Specific Patterns Of Neural Activity Research Articles

Functional near-infrared spectroscopy and language development: An integrative review.

Functional near-infrared spectroscopy (fNIRS) stands poised to revolutionize our understanding of auditory detection, speech perception, and language development in infants. In this study, we conducted a meticulous integrative review across Medline, Scopus, and LILACS databases, targeting investigations utilizing fNIRS to explore language-related features and cortical activation during auditory stimuli in typical infants. We included studies that used the NIRS technique to study language and cortical activation in response to auditory stimuli in typical infants between 0 and 3years old. We used the ROBINS-I tool to assess the quality and the risk of bias in the studies. Our analysis, encompassing 66 manuscripts, is presented in standardized tables for streamlined data extraction. We meticulously correlated findings with children's developmental stages, delineating crucial insights into brain development and its intricate interplay with language outcomes. Although most studies have a high risk for overall bias, especially due to the high loss of data in NIRS studies, the low risk in the other domains is predominant and homogeneous among the studies. Highlighted are the unique advantages of fNIRS for pediatric studies, underscored by its innate suitability for use in children. This review accentuates fNIRS' capacity to elucidate the neural correlates of language processing and the sequential steps of language acquisition. From birth, infants exhibit abilities that lay the foundation for language development. The progression from diffuse to specific neural activation patterns is extremely influenced by exposure to languages, social interaction, and prosodic features and, reflects the maturation of brain networks involved in language processing. In conclusion, fNIRS emerges as an indispensable functional imaging modality, providing insights into the temporal dynamics of language acquisition and associated developmental milestones. This synthesis presents the pivotal role of fNIRS in advancing our comprehension of early language development and paves the way for future research endeavors in this domain.

Specific patterns of neural activity in the hippocampus after massed or distributed spatial training

Training with long inter-session intervals, termed distributed training, has long been known to be superior to training with short intervals, termed massed training. In the present study we compared c-Fos expression after massed and distributed training protocols in the Morris water maze to outline possible differences in the learning-induced pattern of neural activation in the dorsal CA1 in the two training conditions. The results demonstrate that training and time lags between learning opportunities had an impact on the pattern of neuronal activity in the dorsal CA1. Mice trained with the distributed protocol showed sustained neuronal activity in the postero-distal component of the dorsal CA1. In parallel, in trained mice we found more active cells that tended to constitute spatially restricted clusters, whose degree increased with the increase in the time lags between learning trials. Moreover, activated cell assemblies demonstrated increased stability in their spatial organization after distributed as compared to massed training or control condition. Finally, using a machine learning algorithm we found that differences in the number of c-Fos positive cells and their location in the dorsal CA1 could be predictive of the training protocol used. These results suggest that the topographic organization and the spatial location of learning activated cell assemblies might be critical to promote the increased stability of the memory trace induced by distributed training.

Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation

Functional neuroimaging studies indicate that interconnected parts of the subcallosal anterior cingulate cortex (ACC), striatum, and amygdala play a fundamental role in affect in health and disease. Yet, although the patterns of neural activity engaged in the striatum and amygdala during affective processing are well established, especially during reward anticipation, less is known about subcallosal ACC. Here, we recorded neural activity in non-human primate subcallosal ACC and compared this with interconnected parts of the basolateral amygdala and rostromedial striatum while macaque monkeys performed reward-based tasks. Applying multiple analysis approaches, we found that neurons in subcallosal ACC and rostromedial striatum preferentially signal anticipated reward using short bursts of activity that form temporally specific patterns. By contrast, the basolateral amygdala uses a mixture of both temporally specific and more sustained patterns of activity to signal anticipated reward. Thus, dynamic patterns of neural activity across populations of neurons are engaged in affect, especially in subcallosal ACC.

Decoding neural activity to assess individual latent state in ecologically valid contexts.

