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The balance between exploration and exploitation is essential for decision-making. The present study investigated the role of ventromedial orbitofrontal cortex (vmOFC) glutamate neurons in mediating value-based decision-making by first using optogenetics to manipulate vmOFC glutamate activity in rats during a probabilistic reversal learning (PRL) task. Rats that received vmOFC activation during informative feedback completed fewer reversals and exhibited reduced reward sensitivity relative to rats. Analysis with a Q-learning computational model revealed that increased vmOFC activity did not affect the learning rate but instead promoted maladaptive exploration. By contrast, vmOFC inhibition increased the number of completed reversals and increased exploitative behavior. In a separate group of animals, calcium activity of vmOFC glutamate neurons was recorded using fiber photometry. Complementing our results above, we found that suppression of vmOFC activity during the latter part of rewarded trials was associated with improved PRL performance, greater win-stay responding and selecting the correct choice on the next trial. These data demonstrate that excessive vmOFC activity during reward feedback disrupted value-based decision-making by increasing the maladaptive exploration of lower-valued options. Our findings support the premise that pharmacological interventions that normalize aberrant vmOFC glutamate activity during reward feedback processing may attenuate deficits in value-based decision-making.

Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modeled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.

Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.

Depression is a common mental disorder and one of the leading causes of disability around the world. Monoaminergic antidepressants often take weeks to months to work and are not effective for all patients. This has led to a search for a better understanding of the pathogenesis of depression as well as to the development of novel antidepressants. One such novel antidepressant is ketamine, which has demonstrated both clinically promising results and contributed to new explanatory models of depression, including the potential role of neuroplasticity in depression. Early clinical trials are now showing promising results of serotonergic psychedelics for depression; however, their mechanism of action remains poorly understood. This paper seeks to review the effect of depression, classic antidepressants, ketamine, and serotonergic psychedelics on markers of neuroplasticity at a cellular, molecular, electrophysiological, functional, structural, and psychological level to explore the potential role that neuroplasticity plays in the treatment response of serotonergic psychedelics.

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.

Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modelled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.

It is increasingly being recognized that active control of breathing - a key aspect of ancient Vedic meditative practices, can relieve stress and anxiety and improve cognition. However, the underlying mechanisms of respiratory modulation of neurophysiology are just beginning to be elucidated. Research shows that brainstem circuits involved in the motor control of respiration receive input from and can directly modulate activity in subcortical circuits, affecting emotion and arousal. Meanwhile, brain regions involved in the sensory aspects of respiration, such as the olfactory bulb, are like-wise linked with wide-spread brain oscillations; and perturbing olfactory bulb activity can significantly affect both mood and cognition. Thus, via both motor and sensory pathways, there are clear mechanisms by which brain activity is entrained to the respiratory cycle. Here, we review evidence gathered across multiple species demonstrating the links between respiration, entrainment of brain activity and functional relevance for affecting mood and cognition. We also discuss further linkages with cardiac rhythms, and the potential translational implications for biorhythm monitoring and regulation in neuropsychiatric disorders.

As climate change accelerates extreme weather disasters, the mental health of the impacted communities is a rising concern. In a recent study of 725 Californians we showed that individuals that were directly exposed to California’s deadliest wildfire, the Camp Fire of 2018, had significantly greater chronic symptoms of post-traumatic stress disorder, anxiety and depression than control individuals not exposed to the fires. Here, we study a subsample of these individuals: directly exposed (n = 27), indirectly exposed (who witnessed the fire but were not directly impacted, n = 21), versus age and gender-matched non-exposed controls (n = 27). All participants underwent cognitive testing with synchronized electroencephalography (EEG) brain recordings. In our sample, 67% of the individuals directly exposed to the fire reported having experienced recent trauma, while 14% of the indirectly exposed individuals and 0% of the non-exposed controls reported recent trauma exposure. Fire-exposed individuals showed significant cognitive deficits, particularly on the interference processing task and greater stimulus-evoked fronto-parietal activity as measured on this task. Across all subjects, we found that stimulus-evoked activity in left frontal cortex was associated with overall improved interference processing efficiency, suggesting the increased activity observed in fire exposed individuals may reflect a compensatory increase in cortical processes associated with cognitive control. To the best of our knowledge this is the first study to examine the cognitive and underlying neural impacts of recent climate trauma.

