Biology subjects

Bharath, A. A.

Publications and source records attributed to Bharath, A. A..

4 recordsLinked to original sources

bioRxiv2024

Impaired axon regeneration and heightened synaptic dynamics in the injured aged mammalian cortex

How aging affects axon regeneration and synaptic repair in the brain is poorly understood. To study age-related changes in neural circuits, we developed a model of axonal injury in the aged (> 2 years) mouse somatosensory cortex. By directly tracking fluorescently labelled injured axons by multiphoton imaging, we find that while axon degeneration in the aged brain is comparable to the young adult brain, axon regeneration is impaired. We further examine changes in the most common type of cortical synapses, En Passant Boutons (EPBs), and observe a transient and significant increase in the number and size of boutons 6 hours post-lesion. Using a computational model of a recurrent neural network, we examined the functional consequences of these synaptic increases on memory, comparing results to models of the young adult brain. The results suggest that increased synaptic dynamics might enable partial recovery from injury via synaptic re-wiring in the aged brain. HighlightsO_LIImpaired axon regeneration in the aged brain is not due to slowed degeneration. C_LIO_LISynaptic reorganization occurs following axon injury in the aged brain. C_LIO_LISynaptic reorganization may serve as a compensatory mechanism after injury. C_LI

neuroscience↗

bioRxiv2022

Validation of two-dimensional video-based inference of finger kinematics with pose estimation

Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participants hands. However, such requirements can narrow the broader adoption of movement tracking for kinematic assessment outside these laboratory settings, such as in the home. Thus, there is the need for markerless hand motion capture techniques that are easy to use and accurate enough to evaluate the complex movements of the human hand. Several recent studies have validated lower-limb kinematics obtained with a marker-free technique, OpenPose. This investigation examines the accuracy of OpenPose, when applied to images from single RGB cameras, against a gold standard marker-based optical motion capture system that is commonly used for hand kinematics estimation. Participants completed four single-handed activities with right and left hands, including hand abduction and adduction, radial walking, metacarpophalangeal (MCP) joint flexion, and thumb opposition. Accuracy of finger kinematics was assessed using the root mean square error. Mean total active flexion was compared using the Bland-Altman approach, and coefficient of determination of a linear regression. Results showed good agreement for abduction and adduction and thumb opposition activities. Lower agreement between the two methods was observed for radial walking (mean difference between the methods of 5.03{degrees}) and MCP flexion (mean difference of 6.82{degrees}) activities, due to occlusion. This investigation demonstrated that OpenPose, applied to videos captured with monocular cameras, can be used for markerless motion capture for finger tracking with an error below than 11{degrees} and on the order of that which is accepted clinically. Author summaryDecreased hand mobility may limit functionality, and its quantification is fundamental to assess underlying impairments. Optical motion capture technologies are the most accurate means by which to quantify hand motion. As this approach involves placing markers on the skin and recording hand movements using multiple cameras, there are limitations of physical space, time requirements, and financial implications. Therefore, the adoption of these practices is confined to laboratory settings. In clinical settings, goniometry is used to quantify hand range of motion (ROM), but this also involves lengthy processes and requires face-to-face assessments. Alternative solutions have been investigated to quantify hand mobility remotely and support home-based care interventions. However, none has been shown to be accurate enough to replace the gold-standard measurement of hand ROM in clinical settings. Recently, markerless technologies that leverage artificial intelligence have exhibited great potential for human movement analysis, but these studies have validated markerless tracking technologies for the lower limb only. We demonstrate that the validity of these models can be extended to capture hand mobility, making it also possible to assess hand function remotely.

bioengineering↗

bioRxiv2018

Neural Sampling Strategies for Visual Stimulus Reconstruction from Two-photon Imaging of Mouse Primary Visual Cortex

Deciphering the neural code involves interpreting the responses of sensory neurons from the perspective of a downstream population. Performing such a read-out is an important step towards understanding how the brain processes sensory information and has implications for Brain-Machine Interfaces. While previous work has focused on classification algorithms to identify a stimulus in a predefined set of categories, few studies have approached a full-stimulus reconstruction task, especially from calcium imaging recordings. Here, we attempt a pixel-by-pixel reconstruction of complex natural stimuli from two-photon calcium imaging of mouse primary visual cortex. We decoded the activity of 103 neurons from layer 2/3 using an optimal linear estimator and investigated which factors drive the reconstruction performance at the pixel level. We find the density of receptive fields to be the most influential feature. Finally, we use the receptive field data and simulations from a linear-nonlinear Poisson model to extrapolate decoding accuracy as a function of network size. We find that, on this dataset, reconstruction performance can increase by more than 50%, provided that the receptive fields are sampled more uniformly in the full visual field. These results provide practical experimental guidelines to boost the accuracy of full-stimulus reconstruction.

neuroscience↗

bioRxiv2018

Visual reconstruction from 2-photon calcium imaging suggests linear readout properties of neurons in mouse primary visual cortex

Deciphering the neural code, that is interpreting the responses of sensory neurons from the perspective of a downstream population, is an important step towards understanding how the brain processes sensory stimulation. While previous work has focused on classification algorithms to identify the most likely stimulus label in a predefined set of categories, fewer studies have approached a full stimulus reconstruction task. Outstanding questions revolve around the type of algorithm that is most suited to decoding (i.e. full reconstruction, in the context of this study), especially in the presence of strong encoding non-linearities, and the possible role of pairwise correlations. We present, here, the first pixel-by-pixel reconstruction of a complex natural stimulus from 2-photon calcium imaging responses of mouse primary visual cortex (V1). We decoded the activity of approximately 100 neurons from layer 2/3 using an optimal linear estimator and an artificial neural network. We also investigated how much accuracy is lost in this decoding operation when ignoring pairwise neural correlations. We found that a simple linear estimator is sufficient to extract relevant stimulus features from the neural responses, and that it was not significantly outperformed by a non-linear decoding algorithm. The importance of pairwise correlations for reconstruction accuracy was also limited. The results of this study suggest that, conditional on the spatial and temporal limits of the recording technique, V1 neurons display linear readout properties, with low information content in the joint distribution of their activity.

neuroscience↗