Timothy H. Murphy

Increasingly, experiments designed to provide practical perturbations to circuits or behavior are required for hypothesis testing in various disciplines ranging from motor learning to recovery after injury. We present the implementation and efficacy of an open-source closed-loop neurofeedback (CLNF) and closed-loop movement feedback (CLMF) system. In CLNF, we measure mm-scale cortical mesoscale activity with GCaMP6s and provide graded auditory feedback (within ~63 ms) based on changes in dorsal-cortical activation within regions of interest (ROIs) and with a specified rule. Single or dual ROIs (ROI1, ROI2) on the dorsal cortical map were selected as targets. Both motor and sensory regions supported closed-loop training in male and female mice. Mice modulated activity in rule-specific target cortical ROIs to get increasing rewards over days (repeated-measures ANOVA [RM-ANOVA], p=2.83e-5) and adapted to changes in ROI rules (RM-ANOVA, p=8.3e-10, Table 4 for different rule changes). In CLMF, feedback (within ~67 ms) was based on tracking a specified body movement, and rewards were generated when the behavior reached a threshold. For movement training, the group that received graded auditory feedback performed significantly better (RM-ANOVA, p=9.6e-7) than a control group (RM-ANOVA, p=0.49) within 4 training days. Additionally, mice can learn a change in task rule from left forelimb to right forelimb within a day, after a brief performance drop on day 5. Offline analysis of neural data and behavioral tracking revealed changes in the overall distribution of Ca ²⁺ fluorescence values in CLNF and body-part speed values in CLMF experiments. Increased CLMF performance was accompanied by a decrease in task latency and cortical Δ F / F ₀ amplitude during the task, indicating lower cortical activation as the task gets more familiar.

Increasingly, experiments designed to provide practical perturbations to circuits or behavior are required for hypothesis testing in various disciplines ranging from motor learning to recovery after injury. We present the implementation and efficacy of an open-source closed-loop neurofeedback (CLNF) and closed-loop movement feedback (CLMF) system. In CLNF, we measure mm-scale cortical mesoscale activity with GCaMP6s and provide graded auditory feedback (within ∼63 ms) based on changes in dorsal-cortical activation within regions of interest (ROI) and with a specified rule. Single or dual ROIs (ROI1, ROI2) on the dorsal cortical map were selected as targets. Both motor and sensory regions supported closed-loop training in male and female mice. Mice modulated activity in rule-specific target cortical ROIs to get increasing rewards over days (RM ANOVA p=2.83e-5) and adapted to changes in ROI rules (RM ANOVA p=8.3e-10, Table 4 for different rule changes). In CLMF, feedback (within ∼67 ms) was based on tracking a specified body movement, and rewards were generated when the behavior reached a threshold. For movement training, the group that received graded auditory feedback performed significantly better (RM-ANOVA p=9.6e-7) than a control group (RM-ANOVA p=0.49) within four training days. Additionally, mice can learn a change in task rule from left forelimb to right forelimb within a day, after a brief performance drop on day 5. Offline analysis of neural data and behavioral tracking revealed changes in the overall distribution of Ca2+ fluorescence values in CLNF and body-part speed values in CLMF experiments. Increased CLMF performance was accompanied by a decrease in task latency and cortical ΔF/F0 amplitude during the task, indicating lower cortical activation as the task gets more familiar.

The Allen Brain Institute (ABI) and the International Brain Laboratory (IBL) have produced large high quality open behavioural and electrophysiological datasets collected from behaving mice using neuropixels probes. Shared data from these projects are in the form of spike times, raw video footage, scored behaviour, but also local field potentials (LFP). These probes often pass through hippocampus while simultaneously recording from a number of other regions, providing the opportunity to evaluate how hippocampal LFP features during synchronized high-frequency bursts known as sharp-wave ripples (SWRs) impact behavioural task variables or spiking activity on other recorded contacts. Currently, there are no data standards or file formats for sharing SWRs. Here we present the SWR data from the ABI and IBL datasets in a sharable format, which integrates with their APIs. We have extracted, curated using field standards, and shared over 967,431 SWR events from 210 mice from these datasets as well as the code used to process them.