Choice selection strategies and decision making are typically investigated using multiple-choice gambling paradigms that require participants to maximize reward payoff. However, research shows that performance in such paradigms suffers from individual biases towards the frequency of gains to choose smaller local gains over larger longer term gain, also referred to as melioration. Here, we developed a simple two-choice reward task, implemented in 186 healthy human adult subjects across the adult lifespan to understand the behavioral, computational, and neural bases of payoff maximization versus melioration. The observed reward choice behavior on this task was best explained by a reinforcement learning model of differential future reward prediction. Simultaneously recorded and source-localized electroencephalography (EEG) showed that diminished theta-band activations in the right rostral anterior cingulate cortex (rACC) correspond to greater reward payoff maximization, specifically during the presentation of cumulative reward information at the end of each task trial. Notably, these activations (greater rACC theta) predicted self-reported depressed mood symptoms, thereby showcasing a reward processing marker of potential clinical utility.

Interoceptive attention to internal sensory signals is fundamental to mindfulness. In an attention-to-breathing task in 161 adults, we found that consistency of interoceptive attention significantly correlated with performance efficiency across several exteroceptive cognitive domain tasks. EEG source mapping within subjects showed that on low-consistency or distracted trials there was greater recruitment of frontal executive control activity in the dorsolateral prefrontal cortex (DLPFC) and suppression of the posterior default mode network (Precuneus), with increased functional connectivity between these regions. In contrast, high-consistency or attentive trials were associated with greater connectivity between Anterior Cingulate Cortex (ACC) and Insula, key nodes of the cognitive control network for attention monitoring. Notably, individual trait mindfulness was correlated with greater functional connectivity between DLPFC-Precuneus on distracted trials and greater ACC-Insula connectivity on attentive trials. These results showcase dynamic network interactions underlying objective markers of interoceptive attention and subjective rating mindfulness.

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.

E3-ubiquitin ligase Cullin3 (Cul3) is a high confidence risk gene for autism spectrum disorder (ASD) and developmental delay (DD). To investigate how Cul3 mutations impact brain development, we generated a haploinsufficient Cul3 mouse model using CRISPR/Cas9 genome engineering. Cul3 mutant mice exhibited social and cognitive deficits and hyperactive behavior. Brain MRI found decreased volume of cortical regions and changes in many other brain regions of Cul3 mutant mice starting from early postnatal development. Spatiotemporal transcriptomic and proteomic profiling of embryonic, early postnatal and adult brain implicated neurogenesis and cytoskeletal defects as key drivers of Cul3 functional impact. Specifically, dendritic growth, filamentous actin puncta, and spontaneous network activity were reduced in Cul3 mutant mice. Inhibition of small GTPase RhoA, a molecular substrate of Cul3 ligase, rescued dendrite length and network activity phenotypes. Our study identified defects in neuronal cytoskeleton and Rho signaling as the primary targets of Cul3 mutation during brain development.

A fundamental set of cognitive abilities enable humans to efficiently process goal-relevant information, suppress irrelevant distractions, maintain information in working memory, and act flexibly in different behavioral contexts. Yet, studies of human cognition and their underlying neural mechanisms usually evaluate these cognitive constructs in silos, instead of comprehensively in-tandem within the same individual. Here, we developed a scalable, mobile platform, “BrainE” (short for Brain Engagement), to rapidly assay several essential aspects of cognition simultaneous with wireless electroencephalography (EEG) recordings. Using BrainE, we rapidly assessed five aspects of cognition including (1) selective attention, (2) response inhibition, (3) working memory, (4) flanker interference and (5) emotion interference processing, in 102 healthy young adults. We evaluated stimulus encoding in all tasks using the EEG neural recordings, and isolated the cortical sources of the spectrotemporal EEG dynamics. Additionally, we used BrainE in a two-visit study in 24 young adults to investigate the reliability of the neuro-cognitive data as well as its plasticity to transcranial magnetic stimulation (TMS). We found that stimulus encoding on multiple cognitive tasks could be rapidly assessed, identifying common as well as distinct task processes in both sensory and cognitive control brain regions. Event related synchronization (ERS) in the theta (3-7 Hz) and alpha (8-12 Hz) frequencies as well as event related desynchronization (ERD) in the beta frequencies (13-30 Hz) were distinctly observed in each task. The observed ERS/ERD effects were overall anticorrelated. The two-visit study confirmed high test-retest reliability for both cognitive and neural data, and neural responses showed specific TMS protocol driven modulation. We also show that the global cognitive neural responses are sensitive to mental health symptom self-reports. This first study with the BrainE platform showcases its utility in studying neuro-cognitive dynamics in a rapid and scalable fashion.HighlightsRapid and scalable EEG recordings reveal common and distinct cortical activations across five core cognitive tasks.Data acquired across visits one-week-apart show high test-retest reliability for both cognitive and neural measurements.Evoked neural responses during emotion interference processing demonstrate specific short-term plasticity driven by type of neurostimulation.Cognitively evoked neural responses are sensitive to variations in mental health symptoms.

