The hippocampus-prefrontal cortex (HPC-PFC) pathway plays a fundamental role in executive and emotional functions. Neurophysiological studies have begun to unveil the dynamics of HPC-PFC interaction in both immediate demands and long-term adaptations. Disruptions in HPC-PFC functional connectivity can contribute to neuropsychiatric symptoms observed in mental illnesses and neurological conditions, such as schizophrenia, depression, anxiety disorders, and Alzheimer’s disease. Given the role in functional and dysfunctional physiology, it is crucial to understand the mechanisms that modulate the dynamics of HPC-PFC communication. Two of the main mechanisms that regulate HPC-PFC interactions are synaptic plasticity and modulatory neurotransmission. Synaptic plasticity can be investigated inducing long-term potentiation or long-term depression, while spontaneous functional connectivity can be inferred by statistical dependencies between the local field potentials of both regions. In turn, several neurotransmitters, such as acetylcholine, dopamine, serotonin, noradrenaline, and endocannabinoids, can regulate the fine-tuning of HPC-PFC connectivity. Despite experimental evidence, the effects of neuromodulation on HPC-PFC neuronal dynamics from cellular to behavioral levels are not fully understood. The current literature lacks a review that focuses on the main neurotransmitter interactions with HPC-PFC activity. Here we reviewed studies showing the effects of the main neurotransmitter systems in long- and short-term HPC-PFC synaptic plasticity. We also looked for the neuromodulatory effects on HPC-PFC oscillatory coordination. Finally, we review the implications of HPC-PFC disruption in synaptic plasticity and functional connectivity on cognition and neuropsychiatric disorders. The comprehensive overview of these impairments could help better understand the role of neuromodulation in HPC-PFC communication and generate insights into the etiology and physiopathology of clinical conditions.
Sharp wave-ripples (SWRs, 100-250 Hz) are oscillatory events extracellularly recorded in the CA1 subfield of the hippocampus during sleep and quiet wakefulness. SWRs are thought to be involved in the dialogue between the hippocampus and cortical regions to promote memory consolidation during sleep and memory-guided decision making. Many studies employed closed-loop strategies to either detect and abolish SWRs within the hippocampus or manipulate other relevant areas upon ripple detection. However, the code and schematics necessary to replicate the detection system are not always available, which hinders the reproducibility of experiments among different research groups. Furthermore, information about performance is not usually reported. Here, we present the development and validation of an open-source, real-time ripple detection plugin integrated into the Open Ephys GUI. It contains a built-in movement detector based on accelerometer or electromyogram data that prevents false ripple events (due to chewing, grooming, or moving, for instance) from triggering the stimulation/manipulation device. To determine the accuracy of the detection algorithm, we first carried out simulations in Matlab with synthetic and real ripple recordings. Using a specific combination of detection parameters (amplitude threshold of 5 standard deviations above the mean, time threshold of 10 ms, and RMS block size of 7 samples), we obtained a 97% true positive rate and 2.48 false positives per minute on the real data. Next, an Open Ephys plugin based on the same detection algorithm was developed, and a closed-loop system was set up to evaluate the round trip (ripple onset-to-stimulation) latency over synthetic data. The lowest latency obtained was 34.5 ± 0.5 ms. Besides contributing to increased reproducibility, we anticipate that the developed ripple detector plugin will be helpful for many closed-loop applications in the field of systems neuroscience.
Objective. Sharp wave-ripples (SWRs, 100-250 Hz) are oscillatory events extracellularly recorded in the CA1 subfield of the hippocampus during sleep and quiet wakefulness. Many studies employed closed-loop strategies to either detect and abolish SWRs within the hippocampus or manipulate other relevant areas upon ripple detection. However, the code and schematics necessary to replicate the detection system are not always available, which hinders the reproducibility of experiments among different research groups. Furthermore, information about performance is not usually reported. Here, we sought to provide an open-source, validated ripple detector for the scientific community. Approach. We developed and validated a ripple detection plugin integrated into the Open Ephys GUI. It contains a built-in movement detector based on accelerometer or electromyogram data that prevents false ripple events (due to chewing, grooming, or moving, for instance) from triggering the stimulation/manipulation device. Main results. To determine the accuracy of the detection algorithm, we first carried out simulations in Matlab with real ripple recordings. Using a specific combination of detection parameters (amplitude threshold of 5 standard deviations above the mean, time threshold of 10 ms, and RMS block size of 7 samples), we obtained a 97% true positive rate and 2.48 false positives per minute. Next, an Open Ephys plugin based on the same detection algorithm was developed, and a closed-loop system was set up to evaluate the round trip (ripple onset-to-stimulation) latency over synthetic data. The lowest latency obtained was 34.5 ± 0.5 ms. The embedded movement monitoring was effective in reducing false positives and the plugin’s flexibility to detect pathological events was also verified. Significance. Besides contributing to increased reproducibility, we anticipate that the developed ripple detector plugin will be helpful for many closed-loop applications in the field of systems neuroscience.
Thermoelectric power plants have critical units, e.g. boiler that is a complex multivariable system in itself. Such complex units present non-stationary behavior and multiple points of operation, which implies constant changes in variables' setpoints. This work presents a multivariate statistical monitoring methodology, combined with Principal Component Analysis (PCA), to detect changes in operational conditions, adapted to changing process conditions, since strict PCA technique requires stationarity that is not ensured. In addition, performance indices of regulated control loops are used to assess changes in dynamical behavior, which also indicates degradation of a boiler-unit performance. The proposed methodology was implemented in a PIMS environment, integrating a monitoring system, installed in a thermoelectric power plant used as a case study. Experimental results based on actual operational data from the case study power plant are given to illustrate results of the proposed methodology. Resumo: Usinas termelétricas (UTEs) possuem unidades críticas, como caldeira, que é uma unidade multivariável complexa em si. Tais unidades apresentam comportamento não estacionário e múltiplos pontos de operação, o que implica mudanças constantes de referências de variáveis de processo. Apresenta-se uma metodologia de monitoramento estatístico multivariado, combinado com análise de componentes principais, para detecção de mudanças nas condições operacionais, adaptada às condições do processo, uma vez que o uso destas técnicas requer estacionariedade e este não a possui. Além disso, são utilizados índices de desempenho de malhas de controle para auxiliar na indicação de mudanças no comportamento das mesmas e no rastreamento das variáveis mais influentes na degradação de desempenho da caldeira. A metodologia proposta foi implementada numa plataforma PIMS, integrando um sistema de monitoramento, instalado numa UTE usada como estudo de caso. Resultados experimentais com dados de operação da usina estudada validam a proposição e ilustram a metodologia.
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