Uncovering population contributions to the extracellular potential in the mouse visual system using Laminar Population Analysis

Rimehaug, Atle E.; Dale, Anders M.; Arkhipov, Anton; Einevoll, Gaute T.

doi:10.1101/2024.01.15.575805

Cited by 3 publications

References 77 publications

(174 reference statements)

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Kernel-based LFP estimation in detailed large-scale spiking network model of mouse visual cortex

Meneghetti,

Rimehaug,

Einevoll

et al. 2024

Preprint

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Simulations of large-scale neural activity are powerful tools for investigating neural networks. Calculating measurable brain signals like local field potentials (LFPs) bridges the gap between model predictions and experimental observations. However, accurately simulating LFPs from large-scale models has traditionally required highly detailed multicompartmental neuron models, posing significant computational challenges. Here, we demonstrate that a kernel-based method can efficiently and accurately estimate LFPs in a state-of-the-art multicompartmental model of the mouse primary visual cortex (V1). Beyond its computational efficiency, the kernel method aids analysis by disentangling contributions of individual neuronal populations to the LFP. Using this approach, we found that LFPs in the V1 model were dominated by external synaptic inputs, with local synaptic activity playing a minimal role. Our findings establish the kernel method as a powerful tool for LFP estimation in large-scale network models and for uncovering the synaptic mechanisms underlying brain signals.

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Kernel-based LFP estimation in detailed large-scale spiking network model of mouse visual cortex

Meneghetti,

Rimehaug,

Einevoll

et al. 2024

Preprint

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Computational modeling reveals biological mechanisms underlying the whisker-flick EEG

Tharayil,

Isbister,

Neufeld

et al. 2024

Preprint

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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 usein silicomodeling 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 constrainingin siliconeural models.

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Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons

Dura-Bernal,

Herrera,

Lupascu

et al. 2024

J. Neurosci.

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Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.

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Uncovering population contributions to the extracellular potential in the mouse visual system using Laminar Population Analysis

Cited by 3 publications

References 77 publications

Kernel-based LFP estimation in detailed large-scale spiking network model of mouse visual cortex

Kernel-based LFP estimation in detailed large-scale spiking network model of mouse visual cortex

Computational modeling reveals biological mechanisms underlying the whisker-flick EEG

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons

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