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Seseri, N.

Publications and source records attributed to Seseri, N..

2 recordsLinked to original sources

Electrical stimulation elicits space- and parameter-dependent spiking responses in human cortical organoids

Electrical stimulation (ES) is used to treat neuropsychiatric disorders and investigate brain dynamics, yet its effects on human cortical microcircuits remain poorly understood. Cortical organoids provide a unique platform to investigate these mechanisms in isolation from subcortical and long-range cortical inputs. Here we illustrate how cortical organoids respond to ES, identifying the response profiles of isolated cortical circuits while detailing a roadmap of how ES parameters affect the organoid spiking activity. We employed a high-density multielectrode array to record neuronal activity from cortical organoids (n=417 units in N=7 organoids) during ES, systematically varying stimulation frequency, intensity, pulse width, and charge density. By analyzing single unit spiking activity, we found that ES elicits excitatory, inhibitory, and mixed responses in 39%, 12%, and 17% of the units, respectively. On average, this response lasted 100 ms and became stable within 26 trials. The magnitude of both excitatory and inhibitory responses was maximal near the stimulation site and decayed with distance. The response magnitude was inversely correlated with pulse intensity and duration, but not with stimulation frequency and charge density. These findings demonstrate that local cortical circuits are sufficient to initiate the early excitatory phase of the canonical ES response, whose magnitude depends on ES parameters, and can sustain the excitatory phase for over 100 ms. The reduced late inhibitory phase, together with the absence of late excitatory components observed 200 ms after ES in intact adult brains in-vivo, suggests that these phases may depend on neuronal maturation or inter-area connections. Our work thus establishes cortical organoids as a framework for studying the local contributions to ES-induced activity in a developmental model of the human cortex.

neuroscience↗

Statistical Characterization of Cortical-Thalamic Dynamics Evoked by Cortical Stimulation in Mice

ObjectiveStatistical models are powerful tools for describing biological phenomena such as neuronal spiking activity. Although these models have been widely used to study spontaneous and stimulated neuronal activity, they have not yet been applied to analyze responses to electrical cortical stimulation. In this study, we present an innovative approach to characterize neuronal responses to electrical stimulation in the mouse cortex, providing detailed insights into cortical-thalamic dynamics. ApproachOur method applies Mixture Models to analyze the Peri-Stimulus Time Histogram of each neuron, predicting the probability of spiking at specific latencies following the onset of electrical stimuli. By applying this approach, we investigated neuronal responses to cortical stimulation recorded from the motor cortex, somatosensory cortex, and sensorimotor-related thalamic nuclei in the mouse brain. Main resultsThe characterization approach achieved high goodness of fit, and the model features were leveraged by applying machine learning methods for stimulus intensity decoding and classification of brain regions to which a neuron belongs given its response to the stimulus. The Random Forest model demonstrated the highest F1 scores, achieving 92.86% for stimulus intensity decoding and 84.35% for brain zone classification. SignificanceThis study presents a novel statistical framework for characterizing neuronal responses to electrical cortical stimulation, providing quantitative insights into cortical-thalamic dynamics. Our approach achieves high accuracy in stimulus decoding and brain region classification, providing valuable contributions for neuroscience research and neuro-technology applications.

neuroscience↗