Biology subjects

Inayat, S.

Publications and source records attributed to Inayat, S..

9 recordsLinked to original sources

bioRxiv2025

A TRANSPARENT WHEEL-BASED PLATFORM FOR LOCOMOTION-ON-DEMAND AND MULTIMODAL BODY AND FACIAL KINEMATICS IN HEAD-FIXED MICE

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding how the brain transforms sensory input and internal state into coordinated action requires behavioral paradigms that provide precise, multimodal measurements of movement and arousal while remaining compatible with neural recording techniques. Here we present a modular behavioral platform that enables stimulus-evoked locomotion in head-fixed mice using a transparent running wheel combined with air-stream stimulation. The design provides direct ventral access for imaging paw movements while simultaneously capturing body kinematics, facial motion, and eye-related signals from multiple camera views. The system integrates Arduino-based stimulus control, rotary encoder measurements, Raspberry Pi-based videography, and LED-based visual markers for temporal alignment across independently acquired data streams. Using a proof-of-principle dataset from well-trained animals, we show that brief air delivery reliably induces structured locomotion with reproducible trial timing. Optical-flow-based motion metrics and DeepLabCut pose estimation reveal robust, stimulus-locked increases in paw, limb, and facial movements during air-on epochs relative to air-off periods. LED-based event markers enable consistent identification of air-on and air-off epochs across video streams despite differences in sampling rates. Together, these features provide a flexible framework for studying stimulus-driven locomotion and multi-view behavioral dynamics under head fixation, with straightforward compatibility for integration with neural imaging and electrophysiology recording approaches. SO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWQuantifying how the brain generates coordinated movement requires behavioral paradigms that provide precise, multi-view measurements while remaining compatible with neural recording techniques. We introduce a low-cost, modular behavioral platform that enables locomotion-on-demand in head-fixed mice using a transparent running wheel, allowing simultaneous visualization of ventral paw movements, facial dynamics, and eye/pupil-related signals. By combining stimulus-induced locomotion with synchronized multi-camera videography and open-source analysis pipelines, this system overcomes key limitations of existing head-fixed locomotion assays that rely on prolonged reward-based training or provide only limited behavioral readouts.

neuroscience↗

bioRxiv2025

DISTINCT NEURAL SIGNATURES OF HIPPOCAMPAL POPULATION DYNAMICS DURING LOCOMOTION-IN-PLACE

Hippocampal CA1 neurons modulate their activity with movement variables such as time, distance, and speed, yet it remains unclear how these representations reorganize across behavioral states, from externally driven to self-paced movement and immobility. Here, we investigated how sensory events that initiate or terminate locomotion, structure CA1 population codes and how these codes reorganize across sensory-driven locomotion, spontaneous locomotion, and forced immobility. Using two-photon calcium imaging in head-fixed Thy1-GCaMP mice (n = 5) performing the air-induced running task on a non-motorized conveyor belt, we examined neuronal firing-rate modulation across a series of behavioral configurations designed to probe distinct forms of locomotion-in-place. In the No-Brake (locomotion-permitted) condition, the belt rotated freely, allowing animals to execute full cyclic limb movements in response to air stimulation. In the Brake (immobility) condition, the belt was fixed, restricting movement to partial or attempted locomotor motions. Firing-rate modulation with respect to time, distance, and speed was quantified using linear (Pearson correlation) and nonlinear (mutual information) metrics under permutation testing in the natural reference domains. Behaviorally, air stimuli produced faster, sustained running during air-on and more variable, self-paced movement during air-off. Neurally, a larger fraction of CA1 cells was active and significantly modulated during air-off. Within the modulated set, singularly tuned cells (time, distance, or speed) predominated over mixed-tuned cells, and speed-modulated cells peaked earlier after stimulus onset or offset than time- or distance-modulated cells. Under Brake, CA1 activity was predominantly singularly tuned to time or movement-in-place, with stronger movement modulation and engagement post-stimulation. Despite substantial single-cell turnover across configurations and phases, population-level analyses revealed a coherent, air-phase-locked organization and distinct movement-related populations across Brake and No-Brake conditions. These results indicate a state-dependent reweighting of sensorimotor features implemented atop a conserved ensemble scaffold in CA1.

