, we built a genetic circuit for detection of proteasomal degradation (Hys-Deg). The Hys-Deg circuit is based on a tetracycline-controlled transactivator (tTA) variant engineered to interface with the ubiquitin proteasome system (UPS). The tTA variant activates its own expression, generating a positive feedback loop that is triggered by expression of another tTA gene that is constitutively regulated. Guided by predictive modeling, we characterized the hysteretic response of the Hys-Deg circuit. We demonstrated that control of the hysteretic response is achieved by modulating the ratio of expression of constitutive to inducible tTA. We also showed that the system can be finely tuned through dosage of the inducer tetracycline to calibrate the circuit for detection of the desired levels of UPS activation. This study establishes the design rules for building a hysteretic genetic circuit with an autoregulatory feedback loop and provides a synthetic memory module that could be easily integrated into regulatory gene networks to study and engineer complex cellular behaviors.

A Split Transcriptional Repressor That Links Protein Solubility to an Orthogonal Genetic Circuit.

Monitoring the aggregation of proteins within the cellular environment is key to investigating the molecular mechanisms underlying the formation of off-pathway protein assemblies associated with the development of disease and testing therapeutic strategies to prevent the accumulation of non-native conformations. It remains challenging, however, to couple protein aggregation events underlying the cellular pathogenesis of a disease to genetic circuits and monitor their progression in a quantitative fashion using synthetic biology tools. To link the aggregation propensity of a target protein to the expression of an easily detectable reporter, we investigated the use of a transcriptional AND gate system based on complementation of a split transcription factor. We first identified two-fragment tetracycline repressor (TetR) variants that can be regulated via ligand-dependent induction and demonstrated that split TetR variants can function as transcriptional AND gates in both bacteria and mammalian cells. We then adapted split TetR for use as an aggregation sensor. Protein aggregation was detected by monitoring complementation between a larger TetR fragment that serves as a "detector" and a smaller TetR fragment expressed as a fusion to an aggregation-prone protein that serves as a "sensor" of the target protein aggregation status. This split TetR represents a novel genetic component that can be used for a wide range of applications in bacterial as well as mammalian synthetic biology and a much needed cell-based sensor for monitoring a protein's conformational status in complex cellular environments.

A Naturally Encoded Dipeptide Handle for Bioorthogonal Chan-Lam Coupling.

Manipulation of biomacromolecules is ideally achieved through unique and bioorthogonal chemical reactions of genetically encoded, naturally occurring functional groups. The toolkit of methods for site-specific conjugation is limited by selectivity concerns and a dearth of naturally occurring functional groups with orthogonal reactivity. We report that pyroglutamate amide N-H bonds exhibit bioorthogonal copper-catalyzed Chan-Lam coupling at pyroglutamate-histidine dipeptide sequences. The pyroglutamate residue is readily incorporated into proteins of interest by natural enzymatic pathways, allowing specific bioconjugation at a minimalist dipeptide tag.

Genetic and chemical activation of TFEB mediates clearance of aggregated α-synuclein.

Aggregation of α-synuclein (α-syn) is associated with the development of a number of neurodegenerative diseases, including Parkinson's disease (PD). The formation of α-syn aggregates results from aberrant accumulation of misfolded α-syn and insufficient or impaired activity of the two main intracellular protein degradation systems, namely the ubiquitin-proteasome system and the autophagy-lysosomal pathway. In this study, we investigated the role of transcription factor EB (TFEB), a master regulator of the autophagy-lysosomal pathway, in preventing the accumulation of α-syn aggregates in human neuroglioma cells. We found that TFEB overexpression reduces the accumulation of aggregated α-syn by inducing autophagic clearance of α-syn. Furthermore, we showed that pharmacological activation of TFEB using 2-hydroxypropyl-ß-cyclodextrin promotes autophagic clearance of aggregated α-syn. In summary, our findings demonstrate that TFEB modulates autophagic clearance of α-syn and suggest that pharmacological activation of TFEB is a promising strategy to enhance the degradation of α-syn aggregates.