Biology subjects

Denman, D.

Publications and source records attributed to Denman, D..

6 recordsLinked to original sources

bioRxiv2025

Myelin Supports Cortical Circuit Function Underlying Skilled Movement

Primary motor cortex (M1) is among the most heavily myelinated cortical regions and generates tightly coordinated neuronal activity patterns that drive skilled movement. Activity-dependent myelination is required for motor skill acquisition, and myelin loss in demyelinating diseases such as multiple sclerosis leads to motor impairment. Yet how myelination influences neuronal activity underlying skilled behavior remains unclear. By combining in vivo imaging of oligodendrocytes with high density Neuropixels recordings during dexterous reaching, we demonstrate that cuprizone-induced demyelination impairs movement efficiency, and alters cell-type-specific neuronal activity and synchrony in a manner that predicts motor output. Using a computational model constrained by these data, we identify inhibitory axonal propagation failures as a mechanistic link between myelin loss and altered circuit function. Partial remyelination normalizes cortical network-level metrics and reach consistency but leaves smooth movement impaired, revealing a selective vulnerability in inhibitory circuits. These findings close a critical gap between cellular models of demyelination and clinical motor impairment by demonstrating how myelin supports cortical circuit dynamics driving skilled behavior.

neuroscience↗

bioRxiv2025

A Bioelectric Router for Adaptive Isochronous Neurostimulation

ObjectiveMultipolar 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. ApproachThe 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 ResultsThe 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. SignificanceThe 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.

neuroscience↗

bioRxiv2023

Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

neuroscience↗

bioRxiv2022

Multichannel neural spike sorting with spike reduction and positional feature

Sorting neural voltages measured from a multichannel neural probe to extract the single unit activities of neuronal firing, especially in real-time, remains a significant technical challenge, largely due to the large amount of acquired data and the technical difficulties involved in processing and classifying these neural spikes promptly. Most neural spike sorting algorithms focus on sorting neural spikes post hoc for high sorting accuracy, and reducing the processing time generally is not the chief concern. Here we report on two signal processing modifications to our previously developed single-channel real-time spike sorting (Enhanced Growing Neural Gas) algorithm, which is largely based on graph network. Duplicated neural spikes were eliminated and represented by the neural spike with the strongest signal profile, significantly reducing the amount of neural data to be processed. In addition, the channel from which the representing neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features. With these two modifications, the Graph nEtwork Multichannel (GEMsort) neural spike sorting algorithm can rapidly sort neural spikes without requiring significant computer processing power and system memory storage. The parallel processing architecture of GEMsort is particularly suitable for digital hardware implementation to improve processing speed and recording channel scalability. Multichannel synthetic neural spikes and actual neural recordings with Neuropixels probes were used to evaluate the sorting accuracies of the GEMsort algorithm.

neuroscience↗

bioRxiv2018

High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification

Different neuron types serve distinct roles in neural processing. Extracellular electrical recordings are extensively used to study brain function but are typically blind to cell identity. Morpho-electric properties of neurons measured on spatially dense electrode arrays might be useful for distinguishing neuron types. Here we used Neuropixels probes to record from cortical and subcortical regions of the mouse brain. Extracellular waveforms of each neuron were detected across many channels and showed distinct spatiotemporal profiles among brain regions. Classification of neurons by brain region was improved with multi-channel compared to single-channel waveforms. In visual cortex, waveform clustering identified the canonical regular spiking (RS) and fast spiking (FS) classes, but also uncovered a subclass of RS units with unidirectional backpropagating action potentials (BAPs). Moreover, BAPs were observed in many hippocampal RS cells. Overall, waveform analysis of spikes from high-density probes aids neuron identification and can reveal dendritic backpropagation.

neuroscience↗

bioRxiv2018

Visual physiology of the Layer 4 cortical circuit in silico

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.\n\nAUTHOR SUMMARYHow can we capture the incredible complexity of brain circuits in quantitative models, and what can such models teach us about mechanisms underlying brain activity? To answer these questions, we set out to build extensive, bio-realistic models of brain circuitry employing systematic datasets on brain structure and function. Here we report the first modeling results of this project, focusing on the layer 4 of the primary visual cortex (V1) of the mouse. Our simulations reproduced a variety of experimental observations in a large battery of visual stimuli. The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 - in particular, the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion. Simplification of neuronal models led to specific deficiencies in reproducing experimental data, giving insights into how biological details contribute to various aspects of brain activity. To enable future development of more sophisticated models, we make the software code, the model, and simulation results publicly available.

neuroscience↗