Biology subjects

Komiyama, T.

Publications and source records attributed to Komiyama, T..

10 recordsLinked to original sources

bioRxiv2025

Complementary cortical and thalamic contributions to cell-type-specific striatal activity dynamics during movement

Coordinated motor behavior emerges from information flow across brain regions. How long-range inputs drive cell-type-specific activity within motor circuits remains unclear. The dorsolateral striatum (DLS) contains direct- and indirect-pathway medium spiny neurons (dMSNs and iMSNs) with distinct roles in movement control. In mice performing skilled locomotion, we recorded from dMSNs, iMSNs, and their cortical and thalamic inputs identified by monosynaptic rabies tracing. An RNN classifier and clustering analysis revealed functionally heterogeneous subpopulations in each population, with dMSNs preferentially activated at movement onset and offset, and iMSNs during execution. Cortical and thalamic inputs were preferentially activated during onset/offset and execution, respectively, though dMSN- and iMSN-projecting neurons in each region showed similar patterns. Locomotion phase-specific rhythmic activity was found in a subset of thalamic dMSN-projecting neurons and dMSNs. Cortex or thalamus inactivation reduced MSN activity. These findings suggest that corticostriatal and thalamostriatal inputs convey complementary motor signals via shared and cell-type-specific pathways.

neuroscience↗

bioRxiv2025

Optogenetic restoration of neuron subtype-specific cortical activity ameliorates motor deficits in Huntington's Disease mice

Huntingtons disease (HD) is a devastating movement disorder without a current cure. Although the monogenic basis of HD is well-defined, the complex downstream effects that underlie behavioral symptoms are poorly understood. These effects include cortical dysfunctions, yet the role of specific cortical neuronal subtypes in HD symptoms remain largely unexplored. Here, we used longitudinal in vivo two-photon calcium imaging to examine the activity of two cortical inhibitory neuron (IN) subtypes and excitatory corticostriatal projection neurons (CSPNs) in the motor cortex of R6/2 HD mouse model throughout disease progression. We found that motor deficits in R6/2 mice were accompanied by neuron type-specific abnormalities in movement-related activity, including hypoactivity of vasoactive intestinal peptide (VIP)-INs and CSPNs. Optogenetic activation of VIP-INs in R6/2 mice restored healthy levels of activity in VIP-INs and their downstream CSPNs and ameliorated motor deficits in R6/2 mice. Our findings highlight cortical INs as a potential therapeutic target for HD and possibly other neurological diseases.

neuroscience↗

bioRxiv2025

Targeted stimulation of motor cortex neural ensembles drives learned movements

During the execution of learned motor skills, the neural population in the layer 2/3 (L2/3) of the primary motor cortex (M1) expresses a reproducible spatiotemporal activity pattern. It is debated whether M1 actively participates in generating this activity pattern and whether this learned pattern causally drives the learned movement. To address these questions, we utilized in vivo two-photon calcium imaging combined with holographic optogenetic stimulation of functionally defined M1 L2/3 neuronal ensembles in mice performing a skilled lever-pressing task. A brief and synchronous stimulation of [~]20 neurons whose activity onset in voluntary trials precedes movement onsets induced movements that resembled the learned movement, while producing spatiotemporal activity patterns in other M1 neurons that resembled those during the voluntary learned movement. Moreover, trial-by-trial variability of optogenetically triggered population activity correlated with the variability in the induced movements. These trial-by-trial variabilities were predicted by the initial state of M1 population activity immediately preceding the stimulation. In some trials, the stimulation induced the learned activity without inducing overt movements, indicating that the learned activity is not simply a reflection of movements and instead can be induced internally. The stimulation failed to generate movements or learned activity when mice were disengaged from the task. Stimulation of the neurons whose activity followed voluntary movement onsets failed to induce the learned movement in task-engaged mice. Taken together, the learned activity pattern in M1 L2/3 can be generated when the M1 network is prepared at the optimal initial state and receives precise triggering inputs, supporting the active role of M1 in the generation of learned activity and learned movements.

neuroscience↗

bioRxiv2024

Cholinergic feedback for context-specific modulation of sensory representations

The brains ability to prioritize behaviorally relevant sensory information is crucial for adaptive behavior, yet the underlying mechanisms remain unclear. Here, we investigated the role of basal forebrain cholinergic neurons in modulating olfactory bulb (OB) circuits in mice. Calcium imaging of cholinergic feedback axons in OB revealed that their activity is strongly correlated with orofacial movements, with little responses to passively experienced odor stimuli. However, when mice engaged in an odor discrimination task, OB cholinergic axons rapidly shifted their response patterns from movement-correlated activity to odor-aligned responses. Notably, these odor responses during olfactory task engagement were absent in cholinergic axons projecting to the dorsal cortex. The level of odor responses correlated with task performance. Inactivation of OB-projecting cholinergic neurons during task engagement impaired performance and reduced odor responses in OB granule cells. Thus, the cholinergic system dynamically modulates sensory processing in a modality-specific and context-dependent manner, providing a mechanism for a flexible and adaptive sensory prioritization.