Objective.Currently, there exists very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remains unclear as to what extent patterns of neural activity observed under such constraints actually manifest outside the laboratory in a manner that can be used to make accurate inferences about latent states, associated cognitive processes, or proximal behavior. Improving our understanding of when and how specific patterns of neural activity manifest in ecologically valid scenarios would provide validation for laboratory-based approaches that study similar neural phenomena in isolation and meaningful insight into the latent states that occur during complex tasks.Approach.Domain generalization methods, borrowed from the work of the brain-computer interface community, have the potential to capture high-dimensional patterns of neural activity in a way that can be reliably applied across experimental datasets in order to address this specific challenge. We previously used such an approach to decode phasic neural responses associated with visual target discrimination. Here, we extend that work to more tonic phenomena such as internal latent states. We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We apply the trained models to an ecologically valid paradigm in which participants performed multiple, concurrent driving-related tasks while perched atop a six-degrees-of-freedom ride-motion simulator.Main Results.Using the pretrained models, we estimate latent state and the associated patterns of neural activity. As the patterns of neural activity become more similar to those patterns observed in the training data, we find changes in behavior and task performance that are consistent with the observations from the original, laboratory-based paradigms.Significance.These results lend ecological validity to the original, highly controlled, experimental designs and provide a methodology for understanding the relationship between neural activity and behavior during complex tasks.

Neural responses to emotional stimuli across the dissociative spectrum: Common and specific mechanisms.

Departing from existing neurobiological models of dissociation, the current study aims at conducting a quantitative meta-analytic review of neural responses to emotional stimuli among individuals ascribed to the dissociative spectrum (DS). Accordingly, the study explored common and specific brain mechanisms across borderline personality disorder, conversion/somatoform disorders, posttraumatic stress disorder, posttraumatic stress disorder related to repeated interpersonal traumatic experiences, and dissociative disorders. The meta-analysis included studies that administered emotional stimuli during functional magnetic resonance imaging acquisition among individuals included in the DS. There were two conducted meta-analytic procedures: (i) a Bayesian network meta-analysis for a region-of-interest-based approach; and (ii) robust voxel-based approach. Forty-four independent studies were included for a total of 1384 individuals (DS = 741 patients). The network meta-analysis showed specific patterns of neural activity considering an extended brain network involved in emotion regulation for each condition ascribed to the DS. The voxel-based meta-analysis highlighted an increased activity of dorsal anterior cingulate cortex as a common neurological signature of the DS. The common neural feature of the DS captures an implicit appraisal of emotion-eliciting stimuli as threatening and/or noxious for mental and physical integrity of the individual together with painful subjective experiences associated with physiological emotional reactions. Specific brain responses across the DS suggested the engagement in different mechanisms to address emotional stimuli, including implicit avoidance reactions and attempts to overcontrol of affective states together with a disruption of integrative processes of emotional mind-body features.

Recognition Memory Induces Natural LTP-like Hippocampal Synaptic Excitation and Inhibition.

Synaptic plasticity is a cellular process involved in learning and memory by which specific patterns of neural activity adapt the synaptic strength and efficacy of the synaptic transmission. Its induction is governed by fine tuning between excitatory/inhibitory synaptic transmission. In experimental conditions, synaptic plasticity can be artificially evoked at hippocampal CA1 pyramidal neurons by repeated stimulation of Schaffer collaterals. However, long-lasting synaptic modifications studies during memory formation in physiological conditions in freely moving animals are very scarce. Here, to study synaptic plasticity phenomena during recognition memory in the dorsal hippocampus, field postsynaptic potentials (fPSPs) evoked at the CA3–CA1 synapse were recorded in freely moving mice during object-recognition task performance. Paired pulse stimuli were applied to Schaffer collaterals at the moment that the animal explored a new or a familiar object along different phases of the test. Stimulation evoked a complex synaptic response composed of an ionotropic excitatory glutamatergic fEPSP, followed by two inhibitory responses, an ionotropic, GABAA-mediated fIPSP and a metabotropic, G-protein-gated inwardly rectifying potassium (GirK) channel-mediated fIPSP. Our data showed the induction of LTP-like enhancements for both the glutamatergic and GirK-dependent components of the dorsal hippocampal CA3–CA1 synapse during the exploration of novel but not familiar objects. These results support the contention that synaptic plasticity processes that underlie hippocampal-dependent memory are sustained by fine tuning mechanisms that control excitatory and inhibitory neurotransmission balance.

