Neural interactions in the human frontal cortex dissociate reward and punishment learning.

How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.

Decomposing Neural Circuit Function into Information Processing Primitives.

It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.

An information-theoretic quantification of the content of communication between brain regions.

Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information flowing between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional anaytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously hidden feature-specific information flow.

Beta Oscillations in Monkey Striatum Encode Reward Prediction Error Signals.

Reward prediction error (RPE) signals are crucial for reinforcement learning and decision-making as they quantify the mismatch between predicted and obtained rewards. RPE signals are encoded in the neural activity of multiple brain areas, such as midbrain dopaminergic neurons, prefrontal cortex, and striatum. However, it remains unclear how these signals are expressed through anatomically and functionally distinct subregions of the striatum. In the current study, we examined to which extent RPE signals are represented across different striatal regions. To do so, we recorded local field potentials (LFPs) in sensorimotor, associative, and limbic striatal territories of two male rhesus monkeys performing a free-choice probabilistic learning task. The trial-by-trial evolution of RPE during task performance was estimated using a reinforcement learning model fitted on monkeys' choice behavior. Overall, we found that changes in beta band oscillations (15-35 Hz), after the outcome of the animal's choice, are consistent with RPE encoding. Moreover, we provide evidence that the signals related to RPE are more strongly represented in the ventral (limbic) than dorsal (sensorimotor and associative) part of the striatum. To conclude, our results suggest a relationship between striatal beta oscillations and the evaluation of outcomes based on RPE signals and highlight a major contribution of the ventral striatum to the updating of learning processes.SIGNIFICANCE STATEMENT Reward prediction error (RPE) signals are crucial for reinforcement learning and decision-making as they quantify the mismatch between predicted and obtained rewards. Current models suggest that RPE signals are encoded in the neural activity of multiple brain areas, including the midbrain dopaminergic neurons, prefrontal cortex and striatum. However, it remains elusive whether RPEs recruit anatomically and functionally distinct subregions of the striatum. Our study provides evidence that RPE-related modulations in local field potential (LFP) power are dominant in the striatum. In particular, they are stronger in the rostro-ventral rather than the caudo-dorsal striatum. Our findings contribute to a better understanding of the role of striatal territories in reward-based learning and may be relevant for neuropsychiatric and neurologic diseases that affect striatal circuits.

Subcortical-cortical dynamical states of the human brain and their breakdown in stroke.

The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics in the ultra-slow frequency range (<0.01-0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, we identify Dynamic Functional States (DFSs), transient but recurrent clusters of cortical and subcortical regions synchronizing at ultra-slow frequencies. We observe that shifts in cortical clusters are temporally coincident with shifts in subcortical clusters, with cortical regions flexibly synchronizing with either limbic regions (hippocampus/amygdala), or subcortical nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus and cortex, provoke anomalies in the fraction times, dwell times, and transitions between DFSs, causing a bias toward abnormal network integration. Dynamical anomalies observed 2 weeks after stroke recover in time and contribute to explaining neurological impairment and long-term outcome.

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data.

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites1 that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

Two classes of functional connectivity in dynamical processes in networks.

The relationship between network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of recent years. Understanding this relationship is of relevance to a range of disciplines-from neuroscience to geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity, SC) with a (network) representation of the dynamics (functional connectivity, FC). Here, we show that one can distinguish two classes of functional connectivity-one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes-excitations, regular and chaotic oscillators-and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in geomorphology, ecology, systems biology, neuroscience and socio-ecological systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

Stroke-related alterations in inter-areal communication.

