Cortical oscillatory changes in human middle temporal cortex underlying smooth pursuit eye movements

Sex-differences in network level brain dynamics associated with pain sensitivity and pain interference

Neural dynamics can shape human experience, including pain. Pain has been linked to dynamic functional connectivity within and across brain regions of the dynamic pain connectome (consisting of the ascending nociceptive pathway (Asc), descending antinociceptive pathway (Desc), salience network (SN), and the default mode network (DMN)), and also shows sex differences. These linkages are based on fMRI‐derived slow hemodynamics. Here, we utilized the fine temporal resolution of magnetoencephalography (MEG) to measure resting state functional coupling (FCp) related to individual pain perception and pain interference in 50 healthy individuals (26 women, 24 men). We found that pain sensitivity and pain interference were linked to within‐ and cross‐network broadband FCp across the Asc and SN. We also identified sex differences in these relationships: (a) women exhibited greater within‐network static FCp, whereas men had greater dynamic FCp within the dynamic pain connectome; (b) relationship between pain sensitivity and pain interference with FCp in women was commonly found in theta, whereas in men, these relationships were predominantly in the beta and low gamma bands. These findings indicate that dynamic interactions of brain networks underlying pain involve fast brain communication in men but slower communication in women.

Local and large-scale beta oscillatory dysfunction in males with mild traumatic brain injury

Mild traumatic brain injury (mTBI) is impossible to detect with standard neuroradiological assessment such as structural magnetic resonance imaging (MRI). Injury does, however, disrupt the dynamic repertoire of neural activity indexed by neural oscillations. In particular, beta oscillations are reliable predictors of cognitive, perceptual, and motor system functioning, as well as correlating highly with underlying myelin architecture and brain connectivity-all factors particularly susceptible to dysregulation after mTBI. We measured local and large-scale neural circuit function by magnetoencephalography (MEG) with a data-driven model fit approach using the fitting oscillations and one-over f algorithm in a group of young adult men with mTBI and a matched healthy control group. We quantified band-limited regional power and functional connectivity between brain regions. We found reduced regional power and deficits in functional connectivity across brain areas, which pointed to the well-characterized thalamocortical dysconnectivity associated with mTBI. Furthermore, our results suggested that beta functional connectivity data reached the best mTBI classification performance compared with regional power and symptom severity [measured with Sport Concussion Assessment Tool 2 (SCAT2)]. The present study reveals the relevance of beta oscillations as a window into neurophysiological dysfunction in mTBI and also highlights the reliability of neural synchrony biomarkers in disorder classification.NEW & NOTEWORTHY Mild traumatic brain injury (mTBI) disrupts the dynamic repertoire of neural oscillations, but so far beta activity has not been studied. In mTBI, we found reductions in frontal beta and large-scale beta networks, indicative of thalamocortical dysconnectivity and disrupted information flow through cortico-basal ganglia-thalamic circuits. Relatively, connectivity more accurately classifies individual mTBI cases compared with regional power. We show the relevance of beta oscillations in mTBI and the reliability of these markers in classification.

Altered spontaneous brain activity pattern in patients with high myopia using amplitude of low-frequency fluctuation: a resting-state fMRI study

OBJECTIVE

Many previous reports have demonstrated significant neural anatomy changes in the brain of high myopic (HM) patients, whereas the spontaneous brain activity changes in the HM patients at rest are not well studied. Our objective was to use amplitude of low-frequency fluctuation (ALFF) method to investigate the changes in spontaneous brain activity in HM patients and their relationships with clinical features.

METHODS

A total of 38 patients with HM (17 males and 21 females) and 38 healthy controls (HCs) (17 males and 21 females) closely matched in age, sex, and education underwent resting-state functional magnetic resonance imaging scans. The ALFF method was used to assess local features of spontaneous brain activity. The relationship between the mean ALFF signal values in many brain regions and the clinical features in HM patients was calculated by correlation analysis.

RESULTS

Compared with HCs, the HM patients had significantly lower ALFF in the right inferior and middle temporal gyrus, left middle temporal gyrus, left inferior frontal gyrus/putamen, right inferior frontal gyrus/putamen/insula, right middle frontal gyrus, and right inferior parietal lobule and higher ALFF values in the bilateral midcingulate cortex, left postcentral gyrus, and left precuneus/inferior parietal lobule. However, no relationship was found between the mean ALFF signal values of the different areas and the clinical manifestations in HM.

CONCLUSION

The HM patients were affected with brain dysfunction in many regions, which may indicate the presence of neurobiological changes involving deficits in language understanding and attentional control in HM patients.

Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG

This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision – inferred by our behavioural DCM – correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia.

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The brain encodes states of the world probabilistically with means and precisions.

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Precision (inverse variance) may be encoded by the synaptic gain of pyramidal cells.

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We estimate subjects' sensory precision using a model of oculomotor pursuit and DCM.

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We estimate subjects' synaptic gain in V1 using DCM of MEG data during pursuit.

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Estimates of synaptic gain in V1 and sensory precision are significantly correlated.