Neural activity in afferent projections to the infralimbic cortex is associated with individual differences in the recall of fear extinction

Post-traumatic stress disorder (PTSD) is characterized by an impaired ability to extinguish fear responses to trauma-associated cues. Studies in humans and non-human animals point to differences in the engagement of certain frontal cortical regions as key mediators determining whether or not fear extinction is successful, however the neural circuit interactions that dictate the differential involvement of these regions are not well understood. To better understand how individual differences in extinction recall are reflected in differences in neural circuit activity, we labeled projections to the infralimbic cortex (IL) in rats using a retrograde tracer and compared neural activity within, and outside, of IL-projecting neurons. We analyzed these data in groups separated on the basis of how well rats retained extinction memory. We found that within IL-projecting cells, neurons in the posterior paraventricular thalamus showed heightened activity in rats that showed good extinction recall. Outside of the IL-projecting cells, increased Fos activity was observed in good extinction rats in select regions of the claustrum and ventral hippocampus. Our results indicate that differences in extinction recall are associated with a specific pattern of neural activity both within and outside of projections to the IL.

What Neuroscientists Think, and Don't Think, About Consciousness.

The approach the majority of neuroscientists take to the question of how consciousness is generated, it is probably fair to say, is to ignore it. Although there are active research programs looking at correlates of consciousness, and explorations of informational properties of what might be relevant neural ensembles, the tacitly implied mechanism of consciousness in these approaches is that it somehow just happens. This reliance on a “magical emergence” of consciousness does not address the “objectively unreasonable” proposition that elements that have no attributes or properties that can be said to relate to consciousness somehow aggregate to produce it. Neuroscience has furnished evidence that neurons are fundamental to consciousness; at the fine and gross scale, aspects of our conscious experience depend on specific patterns of neural activity – in some way, the connectivity of neurons computes the features of our experience. So how do we get from knowing that some specific configurations of cells produce consciousness to understanding why this would be the case? Behind the voltages and currents electrophysiologists measure is a staggeringly complex system of electromagnetic fields – these are the fundamental physics of neurons and glia in the brain. The brain is entirely made of electromagnetism (EM) phenomena from the level of the atoms up. The EM field literally manifests the computations, or signaling, or information processing/activities performed by connected cellular ensembles that generate a 1st-person perspective. An investigation into the EM field at the cellular scale provides the possibility of identifying the outward signs of a mechanism in fundamental terms (physics), as opposed to merely describing the correlates of our mental abstractions of it.

Volitional control of individual neurons in the human brain.

Brain-machine interfaces allow neuroscientists to causally link specific neural activity patterns to a particular behaviour. Thus, in addition to their current clinical applications, brain-machine interfaces can also be used as a tool to investigate neural mechanisms of learning and plasticity in the brain. Decades of research using such brain-machine interfaces have shown that animals (non-human primates and rodents) can be operantly conditioned to self-regulate neural activity in various motor-related structures of the brain. Here, we ask whether the human brain, a complex interconnected structure of over 80 billion neurons, can learn to control itself at the most elemental scale-a single neuron. We used the unique opportunity to record single units in 11 individuals with epilepsy to explore whether the firing rate of a single (direct) neuron in limbic and other memory-related brain structures can be brought under volitional control. To do this, we developed a visual neurofeedback task in which participants were trained to move a block on a screen by modulating the activity of an arbitrarily selected neuron from their brain. Remarkably, participants were able to volitionally modulate the firing rate of the direct neuron in these previously uninvestigated structures. We found that a subset of participants (learners), were able to improve their performance within a single training session. Successful learning was characterized by (i) highly specific modulation of the direct neuron (demonstrated by significantly increased firing rates and burst frequency); (ii) a simultaneous decorrelation of the activity of the direct neuron from the neighbouring neurons; and (iii) robust phase-locking of the direct neuron to local alpha/beta-frequency oscillations, which may provide some insights in to the potential neural mechanisms that facilitate this type of learning. Volitional control of neuronal activity in mnemonic structures may provide new ways of probing the function and plasticity of human memory without exogenous stimulation. Furthermore, self-regulation of neural activity in these brain regions may provide an avenue for the development of novel neuroprosthetics for the treatment of neurological conditions that are commonly associated with pathological activity in these brain structures, such as medically refractory epilepsy.

