Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.01.23.634598

An Open-Source Joystick Platform for Investigating Forelimb Motor Control, Auditory-Motor Integration, and Value-Based Decision-Making in Head-Fixed Mice

Abstract

Investigation of neural processes underlying motor control requires behavioral readouts that capture the richness of actions, including both categorical (choice-based) information and motor execution (kinematics). We present an open-source platform for behavioral training of head-fixed mice that combines a stationary or retractable forelimb-based joystick, sound-presentation system, capacitive lick sensor, and water reward dispenser. The setup allows for the creation of multiple behavioral paradigms, two of which are highlighted here: a two-alternative forced-choice auditory-motor discrimination paradigm, and a two-armed bandit value-based decision-making task. In the auditory-motor paradigm, mice learn to report high or low frequency tones by pushing or pulling the joystick. In the value-based paradigm, mice learn to push or pull the joystick based on the history of rewarded trials. In addition to reporting categorical choices, this setup provides a rich dataset of motor parameters that reflect components of the underlying learning and decision processes in both of these tasks. These kinematic parameters (including joystick speed and displacement, Frechet similarity of trajectories, tortuosity, angular standard deviation, and movement vigor) provide key additional insights into the motor execution of choices that are not as readily assessed in other paradigms. The systems flexibility of task design, joystick readout, and ease of construction represent an advance compared to currently available manipulandum tasks in mice. We provide detailed schematics for constructing the setup and protocols for behavioral training using both paradigms, with the hope that this open-source resource is readily adopted by neuroscientists interested in mechanisms of sensorimotor integration, motor control, and choice behavior. Significance StatementBehavioral paradigms for experiments in head-restrained mice are important for investigating the relationship between neural activity and behavior. However, behavioral setups are often constrained by high cost, design complexity, and implementation challenges. Here, we present an open-source platform for behavioral training of head-fixed mice using a joystick manipulandum. The setup allows for the creation of multiple behavioral paradigms, including an auditory-motor discrimination paradigm, and a value-based decision-making task. We include detailed instructions for construction and implementation of the entire open-source behavioral platform.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Linares-Garcia, I., Iliakis, E. A., Juliani, S. E., Ramirez, A. N., Woolley, J., Diaz-Hernandez, E., Fuccillo, M. V., Margolis, D. J.. 2025-01-23. An Open-Source Joystick Platform for Investigating Forelimb Motor Control, Auditory-Motor Integration, and Value-Based Decision-Making in Head-Fixed Mice. https://doi.org/10.1101/2025.01.23.634598

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