Decoding state-dependent cortical-cerebellar cellular functional connectivity in the mouse brain.

The cortex and cerebellum form multi-synaptic reciprocal connections. We investigate the functional connectivity between single spiking cerebellar neurons and the population activity of the mouse dorsal cortex using mesoscale imaging. Cortical representations of individual cerebellar neurons vary significantly across different brain states but are drawn from a common set of cortical networks. These cortical-cerebellar connectivity features are observed in mossy fibers and Purkinje cells as well as neurons in different cerebellar lobules, albeit with variations across cell types and regions. Complex spikes of Purkinje cells preferably associate with the sensorimotor cortex, whereas simple spikes display more diverse cortical connectivity patterns. The spontaneous functional connectivity patterns align with cerebellar neurons' functional responses to external stimuli in a modality-specific manner. The tuning properties of subsets of cerebellar neurons differ between anesthesia and awake states, mirrored by state-dependent changes in their long-range functional connectivity patterns with mesoscale cortical activity.

Deep behavioural phenotyping of the Q175 Huntington disease mouse model: effects of age, sex, and weight.

BACKGROUND: Huntington disease (HD) is a neurodegenerative disorder with complex motor and behavioural manifestations. The Q175 knock-in mouse model of HD has gained recent popularity as a genetically accurate model of the human disease. However, behavioural phenotypes are often subtle and progress slowly in this model. Here, we have implemented machine-learning algorithms to investigate behaviour in the Q175 model and compare differences between sexes and disease stages. We explore distinct behavioural patterns and motor functions in open field, rotarod, water T-maze, and home cage lever-pulling tasks. RESULTS: In the open field, we observed habituation deficits in two versions of the Q175 model (zQ175dn and Q175FDN, on two different background strains), and using B-SOiD, an advanced machine learning approach, we found altered performance of rearing in male manifest zQ175dn mice. Notably, we found that weight had a considerable effect on performance of accelerating rotarod and water T-maze tasks and controlled for this by normalizing for weight. Manifest zQ175dn mice displayed a deficit in accelerating rotarod (after weight normalization), as well as changes to paw kinematics specific to males. Our water T-maze experiments revealed response learning deficits in manifest zQ175dn mice and reversal learning deficits in premanifest male zQ175dn mice; further analysis using PyMouseTracks software allowed us to characterize new behavioural features in this task, including time at decision point and number of accelerations. In a home cage-based lever-pulling assessment, we found significant learning deficits in male manifest zQ175dn mice. A subset of mice also underwent electrophysiology slice experiments, revealing a reduced spontaneous excitatory event frequency in male manifest zQ175dn mice. CONCLUSIONS: Our study uncovered several behavioural changes in Q175 mice that differed by sex, age, and strain. Our results highlight the impact of weight and experimental protocol on behavioural results, and the utility of machine learning tools to examine behaviour in more detailed ways than was previously possible. Specifically, this work provides the field with an updated overview of behavioural impairments in this model of HD, as well as novel techniques for dissecting behaviour in the open field, accelerating rotarod, and T-maze tasks.

Scholar Metrics Scraper (SMS): automated retrieval of citation and author data.

Academic departments, research clusters and evaluators analyze author and citation data to measure research impact and to support strategic planning. We created Scholar Metrics Scraper (SMS) to automate the retrieval of bibliometric data for a group of researchers. The project contains Jupyter notebooks that take a list of researchers as an input and exports a CSV file of citation metrics from Google Scholar (GS) to visualize the group's impact and collaboration. A series of graph outputs are also available. SMS is an open solution for automating the retrieval and visualization of citation data.

Meso-Py: Dual Brain Cortical Calcium Imaging in Mice during Head-Fixed Social Stimulus Presentation.

