Prestimulus EEG alpha oscillations modulate task-related fMRI BOLD responses to auditory stimuli

The timing of transcranial magnetic stimulation relative to the phase of prefrontal alpha EEG modulates downstream target engagement

BACKGROUND

The communication through coherence model posits that brain rhythms are synchronized across different frequency bands and that effective connectivity strength between interacting regions depends on their phase relation. Evidence to support the model comes mostly from electrophysiological recordings in animals while evidence from human data is limited.

METHODS

Here, an fMRI-EEG-TMS (fET) instrument capable of acquiring simultaneous fMRI and EEG during noninvasive single pulse TMS applied to dorsolateral prefrontal cortex (DLPFC) was used to test whether prefrontal EEG alpha phase moderates TMS-evoked top-down influences on subgenual, rostral and dorsal anterior cingulate cortex (ACC). Six runs (276 total trials) were acquired in each participant. Phase at each TMS pulse was determined post-hoc using single-trial sorting. Results were examined in two independent datasets: healthy volunteers (HV) (n = 11) and patients with major depressive disorder (MDD) (n = 17) collected as part of an ongoing clinical trial.

RESULTS

In both groups, TMS-evoked functional connectivity between DLPFC and subgenual ACC (sgACC) depended on the EEG alpha phase. TMS-evoked DLPFC to sgACC fMRI-derived effective connectivity (EC) was modulated by EEG alpha phase in healthy volunteers, but not in the MDD patients. Top-down EC was inhibitory for TMS pulses during the upward slope of the alpha wave relative to TMS timed to the downward slope of the alpha wave. Prefrontal EEG alpha phase dependent effects on TMS-evoked fMRI BOLD activation of the rostral anterior cingulate cortex were detected in the MDD patient group, but not in the healthy volunteer group.

DISCUSSION

Results demonstrate that TMS-evoked top-down influences vary as a function of the prefrontal alpha rhythm, and suggest potential clinical applications whereby TMS is synchronized to the brain's internal rhythms in order to more efficiently engage deep therapeutic targets.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.

Sensing and seeing associated with overlapping occipitoparietal activation in simultaneous EEG-fMRI

The presence of a change in a visual scene can influence brain activity and behavior, even in the absence of full conscious report. It may be possible for us to sense that such a change has occurred, even if we cannot specify exactly where or what it was. Despite existing evidence from electroencephalogram (EEG) and eye-tracking data, it is still unclear how this partial level of awareness relates to functional magnetic resonance imaging (fMRI) blood oxygen level dependent (BOLD) activation. Using EEG, fMRI, and a change blindness paradigm, we found multi-modal evidence to suggest that sensing a change is distinguishable from being blind to it. Specifically, trials during which participants could detect the presence of a colour change but not identify the location of the change (sense trials), were compared to those where participants could both detect and localise the change (localise or see trials), as well as change blind trials. In EEG, late parietal positivity and N2 amplitudes were larger for localised changes only, when compared to change blindness. However, ERP-informed fMRI analysis found no voxels with activation that significantly co-varied with fluctuations in single-trial late positivity amplitudes. In fMRI, a range of visual (BA17,18), parietal (BA7,40), and mid-brain (anterior cingulate, BA24) areas showed increased fMRI BOLD activation when a change was sensed, compared to change blindness. These visual and parietal areas are commonly implicated as the storage sites of visual working memory, and we therefore argue that sensing may not be explained by a lack of stored representation of the visual display. Both seeing and sensing a change were associated with an overlapping occipitoparietal network of activation when compared to blind trials, suggesting that the quality of the visual representation, rather than the lack of one, may result in partial awareness during the change blindness paradigm.

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI

Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.

Oscillations in the auditory system and their possible role

GOURÉVITCH, B., C. Martin, O. Postal, J.J. Eggermont. Oscillations in the auditory system, their possible role. NEUROSCI BIOBEHAV REV XXX XXX-XXX, 2020. - Neural oscillations are thought to have various roles in brain processing such as, attention modulation, neuronal communication, motor coordination, memory consolidation, decision-making, or feature binding. The role of oscillations in the auditory system is less clear, especially due to the large discrepancy between human and animal studies. Here we describe many methodological issues that confound the results of oscillation studies in the auditory field. Moreover, we discuss the relationship between neural entrainment and oscillations that remains unclear. Finally, we aim to identify which kind of oscillations could be specific or salient to the auditory areas and their processing. We suggest that the role of oscillations might dramatically differ between the primary auditory cortex and the more associative auditory areas. Despite the moderate presence of intrinsic low frequency oscillations in the primary auditory cortex, rhythmic components in the input seem crucial for auditory processing. This allows the phase entrainment between the oscillatory phase and rhythmic input, which is an integral part of stimulus selection within the auditory system.