Neuro-Computational Foundations of Moral Preferences.

Moral preferences pervade many aspects of our lives, dictating how we ought to behave, whom we can marry and even what we eat. Despite their relevance, one fundamental question remains unanswered: where do individual moral preferences come from? It is often thought that all types of preferences reflect properties of domain-general neural decision mechanisms that employ a common ‘neural currency’ to value choice options in many different contexts. This view, however, appears at odds with the observation that many humans consider it intuitively wrong to employ the same scale to compare moral value (e.g. of a human life) with material value (e.g. of money). In this paper, we directly test if moral subjective values are represented by similar neural processes as financial subjective values. In a study combining functional magnetic resonance imaging with a novel behavioral paradigm, we identify neural representations of the subjective values of human lives or financial payoffs by means of structurally identical computational models. Correlating isomorphic model variables from both domains with brain activity reveals specific patterns of neural activity that selectively represent values in the moral (right temporo-parietal junction) or financial (ventral-medial prefrontal cortex) domain. Intriguingly, our findings show that human lives and money are valued in (at least partially) distinct neural currencies, supporting theoretical proposals that human moral behavior is guided by processes that are distinct from those underlying behavior driven by personal material benefit.

Probing neural codes with two-photon holographic optogenetics.

Optogenetics ushered in a revolution in how neuroscientists interrogate brain function. Because of technical limitations, the majority of optogenetic studies have used low spatial resolution activation schemes that limit the types of perturbations that can be made. However, neural activity manipulations at finer spatial scales are likely to be important to more fully understand neural computation. Spatially precise multiphoton holographic optogenetics promises to address this challenge and opens up many new classes of experiments that were not previously possible. More specifically, by offering the ability to recreate extremely specific neural activity patterns in both space and time in functionally defined ensembles of neurons, multiphoton holographic optogenetics could allow neuroscientists to reveal fundamental aspects of the neural codes for sensation, cognition and behavior that have been beyond reach. This Review summarizes recent advances in multiphoton holographic optogenetics that substantially expand its capabilities, highlights outstanding technical challenges and provides an overview of the classes of experiments it can execute to test and validate key theoretical models of brain function. Multiphoton holographic optogenetics could substantially accelerate the pace of neuroscience discovery by helping to close the loop between experimental and theoretical neuroscience, leading to fundamental new insights into nervous system function and disorder.

Accumbal D1 and D2 medium spiny neurons control distinct learning parameters in complex behavior