neuroscience↗

bioRxiv2023

Cellular Calcium Activity at Depth Predicted from Surface Potential Recordings using Ultra-high Density Transparent Graphene Arrays

Recording brain activity with high spatial and high temporal resolution across deeper layers of cortex has been a long-sought methodology to study how neural information is coded, stored, and processed by neural circuits and how it leads to cognition and behavior. Electrical and optical neural recording technologies have been the key tools in neurophysiology studies toward a comprehensive understanding of the neural dynamics. The advent of optically transparent neural microelectrodes has facilitated multimodal experiments combining simultaneous electrophysiological recordings from the brain surface with optical imaging and stimulation of neural activity. A remaining challenge is to scale down electrode dimensions to single -cell size and increase the density to record neural activity with high spatial resolution across large areas to capture nonlinear neural dynamics at multiple spatial and temporal scales. Here, we developed microfabrication techniques to create transparent graphene microelectrodes with ultra-small openings and a large, completely transparent recording area. We achieved this by using long graphene microwires without any gold extensions in the field of view. To overcome the quantum capacitance limit of graphene and scale down the microelectrode diameter to 20 m, we used Pt nanoparticles. To prevent open circuit failure due to defects and disconnections in long graphene wires, we employed interlayer doped double layer graphene (id-DLG) and demonstrated cm-scale long transparent graphene wires with microscale width and low resistance. Combining these two advances, we fabricated high-density microelectrode arrays up to 256 channels. We conducted multimodal experiments, combining recordings of cortical potentials with high-density transparent arrays with two-photon calcium imaging from layer 1 (L1) and layer 2/3 (L2/3) of the V1 area of mouse visual cortex. High-density recordings showed that the visual evoked responses are more spatially localized for high-frequency bands, particularly for the multi-unit activity (MUA) band. The MUA power was found to be strongly correlated with the cellular calcium activity. Leveraging this strong correlation, we applied dimensionality reduction techniques and neural networks to demonstrate that single-cell (L2/3) and average (L1 and L2/3) calcium activities can be decoded from surface potentials recorded by high-density transparent graphene arrays. Our high-density transparent graphene electrodes, in combination with multimodal experiments and computational methods, could lead to the development of minimally invasive neural interfaces capable of recording neural activity from deeper layers without requiring depth electrodes that cause damage to the tissue. This could potentially improve brain computer interfaces and enable less invasive treatments for neurological disorders.

neuroscience↗

bioRxiv2022

Rewiring Dynamics of Functional Connectome in Motor Cortex during Motor Skill Learning

The brains connectome continually rewires throughout the life of an organism. In this study, we sought to elucidate the operational principles of such rewiring by analyzing the functional connectomes in mouse primary motor cortex (M1) during a 14-session (day) lever-press task learning in response to an auditory cue. Specifically, we employed Calcium imaging recordings of L2/3 and L5 of M1 in awake mice to reconstruct and analyze functional connectomes across learning sessions. Our results show that functional connectomes in L2/3 and L5 follow a similar learning-induced rewiring trajectory. More specifically, the connectomes rewire in a biphasic manner, where functional connectivity increases over the first few learning sessions, and then, it is gradually pruned to return to a homeostatic level of network density. We demonstrated that the increase of network connectivity in L2/3 connectomes, but not in L5, generates neuronal co-firing activity that correlates with higher motor performance (shorter cue-to-reward time), while motor performance remains relatively stable throughout the pruning phase. The results show a biphasic rewiring principle that involves the maximization of reward / performance and maintenance of network density. Finally, we demonstrated that the connectome rewiring in L2/3 is clustered around a core set of movement-associated neurons that form a highly interconnected hub in the connectomes, and that the activity of these core neurons stably encodes movement throughout learning. Significance StatementConnectomes in the motor cortex rewire during motor skill learning, but the operational principle behind this rewiring is yet to be determined. Here, we characterized the rewiring dynamics of functional connectomes in L2/3 and L5 of M1 in mice engaging in a lever-press learning, using two-photon fluorescence microscopy data. We identified a universal biphasic rewiring trajectory across animals and layers in the motor cortex that reflects two objectives: an exploratory phase that increases functional connectivity and optimizes motor performance, and a pruning phase that brings connectivity back to a homeostatic level while maintaining motor performance. We found further that connectome rewiring during motor skill learning concentrates around a core set of highly interconnected neurons in L2/3 that reliably encode movements.

