Andrea Brovelli

High gamma frequency oscillatory activity dissociates attention from intention in the human premotor cortex

The premotor cortex is well known for its role in motor planning. In addition, recent studies have shown that it is also involved in nonmotor functions such as attention and memory, a notion derived from both animal neurophysiology and human functional imaging. The present study is an attempt to bridge the gap between these experimental techniques in the human brain, using a task initially designed to dissociate attention from intention in the monkey, and recently adapted for a functional magnetic resonance imaging (fMRI) study [Simon, S.R., Meunier, M., Piettre, L., Berardi, A.M., Segebarth, C.M., Boussaoud, D. (2002). Spatial attention and memory versus motor preparation: premotor cortex involvement as revealed by fMRI. J. Neurophysiol., 88, 2047-57]. Intracranial EEG was recorded from the cortical regions preferentially active in the spatial attention and/or working memory task and those involved in motor intention. The results show that, among the different intracranial EEG responses, only the high gamma frequency (60-200 Hz) oscillatory activity both dissociates attention/memory from motor intention and spatially colocalizes with the fMRI-identified premotor substrates of these two functions. This finding provides electrophysiological confirmation that the human premotor cortex is involved in spatial attention and/or working memory. Additionally, it provides timely support to the idea that high gamma frequency oscillations are involved in the cascade of neural processes underlying the hemodynamic responses measured with fMRI [Logothetis, N.K., Pauls, J., Augath, M., Trinath, T. and Oeltermann, A. (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature, 412, 150-7], and suggests a functional selectivity of the gamma oscillations that could be critical for future EEG investigations, whether experimental or clinical.

Visuo-motor learning with combination of different rates of motor imagery and physical practice

Sports psychology suggests that mental rehearsal facilitates physical practice in athletes and clinical rehabilitation attempts to use mental rehearsal to restore motor function in hemiplegic patients. Our aim was to examine whether mental rehearsal is equivalent to physical learning, and to determine the optimal proportions of real execution and rehearsal. Subjects were asked to grasp an object and insert it into an adapted slot. One group (G0) practiced the task only by physical execution (240 trials); three groups imagined performing the task in different rates of trials (25%, G25; 50%, G50; 75%, G75), and physically executed movements for the remaining trials; a fourth, control group imagined a visual rotation task in 75% of the trials and then performed the same motor task as the others groups. Movement time (MT) was compared for the first and last physical trials, together with other key trials, across groups. All groups learned, suggesting that mental rehearsal is equivalent to physical motor learning. More importantly, when subjects rehearsed the task for large numbers of trials (G50 and G75), the MT of the first executed trial was significantly shorter than the first executed trial in the physical group (G0), indicating that mental practice is better than no practice at all. Comparison of the first executed trial in G25, G50 and G75 with the corresponding trials in G0 (61, 121 and 181 trials), showed equivalence between mental and physical practice. At the end of training, the performance was much better with high rates of mental practice (G50/G75) compared to physical practice alone (G0), especially when the task was difficult. These findings confirm that mental rehearsal can be beneficial for motor learning and suggest that imagery might be used to supplement or partly replace physical practice in clinical rehabilitation.

Multivoxel pattern analysis for FMRI data: a review.

Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves.

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).

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. Hum Brain Mapp 37:1573‐1592, 2016.

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.

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.

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 actions 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.

Statistical Analysis of Single-Trial Granger Causality Spectra

Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based on t-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.

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

Visual Abstract 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.