Beyond causing local ischemia and cell damage at the site of injury, stroke strongly affects long-range anatomical connections, perturbing the functional organization of brain networks. Several studies reported functional connectivity abnormalities parallelling both behavioral deficits and functional recovery across different cognitive domains. FC alterations suggest that long-range communication in the brain is altered after stroke. However, standard FC analyses cannot reveal the directionality and time scale of inter-areal information transfer. We used resting-state fMRI and covariance-based Granger causality analysis to quantify network-level information transfer and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was significantly decreased with respect to healthy controls. Second, stroke caused inter-hemispheric asymmetries, as information transfer within the affected hemisphere and from the affected to the intact hemisphere was significantly reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they correlated with impaired performance in several behavioral domains. Overall, our findings support the hypothesis that stroke provokes asymmetries between the affected and spared hemisphere, with different functional consequences depending on which hemisphere is lesioned.

A generative spiking neural-network model of goal-directed behaviour and one-step planning.

In mammals, goal-directed and planning processes support flexible behaviour used to face new situations that cannot be tackled through more efficient but rigid habitual behaviours. Within the Bayesian modelling approach of brain and behaviour, models have been proposed to perform planning as probabilistic inference but this approach encounters a crucial problem: explaining how such inference might be implemented in brain spiking networks. Recently, the literature has proposed some models that face this problem through recurrent spiking neural networks able to internally simulate state trajectories, the core function at the basis of planning. However, the proposed models have relevant limitations that make them biologically implausible, namely their world model is trained 'off-line' before solving the target tasks, and they are trained with supervised learning procedures that are biologically and ecologically not plausible. Here we propose two novel hypotheses on how brain might overcome these problems, and operationalise them in a novel architecture pivoting on a spiking recurrent neural network. The first hypothesis allows the architecture to learn the world model in parallel with its use for planning: to this purpose, a new arbitration mechanism decides when to explore, for learning the world model, or when to exploit it, for planning, based on the entropy of the world model itself. The second hypothesis allows the architecture to use an unsupervised learning process to learn the world model by observing the effects of actions. The architecture is validated by reproducing and accounting for the learning profiles and reaction times of human participants learning to solve a visuomotor learning task that is new for them. Overall, the architecture represents the first instance of a model bridging probabilistic planning and spiking-processes that has a degree of autonomy analogous to the one of real organisms.

Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals.

Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience.

Neural mechanisms mediating degrees of strategic uncertainty.

In social interactions, strategic uncertainty arises when the outcome of one's choice depends on the choices of others. An important question is whether strategic uncertainty can be resolved by assessing subjective probabilities to the counterparts' behavior, as if playing against nature, and thus transforming the strategic interaction into a risky (individual) situation. By means of functional magnetic resonance imaging with human participants we tested the hypothesis that choices under strategic uncertainty are supported by the neural circuits mediating choices under individual risk and deliberation in social settings (i.e. strategic thinking). Participants were confronted with risky lotteries and two types of coordination games requiring different degrees of strategic thinking of the kind 'I think that you think that I think etc.' We found that the brain network mediating risk during lotteries (anterior insula, dorsomedial prefrontal cortex and parietal cortex) is also engaged in the processing of strategic uncertainty in games. In social settings, activity in this network is modulated by the level of strategic thinking that is reflected in the activity of the dorsomedial and dorsolateral prefrontal cortex. These results suggest that strategic uncertainty is resolved by the interplay between the neural circuits mediating risk and higher order beliefs (i.e. beliefs about others' beliefs).

Local or Not Local: Investigating the Nature of Striatal Theta Oscillations in Behaving Rats.

In the cortex and hippocampus, neuronal oscillations of different frequencies can be observed in local field potentials (LFPs). LFPs oscillations in the theta band (6-10 Hz) have also been observed in the dorsolateral striatum (DLS) of rodents, mostly during locomotion, and have been proposed to mediate behaviorally-relevant interactions between striatum and cortex (or between striatum and hippocampus). However, it is unclear if these theta oscillations are generated in the striatum. To address this issue, we recorded LFPs and spiking activity in the DLS of rats performing a running sequence on a motorized treadmill. We observed an increase in rhythmical activity of the LFP in the theta-band during run compared to rest periods. However, several observations suggest that these oscillations are mainly generated outside of the striatum. First, theta oscillations disappeared when LFPs were rereferenced against a striatal recording electrode and the imaginary coherence between LFPs recorded at different locations within the striatum was null. Second, 8% of the recorded neurons had their spiking activity phase-locked to the theta rhythm. Third, Granger causality analyses between LFPs simultaneously recorded in the cortex and the striatum revealed that the interdependence between these two signals in the theta range was mostly accounted for by a common external source. The most parsimonious interpretation of these results is that theta oscillations observed in striatal LFPs are largely contaminated by volume-conducted signals. We propose that striatal LFPs are not optimal proxies of network dynamics in the striatum and should be interpreted with caution.