The default-mode network (DMN) in humans consists of a set of brain regions that, as measured with functional magnetic resonance imaging (fMRI), show both intrinsic correlations with each other and suppression during externally oriented tasks. Resting-state fMRI studies have previously identified similar patterns of intrinsic correlations in overlapping brain regions in rodents (A29C/posterior cingulate cortex, parietal cortex, and medial temporal lobe structures). However, due to challenges with performing rodent behavior in an MRI machine, it is still unclear whether activity in rodent DMN regions are suppressed during externally oriented visual tasks. Using distributed local field potential measurements in rats, we have discovered that activity in DMN brain regions noted above show task-related suppression during an externally oriented visual task at alpha and low beta-frequencies. Interestingly, this suppression (particularly in posterior cingulate cortex) was linked with improved performance on the task. Using electroencephalography recordings from a similar task in humans, we identified a similar suppression of activity in posterior cingulate cortex at alpha/low beta-frequencies. Thus, we have identified a common electrophysiological marker of DMN suppression in both rodents and humans. This observation paves the way for future studies using rodents to probe circuit-level functioning of DMN function. Significance Here we show that alpha/beta frequency oscillations in rats show key features of DMN activity, including intrinsic correlations between DMN brain regions, task-related suppression, and interference with attention/decision-making. We found similar task-related suppression at alpha/low beta-frequencies of DMN activity in humans.

Mental health continues to be a rising concern for the global population during the COVID-19 pandemic (1, 2). Addressing these issues requires us to consider the COVID-19 pandemic as a major, global and chronic life-stressor. Stresses related to the pandemic include the risk/fear of getting infected; social isolation; lack of schooling for children; potential for increased interpersonal conflict (domestic violence/trauma), and job/income loss (1, 2). While exact numbers are difficult to come by, early evidence suggests that the global COVID-19 pandemic has led to an increase in acute stress, along with a corollary increase in anxiety and depression across multiple countries and populations studied (1, 3–5). However, these stressors have already lasted for almost a year and may continue as lockdowns continue to be enforced worldwide, thus making them chronic rather than acute in nature. Moreover, despite the advent of vaccines, public health experts, such as Dr. Fauci (head of the US National Institute of Allergy and Infection Disease) suggested a return to normality might only occur at the end of 2021 in the US. Viewing the effects of COVID-19 pandemic as a chronic stressor is important for two reasons: (1) it suggests that the long-term effect of pandemic-related stress may be a worsening of both chronic physical and mental health, both of which are well-documented related to the impacts of chronic stress (6–8). (2) It suggests that specific, practical, affordable, and globally implementable strategies shown to help with chronic stress can be utilized.

Sleep is known to be important for promoting recovery after brain injuries such as stroke. Yet, it remains unclear how such injuries affect neural processing during sleep and how to precisely enhance sleep-dependent memory processing during recovery. Using an experimental model of focal cortical stroke in rats along with long-term electrophysiological monitoring of neural firing and sleep microarchitecture, here we show that sleep-dependent neural processing is altered after stroke induction. Specifically, we found that the precise coupling of spindles to global slow- oscillations (SO), a phenomenon that is known to be important for memory consolidation, appeared to be disrupted by a pathological increase in “isolated” local delta waves. The transition from this pathological to a more physiological sleep state – with both a reduction in isolated delta waves and increased spindle coupling to SO – was associated with sustained performance gains after task training during recovery. Interestingly, post-injury sleep processing could be pushed towards a more physiological state via a pharmacological reduction of tonic GABA. Together, our results suggest that sleep processing after cortical brain injuries may be impaired due to an increase in local delta waves and that restoration of physiological processing is important for recovery of task performance.