Value-based decision-making is at the core of nearly all motivated behaviors and requires the ability to associate outcomes with specific actions and make adaptive decisions about future behavioral action. At the core of value-based decision-making and reinforcement is the nucleus accumbens (NAc), which is integrally involved in learning, selecting, and executing goal-oriented behaviors. The NAc is a heterogeneous population primarily composed of D1 and D2 medium spiny projection (MSN) neurons that are thought to have opposing roles in behavior, in which D1 MSNs promote reward and D2 MSNs promote aversion. By expressing channelrhodopsin selectively in D1- and D2- populations (using D1-Cre and A2A-Cre mice) in the NAc core, we show that mice will nose poke for optical self-stimulation of both cell types, suggesting D2-MSN activity is not inherently aversive. While optogenetic approaches give some information about how cellular activation can modulate behavior, they eliminate the temporally specific neural activity patterns that encode information in behaving animals. To understand how real-time activity in these populations is linked to behavioral execution, we expressed the genetically encoded calcium indicator (GCaMP6f) within D1 and D2 MSNs coupled with in vivo fiber photometry to record from these cell populations in awake and behaving animals during pavlovian and operant conditioning tasks. Utilizing complex reinforcement schedules that allow dissociation of stimulus value, outcome, cue learning, and action, we show that D1 MSNs respond to the presence and intensity of unconditioned stimuli – regardless of value. Conversely, D2 MSNs respond to a mismatch between what is expected and what is received and thus encode errors in prediction in a value-independent fashion. To establish a causal link between the information encoded within these neuronal populations and behavioral output, we next expressed an inhibitory opsin (halorhodopsin) selectively in either D1- or D2-MSNs to inhibit these cells at specific time points during a fear conditioning task. We show that inhibition at select time points, either by inhibiting D2-MSN activity during the predictive cue or D1-MSN activity during the shock outcome, disrupts learning of the association. We thus provide foundational evidence for the discrete aspects of information that are encoded within these cellular populations.

Learning to control the brain through adaptive closed-loop patterned stimulation

Objective. Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed a model-free, adaptive, closed-loop stimulation (ACLS) system that learns to use multi-site electrical stimulation to control the pattern of activity of a population of neurons. Approach. The ACLS system combined multi-electrode electrophysiological recordings with multi-site electrical stimulation to simultaneously record the activity of a population of 5–15 multiunit neurons and deliver spatially-patterned electrical stimulation across 4–16 sites. Using a closed-loop learning system, ACLS iteratively updated the pattern of stimulation to reduce the difference between the observed neural response and a specific target pattern of firing rates in the recorded multiunits. Main results. In silico and in vivo experiments showed ACLS learns to produce specific patterns of neural activity (in ∼15 min) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. Significance. Our results show an ACLS system that can learn, in real-time, to generate specific patterns of neural activity. This work provides a framework for using model-free closed-loop learning to control neural activity.

Spatially confined responses of mouse visual cortex to intracortical magnetic stimulation from micro-coils

Objective. Electrical stimulation via microelectrodes implanted in cortex has been suggested as a potential treatment for a wide range of neurological disorders. Despite some success however, the effectiveness of conventional electrodes remains limited, in part due to an inability to create specific patterns of neural activity around each electrode and in part due to challenges with maintaining a stable interface. The use of implantable micro-coils to magnetically stimulate the cortex has the potential to overcome these limitations because the asymmetric fields from coils can be harnessed to selectively activate some neurons, e.g. vertically-oriented pyramidal neurons while avoiding others, e.g. horizontally-oriented passing axons. In vitro experiments have shown that activation is indeed confined with micro-coils but their effectiveness in the intact brain of living animals has not been evaluated. Approach. To assess the efficacy of stimulation, a 128-channel custom recording microelectrode array was positioned on the surface of the visual cortex (ECoG) in anesthetized mice and responses to magnetic and electric stimulation were compared. Stimulation was delivered from electrodes or micro-coils implanted through a hole in the center of the recording array at a rate of 200 pulses per second for 100 ms. Main results. Both electric and magnetic stimulation reliably elicited cortical responses, although activation from electric stimulation was spatially expansive, often extending more than 1 mm from the stimulation site, while activation from magnetic stimulation was typically confined to a ∼300 µm diameter region around the stimulation site. Results were consistent for stimulation of both cortical layer 2/3 and layer 5 as well as across a range of stimulus strengths. Significance. The improved focality with magnetic stimulation suggests that the effectiveness of cortical stimulation can be improved. Improved focality may be particularly attractive for cortical prostheses that require high spatial resolution, e.g. devices that target sensory cortex, as it may lead to improved acuity.

