Enabling motor control by epidural electrical stimulation of the spinal cord is a promising therapeutic technique for the recovery of motor function after a spinal cord injury (SCI). Although epidural electrical stimulation has resulted in improvement in hindlimb motor function, it is unknown whether it has any therapeutic benefit for improving forelimb fine motor function after a cervical SCI. We tested whether trains of pulses delivered at spinal cord segments C6 and C8 would facilitate the recovery of forelimb fine motor control after a cervical SCI in rats. Rats were trained to reach and grasp sugar pellets. Immediately after a dorsal funiculus crush at C4, the rats showed significant deficits in forelimb fine motor control. The rats were tested to reach and grasp with and without cervical epidural stimulation for 10 weeks post-injury. To determine the best stimulation parameters to activate the cervical spinal networks involved in forelimb motor function, monopolar and bipolar currents were delivered at varying frequencies (20, 40, and 60 Hz) concomitant with the reaching and grasping task. We found that cervical epidural stimulation increased reaching and grasping success rates compared to the no stimulation condition. Bipolar stimulation (C6− C8+ and C6+ C8−) produced the largest spinal motor-evoked potentials (sMEPs) and resulted in higher reaching and grasping success rates compared with monopolar stimulation (C6− Ref+ and C8− Ref+). Forelimb performance was similar when tested at stimulation frequencies of 20, 40, and 60 Hz. We also found that the EMG activity in most forelimb muscles as well as the co-activation between flexor and extensor muscles increased post-injury. With epidural stimulation, however, this trend was reversed indicating that cervical epidural spinal cord stimulation has therapeutic potential for rehabilitation after a cervical SCI.

The medial prefrontal cortex (mPFC) and its abundant connections with other brain regions play key roles in memory, cognition, decision making, social behaviors, and mood. Dysfunction in mPFC is implicated in psychiatric disorders in which these behaviors go awry. The prolonged maturation of mPFC likely enables complex behaviors to emerge, but also increases their vulnerability to disruption. Many foundational studies have characterized either mPFC synaptic or behavioral development without establishing connections between them. Here, we review this rich body of literature, aligning major events in mPFC development with the maturation of complex behaviors. We focus on emotional memory and cognitive flexibility, and highlight new work linking mPFC circuit disruption to alterations of these behaviors in disease models. We advance new hypotheses about the causal connections between mPFC synaptic development and behavioral maturation and propose research strategies to establish an integrated understanding of neural architecture and behavioral repertoires.

Quantitative descriptions of animal behavior are essential to study the neural substrates of cognitive and emotional processes. Analyses of naturalistic behaviors are often performed by hand or with expensive, inflexible commercial software. Recently, machine learning methods for markerless pose estimation enabled automated tracking of freely moving animals, including in labs with limited coding expertise. However, classifying specific behaviors based on pose data requires additional computational analyses and remains a significant challenge for many groups. We developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a simple, flexible software program that can detect behavior from video timeseries and can analyze the results of experimental assays. BehaviorDEPOT calculates kinematic and postural statistics from keypoint tracking data and creates heuristics that reliably detect behaviors. It requires no programming experience and is applicable to a wide range of behaviors and experimental designs. We provide several hard-coded heuristics. Our freezing detection heuristic achieves above 90% accuracy in videos of mice and rats, including those wearing tethered head-mounts. BehaviorDEPOT also helps researchers develop their own heuristics and incorporate them into the software’s graphical interface. Behavioral data is stored framewise for easy alignment with neural data. We demonstrate the immediate utility and flexibility of BehaviorDEPOT using popular assays including fear conditioning, decision-making in a T-maze, open field, elevated plus maze, and novel object exploration.

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To understand how the brain produces behavior, we must elucidate the relationships between neuronal connectivity and function. The medial prefrontal cortex (mPFC) is critical for complex functions including decision-making and mood. mPFC projection neurons collateralize extensively, but the relationships between mPFC neuronal activity and brain-wide connectivity are poorly understood. We performed whole-brain connectivity mapping and fiber photometry to better understand the mPFC circuits that control threat avoidance in male and female mice. Using tissue clearing and light sheet fluorescence microscopy, we mapped the brain-wide axon collaterals of populations of mPFC neurons that project to nucleus accumbens (NAc), ventral tegmental area (VTA), or contralateral mPFC (cmPFC). We present DeepTraCE, for quantifying bulk-labeled axonal projections in images of cleared tissue, and DeepCOUNT, for quantifying cell bodies. Anatomical maps produced with DeepTraCE aligned with known axonal projection patterns and revealed class-specific topographic projections within regions. Using TRAP2 mice and DeepCOUNT, we analyzed whole-brain functional connectivity underlying threat avoidance. PL was the most highly connected node with functional connections to subsets of PL-cPL, PL-NAc and PL-VTA target sites. Using fiber photometry, we found that during threat avoidance, cmPFC and NAc-projectors encoded conditioned stimuli, but only when action was required to avoid threats. mPFC-VTA neurons encoded learned but not innate avoidance behaviors. Together our results present new and optimized approaches for quantitative whole-brain analysis and indicate that anatomically-defined classes of mPFC neurons have specialized roles in threat avoidance.SIGNIFICANCE STATEMENT:Understanding how the brain produces complex behaviors requires detailed knowledge of the relationships between neuronal connectivity and function. The medial prefrontal cortex (mPFC) plays a key role in learning, mood, and decision-making, including evaluating and responding to threats. mPFC dysfunction is strongly linked to fear, anxiety and mood disorders. Although mPFC circuits are clear therapeutic targets, gaps in our understanding of how they produce cognitive and emotional behaviors prevent us from designing effective interventions. To address this, we developed a high-throughput analysis pipeline for quantifying bulk-labeled fluorescent axons (DeepTraCE) or cell bodies (DeepCOUNT) in intact cleared brains. Using DeepTraCE, DeepCOUNT, and fiber photometry, we performed detailed anatomical and functional mapping of mPFC neuronal classes, identifying specialized roles in threat avoidance.