We present a cost-effective, compact foot-print, and open-source Raspberry Pi-based widefield imaging system. The compact nature allows the system to be used for close-proximity dual-brain cortical mesoscale functional-imaging to simultaneously observe activity in two head-fixed animals in a staged social touch-like interaction. We provide all schematics, code, and protocols for a rail system where head-fixed mice are brought together to a distance where the macrovibrissae of each mouse make contact. Cortical neuronal functional signals (GCaMP6s; genetically encoded Ca2+ sensor) were recorded from both mice simultaneously before, during, and after the social contact period. When the mice were together, we observed bouts of mutual whisking and cross-mouse correlated cortical activity across the cortex. Correlations were not observed in trial-shuffled mouse pairs, suggesting that correlated activity was specific to individual interactions. Whisking-related cortical signals were observed during the period where mice were together (closest contact). The effects of social stimulus presentation extend outside of regions associated with mutual touch and have global synchronizing effects on cortical activity.

Mesotrode chronic simultaneous mesoscale cortical imaging and subcortical or peripheral nerve spiking activity recording in mice.

Brain function originates from hierarchical spatial-temporal neural dynamics distributed across cortical and subcortical networks. However, techniques available to assess large-scale brain network activity with single-neuron resolution in behaving animals remain limited. Here, we present Mesotrode that integrates chronic wide-field mesoscale cortical imaging and compact multi-site cortical/subcortical cellular electrophysiology in head-fixed mice that undergo self-initiated running or orofacial movements. Specifically, we harnessed the flexibility of chronic multi-site tetrode recordings to monitor single-neuron activity in multiple subcortical structures while simultaneously imaging the mesoscale activity of the entire dorsal cortex. A mesoscale spike-triggered averaging procedure allowed the identification of cortical activity motifs preferentially associated with single-neuron spiking. Using this approach, we were able to characterize chronic single-neuron-related functional connectivity maps for up to 60 days post-implantation. Neurons recorded from distinct subcortical structures display diverse but segregated cortical maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. We extended the capability of Mesotrode by implanting the micro-electrode at the facial motor nerve and found that facial nerve spiking is functionally associated with the PTA, RSP, and M2 network, and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice. These findings demonstrate that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods, generating multimodal and multi-scale network activity from a single implant, offering new insights into the neural mechanisms underlying specific behaviors.

Standardized 3D test object for multi-camera calibration during animal pose capture.

Accurate capture of animal behavior and posture requires the use of multiple cameras to reconstruct three-dimensional (3D) representations. Typically, a paper ChArUco (or checker) board works well for correcting distortion and calibrating for 3D reconstruction in stereo vision. However, measuring the error in two-dimensional (2D) is also prone to bias related to the placement of the 2D board in 3D. We proposed a procedure as a visual way of validating camera placement, and it also can provide some guidance about the positioning of cameras and potential advantages of using multiple cameras. We propose the use of a 3D printable test object for validating multi-camera surround-view calibration in small animal video capture arenas. The proposed 3D printed object has no bias to a particular dimension and is designed to minimize occlusions. The use of the calibrated test object provided an estimate of 3D reconstruction accuracy. The approach reveals that for complex specimens such as mice, some view angles will be more important for accurate capture of keypoints. Our method ensures accurate 3D camera calibration for surround image capture of laboratory mice and other specimens.

PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment.

PyMouseTracks (PMT) is a scalable and customizable computer vision and radio frequency identification (RFID)-based system for multiple rodent tracking and behavior assessment that can be set up within minutes in any user-defined arena at minimal cost. PMT is composed of the online Raspberry Pi (RPi)-based video and RFID acquisition with subsequent offline analysis tools. The system is capable of tracking up to six mice in experiments ranging from minutes to days. PMT maintained a minimum of 88% detections tracked with an overall accuracy >85% when compared with manual validation of videos containing one to four mice in a modified home-cage. As expected, chronic recording in home-cage revealed diurnal activity patterns. In open-field, it was observed that novel noncagemate mouse pairs exhibit more similarity in travel trajectory patterns than cagemate pairs over a 10-min period. Therefore, shared features within travel trajectories between animals may be a measure of sociability that has not been previously reported. Moreover, PMT can interface with open-source packages such as DeepLabCut and Traja for pose estimation and travel trajectory analysis, respectively. In combination with Traja, PMT resolved motor deficits exhibited in stroke animals. Overall, we present an affordable, open-sourced, and customizable/scalable mouse behavior recording and analysis system.