Objective: Ketamine is a glutamatergic drug with potent and rapid acting effects for the treatment of depression. Little is known about the effectiveness of intranasal (S)-ketamine for treating patients with comorbid depression and PTSD. Methods: We retrospectively analyzed clinical outcomes in 35 patients with both depression and PTSD referred for (S)-ketamine treatments at the VA San Diego Neuromodulation Clinic between Jan 2020 to March 2021. Outcomes were calculated across the first 8 treatments (4 weeks, induction phase) using a repeated measures ANOVA to measure change in symptoms over treatments. In a smaller sub-group (n=19) we analyzed whether (S)-ketamine continued to show effects during the maintenance period (after 4 weeks. Sub-group analyses based on antidepressant response and general linear models were also performed to identify predictors of treatment response. Results: During the induction phase of treatment there was an absolute reduction of 4.6 +/- 0.9 on the patient health questionnaire-9 (PHQ-9) rating scale for depression, from 19.6 +/- 0.8 at treatment 1 to 14.9 +/- 0.9 at treatment 8 (week 4) (F(7,238)=6, p 50%) at treatment 8. There was an absolute reduction of 12.1 +/- 2.5 on the patient checklist 5 (PCL5) rating scale for PTSD, from 54.4 +/- 2 at treatment 1 down to 42.3 +/- 2.5 at treatment 8 (F(7,238)=10.8, p 30%) for PTSD at this time-point. After transitioning to maintenance treatments, depression and PTSD symptoms remained stable or improved. Conclusions: Our results show that (S)-ketamine can improve both depression and PTSD symptoms in Veterans with dual-diagnoses. The effects of (S)-ketamine on PTSD were temporally and individually distinct from those on depression, suggesting potentially different modes of action on the two disorders. This work warrants further research in studying intranasal (S)-ketamine for PTSD. Funding Information: This work was supported by awards from the Department of Veterans Affairs, Veterans Health Administration (Career Development Award: 7IK2BX003308 to D.S.R.), and funding from the Center of Excellence for Stress and Mental Health (CESAMH) at the San Diego VA Medical Center, as well as from the National Institute of Mental Health (K23 MH119375-01 to E.E.L.) Declaration of Interests: None of the authors have any competing interests to declare, financial or otherwise. Ethics Approval Statement: This study was approved as an IRB-exemption by the VA San Diego Medical Center IRB committee.

Plasticity within the corticostriatal network is known to regulate the balance between behavioral flexibility and automaticity. Repeated training of an action has been shown to bias behavior towards automaticity, suggesting that training may trigger activity-dependent corticostriatal plasticity. However, surprisingly little is known about the natural activity patterns that may drive plasticity or when they occur during long-term training. Here we chronically monitored neural activity from primary motor cortex (M1) and the dorsolateral striatum (DLS) during both training and offline periods, i.e., time away from training including sleep, throughout the development of an automatic reaching action. We first show that blocking striatal NMDA receptors during offline periods prevents the emergence of behavioral consistency, a hallmark of automaticity. We then show that, throughout the development of an automatic reaching action, corticostriatal functional connectivity increases during offline periods. Such increases track the emergence of consistent behavior and predictable cross-area neural dynamics. We then identify sleep spindles during non-REM sleep (NREM) as uniquely poised to mediate corticostriatal plasticity during offline periods. We show that sleep spindles are periods of maximal corticostriatal transmission within offline periods, that sleep spindles in post-training NREM reactivate neurons across areas, and that sleep-spindle modulation in post-training NREM is linked to observable changes in spiking relationships between individual pairs of M1 and DLS neurons. Our results indicate that offline periods, in general, and sleep spindles, specifically, play an important role in regulating behavioral flexibility through corticostriatal network plasticity.