Developing high-quality training data is essential for tailoring large language models (LLMs) to specialized applications like mental health. To address privacy and legal constraints associated with real patient data, we designed a synthetic patient and interview generation framework that can be tailored to regional patient demographics. This system employs two locally run instances of Llama 3.3:70B: one as the interviewer and the other as the patient. These models produce contextually rich interview transcripts, structured by a customizable question bank, with lexical diversity similar to normal human conversation. We calculate median Distinct-1 scores of 0.44 and 0.33 for the patient and interview assistant model outputs respectively compared to 0.50 ± 0.11 as the average for 10,000 episodes of a radio program dialog. Central to this approach is the patient generation process, which begins with a locally run Llama 3.3:70B model. Given the full question bank, the model generates a detailed profile template, combining predefined variables (e.g., demographic data or specific conditions) with LLM-generated content to fill in contextual details. This hybrid method ensures that each patient profile is both diverse and realistic, providing a strong foundation for generating dynamic interactions. Demographic distributions of generated patient profiles were not significantly different from real-world population data and exhibited expected variability. Additionally, for the patient profiles we assessed LLM metrics and found an average Distinct-1 score of 0.8 (max = 1) indicating diverse word usage. By integrating detailed patient generation with dynamic interviewing, the framework produces synthetic datasets that may aid the adoption and deployment of LLMs in mental health settings.

Across cortex, a substantial component of observed neuronal activity can be explained by movement. Voluntary movements elicit cortical activity that contain elements related to both motor planning and sensory activity. However, automatic movements that do not require conscious processing, for example grooming or drinking, are to a large extent controlled by subcortical systems such as the brainstem. While the cortex may not be required to generate these automatic behaviors, it is unclear whether cortical activity represents the movements associated with them. In this work, we use a simple procedure to stimulate stereotyped grooming behaviors in the head-fixed mouse while measuring neuronal function across the dorsal cortex. We find specific cortical representations of grooming both at a mesoscale network-level and single-cell resolution. Mesoscale cortical activation was most prominent at the onset of grooming episodes, and declined to baseline levels despite continuous engagement in the behavior. Further stratification of grooming component movements revealed that more directed and unilateral grooming movements had greater cortical associated responses than more stereotyped bilateral movements. These findings help to frame the impact of forms of automatic movements on large scale cortical ensembles and suggest they engage specific and transient cellular and regional cortical ensembles.

Across cortex, a substantial component of observed neuronal activity can be explained by movement. Voluntary movements elicit cortical activity that contain elements related to both motor planning and sensory activity. However, automatic movements that do not require conscious processing, for example grooming or drinking, are to a large extent controlled by subcortical systems such as the brainstem. While the cortex may not be required to generate these automatic behaviors, it is unclear whether cortical activity represents the movements associated with them. In this work, we use a simple procedure to stimulate stereotyped grooming behaviors in the head-fixed mouse while measuring neuronal function across the dorsal cortex. We find specific cortical representations of grooming both at a mesoscale network-level and single-cell resolution. Mesoscale cortical activation was most prominent at the onset of grooming episodes, and declined to baseline levels despite continuous engagement in the behavior. Further stratification of grooming component movements revealed that more directed and unilateral grooming movements had greater cortical associated responses than more stereotyped bilateral movements. These findings help to frame the impact of forms of automatic movements on large scale cortical ensembles and suggest they engage specific and transient cellular and regional cortical ensembles.

