Biology subjects

Tharayil, J. J.

Publications and source records attributed to Tharayil, J. J..

4 recordsLinked to original sources

bioRxiv2025

iCSD can produce spurious results in dense electrode arrays

Estimation of the current source density (CSD) is a commonly-used method to interpret local field potential (LFP) signals by estimating the location of the neural sinks and sources of current that give rise to the LFP. We show analytically that, when the inter-electrode spacing is small relative to the width of the current distribution, commonly used methods to estimate the CSD produces spurious results, calculating true sources as sinks and vice versa. By simulating a biologically-detailed subvolume of the rat somatosensory cortex with over 200,000 biophysically-detailed neurons, we show that, for high-density recording electrodes, the estimated CSD diverges from expected results, necessitating awareness and careful interpretation of CSD results.

neuroscience↗

bioRxiv2024

Computational modeling reveals biological mechanisms underlying the whisker-flick EEG

Whisker flick stimulation is a commonly used protocol to investigate somatosensory processing in rodents. Neural activity in the brain evoked by whisker flicks produces a characteristic EEG waveform recorded at the skull, known as a somatosensory evoked potential. In this paper, we use in silico modeling to identify the neural populations that serve as sources and targets of the synaptic currents contributing to this signal (presynaptic and postsynaptic populations, respectively). The initial positive deflection of the EEG waveform is driven largely by direct thalamic inputs to Layer 2/3 and Layer 5 pyramidal cells, though interestingly, L5-L5 inhibition plays a modulatory role, reducing the amplitude and width of the deflection. This suggests that increasing thalamocortical connectivity and decreasing L5-L5 inhibition may be responsible for some of the changes observed in the EEG waveform over the course of development. The negative deflection is driven by a more complex mix of sources, including both thalamic and recurrent cortical connectivity. We demonstrate that small changes to the local connectivity of the circuit, particularly to perisomatic inhibitory targeting, can have an important impact on the recorded EEG, without substantially affecting firing rates, suggesting that EEG may be useful in constraining in silico neural models.

neuroscience↗

bioRxiv2024

Simulation Insights on the Compound Action Potential in Multifascicular Nerves

ObjectiveDevelop an efficient method for simulating evoked compound action potential (eCAP) signals from complex nerves to help optimize and interpret eCAP recordings; validate it through comparison with measured vagus nerve eCAP recordings; elucidate the subtle interplay giving rise to specific eCAP signal shapes and magnitudes. ApproachWe developed an extended reciprocity theorem approach to model neuron signals in heterogeneous environments, and use it to study analytically the single fibre action potential. We then established a semi-analytic model that also uses hybrid electromagnetic-electrophysiological simulations to model eCAP signals from complex nerves populated with heterogeneous fiber populations of fibers. A cuff electrode was used to measure activity induced by vagus nerve stimulation in in vivo porcine experiments; these measurements were compared with signals produced by the model. Main ResultsThe semi-analytic model produces signals that approximate the shape and amplitude of in vivo measurements. Partially activated fascicles contribute substantially to the signal, as eCAP contributions from smoothly varying fiber calibers in fully activated ones partially cancel. As a result, eCAP magnitude does not depend monotonically on the stimulation current and recruitment level. Because the eCAP is sensitive to the degree of activation in individual fascicles, and to the location of the recording electrodes with respect to individual fascicles, the contributions of different fascicles to the recorded eCAP signals vary significantly with changes in the shape and placement of the stimulus and the recording electrodes. SignificanceOur method can be used to rapidly assess new stimulation and recording setups involving complex nerves and neurovascular bundles, e.g., to maximize signal information content, for closed-loop control in bioelectronic medicine applications, and potentially to non-destructively reconstruct structural and functional nerve topologies through inverse problem solving.

neuroscience↗

bioRxiv2024

BlueRecording: A Pipeline for the efficient calculation of extracellular recordings in large-scale neural circuit models

As the size and complexity of network simulations accessible to computational neuroscience grows, new avenues open for research into extracellularly recorded electric signals. Biophysically detailed simulations permit the identification of the biological origins of the different components of recorded signals, the evaluation of signal sensitivity to different anatomical, physiological, and geometric factors, and selection of recording parameters to maximize the signal information content. Simultaneously, virtual extracellular signals produced by these networks may become important metrics for neuro-simulation validation. To enable efficient calculation of extracellular signals from large neural network simulations, we have developed BlueRecording, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. In particular, we implement a general form of the reciprocity theorem, which is capable of handling non-dipolar current sources, such as may be found in long axons and recordings close to the current source, as well as complex tissue anatomy, dielectric heterogeneity, and electrode geometries. To our knowledge, this is the first application of this generalized (i.e., non-dipolar) reciprocity-based approach to simulate EEG recordings. We use these tools to calculate extracellular signals from an in silico model of the rat somatosensory cortex and hippocampus and to study signal contribution differences between regions and cell types.

neuroscience↗