Neural plasticity - the capacity of the brain to change - has been described at almost every level of the nervous system. These include changes at the single neuron level in gene expression, protein phosphorylation and cellular distribution of proteins; changes in the morphology of spines and dendrites; changes in the synaptic signaling efficacy between different populations of neurons; and finally, large-scale changes in the organization or function of entire brain regions. This last type, denoted as cortical reorganization or map plasticity, is of special importance because it is believed to represent the large-scale integration of the many plastic changes that occur at the cellular and systems levels. functional maps of these areas of the brain before and after different experimental manipulations. One of the first examples of such cortical reorganization was demonstrated in the somatosensory cortex following digit amputation in macaque monkeys [1]. Further studies demonstrated somatosensory cortical reorganization following other peripheral manipulations [2], skilled training on a tactile paradigm [3, 4], and recovery of function after a cortical injury [5]. It was first surmised that the neural correlates of map plasticity would be the same, regardless of whether such plasticity occurred following cortical injury, peripheral injury or behavioral training [6]. Thus, a finding describing the importance of acetylcholine for map plasticity following skilled motor learning [7] was taken as evidence that acetylcholine was necessary for all forms of cortical map plasticity. In this dissertation, we challenged that assertion. Specifically, we have shown that the neural processes underlying cortical map plasticity vary depending on the experimental paradigm used to elicit it. Further, we demonstrate that certain aspects of this plasticity are specific to behavioral experience. We used the motor cortex of rats as a model system to study cortical reorganization following different types of injuries (both peripheral and central), as well as different types of behavioral experiences, including motor development during the juvenile period and skilled motor learning in adulthood. We first studied the role acetylcholine plays in these different types of motor cortical plasticity. We found that the basal forebrain cholinergic system, the primary source of acetylcholine in the cortex, is required in adult animals for behaviorally driven forms of cortical plasticity, but not for plasticity that occurs spontaneously following nervous system injury. We also found that this cholinergic system is necessary for the normal development of the cortical motor system. We next proceeded to study whether cortical reorganization is ever associated with axonal plasticity of the corticospinal tract at the level of the spinal cord. As axonal plasticity of other fibers has been described following cortical lesions, these neurons were traced following a cortical injury and rehabilitation paradigm previously developed in the lab. We found no evidence of plasticity of the corticospinal tract system following either a brain injury alone, or a brain injury in conjunction with rehabilitation training. Map plasticity of the motor cortex occurs in many contexts, and is thus not by itself an indication of skilled motor behavior. In searching for a paradigm to study motor cortex plasticity that occurs primarily in the context of skilled motor behavior, we adopted a stimulation paradigm used by others to evoke higher-level encoding of motor movements. Using this long-term stimulation paradigm, we found a form of cortical plasticity that occurs only in the context of rehabilitation following a cortical lesion. Plasticity of these complex movement maps correlated with the functional recovery of the animals, validating their behavioral relevance. We conclude that there are many different neural correlates underlying map plasticity of the motor cortex. In order to utilize this knowledge to enhance recovery following injury, it is essential to understand which neural changes have behavioral and functional relevance. 1. Merzenich, M.M., et al., Somatosensory cortical map changes following digit amputation in adult monkeys. J Comp Neurol, 1984. 224(4): p. 591-605. 2. Merzenich, M.M. and W.M. Jenkins, Reorganization of cortical representations of the hand following alterations of skin inputs induced by nerve injury, skin island transfers, and experience. J Hand Ther, 1993. 6(2): p. 89-104. 3. Recanzone, G.H., et al., Topographic reorganization of the hand representation in cortical area 3b owl monkeys trained in a frequency- discrimination task. J Neurophysiol, 1992. 67(5): p. 1031- 56. 4. Recanzone, G.H., C.E. Schreiner, and M.M. Merzenich, Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. J Neurosci, 1993. 13(1): p. 87-103. 5. Xerri, C., et al., Plasticity of primary somatosensory cortex paralleling sensorimotor skill recovery from stroke in adult monkeys. J Neurophysiol, 1998. 79(4): p. 2119-48. 6. Cruikshank, S.J. and N.M. Weinberger, Evidence for the Hebbian hypothesis in experience-dependent physiological plasticity of neocortex: a critical review. Brain Res Brain Res Rev, 1996. 22(3): p. 191-228. 7. Conner, J.M., et al., Lesions of the Basal forebrain cholinergic system impair task acquisition and abolish cortical plasticity associated with motor skill learning. Neuron, 2003. 38(5): p. 819-29

Brain-machine interfaces can allow neural control over assistive devices. They also provide an important platform for studying neural plasticity. Recent studies have suggested that optimal engagement of learning is essential for robust neuroprosthetic control. However, little is known about the neural processes that may consolidate a neuroprosthetic skill. On the basis of the growing body of evidence linking slow-wave activity (SWA) during sleep to consolidation, we examined whether there is offline processing after neuroprosthetic learning. Using a rodent model, we found that, after successful learning, task-related units specifically experienced increased locking and coherency to SWA during sleep. Moreover, spike-spike coherence among these units was substantially enhanced. These changes were not present with poor skill acquisition or after control awake periods, demonstrating the specificity of our observations to learning. Notably, the time spent in SWA predicted the performance gains. Thus, SWA appears to be involved in offline processing after neuroprosthetic learning.