Water-Reaching Platform for Longitudinal Assessment of Cortical Activity and Fine Motor Coordination Defects in a Huntington Disease Mouse Model.

Huntington disease (HD), caused by dominantly inherited expansions of a CAG repeat results in characteristic motor dysfunction. Although gross motor defects have been extensively characterized in multiple HD mouse models using tasks such as rotarod and beam walking, less is known about forelimb deficits. We develop a high-throughput alternating reward/nonreward water-reaching task and training protocol conducted daily over approximately two months to simultaneously monitor forelimb impairment and mesoscale cortical changes in GCaMP activity, comparing female zQ175 (HD) and wild-type (WT) littermate mice, starting at ∼5.5 months. Behavioral analysis of the water-reaching task reveals that HD mice, despite learning the water-reaching task as proficiently as wild-type mice, take longer to learn the alternating event sequence as evident by impulsive (noncued) reaches and initially display reduced cortical activity associated with successful reaches. At this age gross motor defects determined by tapered beam assessment were not apparent. Although wild-type mice displayed no significant changes in cortical activity and reaching trajectory throughout the testing period, HD mice exhibited an increase in cortical activity, especially in the secondary motor and retrosplenial cortices, over time, as well as longer and more variable reaching trajectories by approximately seven months. HD mice also experienced a progressive reduction in successful performance. Tapered beam and rotarod tests as well as reduced DARPP-32 expression (striatal medium spiny neuron marker) after water-reaching assessment confirmed HD pathology. The water-reaching task can be used to inform on a daily basis, HD and other movement disorder onset and manifestation, therapeutic intervention windows, and test drug efficacy.

Chronic multiscale resolution of mouse brain networks using combined mesoscale cortical imaging and subcortical fiber photometry.

Significance: Genetically encoded optical probes to image calcium levels in neurons in vivo are used widely as a real-time measure of neuronal activity in the brain. Mesoscale calcium imaging through a cranial window provides a method of studying the interaction of circuit activity between cortical areas but lacks access to subcortical regions. Aim: We have developed an optical and surgical preparation that preserves wide-field imaging of the cortical surface while also permitting access to specific subcortical networks. Approach: This was achieved using an optical fiber implanted in the striatum, along with a bilateral widefield cranial window, enabling simultaneous mesoscale cortical imaging and subcortical fiber photometry recording of calcium signals in a transgenic animal expressing GCaMP. Subcortical signals were collected from the dorsal regions of the striatum. We combined this approach with multiple sensory-motor tasks, including specific auditory and visual stimulation, and video monitoring of animal movements and pupillometry during head-fixed behaviors. Results: We found high correlations between cortical and striatal activity in response to sensory stimulation or movement. Furthermore, spontaneous activity recordings revealed that specific motifs of cortical activity are correlated with presynaptic activity recorded in the striatum, enabling us to select for corticostriatal activity motifs. Conclusion: We believe that this method can be utilized to reveal not only global patterns but also cell-specific connectivity that provides insight into corticobasal ganglia circuit organization.

Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement.

Volition - the sense of control or agency over one's voluntary actions - is widely recognized as the basis of both human subjective experience and natural behavior in nonhuman animals. Several human studies have found peaks in neural activity preceding voluntary actions, for example the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. Others propose that random processes underlie and explain pre-movement neural activity. Here, we seek to address these issues by evaluating whether pre-movement neural activity in mice contains structure beyond that present in random neural activity. Implementing a self-initiated water-rewarded lever-pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls could be predicted between 3 and 5 s (or more in some cases) prior to movement. We found inhibition of motor cortex starting at approximately 5 s prior to lever pulls and activation of motor cortex starting at approximately 2 s prior to a random unrewarded left limb movement. We show that mice, like humans, are biased toward commencing self-initiated actions during specific phases of neural activity but that the pre-movement neural code changes over time in some mice and is widely distributed as behavior prediction improved when using all vs. single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding self-initiated action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated action could be preserved between mice and humans.

Multiscale imaging informs translational mouse modeling of neurological disease.