Individual Alpha Frequency Predicts Perceived Visuotactile Simultaneity

Temporal encoding is a key feature in multisensory processing that leads to the integration versus segregation of perceived events over time. Whether or not two events presented at different offsets are perceived as simultaneous varies widely across the general population. Such tolerance to temporal delays is known as the temporal binding window (TBW). It has been recently suggested that individual oscillatory alpha frequency (IAF) peak may represent the electrophysiological correlate of TBW, with IAF also showing a wide variability in the general population (8–12 Hz). In our work, we directly tested this hypothesis by measuring each individual's TBW during a visuotactile simultaneity judgment task while concurrently recording their electrophysiological activity. We found that the individual's TBW significantly correlated with their left parietal IAF, such that faster IAF accounted for narrower TBW. Furthermore, we found that higher prestimulus alpha power measured over the same left parietal regions accounted for more veridical responses of non-simultaneity, which may be explained either by accuracy in perceptual simultaneity or, alternatively, in line with recent proposals by a shift in response bias from more conservative (high alpha power) to more liberal (low alpha power). We propose that the length of an alpha cycle constrains the temporal resolution within which perceptual processes take place.

Trial-by-trial surprise-decoding model for visual and auditory binary oddball tasks

Having to survive in a continuously changing environment has driven the human brain to actively predict the future state of its surroundings. Oddball tasks are specific types of experiments in which this nature of the human brain is studied. Detailed mathematical models have been constructed to explain the brain's perception in these tasks. These models consider a subject as an ideal observer who abstracts a hypothesis from the previous stimuli, and estimates its hyper-parameters - in order to make the next prediction. The corresponding prediction error is assumed to manifest the subjective surprise of the brain. While the approach of earlier works to this problem has been to suggest an encoding model, we investigated the reverse model: if the stimuli's surprise is assumed as the cause of the observer's surprise, it must be possible to decode the surprise of each stimulus, for every single subject, given only their neural responses, i.e. to tell how unexpected a specific stimulus has been for them. Employing machine learning tools, we developed a surprise decoding model for binary oddball tasks. We constructed our model using the ideal observer proposed by Meyniel et al. in 2016, and applied it to three datasets, one with visual, one with auditory, and one with both visual and auditory stimuli. We demonstrated that our decoding model performs very well for both of the sensory modalities with or without the presence of the subject's motor response.

Unsuccessful reduction of high-frequency alpha activity during cognitive activation in schizophrenia

AIMS

Electroencephalogram (EEG) alpha activity during resting state reflects the 'readiness' of an individual to respond to the environment; this includes the performance of cognitive processes. Alpha activity is reported to be attenuated in schizophrenia (SCZ). Understanding the interaction between alpha activity during rest and when cognitively engaged may provide insights into the neural circuitry, which is dysfunctional in SCZ. This study investigated the changes of alpha activity between resting state and cognitive engagement in SCZ patients.

METHODS

Thirty-four SCZ patients and 29 healthy controls (HC) were recruited. EEG was performed in the resting state and during an auditory P300 task. All experimental procedures followed the relevant institutional guidelines and regulations.

RESULTS

In SCZ, high-frequency alpha activity was reduced in the resting state. High-frequency alpha source density was decreased in both the resting-state and a P300 task condition in patients, compared to HC. HC, but not SCZ patients, showed a reduction in high-frequency alpha source density during the P300 task compared to the resting state. The negative correlation between high-frequency alpha source density in the resting state and positive symptoms was significant.

CONCLUSIONS

High-frequency alpha activity in SCZ patients and its unsuccessful reduction during cognitive processing may be biological markers of SCZ.

Pre-target alpha power predicts the speed of cued target discrimination

The human visual system selects information from dense and complex streams of spatiotemporal input. This selection process is aided by prior knowledge of the features, location, and temporal proximity of an upcoming stimulus. In the laboratory, this knowledge is often conveyed by cues, preceding a task-relevant target stimulus. Response speed in cued selection tasks varies within and across participants and is often thought to index efficient selection of a cued feature, location, or moment in time. The present study used a reverse correlation approach to identify neural predictors of efficient target discrimination: Participants identified the orientation of a sinusoidal grating, which was presented in one hemifield following the presentation of bilateral visual cues that carried temporal but not spatial information about the target. Across different analytic approaches, faster target responses were predicted by larger alpha power preceding the target. These results suggest that heightened pre-target alpha power during a cue period may index a state that is beneficial for subsequent target processing. Our findings are broadly consistent with models that emphasize capacity sharing across time, as well as models that link alpha oscillations to temporal predictions regarding upcoming events.

fMRIPrep: a robust preprocessing pipeline for functional MRI

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.

Is conscious perception a series of discrete temporal frames?