Dynamic Reconfiguration of Visuomotor-Related Functional Connectivity Networks.

Cognitive functions arise from the coordination of large-scale brain networks. However, the principles governing interareal functional connectivity dynamics (FCD) remain elusive. Here, we tested the hypothesis that human executive functions arise from the dynamic interplay of multiple networks. To do so, we investigated FCD mediating a key executing function, known as arbitrary visuomotor mapping, using brain connectivity analyses of high-gamma activity recorded using MEG and intracranial EEG. Visuomotor mapping was found to arise from the dynamic interplay of three partly overlapping cortico-cortical and cortico-subcortical functional connectivity (FC) networks. First, visual and parietal regions coordinated with sensorimotor and premotor areas. Second, the dorsal frontoparietal circuit together with the sensorimotor and associative frontostriatal networks took the lead. Finally, cortico-cortical interhemispheric coordination among bilateral sensorimotor regions coupled with the left frontoparietal network and visual areas. We suggest that these networks reflect the processing of visual information, the emergence of visuomotor plans, and the processing of somatosensory reafference or action's outcomes, respectively. We thus demonstrated that visuomotor integration resides in the dynamic reconfiguration of multiple cortico-cortical and cortico-subcortical FC networks. More generally, we showed that visuomotor-related FC is nonstationary and displays switching dynamics and areal flexibility over timescales relevant for task performance. In addition, visuomotor-related FC is characterized by sparse connectivity with density <10%. To conclude, our results elucidate the relation between dynamic network reconfiguration and executive functions over short timescales and provide a candidate entry point toward a better understanding of cognitive architectures. SIGNIFICANCE STATEMENT: Executive functions are supported by the dynamic coordination of neural activity over large-scale networks. The properties of large-scale brain coordination processes, however, remain unclear. Using tools combining MEG and intracranial EEG with brain connectivity analyses, we provide evidence that visuomotor behaviors, a hallmark of executive functions, are mediated by the interplay of multiple and spatially overlapping subnetworks. These subnetworks span visuomotor-related areas, the cortico-cortical and cortico-subcortical interactions of which evolve rapidly and reconfigure over timescales relevant for behavior. Visuomotor-related functional connectivity dynamics are characterized by sparse connections, nonstationarity, switching dynamics, and areal flexibility. We suggest that these properties represent key aspects of large-scale functional networks and cognitive architectures.

Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy.

The reference electrophysiological pattern at seizure onset is the "rapid discharge," as visible on intracerebral electroencephalography (EEG). This discharge typically corresponds to a decrease of synchrony across brain areas. In contrast, the preictal period can exhibit patterns of increased synchrony, which can be quantified by network measures. Our objective was to compare preictal synchrony with a quantification of the rapid discharge as provided by the epileptogenicity index (EI). We investigated 24 seizures from 12 patients recorded by stereotaxic EEG (SEEG). Seizures were classified visually as containing preictal synchrony or not. We computed pairwise nonlinear correlation (h(2)) across channels in the 8 sec preceding the rapid discharge. The sum of ingoing and outgoing links (IN and OUT node strength), as well as the sum of all links (total strength, TOT) were computed for each region. We tested several filtering schemes, and quantified the capacity of each strength measure to serve as a detector of regions with high EI values using a receiver operating characteristic (ROC) analysis. We found that the best correspondence between node strength and EI was obtained for the OUT and TOT measures, for signals filtered in the 15-40 Hz band-that is, for the band corresponding to the spiky part of epileptic discharges. In agreement with these results, we also found that the ROC results were improved when considering only seizures with visible synchronous patterns in the preictal period. Our results suggest that measuring strength of preictal connectivity graphs can bring useful clinical information on the epileptogenic zone.

