Cognitive impairments and psychiatric symptoms affect up to half of temporal lobe epilepsy patients and are often more detrimental to their quality of life than the seizures themselves. Evidence indicates that the neurobiology of epileptogenesis shares common pathophysiological mechanisms with psychiatric comorbidities. However, these mechanisms and how they relate to specific behavioral alterations are unclear. We hypothesized that a dysfunctional communication between the hippocampus (HPC) and the prefrontal cortex (PFC), as a consequence of epileptogenesis, would be linked to behavioral and cognitive symptoms observed in the comorbidities of temporal lobe epilepsy. Here, we performed a multilevel study to investigate behavioral, electrophysiological, histopathological, and neurochemical long-term consequences of early-life Status Epilepticus in male rats. We found that adult animals submitted to early-life seizure (ELS) presented behavioral alterations typically found in animal models of psychosis, such as hyperlocomotion, reduction in sensorimotor gating, working memory deficits, and sensitivity to psychostimulants. Noteworthy, ELS rats did not exhibit neuronal loss. Instead, sensorimotor alterations were associated with increased neuroinflammation, as verified by glial fibrillary acidic protein (GFAP) expression, and altered dopamine neurotransmission. Surprisingly, cognitive deficits were linked to an aberrant increase in HPC-PFC long-term potentiation (LTP). Furthermore, ELS rats displayed an abnormal brain state during active behavior characterized by oscillatory dynamics oddly similar to REM sleep. Our results point to impaired hippocampal-prefrontal network dynamics as a possible pathophysiological mechanism by which an epileptogenic insult can cause behavioral changes without neuronal loss. These convergent patterns of dysfunctional activity between epileptogenesis and psychosis bear translational implications for understanding psychiatric and cognitive comorbidities in epilepsy.
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.
The COVID-19 pandemic affected almost all aspects of our lives, including the Education sector and the way of teaching and learning. In March 2020, health authorities in Brazil have imposed social isolation and the interruption of on-site activities in schools and universities. In this context, the Federal University of Minas Gerais (UFMG), one of the largest universities in Brazil and Latin America, developed the Emergency Remote Learning (ERL) that allowed the return of classes in an online format and supported students to obtain access to equipment and Internet network. Within this new perspective, the Undergraduate Teaching Assistant (UTA) program of the Department of Physiology and Biophysics (DFIB) has been explored strategies to minimize the impact of the absence of face-to-face classes. Using different available tools in online platforms and social media, such as, Microsoft Teams, Youtube animated video classes, and Instagram, UTA program assisted more than 500 undergraduate students and strongly supported professors during ERL. In just over a year, our video classes on Youtube Channel reached about 40k views. Most of the students reported their questions were fully and quickly solved by UTA program. Collectively, our results indicate that the strategies implemented by the UTA program helped the undergraduate students and professors to adapt to a remote learning format, keeping the quality of the education.
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.
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