This paper reviews proposals that conscious perception consists, in whole or part, of successive discrete temporal frames on the sub-second time scale, each frame containing information registered as simultaneous or static. Although the idea of discrete frames in conscious perception cannot be regarded as falsified, there are many problems. Evidence does not consistently support any proposed duration or range of durations for frames. EEG waveforms provide evidence of periodicity in brain activity, but not necessarily in conscious perception. Temporal properties of perceptual processes are flexible in response to competing processing demands, which is hard to reconcile with the relative inflexibility of regular frames. There are also problems concerning the definition of frames, the need for informational connections between frames, the means by which boundaries between frames are established, and the apparent requirement for a storage buffer for information awaiting entry to the next frame.

The spatiotemporal pattern of pure tone processing: A single-trial EEG-fMRI study

Although considerable research has been published on pure tone processing, its spatiotemporal pattern is not well understood. Specifically, the link between neural activity in the auditory pathway measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) markers of pure tone processing in the P1, N1, P2, and N4 components is not well established. In this study, we used single-trial EEG-fMRI as a multi-modal fusion approach to integrate concurrently acquired EEG and fMRI data, in order to understand the spatial and temporal aspects of the pure tone processing pathway. Data were recorded from 33 subjects who were presented with stochastically alternating pure tone sequences with two different frequencies: 200 and 6400 Hz. Brain network correlated with trial-to-trial variability of the task-discriminating EEG amplitude was identified. We found that neural responses responding to pure tone perception are spatially along the auditory pathway and temporally divided into three stages: (1) the early stage (P1), wherein activation occurs in the midbrain, which constitutes a part of the low level auditory pathway; (2) the middle stage (N1, P2), wherein correlates were found in areas associated with the posterodorsal auditory pathway, including the primary auditory cortex and the motor cortex; (3) the late stage (N4), wherein correlation was found in the motor cortex. This indicates that trial-by-trial variation in neural activity in the P1, N1, P2, and N4 components reflects the sequential engagement of low- and high-level parts of the auditory pathway for pure tone processing. Our results demonstrate that during simple pure tone listening tasks, regions associated with the auditory pathway transiently correlate with trial-to-trial variability of the EEG amplitude, and they do so on a millisecond timescale with a distinct temporal ordering.

Network Configurations in the Human Brain Reflect Choice Bias during Rapid Face Processing

Network interactions are likely to be instrumental in processes underlying rapid perception and cognition. Specifically, high-level and perceptual regions must interact to balance pre-existing models of the environment with new incoming stimuli. Simultaneous electroencephalography (EEG) and fMRI (EEG/fMRI) enables temporal characterization of brain-network interactions combined with improved anatomical localization of regional activity. In this paper, we use simultaneous EEG/fMRI and multivariate dynamical systems (MDS) analysis to characterize network relationships between constitute brain areas that reflect a subject's choice for a face versus nonface categorization task. Our simultaneous EEG and fMRI analysis on 21 human subjects (12 males, 9 females) identifies early perceptual and late frontal subsystems that are selective to the categorical choice of faces versus nonfaces. We analyze the interactions between these subsystems using an MDS in the space of the BOLD signal. Our main findings show that differences between face-choice and house-choice networks are seen in the network interactions between the early and late subsystems, and that the magnitude of the difference in network interaction positively correlates with the behavioral false-positive rate of face choices. We interpret this to reflect the role of saliency and expectations likely encoded in frontal "late" regions on perceptual processes occurring in "early" perceptual regions.SIGNIFICANCE STATEMENT Our choices are affected by our biases. In visual perception and cognition such biases can be commonplace and quite curious-e.g., we see a human face when staring up at a cloud formation or down at a piece of toast at the breakfast table. Here we use multimodal neuroimaging and dynamical systems analysis to measure whole-brain spatiotemporal dynamics while subjects make decisions regarding the type of object they see in rapidly flashed images. We find that the degree of interaction in these networks accounts for a substantial fraction of our bias to see faces. In general, our findings illustrate how the properties of spatiotemporal networks yield insight into the mechanisms of how we form decisions.

Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function

BACKGROUND

Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years.

OBJECTIVE

Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli.

METHODS

In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors.

RESULTS

The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results.

CONCLUSIONS

The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

Threat of shock increases excitability and connectivity of the intraparietal sulcus

Anxiety disorders affect approximately 1 in 5 (18%) Americans within a given 1 year period, placing a substantial burden on the national health care system. Therefore, there is a critical need to understand the neural mechanisms mediating anxiety symptoms. We used unbiased, multimodal, data-driven, whole-brain measures of neural activity (magnetoencephalography) and connectivity (fMRI) to identify the regions of the brain that contribute most prominently to sustained anxiety. We report that a single brain region, the intraparietal sulcus (IPS), shows both elevated neural activity and global brain connectivity during threat. The IPS plays a key role in attention orienting and may contribute to the hypervigilance that is a common symptom of pathological anxiety. Hyperactivation of this region during elevated state anxiety may account for the paradoxical facilitation of performance on tasks that require an external focus of attention, and impairment of performance on tasks that require an internal focus of attention.