A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship.

Sleep is known to promote recovery after stroke. Yet it remains unclear how stroke affects neural processing during sleep. Using an experimental stroke model in rats along with electrophysiological monitoring of neural firing and sleep microarchitecture, here we show that sleep processing is altered by stroke. We find that the precise coupling of spindles to global slow oscillations (SOs), a phenomenon that is known to be important for memory consolidation, is disrupted by a pathological increase in "isolated" local delta waves. The transition from this pathological to a physiological state-with increased spindle coupling to SO-is associated with sustained performance gains during recovery. Interestingly, post-injury sleep could be pushed toward a physiological state via a pharmacological reduction of tonic γ-aminobutyric acid (GABA). Together, our results suggest that sleep processing after stroke is impaired due to an increase in delta waves and that its restoration can be important for recovery.

Intracortical brain-machine interfaces (BMIs) may eventually restore function in those with motor disability after stroke. However, current research into the development of intracortical BMIs has focused on subjects with largely intact cortical structures, such as those with spinal cord injury. Although the stroke perilesional cortex (PLC) has been hypothesized as a potential site for a BMI, it remains unclear whether the injured motor cortical network can support neuroprosthetic control directly. Using chronic electrophysiological recordings in a rat stroke model, we demonstrate here the PLCs capacity for neuroprosthetic control and physiological plasticity. We initially found that the perilesional network demonstrated abnormally increased slow oscillations that also modulated neural firing. Despite these striking abnormalities, neurons in the perilesional network could be modulated volitionally to learn neuroprosthetic control. The rate of learning was surprisingly similar regardless of the electrode distance from the stroke site and was not significantly different from intact animals. Moreover, neurons achieved similar task-related modulation and, as an ensemble, formed cell assemblies with learning. Such control was even achieved in animals with poor motor recovery, suggesting that neuroprosthetic control is possible even in the absence of motor recovery. Interestingly, achieving successful control also reduced locking to abnormal oscillations significantly. Our results thus suggest that, despite the disrupted connectivity in the PLC, it may serve as an effective target for neuroprosthetic control in those with poor motor recovery after stroke.

Background

Rodent forelimb reaching behaviors are commonly assessed using a single-pellet reach-to-grasp task. While the task is widely recognized as a very sensitive measure of distal limb function, it is also known to be very labor-intensive, both for initial training and the daily assessment of function.

New method

Using components developed by open-source electronics platforms, we have designed and tested a low-cost automated behavioral box to measure forelimb function in rats. Our apparatus, made primarily of acrylic, was equipped with multiple sensors to control the duration and difficulty of the task, detect reach outcomes, and dispense pellets. Our control software, developed in MATLAB, was also used to control a camera in order to capture and process video during reaches. Importantly, such processing could monitor task performance in near real-time.

Results

We further demonstrate that the automated apparatus can be used to expedite skill acquisition, thereby increasing throughput as well as facilitating studies of early versus late motor learning. The setup is also readily compatible with chronic electrophysiological monitoring.

Comparison with existing methods

Compared to a previous version of this task, our setup provides a more efficient method to train and test rodents for studies of motor learning and recovery of function after stroke. The unbiased delivery of behavioral cues and outcomes also facilitates electrophysiological studies.

Conclusions

In summary, our automated behavioral box will allow high-throughput and efficient monitoring of rat forelimb function in both healthy and injured animals.

Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual's best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.

A remarkable feature of motor control is the ability to coordinate movements across distinct body parts into a consistent, skilled action. To reach and grasp an object, 'gross' arm and 'fine' dexterous movements must be coordinated as a single action. How the nervous system achieves this coordination is currently unknown. One possibility is that, with training, gross and fine movements are co-optimized to produce a coordinated action; alternatively, gross and fine movements may be modularly refined to function together. To address this question, we recorded neural activity in the primary motor cortex and dorsolateral striatum during reach-to-grasp skill learning in rats. During learning, the refinement of fine and gross movements was behaviorally and neurally dissociable. Furthermore, inactivation of the primary motor cortex and dorsolateral striatum had distinct effects on skilled fine and gross movements. Our results indicate that skilled movement coordination is achieved through emergent modular neural control.