neuroscience↗

bioRxiv2021

PatchWarp: Corrections of non-uniform image distortions in two-photon calcium imaging data by patchwork affine transformations

Two-photon microscopy has been widely used to record the activity of populations of individual neurons at high spatial resolution in behaving animals. The ability to perform imaging for an extended period of time allows the investigation of activity changes associated with behavioral states and learning. However, imaging often accompanies shifts of the imaging field, including rapid ([~]100ms) translation and slow, spatially non-uniform distortion. To combat this issue and obtain a stable time series of the target structures, motion correction algorithms are commonly applied. However, typical motion correction algorithms are limited to full field translation of images and are unable to correct non-uniform distortions. Here, we developed a novel algorithm, PatchWarp, to robustly correct slow image distortion for calcium imaging data. PatchWarp is a two-step algorithm with rigid and non-rigid image registrations. To correct non-uniform image distortions, it splits the imaging field and estimates the best affine transformation matrix for each of the subfields. The distortion-corrected subfields are stitched together like a patchwork to reconstruct the distortion-corrected imaging field. We show that PatchWarp robustly corrects image distortions of calcium imaging data collected from various cortical areas through glass window or GRIN lens with a higher accuracy than existing non-rigid algorithms. Furthermore, it provides a fully automated method of registering images from different imaging sessions for longitudinal neural activity analyses. PatchWarp improves the quality of neural activity analyses and would be useful as a general approach to correct image distortions in a wide range of disciplines.

neuroscience↗

bioRxiv2021

Decoding of cortex-wide brain activity from local recordings of neural potentials

ObjectiveElectrical recordings of neural activity from brain surface have been widely employed in basic neuroscience research and clinical practice for investigations of neural circuit functions, brain-computer interfaces, and treatments for neurological disorders. Traditionally, these surface potentials have been believed to mainly reflect local neural activity. It is not known how informative the locally recorded surface potentials are for the neural activities across multiple cortical regions. ApproachTo investigate that, we perform simultaneous local electrical recording and wide-field calcium imaging in awake head-fixed mice. Using a recurrent neural network model, we try to decode the calcium fluorescence activity of multiple cortical regions from local electrical recordings. Main resultsThe mean activity of different cortical regions could be decoded from locally recorded surface potentials. Also, each frequency band of surface potentials differentially encodes activities from multiple cortical regions so that including all the frequency bands in the decoding model gives the highest decoding performance. Despite the close spacing between recording channels, surface potentials from different channels provide complementary information about the large-scale cortical activity and the decoding performance continues to improve as more channels are included. Finally, we demonstrate the successful decoding of whole dorsal cortex activity at pixel-level using locally recorded surface potentials. SignificanceThese results show that the locally recorded surface potentials indeed contain rich information of the large-scale neural activities, which could be further demixed to recover the neural activity across individual cortical regions. In the future, our cross-modality inference approach could be adapted to virtually reconstruct cortex-wide brain activity, greatly expanding the spatial reach of surface electrical recordings without increasing invasiveness. Furthermore, it could be used to facilitate imaging neural activity across the whole cortex in freely moving animals, without requirement of head-fixed microscopy configurations.

neuroscience↗

bioRxiv2020

FARCI: Fast and Robust Connectome Inference

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using gold standard datasets from the Neural Connectomics Challenge (NCC) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison to the best performing algorithm in the NCC, FARCI produces more accurate networks over different network sizes and subsampling, while providing over two orders of magnitude faster computational speed.

neuroscience↗

bioRxiv2018

Alopecia areata susceptibility variant identified by MHC risk haplotype sequencing reproduces symptomatic patched hair loss in mice

BackgroundAlopecia areata (AA) is a highly heritable multifactorial and complex disease. However, no convincing susceptibility gene has yet been pinpointed in the major histocompatibility complex (MHC), a region in the human genome known to be associated with AA as compared to other regions.\n\nResultsBy sequencing MHC risk haplotypes, we identified a variant (rs142986308, p.Arg587Trp) in the coiled-coil alpha-helical rod protein 1 (CCHCR1) gene as the only non-synonymous variant in the AA risk haplotype. Using CRISPR/Cas9 for allele-specific genome editing, we then phenocopied AA symptomatic patched hair loss in mice engineered to carry the Cchcr1 risk allele. Skin biopsies of these alopecic mice showed strong up-regulation of hair-related genes, including hair keratin and keratin-associated proteins (KRTAPs). Using transcriptomics findings, we further identified CCHCR1 as a novel component of hair shafts and cuticles in areas where the engineered alopecic mice displayed fragile and impaired hair.\n\nConclusionsThese results suggest an alternative mechanism for the aetiology of AA based on aberrant keratinization, in addition to generally well-known autoimmune events.

genomics↗