MarsAtlas: A cortical parcellation atlas for functional mapping.

An open question in neuroimaging is how to develop anatomical brain atlases for the analysis of functional data. Here, we present a cortical parcellation model based on macroanatomical information and test its validity on visuomotor-related cortical functional networks. The parcellation model is based on a recently developed cortical parameterization method (Auzias et al., [2013]: IEEE Trans Med Imaging 32:873-887), called HIP-HOP. This method exploits a set of primary and secondary sulci to create an orthogonal coordinate system on the cortical surface. A natural parcellation scheme arises from the axes of the HIP-HOP model running along the fundus of selected sulci. The resulting parcellation scheme, called MarsAtlas, complies with dorsoventral/rostrocaudal direction fields and allows inter-subject matching. To test it for functional mapping, we analyzed a MEG dataset collected from human participants performing an arbitrary visuomotor mapping task. Single-trial high-gamma activity, HGA (60-120 Hz), was estimated using spectral analysis and beamforming techniques at cortical areas arising from a Talairach atlas (i.e., Brodmann areas) and MarsAtlas. Using both atlases, we confirmed that visuomotor associations involve an increase in HGA over the sensorimotor and fronto-parietal network, in addition to medial prefrontal areas. However, MarsAtlas provided: (1) crucial functional information along both the dorsolateral and rostrocaudal direction; (2) an increase in statistical significance. To conclude, our results suggest that the MarsAtlas is a valid anatomical atlas for functional mapping, and represents a potential anatomical framework for integration of functional data arising from multiple techniques such as MEG, intracranial EEG and fMRI.

Modeling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning.

Current learning theory provides a comprehensive description of how humans and other animals learn, and places behavioral flexibility and automaticity at heart of adaptive behaviors. However, the computations supporting the interactions between goal-directed and habitual decision-making systems are still poorly understood. Previous functional magnetic resonance imaging (fMRI) results suggest that the brain hosts complementary computations that may differentially support goal-directed and habitual processes in the form of a dynamical interplay rather than a serial recruitment of strategies. To better elucidate the computations underlying flexible behavior, we develop a dual-system computational model that can predict both performance (i.e., participants' choices) and modulations in reaction times during learning of a stimulus-response association task. The habitual system is modeled with a simple Q-Learning algorithm (QL). For the goal-directed system, we propose a new Bayesian Working Memory (BWM) model that searches for information in the history of previous trials in order to minimize Shannon entropy. We propose a model for QL and BWM coordination such that the expensive memory manipulation is under control of, among others, the level of convergence of the habitual learning. We test the ability of QL or BWM alone to explain human behavior, and compare them with the performance of model combinations, to highlight the need for such combinations to explain behavior. Two of the tested combination models are derived from the literature, and the latter being our new proposal. In conclusion, all subjects were better explained by model combinations, and the majority of them are explained by our new coordination proposal.

Characterization of Cortical Networks and Corticocortical Functional Connectivity Mediating Arbitrary Visuomotor Mapping.