Neural Activity Across the Dorsolateral Prefrontal Cortex and Risk for Suicidal Ideation and Self-Injury

The current study investigated the neural correlates of non-suicidal self-injury (NSSI) and suicidal ideation across the dorsolateral prefrontal cortex. Two-hundred ninety-six undergraduates solved anagram tasks while being monitored by a functional near-infrared spectroscopy device, and completed a questionnaire assessing behaviors and symptoms. Repeated measures analysis of variance revealed location-specific changes in neural activity based on NSSI, suicidal ideation, task type, and cognitive demand. The presence of suicidal ideation was associated with specific patterns of neural activity, modified by sex and task type. Interestingly, participants who engaged in NSSI exhibited some deactivation of the dlPFC when faced with more difficult cognitive challenges. Future research on these processes may allow for noninvasive imaging techniques to help screen risk for suicidality and NSSI.

Ventral Striatum Encodes the Value of Cortical Neural States and Dorsal Striatum the Value of Cortical Trajectories During Operant Learning

During operant learning, reinforcement strengthens and weakens neural connections to increase the probability of obtaining reward. Critical to this process, the ventral striatum has been suggested to encode the value of states, and the dorsal striatum to encode the value of particular actions given a particular state. However, how states and actions correspond to specific patterns of neural activity in the brain is not well understood. To address this, we investigated the role of the ventral and dorsal striatum in encoding the value of states and trajectories of cortical activity that predict reward. We trained rats to volitionally control an auditory cursor by modulating neurons in motor cortex to obtain reward, while recording activity in the ventral and dorsal striatum. Using reinforcement learning models, we were able to obtain estimates of the value of current cortical states versus the value of cortical trajectories among states, and these value estimates were differentially associated with activity in distinct subregions of the striatum. Our results show that the ventral striatum assesses the value of neural states while the dorsal striatum assesses the value of the trajectories between those states. These findings highlight the role of specific circuits in neural reinforcement.

The Relationship Between Glutamate Dynamics and Activity-Dependent Synaptic Plasticity.

The spatiotemporal dynamics of excitatory neurotransmission must be tightly regulated to achieve efficient synaptic communication. By limiting spillover, glutamate transporters are believed to prevent excessive activation of extrasynaptically located receptors that can impair synaptic plasticity. While glutamate transporter expression is reduced in numerous neurodegenerative diseases, the contributions of transporter dysfunction to disease pathophysiology remain ambiguous as the fundamental relationship between glutamate dynamics and plasticity, and the mechanisms linking these two phenomena, remain poorly understood. Here, we combined electrophysiology and real-time high-speed imaging of extracellular glutamate transients during LTP induction and characterized the sensitivity of the relationship between glutamate dynamics during theta burst stimulation (TBS) and the resulting magnitude of LTP consolidation, both in control conditions and following selective and nonselective glutamate transporter blockade. Glutamate clearance times were negatively correlated with LTP magnitude following nonselective glutamate transporter inhibition but not following selective blockade of a majority of GLT-1, the brain's most abundant glutamate transporter. Although glutamate transporter inhibition reduced the postsynaptic population response to TBS, calcium responses to TBS were greatly exaggerated. The source of excess calcium was dependent on NMDARs, L-type VGCCs, GluA2-lacking AMPARs, and internal calcium stores. Surprisingly, inhibition of L-type VGCCs, but not GluA2-lacking AMPARs or ryanodine receptors, was required to restore robust LTP. In all, these data provide a detailed understanding of the relationship between glutamate dynamics and plasticity and uncover important mechanisms by which poor glutamate uptake can negatively impact LTP consolidation.SIGNIFICANCE STATEMENT Specific patterns of neural activity can promote long-term changes in the strength of synaptic connections through a phenomenon known as synaptic plasticity. Synaptic plasticity is well accepted to represent the cellular mechanisms underlying learning and memory, and many forms of plasticity are initiated by the excitatory neurotransmitter glutamate. While essential for rapid cellular communication in the brain, excessive levels of extracellular glutamate can negatively impact brain function. In this study, we demonstrate that pharmacological manipulations that increase the availability of extracellular glutamate during neural activity can have profoundly negative consequences on synaptic plasticity. We identify mechanisms through which excess glutamate can negatively influence synaptic plasticity, and we discuss the relevance of these findings to neurodegenerative diseases and in the aging brain.