neuroscience↗

bioRxiv2023

Information-theory analysis of mouse string-pulling agrees with Fitts's Law: Increasing task difficulty engages multiple sensorimotor modalities in a dual oscillator behavior

Mouse string pulling, in which a mouse reels in a string with hand-over-hand movements, can provide insights into skilled motor behavior, neurological status, and cognitive function. The task involves two oscillatory movements connected by the string. The snout tracks the pendulum movement of the string produced by hand-over-hand pulls and so guides the hands to grasp the string. The present study examines the allocation of time required to pull strings of varying diameter. Movement is also described with end-point measures, string-pulling topography with 2D markerless pose estimates based on transfer learning with deep neural networks, and Mat-lab image-segmentation and heuristic algorithms for object tracking. With reduced string diameter, mice took longer to pull 60cm long strings. They also made more pulling cycles, misses, and mouth engagements, and displayed changes in the amplitude and frequency of pull cycles. The time measures agree with Fittss law in showing that increased task difficulty slows behavior and engages multiple compensatory sensorimotor modalities. The analysis reveals that time is a valuable resource in skilled motor behavior and information-theory can serve as a measure of its effective use.

animal behavior and cognition↗

bioRxiv2022

Generation of a hybrid AppNL-G-F/NL-G-F xThy1-GCaMP6s+/- Alzheimer disease mouse mitigates the behavioral and hippocampal encoding deficits of APP knock-in mutations of AppNL-G-F/NL-G-F mice

In contrast to most transgenic mouse models of Alzheimer disease (AD), knock-in mice expressing familial AD-linked mutations of the amyloid precursor protein (App) gene exhibit stereotypical age-dependent amyloid beta (A{beta}) pathology and cognitive impairment without physiologically unrealistic App overexpression. This study investigated the effect of familial AD-linked App mutations on hippocampal CA1 neuronal activity and function. To enable calcium imaging of neuronal activity, AppNL-G-F/NL-G-F knock-in (APPki) mice were crossed with Thy1-GCaMP6s+/- (C-TG) mice to generate AppNL-G-F/NL-G-FxThy1-GCaMP6s+/- (A-TG) mice, which were characterized at 12 months of age. A-TG mice exhibited A{beta} pathology in the hippocampus. In several configurations of an air-induced running task, A-TG mice and C-TG mice were equally successful in learning to run or to stay immobile. In the Morris water place test, A-TG mice were impaired, but learned the task. Comparisons of hippocampal CA1 neuronal activity in the air-induced running task showed that A-TG mice displayed neuronal hypoactivity both during movement and immobility. A-TG mice and C-TG CA1 neuronal encoding of distance or time in the air induced running task were not different. These results suggest that knock-in of familial AD-linked mutations in A-TG mice results in A{beta} pathology, neuronal hypoactivity, and cognitive impairment without severely affecting CA1 neuronal encoding. In comparison to APPki mice, A-TG mice had less severe AD-like memory impairments at 12 months of age (Saito et al., 2014; Mehla et al., 2019), suggesting that the disease onset was delayed in A-TG mice. The effect of APP mutations may have been mitigated through genetic mechanisms when APPKi mice were crossed with C-TG mice.

neuroscience↗

bioRxiv2022

Conjunctive and complementary CA1 hippocampal cell populations relate sensory events to immobility and locomotion