Multiscale neurophysiology reveals that simple motor actions are associated with changes in neuronal firing in virtually every brain region studied. Accordingly, the assessment of focal pathology such as stroke or progressive neurodegenerative diseases must also extend widely across brain areas. To derive mechanistic information through imaging, multiple resolution scales and multimodal factors must be included, such as the structure and function of specific neurons and glial cells and the dynamics of specific neurotransmitters. Emerging multiscale methods in preclinical animal studies that span micro- to macroscale examinations fill this gap, allowing a circuit-based understanding of pathophysiological mechanisms. Combined with high-performance computation and open-source data repositories, these emerging multiscale and large field-of-view techniques include live functional ultrasound, multi- and single-photon wide-scale light microscopy, video-based miniscopes, and tissue-penetrating fiber photometry, as well as variants of post-mortem expansion microscopy. We present these technologies and outline use cases and data pipelines to uncover new knowledge within animal models of stroke, Alzheimer's disease, and movement disorders.

Towards a Visualizable, De-identified Synthetic Biomarker of Human Movement Disorders.

Human motion analysis has been a common thread across modern and early medicine. While medicine evolves, analysis of movement disorders is mostly based on clinical presentation and trained observers making subjective assessments using clinical rating scales. Currently, the field of computer vision has seen exponential growth and successful medical applications. While this has been the case, neurology, for the most part, has not embraced digital movement analysis. There are many reasons for this including: the limited size of labeled datasets, accuracy and nontransparent nature of neural networks, and potential legal and ethical concerns. We hypothesize that a number of opportunities are made available by advancements in computer vision that will enable digitization of human form, movements, and will represent them synthetically in 3D. Representing human movements within synthetic body models will potentially pave the way towards objective standardized digital movement disorder diagnosis and building sharable open-source datasets from such processed videos. We provide a perspective of this emerging field and describe how clinicians and computer scientists can navigate this new space. Such digital movement capturing methods will be important for both machine learning-based diagnosis and computer vision-aided clinical assessment. It would also supplement face-to-face clinical visits and be used for longitudinal monitoring and remote diagnosis.

Altered cortical processing of sensory input in Huntington disease mouse models.

Huntington disease (HD), a hereditary neurodegenerative disorder, manifests as progressively impaired movement and cognition. Although early abnormalities of neuronal activity in striatum are well established in HD models, there are fewer in vivo studies of the cortex. Here, we record local field potentials (LFPs) in YAC128 HD model mice versus wild-type mice. In multiple cortical areas, limb sensory stimulation evokes a greater change in LFP power in YAC128 mice. Mesoscopic imaging using voltage-sensitive dyes reveals more extensive spread of evoked sensory signals across the cortical surface in YAC128 mice. YAC128 layer 2/3 sensory cortical neurons ex vivo show increased excitatory events, which could contribute to enhanced sensory responses in vivo. Cortical LFP responses to limb stimulation, visual and auditory input are also significantly increased in zQ175 HD mice. Results presented here extend knowledge of HD beyond ex vivo studies of individual neurons to the intact cortical network.

MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning.

Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis.

A three-dimensional virtual mouse generates synthetic training data for behavioral analysis.

We developed a three-dimensional (3D) synthetic animated mouse based on computed tomography scans that is actuated using animation and semirandom, joint-constrained movements to generate synthetic behavioral data with ground-truth label locations. Image-domain translation produced realistic synthetic videos used to train two-dimensional (2D) and 3D pose estimation models with accuracy similar to typical manual training datasets. The outputs from the 3D model-based pose estimation yielded better definition of behavioral clusters than 2D videos and may facilitate automated ethological classification.

Gamma frequency activation of inhibitory neurons in the acute phase after stroke attenuates vascular and behavioral dysfunction.

Alterations in gamma oscillations occur in several neurological disorders, and the entrainment of gamma oscillations has been recently proposed as a treatment for neurodegenerative disease. Optogenetic stimulation enhances recovery in models of stroke when applied weeks after injury; however, the benefits of acute brain stimulation have not been investigated. Here, we report beneficial effects of gamma-frequency modulation in the acute phase, within 1 h, after stroke. Transgenic VGAT-ChR2 mice are subject to awake photothrombotic stroke in an area encompassing the forelimb sensory and motor cortex. Optogenetic stimulation at 40 Hz in the peri-infarct zone recovers neuronal activity 24 h after stroke in motor and parietal association areas, as well as blood flow over the first week after stroke. Stimulation significantly reduces lesion volume and improves motor function. Our results suggest that acute-phase modulation of cortical oscillatory dynamics may serve as a target for neuroprotection against stroke.