ObjectiveA promising application of BCI technology is in the development of personalized therapies that can target neural circuits linked to mental or physical disabilities. Typical BCIs, however, offer limited value due to simplistic designs and poor understanding of the conditions being treated. Building BCIs on more solid grounds may require the characterization of the brain dynamics supporting cognition and behavior at multiple scales, from single-cell and local field potential (LFP) recordings in animals to non-invasive electroencephalography (EEG) in humans. Despite recent efforts, a unifying software framework to support closed-loop studies in both animals and humans, is still lacking. The objective of this paper is to develop such a neurotechnology software framework.ApproachHere we develop the Simulink for Brain Signal Interfaces library (SimBSI). Simulink is a mature graphical programming environment within MATLAB that has gained traction for processing electrophysiological data. SimBSI adds to this ecosystem: 1) advanced human EEG source imaging, 2) cross-species multimodal data acquisition based on the Lab Streaming Layer library, and 3) a graphical experimental design platform.Main resultsWe used several examples to demonstrate the capabilities of the library, ranging from simple signal processing, to online EEG source imaging, cognitive task design, and closed-loop neuromodulation. We further demonstrate the simplicity of developing a sophisticated experimental environment for rodents within this environment.SignificanceWith the SimBSI library we hope to aid BCI practitioners of dissimilar backgrounds in the development of, much needed, single and cross-species closed-loop neuroscientific experiments. These experiments may provide the necessary mechanistic data for BCIs to become effective therapeutic tools.

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.

A key aspect of behavioral inhibition is the ability to wait before acting. Failures in this form of inhibition result in impulsivity and are commonly observed in various neuropsychiatric disorders. Prior evidence has implicated medial frontal cortex, motor cortex, orbitofrontal cortex (OFC), and ventral striatum in various aspects of inhibition. Here, using distributed recordings of brain activity [with local-field potentials (LFPs)] in rodents, we identified oscillatory patterns of activity linked with action and inhibition. Low-frequency (δ) activity within motor and premotor circuits was observed in two distinct networks, the first involved in cued, sensory-based responses and the second more generally in both cued and delayed actions. By contrast, θ activity within prefrontal and premotor regions (medial frontal cortex, OFC, ventral striatum, and premotor cortex) was linked with inhibition. Connectivity at θ frequencies was observed within this network of brain regions. Interestingly, greater connectivity between primary motor cortex (M1) and other motor regions was linked with greater impulsivity, whereas greater connectivity between M1 and inhibitory brain regions (OFC, ventral striatum) was linked with improved inhibition and diminished impulsivity. We observed similar patterns of activity on a parallel task in humans: low-frequency activity in sensorimotor cortex linked with action, θ activity in OFC/ventral prefrontal cortex (PFC) linked with inhibition. Thus, we show that δ and θ oscillations form distinct large-scale networks associated with action and inhibition, respectively.

Recent work has highlighted the importance of transient low-frequency oscillatory (LFO

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.

Previous studies reported that early postnatal cholinergic lesions severely perturb early cortical development, impairing neuronal cortical migration and the formation of cortical dendrites and synapses. These severe effects of early postnatal cholinergic lesions preclude our ability to understand the contribution of cholinergic systems to the later-stage maturation of topographic cortical representations. To study cholinergic mechanisms contributing to the later maturation of motor cortical circuits, we first characterized the temporal course of cortical motor map development and maturation in rats. In this study, we focused our attention on the maturation of cortical motor representations after postnatal day 25 (PND 25), a time after neuronal migration has been accomplished and cortical volume has reached adult size. We found significant maturation of cortical motor representations after this time, including both an expansion of forelimb representations in motor cortex and a shift from proximal to distal forelimb representations to an extent unexplainable by simple volume enlargement of the neocortex. Specific cholinergic lesions placed at PND 24 impaired enlargement of distal forelimb representations in particular and markedly reduced the ability to learn skilled motor tasks as adults. These results identify a novel and essential role for cholinergic systems in the late refinement and maturation of cortical circuits. Dysfunctions in this system may constitute a mechanism of late-onset neurodevelopmental disorders such as Rett syndrome and schizophrenia.