Improved motor imagery brain-computer interface performance via adaptive modulation filtering and two-stage classification

Electroencephalogram (EEG) based brain-computer interfaces (BCI) monitor neural activity and translate these signals into actions and/or decisions, with the final goal of enabling users to interact with a computer using only their thoughts. To this end, users must produce specific neural activity patterns that are used by the system as control signals. A common task used to elicit such signals is motor imagery (MI), where specific patterns are elicited in the sensorimotor cortex during imagination of movements (e.g., of the hands, arms, feet or tongue). The processing pipeline typically used in EEG-BCIs consists of three stages: pre-processing, feature extraction, and classification. Here, we propose innovations in pre-processing and classification and quantify the gains achieved on 4-class MI-based BCI performance. More specifically, for the pre-processing stage, we propose the concept of spectro-temporal filtering as we show that MI-elicited neural patterns have varying amplitude modulation variations relative to artifacts. For the classification stage, in turn, a two-step classification method is proposed. First, LDA classifiers are used to discriminate between different pair-wise MI tasks. Next, a naive Bayes classifier is used to predict the final task performed by the user based on the weighted outputs of the LDA classifiers. Experimental results showed that the proposed system outperformed the first-place winner of the BCI competition IV by 3.5 %.

Chronic stability of single-channel neurophysiological correlates of gross and fine reaching movements in the rat.

While substantial task-related neural activity has been observed during motor tasks in rodent primary motor cortex and premotor cortex, the long-term stability of these responses in healthy rats is uncertain, limiting the interpretability of longitudinal changes in the specific patterns of neural activity associated with learning or motor recovery following injury. This study examined the stability of task-related neural activity associated with execution of two distinct reaching tasks in healthy rodents. A novel automated rodent behavioral apparatus was constructed and rats were trained to perform a reaching task combining a ‘gross’ lever press and a ‘fine’ pellet retrieval. In each animal, two chronic microelectrode arrays were implanted in motor cortex spanning the caudal forelimb area (rodent primary motor cortex) and the rostral forelimb area (rodent premotor cortex). We recorded multiunit spiking and local field potential activity from 10 days to 7–10 weeks post-implantation to characterize the patterns of neural activity observed during each task component and analyzed the consistency of channel-specific task-related neural activity. Task-related changes in neural activity were observed on the majority of channels. While the task-related changes in multi-unit spiking and local field potential spectral power were consistent over several weeks, spectral power changes were more stable, despite the trade-off of decreased spatial and temporal resolution. These results show that neural activity in rodent primary and premotor cortex is associated with specific phases of reaching movements with stable patterns of task-related activity across time, establishing the relevance of the rodent for future studies designed to examine changes in task-related neural activity during recovery from focal cortical lesions.

Structural and functional neural correlates of vigilant and avoidant regulation style

BackgroundRegulation of emotional arousal is a relevant factor for mental health. The investigation of neural underpinnings of regulation styles in healthy individuals may provide important insights regarding potential risk factors. To fill the gap of structural correlates of regulation styles and to expand previous results, we focused on the association between brain structure, neural responsiveness and vigilant/avoidant regulation style. MethodsIn n = 302 healthy individuals regulation style was assessed with the Mainz Coping Inventory (MCI). Participants underwent structural and functional MRI during an emotion-processing paradigm. Structural MRI (voxel-based morphometry) and functional MRI were analysed in two regions of interest (amygdala and anterior cingulate cortex [ACC]). ResultsRegulation styles did not show an association with brain structure after correction for gender, age, trait anxiety, depressive symptoms. During emotion processing, a vigilant regulation style was negatively associated with ACC activation. LimitationsThe cross-sectional study in a non-pathological sample is not adequate to unveil causalities or draw conclusions regarding prevention interventions. ConclusionRegulation styles are associated with specific neural activation patterns. The association of a high-vigilant regulation style and low ACC activation during emotion processing in healthy participants might be a potential risk factor.