This study investigated the dynamics of recruitment of cells in the CA1 region of the hippocampus in response to sensory stimuli presented during immobility, movement, and their transitions. Two-photon calcium imaging of somal activity in CA1 neuron populations was done in head fixed mice. Sensory stimuli, either a light flash or an air stream, were delivered to the mice when at rest, when moving spontaneously, and while they were induced to run a fixed distance on the conveyor belt. Overall, 99% of 2083 identified cells (from 5 mice) were active across one or more of 20 sensorimotor events. A larger proportion of cells were active during locomotion. Nevertheless, for any given sensorimotor event, only about 17% of cells were active. When considering pairs of sensorimotor event types, the active cell population consisted of conjunctive (C [isin] A and B) cells, active across both events, and complementary (C [isin] A not B or C [isin] B not A) cells that were active only during individual events. Whereas conjunctive cells characterised stable representations of repeated sensorimotor events, complementary cells characterised recruitment of new cells for encoding novel sensorimotor events. The moment-to-moment recruitment of conjunctive and complementary cells across changing sensorimotor events signifies the involvement of the hippocampus in functional networks integrating sensory information with ongoing movement. This role of the hippocampus is well suited for movement guidance that secondarily might include spatial behavior, episodic learning and memory, context representation, and scene construction.

neuroscience↗

bioRxiv2020

Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice

Brain activity propagates across the cortex in diverse spatiotemporal patterns, both as a response to sensory stimulation and during spontaneous activity. Despite been extensively studied, the relationship between the characteristics of such patterns during spontaneous and evoked activity is not completely understood. To investigate this relationship, we compared visual, auditory, and tactile evoked activity patterns elicited with different stimulus strengths and spontaneous activity motifs in lightly anesthetized and awake mice using mesoscale wide-field voltage-sensitive dye and glutamate imaging respectively. The characteristics of cortical activity that we compared include amplitude, speed, direction, and complexity of propagation trajectories in spontaneous and evoked activity patterns. We found that the complexity of the propagation trajectories of spontaneous activity, quantified as their fractal dimension, is higher than the one from sensory evoked responses. Moreover, the speed and direction of propagation, are modulated by the amplitude during both, spontaneous and evoked activity. Finally, we found that spontaneous activity had similar amplitude and speed when compared to evoked activity elicited with low stimulus strengths. However, this similarity gradually decreased when the strength of stimuli eliciting evoked responses increased. Altogether, these findings are consistent with the fact that even primary sensory areas receive widespread inputs from other cortical regions, and that, during rest, the cortex tends to reactivate traces of complex, multi-sensory experiences that may have occurred in a range of different behavioural contexts.

neuroscience↗

bioRxiv2019

A toolbox for automated video analysis of rodents engaged in string-pulling: Phenotyping motor behavior of mice for sensory, whole-body and bimanual skilled hand function

String-pulling in rodents (rats and mice) is a task in which animals make hand-over-hand movements to spontaneously reel in a string with or without a food reward attached to its end. The task elicits bilateral skilled hand movements for which rodents require little training. The task is suitable for phenotyping physiology and pathophysiology of sensorimotor integration in rodent models of neurological and motor disorders. Because a rodent stands in the same location and its movements are repetitive, the task lends itself to quantification of topographical and kinematic parameters for on-line tactile tracking of the string, skilled hand movements for grasping, and rhythmical bilateral forearm movements to advance the string. Here we describe a Matlab(R) based software with a graphical user interface to assist researchers in analyzing the video record of string pulling. The software allows global characterization of position and motion using optical flow estimation, descriptive statistics, principal component, and independent component analyses as well as temporal measures of Fano factor, entropy, and Higuchi fractal dimension. Based on image segmentation and object tracking heuristic algorithms, the software also allows independent tracking of the body, ears, nose, and forehands for estimation of kinematic parameters such as body length, body angle, head roll, head yaw, head pitch, movement paths and speed of hand movement. The utility of the task and that of the software is presented by describing mouse strain characteristics in string-pulling behavior of two strains of mice, C57BL/6 and Swiss Webster. Postural and skilled hand kinematic differences that characterize the strains highlight the utility of the task and assessment methods for phenotypic and neurological analysis of healthy and rodent models of diseases such as Parkinsons, Huntingtons, Alzheimers and other neurological and motor disorders. Significance statementMouse models are used to investigate the physiology and pathophysiology of motor deficits observed in human neurological conditions, for testing substances for therapeutic drug development, and to investigate the role of neural systems and their genetic basis in the expression of behavior. Behavioral tasks involving unconditioned and natural behavior can provide rich insights into motor performance in animal models and analyses can be aided by the automated processing of video data for reliable quantification and high throughput.