PiDose: an open-source system for accurate and automated oral drug administration to group-housed mice.

Drug treatment studies in laboratory mice typically employ manual administration methods such as injection or gavage, which can be time-consuming to perform over long periods and cause substantial stress in animals. These stress responses may mask or enhance treatment effects, increasing the risk of false positive or negative results and decreasing reliability. To address the lack of an automated method for drug treatment in group-housed mice, we have developed PiDose, a home-cage attached device that weighs individual animals and administers a daily dosage of drug solution based on each animal's bodyweight through their drinking water. Group housed mice are identified through the use of RFID tagging and receive both regular water and drug solution drops by licking at a spout within the PiDose module. This system allows animals to be treated over long periods (weeks to months) in a fully automated fashion, with high accuracy and minimal experimenter interaction. PiDose is low-cost and fully open-source and should prove useful for researchers in both translational and basic research.

Automated task training and longitudinal monitoring of mouse mesoscale cortical circuits using home cages.

We report improved automated open-source methodology for head-fixed mesoscale cortical imaging and/or behavioral training of home cage mice using Raspberry Pi-based hardware. Staged partial and probabilistic restraint allows mice to adjust to self-initiated headfixation over 3 weeks' time with ~50% participation rate. We support a cue-based behavioral licking task monitored by a capacitive touch-sensor water spout. While automatically head-fixed, we acquire spontaneous, movement-triggered, or licking task-evoked GCaMP6 cortical signals. An analysis pipeline marked both behavioral events, as well as analyzed brain fluorescence signals as they relate to spontaneous and/or task-evoked behavioral activity. Mice were trained to suppress licking and wait for cues that marked the delivery of water. Correct rewarded go-trials were associated with widespread activation of midline and lateral barrel cortex areas following a vibration cue and delayed frontal and lateral motor cortex activation. Cortical GCaMP signals predicted trial success and correlated strongly with trial-outcome dependent body movements.

Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks.

Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut-a robust movement-tracking deep neural network framework-which enables real-time estimation of paw and digit movements of mice. Using this approach, we demonstrate movement-generated feedback by triggering a USB-GPIO (general-purpose input/output)-controlled LED when the movement of one paw, but not the other, selectively exceeds a preset threshold. The mean time delay between paw movement initiation and LED flash was 44.41 ms (SD = 36.39 ms), a latency sufficient for applying behaviorally triggered feedback. We adapt DeepLabCut for real-time tracking as an open-source package we term DeepCut2RealTime. The ability of the package to rapidly assess animal behavior was demonstrated by reinforcing specific movements within water-restricted, head-fixed mice. This system could inform future work on a behaviorally triggered "closed loop" brain-machine interface that could reinforce behaviors or deliver feedback to brain regions based on prespecified body movements.

Stress impacts sensory variability through cortical sensory activity motifs.

Medically unexplained symptoms in depression are common. These individual-specific complaints are often considered an 'idiom of distress', yet animal studies suggest that cortical sensory representations are flexible and influenced by spontaneous cortical activity. We hypothesized that stress would reveal activity dynamics in somatosensory cortex resulting in greater sensory-evoked response variability. Using millisecond resolution in vivo voltage sensitive dye (VSD) imaging in mouse neocortex, we characterized spontaneous regional depolarizations within limb and barrel regions of somatosensory cortex, or spontaneous sensory motifs, and their influence on sensory variability. Stress revealed an idiosyncratic increase in spontaneous sensory motifs that is normalized by selective serotonin reuptake inhibitor treatment. Spontaneous motif frequency is associated with increased variability in sensory-evoked responses, and we optogenetically demonstrate that regional depolarization in somatosensory cortex increases sensory-evoked variability for seconds. This reveals a putative circuit level target for changes in sensory processing and for unexplained physical complaints in stress-related psychopathology.