Myelin loss induces deficits in action potential propagation that result in neural dysfunction and contribute to the pathophysiology of neurodegenerative diseases, injury conditions, and aging. Because remyelination is often incomplete, better understanding endogenous remyelination and developing remyelination therapies that seek to restore neural function are clinical imperatives. Here, we used
Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.
Abstract The mammalian visual system, from retina to neocortex, has been extensively studied at both anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, but characterization of cellular-level functional interactions across multiple levels of this hierarchy is lacking, partially due to the challenge of simultaneously recording activity across numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory ) that surveys spiking from units in six cortical and two thalamic regions responding to a battery of visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas . Classical functional measures of hierarchy, including visual response latency, receptive field size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual system.
Abstract Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of neurons in the brain. While these two modalities have distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging or electrophysiology. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging. This work explores which data transformations are most useful for explaining these modality-specific discrepancies. We show that the higher selectivity in imaging can be partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could not reconcile differences in responsiveness without sub-selecting neurons based on event rate or level of signal contamination. This suggests that differences in responsiveness more likely reflect neuronal sampling bias or cluster-merging artifacts during spike sorting of electrophysiological recordings, rather than flaws in event detection from fluorescence time series. This work establishes the dominant impacts of the two modalities’ respective biases on a set of functional metrics that are fundamental for characterizing sensory-evoked responses.
Mammalian visual behaviors, as well as responses in the neural systems underlying these behaviors, are driven by luminance and color contrast. With constantly improving tools for measuring activity in cell-type-specific populations in the mouse during visual behavior, it is important to define the extent of luminance and color information that is behaviorally accessible to the mouse. A non-uniform distribution of cone opsins in the mouse retina potentially complicates both luminance and color sensitivity; opposing gradients of short (UV-shifted) and middle (blue/green) cone opsins suggest that color discrimination and wavelength-specific luminance contrast sensitivity may differ with retinotopic location. Here we ask how well mice can discriminate color and wavelength-specific luminance changes across visuotopic space. We found that mice were able to discriminate color and were able to do so more broadly across visuotopic space than expected from the cone-opsin distribution. We also found wavelength-band-specific differences in luminance sensitivity.
Abstract Objective Multipolar intracranial electrical brain stimulation (iEBS) is a method that has potential to improve clinical applications of mono- and bipolar iEBS. Current tools for researching multipolar iEBS are proprietary, can have high entry costs, lack flexibility in managing different stimulation parameters and electrodes, and can include clinical features unnecessary for the requisite exploratory research. This is a factor limiting the progress in understanding and applying multipolar iEBS effectively. To address these challenges, we developed the Bioelectric Router for Adaptive Isochronous Neuro stimulation (BRAINS) board. Approach The BRAINS board is a cost-effective and customizable device designed to facilitate multipolar stimulation experiments across a 16-channel electrode array using common research electrode setups. The BRAINS board interfaces with a microcontroller, allowing users to configure each channel for cathodal or anodal input, establish a grounded connection, or maintain a floating state. The design prioritizes ease of integration by leveraging standard tools like a microcontroller and an analog signal isolators while providing options to customize setups according to experimental conditions. It also ensures output isolation, reduces noise, and supports remote configuration changes for rapid switching of electrode states. To test the efficacy of the board, we performed bench-top validation of monopolar, bipolar, and multipolar stimulation regimes. The same regimes were tested in vivo in mouse primary visual cortex and measured using Neuropixel recordings. Main Results The BRAINS board demonstrates no meaningful differences in Root Mean Square Error (RMSE) noise or signal-to-noise ratio compared to the baseline performance of the isolated stimulator alone. The board supports configuration changes at a rate of up to 600 Hz without introducing residual noise, enabling high-frequency switching necessary for temporally multiplexed multipolar stimulation. Significance The BRAINS board represents a significant advancement in exploratory brain stimulation research by providing a user-friendly, customizable, open source, and cost-effective tool capable of conducting sophisticated, reproducible, and finely controlled stimulation experiments. With a capacity for effectively real-time information processing and efficient parameter exploration the BRAINS board can enhance both exploratory research on iEBS and enable improved clinical use of multipolar and closed-loop iEBS.
NWB files for a novel Neuropixels electrophysiology dataset from transgenic mice expressing GCaMP6f, as well as additional wild type mice. This dataset was used in a comparison of electrophysiology and two-photon imaging data, which appears in Siegle, Ledochowitsch et al. (2021) eLife. For information about experimental procedures, see Siegle, Jia et al. (2021) Nature 592, 86-92 (https://www.nature.com/articles/s41586-020-03171-x). For information about file contents, see https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html Files were generated with a custom branch of the AllenSDK, available at https://github.com/jsiegle/allensdk/tree/ophys-ephys. We recommend using the same branch to interact with these files.
