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The neurophysiological response to visual stimulation in both humans and animals is characterized by an increase in high frequency amplitude peaking in the gamma range (40–100Hz) and a suppression of low frequency amplitude peaking in the alpha range (10–16Hz). Due to the large number of studies linking amplitude and peak frequency to perception and neurological disorders, there is great interest in understanding the basis of inter-subject variability in gamma and alpha responses. To address this, we measured gamma and alpha amplitude and peak frequency of response to visual stimulation in 42 healthy humans. Using FMRI to delineate active cortical tissue in the same subjects, we correlated these neurophysiological metrics with two structural metrics: distance from active cortex to electrode, and dipole cancellation over active cortex. We find that distance strongly predicted inter-subject gamma amplitude, but had little effect on alpha amplitude, while cancellation had little effect on gamma or alpha amplitude. Neither alpha peak frequency nor gamma peak frequency correlated with our structural metrics. These results suggest that inter-subject variability in gamma amplitude may reflect gross morphology rather than neurophysiological variability, and should be interpreted with caution, while peak frequency may serve as a more sensitive metric of differences in neuronal activity across subjects.

Recent work in early visual cortex of humans has shown that the BOLD signal exhibits contrast dependent orientation tuning, with an inverse oblique effect (oblique > cardinal) at high contrast and a horizontal effect (vertical > horizontal) at low contrast. This finding is at odds with decades of neurophysiological research demonstrating contrast invariant orientation tuning in primate visual cortex, yet the source of this discrepancy is unclear. We hypothesized that contrast dependent BOLD orientation tuning may arise due to contrast dependent influences of feedforward (FF) and feedback (FB) synaptic activity, indexed through gamma and alpha rhythms, respectively. To quantify this, we acquired EEG and BOLD in healthy humans to generate and compare orientation tuning curves across all neural frequency bands with BOLD. As expected, BOLD orientation selectivity in V1 was contrast dependent, preferring oblique orientations at high contrast and vertical at low contrast. On the other hand, EEG orientation tuning was contrast invariant, though frequency-specific, with an inverse-oblique effect in the gamma band (FF) and a horizontal effect in the alpha band (FB). Therefore, high-contrast BOLD orientation tuning closely matched FF activity, while at low contrast, BOLD best resembled FB orientation tuning. These results suggest that contrast dependent BOLD orientation tuning arises due to the reduced contribution of FF input to overall neurophysiological activity at low contrast, shifting BOLD orientation tuning towards the orientation preferences of FB at low contrast.

Monetary rewards and punishments have been shown to respectively enhance retention of motor memories and short-term motor performance, but their underlying neural bases in the context of motor control tasks remain unclear. Using electroencephalography (EEG), the present study tested the hypothesis that monetary rewards and punishments are respectively reflected in post-feedback beta-band (20-30 Hz) and theta-band (3-8 Hz) oscillatory power. While participants performed upper limb reaching movements toward visual targets using their right hand, the delivery of monetary rewards and punishments was manipulated as well as their probability (i.e., by changing target size). Compared to unrewarded and unpunished trials, monetary rewards and the successful avoidance of punishments both entailed greater beta-band power at left central electrodes overlaying contralateral motor areas. In contrast, monetary punishments and reward omissions both entailed increased theta-band power at fronto-central scalp sites. Additional analyses revealed that beta-band power was further increased when rewards were lowly probable. In light of previous work demonstrating similar beta-band modulations in basal ganglia during reward processing, the present results may reflect functional communication of reward-related information between the basal ganglia and motor cortical regions. In turn, the increase in fronto-central theta-band power after monetary punishments may reflect an emphasized cognitive need for behavioral adjustments. Globally, the present work identifies possible neural substrates for the growing behavioral evidence showing beneficial effects of monetary feedback on motor learning and performance.

International audience; The human brain can be described as a network of specialized and spatially distributed regions. The activity of individual regions can be estimated using electroencephalography and the structure of the network can be measured using diffusion magnetic resonance imaging. However, the communication between the different cortical regions occurring through the white matter, coined information flow, cannot be observed by either modalities independently. Here, we present a new method to infer information flow in the white matter of the brain from joint diffusion MRI and EEG measurements. This is made possible by the millisecond resolution of EEG which makes the transfer of information from one region to another observable. A subject specific Bayesian network is built which captures the possible interactions between brain regions at different times. This network encodes the connections between brain regions detected using diffusion MRI tractography derived white matter bundles and their associated delays. By injecting the EEG measurements as evidence into this model, we are able to estimate the directed dynamical functional connectivity whose delays are supported by the diffusion MRI derived structural connectivity. We present our results in the form of information flow diagrams that trace transient communication between cortical regions over a functional data window. The performance of our algorithm under different noise levels is assessed using receiver operating characteristic curves on simulated data. In addition, using the well-characterized visual motor network as grounds to test our model, we present the information flow obtained during a reaching task following left or right visual stimuli. These promising results present the transfer of information from the eyes to the primary motor cortex. The information flow obtained using our technique can also be projected back to the anatomy and animated to produce videos of the information path through the white matter, opening a new window into multi-modal dynamic brain connectivity.