Huntington disease (HD) is a genetic neurodegenerative disorder characterized by progressive motor dysfunction, cognitive decline, and neuropsychiatric symptoms. Assessing early motor skill deficits in HD mouse models is challenging with traditional behavioral tasks. This study uses a home cage-based lever-pulling task, PiPaw2.0, to evaluate motor learning in 6–7 months-old zQ175 knock-in HD mice in a more naturalistic environment. In this task, mice learn to pull a lever for a water reward, with the requirement to hold the lever within a specific goal range for a required hold time. As the mice improved, the required hold time increased, thereby gradually increasing the task demands. Both wild type (WT) and zQ175 mice initially showed similar task engagement, but zQ175 mice had significant deficits in adapting to increasing hold time. The WT mice refined their strategies over time, shifting from random to more precise lever pulls, while zQ175 mice failed to make this adjustment, maintaining erratic performance. Additionally, in group-housing WT mouse lever performance benefited from peer interactions, an effect absent in zQ175 mice. Post-task neural assessments revealed that WT mice developed experience-mediated synaptic plasticity in the left striatum (contralateral to lever-pulling paw), while zQ175 mice showed no significant changes, consistent with known corticostriatal plasticity impairments in HD mouse models. In conclusion, our findings demonstrate the effectiveness of group-housed, home cage-based assessments for evaluating motor learning and adaptation in HD mouse models. This study provides insights into the motor control and adaptive learning deficits in HD, emphasizing the value of automated home cage systems in advancing neurodegenerative disease research and highlighting the importance of peer influences on performance.

The availability of large-scale neuronal population datasets necessitates new methods to model population dynamics and extract interpretable, scientifically translatable insights. Existing deep learning methods often overlook the biological mechanisms underlying population activity and thus exhibit suboptimal performance with neuronal data and provide little to no interpretable information about neurons and their interactions. In response, we introduce SynapsNet, a novel deep-learning framework that effectively models population dynamics and functional interactions between neurons. Within this biologically realistic framework, each neuron, characterized by a latent embedding, sends and receives currents through directed connections. A shared decoder uses the input current, previous neuronal activity, neuron embedding, and behavioral data to predict the population activity in the next time step. Unlike common sequential models that treat population activity as a multichannel time series, SynapsNet applies its decoder to each neuron (channel) individually, with the learnable functional connectivity serving as the sole pathway for information flow between neurons. Our experiments, conducted on mouse cortical activity from publicly available datasets and recorded using the two most common population recording modalities (Ca imaging and Neuropixels) across three distinct tasks, demonstrate that SynapsNet consistently outperforms existing models in forecasting population activity. Additionally, our experiments on both real and synthetic data showed that SynapsNet accurately learns functional connectivity that reveals predictive interactions between neurons.

Increasingly, experiments designed to provide practical perturbations to circuits or behavior are required for hypothesis testing in various disciplines ranging from motor learning to recovery after injury. We present the implementation and efficacy of an open-source closed-loop neurofeedback (CLNF) and closed-loop movement feedback (CLMF) system. In CLNF, we measure mm-scale cortical mesoscale activity with GCaMP6s and provide graded auditory feedback (within ~63 ms) based on changes in dorsal-cortical activation within regions of interest (ROI) and with a specified rule. Single or dual ROIs (ROI1, ROI2) on the dorsal cortical map were selected as targets. Both motor and sensory regions supported closed-loop training in male and female mice. Mice modulated activity in rule-specific target cortical ROIs to get increasing rewards over days (RM ANOVA p=2.83e-5) and adapted to changes in ROI rules (RM ANOVA p=8.3e-10, Table 4 for different rule changes). In CLMF, feedback (within ~67 ms) was based on tracking a specified body movement, and rewards were generated when the behavior reached a threshold. For movement training, the group that received graded auditory feedback performed significantly better (RM-ANOVA p=9.6e-7) than a control group (RM-ANOVA p=0.49) within four training days. Additionally, mice can learn a change in task rule from left forelimb to right forelimb within a day, after a brief performance drop on day 5. Offline analysis of neural data and behavioral tracking revealed changes in the overall distribution of Ca2+ fluorescence values in CLNF and body-part speed values in CLMF experiments. Increased CLMF performance was accompanied by a decrease in task latency and cortical ΔF/F0 amplitude during the task, indicating lower cortical activation as the task gets more familiar.