neuroscience↗

bioRxiv2018

Low acetylcholine during early sleep is important for motor memory consolidation

The synaptic homeostasis theory of sleep proposes that low neurotransmitter activity in sleep is optimal for memory consolidation. We tested this theory by asking whether increasing acetylcholine levels during early sleep would disrupt motor memory consolidation. We trained separate groups of adult mice on the rotarod walking and skilled reaching for food tasks, and after training, administered physostigmine, an acetylcholinesterase inhibitor, to increase cholinergic tone in subsequent sleep. Post-sleep testing suggested that physostigmine impaired motor skill acquisition. Home-cage video monitoring and electrophysiology revealed that physostigmine disrupted sleep structure, delayed non-rapid-eye-movement sleep onset, and reduced slow-wave power in the hippocampus and cortex. The impaired motor performance with physostigmine, however, was not solely due to its effects on sleep structure, as one hour of sleep deprivation after training did not impair rotarod performance. A reduction in cholinergic tone by inactivation of cholinergic neurons during early sleep also affected rotarod performance. Administration of agonists and antagonists of muscarinic and nicotinic acetylcholine receptors revealed that activation of muscarinic receptors during early sleep impaired rotarod performance. The experiments suggest that the increased slow wave activity and inactivation of muscarinic receptors during early sleep due to reduced acetylcholine contribute to motor memory consolidation.

neuroscience↗

bioRxiv2016

Optical-flow analysis toolbox for characterization of spatiotemporal dynamics in mesoscale optical imaging of brain activity

Wide-field optical imaging techniques constitute powerful tools to sample and study mesoscale neuronal activity. The sampled data constitutes a sequence of image frames in which one can perceive the flow of brain activity starting and terminating at source and sink locations respectively. The most common data analyses include qualitative assessment to identify sources and sinks of activity as well as their trajectories. The quantitative analyses is mostly based on computing the temporal variation of the intensity of pixels while a few studies have also reported estimates of wave motion using optical-flow techniques from computer vision. A comprehensive toolbox for the quantitative analyses of mesoscale brain activity data however is still missing. We present a graphical-user-interface based Matlab(R) toolbox for investigating the spatiotemporal dynamics of mesoscale brain activity using optical-flow analyses. The toolbox includes the implementation of three optical-flow methods namely Horn-Schunck, Combined Local-Global, and Temporospatial algorithms for estimating velocity vector fields of perceived flow in mesoscale brain activity. From the velocity vector fields we determine the locations of sources and sinks as well as the trajectories and temporal velocities of activity flow. Using our toolbox, we compare the efficacy of the three optical-flow methods for determining spatiotemporal dynamics by using simulated data. We also demonstrate the application of optical-flow methods onto sensory-evoked calcium and voltage imaging data. Our results indicate that the combined local-global method we employ, yields results that correlate with the manual assessment. The automated approach permits rapid and effective quantification of mesoscale brain dynamics and may facilitate the study of brain function in response to new experiences or pathology.\n\nConflicts of Interestnone\n\nAuthor contribution statementMHM, MM, NV, and SI designed the study. NA and SI wrote Matlab(R) code for the toolbox and designed the simulated data. MHM, and NA performed the experiments. NA and SI analyzed the data. SI, NA, and MHM wrote the manuscript.

neuroscience↗