Positron emission tomography using [18F]-fluorodeoxyglucose (PET-FDG) is the primary imaging modality used to measure glucose metabolism in the brain (CMRGlu). CMRGlu has been used as a biomarker of brain aging and neurodegenerative diseases, but the complexity and invasive nature of PET often limits its use in research. There is therefore great interest in developing non-invasive metrics for estimating brain CMRGlu. We therefore investigated resting state fMRI metrics such as regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF) and regional global connectivity (Closeness) with multiple analytical approaches to determine their relationship to CMRGlu. We investigated this relation in two distinct cognitively healthy populations separated by age (27 young adults and 35 older adults). Overall, we found that both regionally and across participants, ReHo strongly correlated with CMRGlu in healthy young and older adults. Moreover, ReHo demonstrated the same age-related differences as CMRGlu throughout all cortical regions, particularly in the default network and frontal areas.

Available online 9 May 2014; International audience; Diffusion MRI tractography is often used to estimate structural connections between brain areas and there is a fast-growing interest in quantifying these connections based on their position, shape, size and length. However, a portion of the connections reconstructed with tractography is biased by their position, shape, size and length. Thus, connections reconstructed are not equally distributed in all white matter bundles. Quantitative measures of connectivity based on the streamline distribution in the brain such as streamline count (density), average length and spatial extent (volume) are biased by erroneous streamlines produced by tractography algorithms. In this paper, solutions are proposed to reduce biases in the streamline distribution. First, we propose to optimize tractography parameters in terms of connectivity. Then, we propose to relax the tractography stopping criterion with a novel probabilistic stopping criterion and a particle filtering method, both based on tissue partial volume estimation maps calculated from a T1-weighted image. We show that optimizing tractography parameters, stopping and seeding strategies can reduce the biases in position, shape, size and length of the streamline distribution. These tractography biases are quantitatively reported using in-vivo and synthetic data. This is a critical step towards producing tractography results for quantitative structural connectivity analysis.

Transcranial magnetic stimulation (TMS) can non-invasively modify cortical neural activity by means of a time-varying magnetic field. For example, in cognitive neuroscience, it is applied to create reversible “virtual lesions” in healthy humans (usually assessed as diminished performance in a behavioral task), thereby helping to establish causal structure–function relationships. Despite its widespread use, it is still rather unclear how TMS acts on existing, task-related neural activity, potentially resulting in a measurable effect on the behavioral level. Here, we deliver TMS to early visual areas while recording EEG in order to directly characterize the interaction between TMS-evoked (TEPs) and visual-evoked potentials (VEPs). Simultaneously, the subjectslsquo; performance is assessed in a visual forced-choice task. This allows us to compare the TMS effects on the VEPs across different levels of behavioral impairment. By systematically varying the stimulation intensity, we demonstrate tha t TMS strongly enhances the overall visual stimulus-related activity (rather than disrupting it). This enhancement effect saturates when behavior is impaired. This might indicate that the neural coding of the visual stimulus is robust to noise within a certain dynamic range (as indexed by the enhancement). Strong disturbances might saturate this range, causing behavioral impairment. Variation of the timing between the visual stimulus and the magnetic pulse reveals a “constructive interference” between the TEPs and VEPs: The better the overlap between both evoked potentials, the stronger the interaction effect when TMS and visual stimulation are combined. Importantly, however, this effect is uncorrelated with the strength of behavioral impairment.

Despite the widespread use of EEGs to measure the large-scale dynamics of the human brain, little is known on how the dynamics of EEGs relates to that of the underlying spike rates of cortical neurons. However, progress was made by recent neurophysiological experiments reporting that EEG delta-band phase and gamma-band amplitude reliably predict some complementary aspects of the time course of spikes of visual cortical neurons. To elucidate the mechanisms behind these findings, here we hypothesize that the EEG delta phase reflects shifts of local cortical excitability arising from slow fluctuations in the network input due to entrainment to sensory stimuli or to fluctuations in ongoing activity, and that the resulting local excitability fluctuations modulate both the spike rate and the engagement of excitatory–inhibitory loops producing gamma-band oscillations. We quantitatively tested these hypotheses by simulating a recurrent network of excitatory and inhibitory neurons stimulated with dynamic inputs prese nting temporal regularities similar to that of thalamic responses during naturalistic visual stimulation and during spontaneous activity. The network model reproduced in detail the experimental relationships between spike rate and EEGs, and suggested that the complementariness of the prediction of spike rates obtained from EEG delta phase or gamma amplitude arises from nonlinearities in the engagement of excitatory–inhibitory loops and from temporal modulations in the amplitude of the network input, which respectively limit the predictability of spike rates from gamma amplitude or delta phase alone. The model suggested also ways to improve and extend current algorithms for online prediction of spike rates from EEGs.