Adaptive behaviors are built on the arbitrary linkage of sensory inputs to actions and goals. Although the sensorimotor and associative frontostriatal circuits are known to mediate arbitrary visuomotor mappings, the underlying corticocortico dynamics remain elusive. Here, we take a novel approach exploiting gamma-band neural activity to study the human cortical networks and corticocortical functional connectivity mediating arbitrary visuomotor mapping. Single-trial gamma-power time courses were estimated for all Brodmann areas by combing magnetoencephalographic and MRI data with spectral analysis and beam-forming techniques. Linear correlation and Granger causality analyses were performed to investigate functional connectivity between cortical regions. The performance of visuomotor associations was characterized by an increase in gamma-power and functional connectivity over the sensorimotor and frontoparietal network, in addition to medial prefrontal areas. The superior parietal area played a driving role in the network, exerting Granger causality on the dorsal premotor area. Premotor areas acted as relay from parietal to medial prefrontal cortices, which played a receiving role in the network. Link community analysis further revealed that visuomotor mappings reflect the coordination of multiple subnetworks with strong overlap over motor and frontoparietal areas. We put forward an associative account of the underlying cognitive processes and corticocortical functional connectivity. Overall, our approach and results provide novel perspectives toward a better understanding of how distributed brain activity coordinates adaptive behaviors. SIGNIFICANCE STATEMENT: In everyday life, most of our behaviors are based on the arbitrary linkage of sensory information to actions and goals, such as stopping at a red traffic light. Despite their automaticity, such behaviors rely on the activity of a large brain network and elusive interareal functional connectivity. We take a novel approach exploiting noninvasive recordings of human brain activity to reveal the cortical networks and corticocortical functional connectivity mediating visuomotor mappings. Parietal areas were found to play a driving role in the network, whereas premotor areas acted as relays from parietal to medial prefrontal cortices, which played a receiving role. Overall, our approach and results provide novel perspectives toward a better understanding of how distributed brain activity coordinates adaptive behaviors.

Learning by observation in the macaque monkey under high experimental constraints.

While neuroscience research has tremendously advanced our knowledge about the neural mechanisms of individual learning, i.e. through trial-and-error, it is only recently that neuroscientists have begun to study observational learning, and thus little is known about its neural mechanisms. One limitation is that observational learning has been addressed under unconstrained experimental conditions, not compatible with neuronal recordings. This study examined observational learning in macaque monkeys under the constraining conditions of behavioral neurophysiology. Two animals sat in primate chairs facing each other, with their head fixed. A touch screen was placed face up between the chairs at arm's reach, and the monkeys were trained on an abstract visuomotor associative task. In one experiment, the monkeys alternated the roles of "actor" and "observer". The actor learned to associate visual cues with reaching targets, while the observer "watched" freely. Then, the observer was given the same cue-target associations just performed by the actor, or had to learn new, not previously observed ones. The results show that learning performance is better after observation. In experiment 2, one monkey learned from a human actor who performed the task with errors only, or with successes only in separate blocks. The monkey's gain in performance was higher after observation of errors than after successes. The findings suggest that observational learning can occur even under highly constraining conditions, and open the way for investigating the neuronal correlates of social learning using the methods of behavioral neurophysiology.

Neurophysiological correlates of visuo-motor learning through mental and physical practice.

We have previously shown that mental rehearsal can replace up to 75% of physical practice for learning a visuomotor task (Allami, Paulignan, Brovelli, & Boussaoud, (2008). Experimental Brain Research, 184, 105-113). Presumably, mental rehearsal must induce brain changes that facilitate motor learning. We tested this hypothesis by recording scalp electroencephalographic activity (EEG) in two groups of subjects. In one group, subjects executed a reach to grasp task for 240 trials. In the second group, subjects learned the task through a combination of mental rehearsal for the initial 180 trials followed by the execution of 60 trials. Thus, one group physically executed the task for 240 trials, the other only for 60 trials. Amplitudes and latencies of event-related potentials (ERPs) were compared across groups at different stages during learning. We found that ERP activity increases dramatically with training and reaches the same amplitude over the premotor regions in the two groups, despite large differences in physically executed trials. These findings suggest that during mental rehearsal, neuronal changes occur in the motor networks that make physical practice after mental rehearsal more effective in configuring functional networks for skilful behaviour.

Vicarious neural processing of outcomes during observational learning.

Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sources of information. Here, we combined newly developed experimental tasks and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms that govern such observational learning. Results show that the neural systems involved in individual trial-and-error learning and in action observation and execution both participate in observational learning. In addition, we identified brain areas that specifically activate for others' incorrect outcomes during learning in the posterior medial frontal cortex (pMFC), the anterior insula and the posterior superior temporal sulcus (pSTS).