Biology subjects

Kenyon, R. V.

Publications and source records attributed to Kenyon, R. V..

4 recordsLinked to original sources

bioRxiv2020

V-NeuroStack: 3D Time Stacks for Identifying Patterns in Calcium Imaging Data

BackgroundUnderstanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of cortical neurons in a mouse brain slice. New MethodV-NeuroStack creates 3D time stacks by stacking 2D time frames for a period of 600 seconds. It provides a web interface that enables exploration and analysis of data using a combination of 3D and 2D visualization techniques. Comparison with existing MethodsPrevious attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack can scale data sets with at least a few thousand temporal snapshots. ResultsV-NeuroStacks 3D view is used to explore patterns in the dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of a single neuron or a functional cluster of neurons. ConclusionsV-NeuroStack enables easy exploration and analysis of large spatio-temporal datasets using two visualization paradigms: (a) Space-Time cube (b)Two-dimensional networks, via web interface. It will support future advancements in in vitro and in vivo data capturing techniques and can bring forth novel hypotheses by permitting unambiguous visualization of large-scale patterns in the neuronal activity data.

neuroscience↗

bioRxiv2019

A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

Network analysis of large-scale neuroimaging data has proven to be a particularly challenging computational problem. In this study, we adapt a novel analytical tool, known as the community dynamic inference method (CommDy), which was inspired by social network theory, for the study of brain imaging data from an aging mouse model. CommDy has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network parameters in the auditory and motor cortices using flavoprotein autofluorescence imaging in brain slices and in vivo. Analysis of spontaneous activations in the auditory cortex of slices taken from young and aged animals demonstrated that cortical networks in aged brains were highly fragmented compared to networks observed in young animals. Specifically, the degree of connectivity of each activated node in the aged brains was significantly lower than those seen in the young brain, and multivariate analyses of all derived network metrics showed distinct clusters of these metrics in young vs. aged brains. CommDy network metrics were then used to build a random-forests classifier based on NMDA-receptor blockade data, which successfully recapitulated the aging findings, suggesting that the excitatory synaptic substructure of the auditory cortex may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity obtained from the awake motor cortex, suggesting that the findings in the auditory cortex are reflections of general mechanisms that occur during aging. Therefore, CommDy therefore provides a new dynamic network analytical tool to study the brain and provides links between network-level and synaptic-level dysfunction in the aging brain.

neuroscience↗

bioRxiv2019

Corticothalamic gating of population auditory thalamocortical transmission in mouse

Since the discovery of the receptive field, scientists have tracked receptive field structure to gain insights about mechanisms of sensory processing. At the level of the thalamus and cortex, this linear filter approach has been challenged by findings that populations of cortical neurons respond in a stereotyped fashion to sensory stimuli. Here, we elucidate a possible mechanism by which gating of cortical representations occurs. All-or-none population responses (here called \"ON\" and \"OFF\" responses) were observed in vivo and in vitro in the mouse auditory cortex at near-threshold acoustic or electrical stimulation. ON-responses were associated with previously-described UP states in the auditory cortex. OFF-responses in the cortex were only eliminated by blocking GABAergic inhibition in the thalamus. Opto- and chemogenetic silencing of NTSR-positive corticothalamic layer 6 (CTL6) neurons as well as the pharmacological blocking of the thalamic reticular nucleus (TRN) retrieved the missing cortical responses, suggesting that the corticothalamic feedback inhibition via TRN controls the gating of thalamocortical activity. Moreover, the oscillation of the pre-stimulus activity of corticothalamic cells predicted the cortical ON vs. OFF responses, suggesting that underlying cortical oscillation controls thalamocortical gating. These data suggest that the thalamus may recruit cortical ensembles rather than linearly encoding ascending stimuli and that corticothalamic projections play a key role in selecting cortical ensembles for activation.

neuroscience↗

bioRxiv2019

A computational model of intrathalamic signaling via open-loop thalamo-reticular-thalamic architectures

Propagation of signals across the cerebral cortex is a core component of many cognitive processes and is generally thought to be mediated by direct intracortical connectivity. The thalamus, by contrast, is considered to be devoid of internal connections and organized as a collection of parallel inputs to the cortex. Here, we provide evidence that \"open-loop\" intrathalamic connections involving the thalamic reticular nucleus (TRN) can support propagation of oscillatory activity across the cortex. Recent studies support the existence of open-loop thalamo-reticulo-thalamic (TC-TRN-TC) synaptic motifs in addition to traditional closed-loop architectures. We hypothesized that open-loop structural modules, when connected in series, might underlie thalamic and, therefore cortical, signal propagation. Using a supercomputing platform to simulate thousands of permutations of a thalamo-reticular-cortical network and allowing select synapses to vary both by class and individually, we evaluated the relative capacities of closed- and open-loop TC-TRN-TC synaptic configurations to support both propagation and oscillation. We observed that 1) signal propagation was best supported in networks possessing strong open-loop TC-TRN-TC connectivity; 2) intrareticular synapses were neither primary substrates of propagation nor oscillation; and 3) heterogeneous synaptic networks supported more robust propagation of oscillation than their homogeneous counterparts. These findings suggest that open-loop heterogeneous intrathalamic architectures complement direct intracortical connectivity to facilitate cortical signal propagation.\n\nSignificance StatementInteractions between the dorsal thalamus and thalamic reticular nucleus (TRN) are speculated to contribute to phenomena such as arousal, attention, sleep, and seizures. Despite the importance of the TRN, the synaptic microarchitectures forming the basis for dorsal thalamus-TRN interactions are not fully understood. The computational neural model we present incorporates \"open-loop\" thalamo-reticular-thalamic (TC-TRN-TC) synaptic motifs, which have been experimentally observed. We elucidate how open-loop motifs possess the capacity to shape the propagative properties of signals intrinsic to the thalamus and evaluate the wave dynamics they support relative to closed-loop TC-TRN-TC pathways and intrareticular synaptic connections. Our model also generates predictions regarding how different spatial distributions of reticulothalamic and intrareticular synapses affect these signaling properties.

neuroscience↗