Evidence that smooth pursuit velocity, not eye position, modulates alpha and beta oscillations in human middle temporal cortex

Suppression of 5-25 Hz oscillations have been observed in MT+ during pursuit eye movements, suggesting oscillations that play a role in oculomotor control and/or the integration of extraretinal signals during pursuit. The amplitude of these rhythms appears to covary with head-centered eye position, but an alternative is that they depend on a velocity signal that lags the movement of the eyes. To investigate, we explored how alpha and beta amplitude changes related to ongoing eye movement depended on pursuit at different eccentricities. The results revealed largely identical patterns of modulation in the alpha and beta amplitude, irrespective of the eccentricity at which the pursuit eye movement was performed. The signals we measured therefore do not depend on head-centered position. A second experiment was designed to investigate whether the alpha and beta oscillations depended on the direction of pursuit, as opposed to just speed. We found no evidence that alpha or beta oscillations depended on direction, but there was a significant effect of eye speed on the magnitude of the beta suppression. This suggests distinct functional roles for alpha and beta suppression in pursuit behavior.

Neural correlates of apparent motion perception of impoverished facial stimuli: a comparison of ERP and ERSP activity

Our brains readily decode human movements, as shown by neural responses to face and body motion. N170 event-related potentials (ERPs) are earlier and larger to mouth opening movements relative to closing in both line-drawn and natural faces, and gaze aversions relative to direct gaze in natural faces (Puce and Perrett, 2003; Puce et al., 2000). Here we extended this work by recording both ERP and oscillatory EEG activity (event-related spectral perturbations, ERSPs) to line-drawn faces depicting eye and mouth movements (Eyes: Direct vs Away; Mouth: Closed vs Open) and non-face motion controls. Neural activity was measured in 2 occipito-temporal clusters of 9 electrodes, one in each hemisphere. Mouth opening generated larger N170s than mouth closing, replicating earlier work. Eye motion elicited robust N170s that did not differ between gaze conditions. Control condition differences were seen, and generated the largest N170. ERSP difference plots across conditions in the occipito-temporal electrode clusters (Eyes: Direct vs Away; Mouth: Closed vs Open) showed statistically significant differences in beta and gamma bands for gaze direction changes and mouth opening at similar post-stimulus times and frequencies. In contrast, control stimuli showed activity in the gamma band with a completely different time profile and hemispheric distribution to facial stimuli. ERSP plots were generated in two 9 electrode clusters centered on central sites, C3 and C4. In the left cluster for all stimulus conditions, broadband beta suppression persisted from about 250ms post-motion onset. In the right cluster, beta suppression was seen for control conditions only. Statistically significant differences between conditions were confined between 4 and 15Hz, unlike the occipito-temporal sites where differences occurred at much higher frequencies (high beta/gamma). Our data indicate that N170 amplitude is sensitive to the amount of movement in the visual field, independent of stimulus type. In contrast, occipito-temporal beta and gamma activity differentiates between facial and non-facial motion. Context and stimulus configuration likely plays a role in shaping neural responses, based on comparisons of the current data to previously reported studies. Broadband suppression of central beta activity, and significant low frequency differences were likely stimulus driven and not contingent on behavioral responses.

Accounting for direction and speed of eye motion in planning visually guided manual tracking

Accurate motor planning in a dynamic environment is a critical skill for humans because we are often required to react quickly and adequately to the visual motion of objects. Moreover, we are often in motion ourselves, and this complicates motor planning. Indeed, the retinal and spatial motions of an object are different because of the retinal motion component induced by self-motion. Many studies have investigated motion perception during smooth pursuit and concluded that eye velocity is partially taken into account by the brain. Here we investigate whether the eye velocity during ongoing smooth pursuit is taken into account for the planning of visually guided manual tracking. We had 10 human participants manually track a target while in steady-state smooth pursuit toward another target such that the difference between the retinal and spatial target motion directions could be large, depending on both the direction and the speed of the eye. We used a measure of initial arm movement direction to quantify whether motor planning occurred in retinal coordinates (not accounting for eye motion) or was spatially correct (incorporating eye velocity). Results showed that the eye velocity was nearly fully taken into account by the neuronal areas involved in the visuomotor velocity transformation (between 75% and 102%). In particular, these neuronal pathways accounted for the nonlinear effects due to the relative velocity between the target and the eye. In conclusion, the brain network transforming visual motion into a motor plan for manual tracking adequately uses extraretinal signals about eye velocity.

Which "neural activity" do you mean? fMRI, MEG, oscillations and neurotransmitters

Over the last 20 years, BOLD-FMRI has proved itself to be a powerful and versatile tool for the study of the neural substrate underpinning many of our cognitive and perceptual functions. However, exactly how it is coupled to the underlying neurophysiology, and how this coupling varies across the brain, across tasks and across individuals is still unclear. The story is further complicated by the fact that within the same cortical region, multiple evoked and induced oscillatory effects may be modulated during task execution, supporting different cognitive roles, and any or all of these may have metabolic demands that then drive the BOLD response. In this paper I shall concentrate on one experimental approach to shedding light on this problem i.e. the execution of the same experimental tasks using MEG and fMRI in order to reveal which electrophysiological responses best match the BOLD response spatially, temporally and functionally. The results demonstrate a rich and complex story that does not fit with a simplistic view of BOLD reflecting "neural activity" and suggests that we could consider the coupling between BOLD and the various parameters of neural function as an ill-posed inverse problem. Finally, I describe recent work linking individual variability in both cortical oscillations and the BOLD-fMRI response to variability in endogenous GABA concentration.