A fundamental goal of motor learning is to establish 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 where we could control the causal relationship between neurons and behavior, here we show that sleep–dependent processing is required for credit-assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Importantly, we found a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non–causal activity. Strikingly, decoupling of spiking to slow–oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep plays an essential role in establishing sparse activation patterns that reflect the causal neuron–behavior relationship.

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 PLC's 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.

To investigate functional mechanisms underlying cortical motor plasticity in the intact and injured brain, we used “behaviorally relevant,” long-duration intracortical microstimulation. We now report the existence of complex, multijoint movements revealed with a 500-msec duration intracortical stimulation in rat motor cortex. A consistent topographic distribution of these complex motor patterns is present across the motor cortex in naïve rats. We further document the plasticity of these complex movement patterns after focal cortical injury, with a significant expansion of specific complex movement representations in response to rehabilitative training after injury. Notably, the degree of functional recovery attained after cortical injury and rehabilitation correlates significantly with a specific feature of map reorganization, the ability to reexpress movement patterns disrupted by the initial injury. This evidence suggests the existence of complex movement representations in the rat motor cortex that exhibit plasticity after injury and rehabilitation, serving as a relevant predictor of functional recovery.

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.

The basal forebrain cholinergic system has been implicated in the reorganization of adult cortical sensory and motor representations under many, but not all, experimental conditions. It is still not fully understood which types of plasticity require the cholinergic system and which do not. In this study, we examine the hypothesis that the basal forebrain cholinergic system is required for eliciting plasticity associated with complex cognitive processing (e.g., behavioral experiences that drive cortical reorganization) but is not required for plasticity mediated under behaviorally independent conditions. We used established experimental manipulations to elicit two distinct forms of plasticity within the motor cortex: facial nerve transections evoke reorganization of cortical motor representations independent of behavioral experience, and skilled forelimb training induces behaviorally dependent expansion of forelimb motor representations. In animals that underwent skilled forelimb training in conjunction with a facial nerve lesion, cholinergic mechanisms were required for mediating the behaviorally dependent plasticity associated with the skilled motor training but were not necessary for mediating plasticity associated with the facial nerve transection. These results dissociate the contribution of cholinergic mechanisms to distinct forms of cortical plasticity and support the hypothesis that the forebrain cholinergic system is selectively required for modulating complex forms of cortical plasticity driven by behavioral experience.

Selective attention may be flexibly directed toward particular locations in the visual field (spatial attention) or to entire object configurations (object-based attention). A key question is whether spatial attention plays a direct role in the selection of objects, perhaps by spreading its facilitatory influence throughout the boundaries of an object. We studied the relationship between spatial and object-based attention in a design in which subjects attended to brief offsets of one corner of a real or illusory square form. Object-selective attention was indexed by differences in event-related potential (ERP) amplitudes and blood oxygen level-dependent (BOLD) activations to unattended corner offsets in conditions in which the objects were intact versus fragmented or absent. This design ensured that object-based attention effects were not an artifact of attention being guided by simple directional cues such as parallel lines, which may have occurred in previous studies. Both space-based and object-based attention were associated with enhanced negative ERPs (N1 component at 140–180 ms) that were colocalized with BOLD activations in lateral occipital cortex (LOC). These results provide physiological evidence that directing spatial attention to one part of an object (whether real or illusory) facilitates the processing of the entire object at the level of the LOC and thus contributes directly to object-based selective attention.

The strength of cortical connectivity to the striatum influences the balance between behavioral variability and stability. Learning to consistently produce a skilled action requires plasticity in corticostriatal connectivity associated with repeated training of the action. However, it remains unknown whether such corticostriatal plasticity occurs during training itself or ‘offline’ during time away from training, such as sleep. Here, we monitor the corticostriatal network throughout long-term skill learning in rats and find that non-rapid-eye-movement (NREM) sleep is a relevant period for corticostriatal plasticity. We first show that the offline activation of striatal NMDA receptors is required for skill learning. We then show that corticostriatal functional connectivity increases offline, coupled to emerging consistent skilled movements, and coupled cross-area neural dynamics. We then identify NREM sleep spindles as uniquely poised to mediate corticostriatal plasticity, through interactions with slow oscillations. Our results provide evidence that sleep shapes cross-area coupling required for skill learning.