DOI:http://dx.doi.org/10.7554/eLife.23608.001

Anxiety disorders affect around one in five Americans, and in many cases people experience anxiety so intensely that they have difficulties performing day-to-day activities. To help these people, it is important to understand how anxiety works. Current research suggests that anxiety disorders are caused when the connections in the brain that control our response to threat are either excessively or inappropriately activated. However, it was not clear what causes the anxiety to last for long periods. To better understand this phenomenon, Balderston et al. studied the brains of over 30 volunteers using two types of measurements called magnetoencephalography and fMRI.

In the each experiment, participants experienced periods of threat, where they could receive unpredictable electric shocks. In the first experiment, Balderston et al. measured the brain activity by recording the magnetic fields generated in the brain. In the second experiment, they used fMRI to record changes in the blood flow throughout the brain to measure how the different regions in the brain communicate.

The recordings identified a single part of the brain that increased its activity and changed its communication pattern with the other regions in the brain, when people are anxious. This region in a part of the brain called parietal lobe, is also important for processing attention, which suggests that anxiety might make people also more aware of their surroundings. However, this extra awareness might also make it more difficult for people to concentrate.

Future studies may be able to stimulate this area of the brain through the scalp to potentially reduce anxiety, as the affected area is close to the skull.

DOI:http://dx.doi.org/10.7554/eLife.23608.002

Fusing multiple neuroimaging modalities to assess group differences in perception-action coupling

In the last few decades, non-invasive neuroimaging has revealed macro-scale brain dynamics that underlie perception, cognition and action. Advances in non-invasive neuroimaging target two capabilities; 1) increased spatial and temporal resolution of measured neural activity, and 2) innovative methodologies to extract brain-behavior relationships from evolving neuroimaging technology. We target the second. Our novel methodology integrated three neuroimaging methodologies and elucidated expertise-dependent differences in functional (fused EEG-fMRI) and structural (dMRI) brain networks for a perception-action coupling task. A set of baseball players and controls performed a Go/No-Go task designed to mimic the situation of hitting a baseball. In the functional analysis, our novel fusion methodology identifies 50ms windows with predictive EEG neural correlates of expertise and fuses these temporal windows with fMRI activity in a whole-brain 2mm voxel analysis, revealing time-localized correlations of expertise at a spatial scale of millimeters. The spatiotemporal cascade of brain activity reflecting expertise differences begins as early as 200ms after the pitch starts and lasting up to 700ms afterwards. Network differences are spatially localized to include motor and visual processing areas, providing evidence for differences in perception-action coupling between the groups. Furthermore, an analysis of structural connectivity revealed that the players have significantly more connections between cerebellar and left frontal/motor regions, and many of the functional activation differences between the groups are located within structurally defined network modules that differentiate expertise. In short, our novel method illustrates how multimodal neuroimaging can provide specific macro-scale insights into the functional and structural correlates of expertise development.

Brain dynamics of post-task resting state are influenced by expertise: Insights from baseball players

Post‐task resting state dynamics can be viewed as a task‐driven state where behavioral performance is improved through endogenous, non‐explicit learning. Tasks that have intrinsic value for individuals are hypothesized to produce post‐task resting state dynamics that promote learning. We measured simultaneous fMRI/EEG and DTI in Division‐1 collegiate baseball players and compared to a group of controls, examining differences in both functional and structural connectivity. Participants performed a surrogate baseball pitch Go/No‐Go task before a resting state scan, and we compared post‐task resting state connectivity using a seed‐based analysis from the supplementary motor area (SMA), an area whose activity discriminated players and controls in our previous results using this task. Although both groups were equally trained on the task, the experts showed differential activity in their post‐task resting state consistent with motor learning. Specifically, we found (1) differences in bilateral SMA–L Insula functional connectivity between experts and controls that may reflect group differences in motor learning, (2) differences in BOLD‐alpha oscillation correlations between groups suggests variability in modulatory attention in the post‐task state, and (3) group differences between BOLD‐beta oscillations that may indicate cognitive processing of motor inhibition. Structural connectivity analysis identified group differences in portions of the functionally derived network, suggesting that functional differences may also partially arise from variability in the underlying white matter pathways. Generally, we find that brain dynamics in the post‐task resting state differ as a function of subject expertise and potentially result from differences in both functional and structural connectivity. Hum Brain Mapp 37:4454–4471, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.