DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data

Detailed behavioral analysis is key to understanding the brain-behavior relationship. Here, we present deep learning-based methods for analysis of behavior imaging data in mice and humans. Specifically, we use three different convolutional neural network architectures and five different behavior tasks in mice and humans and provide detailed instructions for rapid implementation of these methods for the neuroscience community. We provide examples of three dimensional (3D) kinematic analysis in the food pellet reaching task in mice, three-chamber test in mice, social interaction test in freely moving mice with simultaneous miniscope calcium imaging, and 3D kinematic analysis of two upper extremity movements in humans (reaching and alternating pronation/supination). We demonstrate that the transfer learning approach accelerates the training of the network when using images from these types of behavior video recordings. We also provide code for post-processing of the data after initial analysis with deep learning. Our methods expand the repertoire of available tools using deep learning for behavior analysis by providing detailed instructions on implementation, applications in several behavior tests, and post-processing methods and annotated code for detailed behavior analysis. Moreover, our methods in human motor behavior can be used in the clinic to assess motor function during recovery after an injury such as stroke

Evidence that Meningeal Mast Cells Can Worsen Stroke Pathology in Mice

Stroke is the leading cause of adult disability and the fourth most common cause of death in the United States. Inflammation is thought to play an important role in stroke pathology, but the factors that promote inflammation in this setting remain to be fully defined. An understudied but important factor is the role of meningeal-located immune cells in modulating brain pathology. Although different immune cells traffic through meningeal vessels en route to the brain, mature mast cells do not circulate but are resident in the meninges. With the use of genetic and cell transfer approaches in mice, we identified evidence that meningeal mast cells can importantly contribute to the key features of stroke pathology, including infiltration of granulocytes and activated macrophages, brain swelling, and infarct size. We also obtained evidence that two mast cell-derived products, interleukin-6 and, to a lesser extent, chemokine (C-C motif) ligand 7, can contribute to stroke pathology. These findings indicate a novel role for mast cells in the meninges, the membranes that envelop the brain, as potential gatekeepers for modulating brain inflammation and pathology after stroke

Meningeal Mast Cells as Key Effectors of Stroke Pathology

Stroke is the leading cause of adult disability in the United States. Because post-stroke inflammation is a critical determinant of damage and recovery after stroke, understanding the interplay between the immune system and the brain after stroke holds much promise for therapeutic intervention. An understudied, but important aspect of this interplay is the role of meninges that surround the brain. All blood vessels travel through the meningeal space before entering the brain parenchyma, making the meninges ideally located to act as an immune gatekeeper for the underlying parenchyma. Emerging evidence suggests that the actions of immune cells resident in the meninges are essential for executing this gatekeeper function. Mast cells (MCs), best known as proinflammatory effector cells, are one of the long-term resident immune cells in the meninges. Here, we discuss recent findings in the literature regarding the role of MCs located in the meningeal space and stroke pathology. We review the latest advances in mouse models to investigate the roles of MCs and MC-derived products in vivo, and the importance of using these mouse models. We examine the concept of the meninges playing a critical role in brain and immune interactions, reevaluate the perspectives on the key effectors of stroke pathology, and discuss the opportunities and challenges for therapeutic development

Defective sphingosine-1-phosphate receptor 1 (S1P1) phosphorylation exacerbates TH17-mediated autoimmune neuroinflammation

Sphingosine-1-phosphate (S1P) signaling regulates lymphocyte egress from lymphoid organs into systemic circulation. Sphingosine phosphate receptor 1 (S1P1) agonist, FTY-720 (Gilenya™) arrests immune trafficking and prevents multiple sclerosis (MS) relapses. However, alternative mechanisms of S1P-S1P1 signaling have been reported. Phosphoproteomic analysis of MS brain lesions revealed S1P1 phosphorylation on S351, a residue crucial for receptor internalization. Mutant mice harboring a S1pr1 gene encoding phosphorylation-deficient receptors [S1P1(S5A)] developed severe experimental autoimmune encephalomyelitis (EAE) due to T helper (TH) 17-mediated autoimmunity in the peripheral immune and nervous system. S1P1 directly activated Janus-like kinase–signal transducer and activator of transcription 3 (JAK-STAT3) pathway via interleukin 6 (IL-6). Impaired S1P1 phosphorylation enhances TH17 polarization and exacerbates autoimmune neuroinflammation. These mechanisms may be pathogenic in MS

Machine Learning for 3D Kinematic Analysis of Movements in Neurorehabilitation

Purpose of reviewRecent advances in the machine learning field, especially in deep learning, provide the opportunity for automated, detailed, and unbiased analysis of motor behavior. Although there has not yet been wide use of these techniques in the motor rehabilitation field, they have great potential. In this review, I describe how the current state of machine learning can be applied to 3D kinematic analysis, and how this will have an impact on neurorehabilitation.Recent findingsApplications of deep learning methods, in the form of convolutional neural networks, have been revolutionary for image analysis such as face recognition and object detection in images, exceeding human level performance. Recent studies have shown applicability of these deep learning approaches to human posture and movement classification. It is to be expected that portable stereo-camera systems will bring 3D pose estimation into the clinical setting and allow the assessment of movement quality in response to interventions. Advances in machine learning can help automate the process of obtaining 3D kinematics of human movements and to identify/classify patterns of movement

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis.

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods often do not capture the richness inherent to the natural behavior. Here, we provide detailed step-by-step instructions with visualizations of our recent methodology, DeepBehavior. The DeepBehavior toolbox uses deep learning frameworks built with convolutional neural networks to rapidly process and analyze behavioral videos. This protocol demonstrates three different frameworks for single object detection, multiple object detection, and three-dimensional (3D) human joint pose tracking. These frameworks return cartesian coordinates of the object of interest for each frame of the behavior video. Data collected from the DeepBehavior toolbox contain much more detail than traditional behavior analysis methods and provides detailed insights to the behavior dynamics. DeepBehavior quantifies behavior tasks in a robust, automated, and precise way. Following the identification of behavior, post-processing code is provided to extract information and visualizations from the behavioral videos

DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data.

Detailed behavioral analysis is key to understanding the brain-behavior relationship. Here, we present deep learning-based methods for analysis of behavior imaging data in mice and humans. Specifically, we use three different convolutional neural network architectures and five different behavior tasks in mice and humans and provide detailed instructions for rapid implementation of these methods for the neuroscience community. We provide examples of three dimensional (3D) kinematic analysis in the food pellet reaching task in mice, three-chamber test in mice, social interaction test in freely moving mice with simultaneous miniscope calcium imaging, and 3D kinematic analysis of two upper extremity movements in humans (reaching and alternating pronation/supination). We demonstrate that the transfer learning approach accelerates the training of the network when using images from these types of behavior video recordings. We also provide code for post-processing of the data after initial analysis with deep learning. Our methods expand the repertoire of available tools using deep learning for behavior analysis by providing detailed instructions on implementation, applications in several behavior tests, and post-processing methods and annotated code for detailed behavior analysis. Moreover, our methods in human motor behavior can be used in the clinic to assess motor function during recovery after an injury such as stroke