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.

Pupillary response is associated with the reset and switching of functional brain networks during salience processing.

The interface between processing internal goals and salient events in the environment involves various top-down processes. Previous studies have identified multiple brain areas for salience processing, including the salience network (SN), dorsal attention network, and the locus coeruleus-norepinephrine (LC-NE) system. However, interactions among these systems in salience processing remain unclear. Here, we simultaneously recorded pupillometry, EEG, and fMRI during an auditory oddball paradigm. The analyses of EEG and fMRI data uncovered spatiotemporally organized target-associated neural correlates. By modeling the target-modulated effective connectivity, we found that the target-evoked pupillary response is associated with the network directional couplings from late to early subsystems in the trial, as well as the network switching initiated by the SN. These findings indicate that the SN might cooperate with the pupil-indexed LC-NE system in the reset and switching of cortical networks, and shed light on their implications in various cognitive processes and neurological diseases.

Neural Encoding of Active Multi-Sensing Enhances Perceptual Decision-Making via a Synergistic Cross-Modal Interaction.

Most perceptual decisions rely on the active acquisition of evidence from the environment involving stimulation from multiple senses. However, our understanding of the neural mechanisms underlying this process is limited. Crucially, it remains elusive how different sensory representations interact in the formation of perceptual decisions. To answer these questions, we used an active sensing paradigm coupled with neuroimaging, multivariate analysis, and computational modeling to probe how the human brain processes multisensory information to make perceptual judgments. Participants of both sexes actively sensed to discriminate two texture stimuli using visual (V) or haptic (H) information or the two sensory cues together (VH). Crucially, information acquisition was under the participants' control, who could choose where to sample information from and for how long on each trial. To understand the neural underpinnings of this process, we first characterized where and when active sensory experience (movement patterns) is encoded in human brain activity (EEG) in the three sensory conditions. Then, to offer a neurocomputational account of active multisensory decision formation, we used these neural representations of active sensing to inform a drift diffusion model of decision-making behavior. This revealed a multisensory enhancement of the neural representation of active sensing, which led to faster and more accurate multisensory decisions. We then dissected the interactions between the V, H, and VH representations using a novel information-theoretic methodology. Ultimately, we identified a synergistic neural interaction between the two unisensory (V, H) representations over contralateral somatosensory and motor locations that predicted multisensory (VH) decision-making performance.SIGNIFICANCE STATEMENT In real-world settings, perceptual decisions are made during active behaviors, such as crossing the road on a rainy night, and include information from different senses (e.g., car lights, slippery ground). Critically, it remains largely unknown how sensory evidence is combined and translated into perceptual decisions in such active scenarios. Here we address this knowledge gap. First, we show that the simultaneous exploration of information across senses (multi-sensing) enhances the neural encoding of active sensing movements. Second, the neural representation of active sensing modulates the evidence available for decision; and importantly, multi-sensing yields faster evidence accumulation. Finally, we identify a cross-modal interaction in the human brain that correlates with multisensory performance, constituting a putative neural mechanism for forging active multisensory perception.

Large-scale network dynamics in neural response to emotionally negative stimuli linked to serotonin 1A binding in major depressive disorder.

Serotonergic dysfunction is implicated in major depressive disorder (MDD), but the mechanisms of this relationship remain elusive. Serotonin 1A (5-HT1A) autoreceptors regulate brain-wide serotonin neuron firing and are positioned to assert large-scale effects on negative emotion. Here we investigated the relationship between raphe 5-HT1A binding and brain-wide network dynamics of negative emotion. 22 healthy-volunteers (HV) and 27 medication-free participants with MDD underwent positron emission tomography (PET) using [11C]CUMI-101 (CUMI) to quantify 5-HT1A binding in midbrain raphe nuclei and functional magnetic resonance imaging (fMRI) scanning during emotionally negative picture viewing. Causal connectivity across regions responsive to negative emotion was estimated in the fMRI data using a multivariate dynamical systems model. During negative picture viewing, MDD subjects demonstrated significant hippocampal inhibition of amygdala, basal-ganglia, thalamus, orbital frontal cortex, inferior frontal gyrus and dorsomedial prefrontal cortex (IFG, dmPFC). MDD-related connectivity was not associated with raphe 5-HT1A binding. However, greater hippocampal inhibition of amygdala, thalamus, IFG and dmPFC correlated with hippocampal 5-HT1A binding. Correlation between hippocampal 5-HT1A binding and the hippocampal inhibition network was specific to MDD but not HV. MDD and HV groups also differed with respect to the correlation between raphe and hippocampal 5-HT1A binding which was more pronounced in HV. These findings suggest that increased hippocampal network inhibition in MDD is linked to hippocampal serotonergic dysfunction which may in turn arise from disrupted linkage in raphe to hippocampus serotonergic circuitry.

Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task.

Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use online neurofeedback to shift an individual's arousal from the right side of the Yerkes-Dodson curve to the left toward a state of improved performance. Specifically, we use a brain-computer interface (BCI) that uses information in the EEG to generate a neurofeedback signal that dynamically adjusts an individual's arousal state when they are engaged in a boundary-avoidance task (BAT). The BAT is a demanding sensory-motor task paradigm that we implement as an aerial navigation task in virtual reality and which creates cognitive conditions that escalate arousal and quickly results in task failure (e.g., missing or crashing into the boundary). We demonstrate that task performance, measured as time and distance over which the subject can navigate before failure, is significantly increased when veridical neurofeedback is provided. Simultaneous measurements of pupil dilation and heart-rate variability show that the neurofeedback indeed reduces arousal. Our work demonstrates a BCI system that uses online neurofeedback to shift arousal state and increase task performance in accordance with the Yerkes-Dodson law.

Spatiospectral brain networks reflective of improvisational experience.

Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical chord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.

Scale-specific dynamics of high-amplitude bursts in EEG capture behaviorally meaningful variability.

Cascading high-amplitude bursts in neural activity, termed avalanches, are thought to provide insight into the complex spatially distributed interactions in neural systems. In human neuroimaging, for example, avalanches occurring during resting-state show scale-invariant dynamics, supporting the hypothesis that the brain operates near a critical point that enables long range spatial communication. In fact, it has been suggested that such scale-invariant dynamics, characterized by a power-law distribution in these avalanches, are universal in neural systems and emerge through a common mechanism. While the analysis of avalanches and subsequent criticality is increasingly seen as a framework for using complex systems theory to understand brain function, it is unclear how the framework would account for the omnipresent cognitive variability, whether across individuals or tasks. To address this, we analyzed avalanches in the EEG activity of healthy humans during rest as well as two distinct task conditions that varied in cognitive demands and produced behavioral measures unique to each individual. In both rest and task conditions we observed that avalanche dynamics demonstrate scale-invariant characteristics, but differ in their specific features, demonstrating individual variability. Using a new metric we call normalized engagement, which estimates the likelihood for a brain region to produce high-amplitude bursts, we also investigated regional features of avalanche dynamics. Normalized engagement showed not only the expected individual and task dependent variability, but also scale-specificity that correlated with individual behavior. Our results suggest that the study of avalanches in human brain activity provides a tool to assess cognitive variability. Our findings expand our understanding of avalanche features and are supportive of the emerging theoretical idea that the dynamics of an active human brain operate close to a critical-like region and not a singular critical-state.

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.

Correlation of neural activity with behavioral kinematics reveals distinct sensory encoding and evidence accumulation processes during active tactile sensing.

Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making.

Extracting multidimensional stimulus-response correlations using hybrid encoding-decoding of neural activity.

In neuroscience, stimulus-response relationships have traditionally been analyzed using either encoding or decoding models. Here we propose a hybrid approach that decomposes neural activity into multiple components, each representing a portion of the stimulus. The technique is implemented via canonical correlation analysis (CCA) by temporally filtering the stimulus (encoding) and spatially filtering the neural responses (decoding) such that the resulting components are maximally correlated. In contrast to existing methods, this approach recovers multiple correlated stimulus-response pairs, and thus affords a richer, multidimensional analysis of neural representations. We first validated the technique's ability to recover multiple stimulus-driven components using electroencephalographic (EEG) data simulated with a finite element model of the head. We then applied the technique to real EEG responses to auditory and audiovisual narratives experienced identically across subjects, as well as uniquely experienced video game play. During narratives, both auditory and visual stimulus-response correlations (SRC) were modulated by attention and tracked inter-subject correlations. During video game play, SRC varied with game difficulty and the presence of a dual task. Interestingly, the strongest component extracted for visual and auditory features of film clips had nearly identical spatial distributions, suggesting that the predominant encephalographic response to naturalistic stimuli is supramodal. The diversity of these findings demonstrates the utility of measuring multidimensional SRC via hybrid encoding-decoding.

A multimodal encoding model applied to imaging decision-related neural cascades in the human brain.

Perception and cognition in the brain are naturally characterized as spatiotemporal processes. Decision-making, for example, depends on coordinated patterns of neural activity cascading across the brain, running in time from stimulus to response and in space from primary sensory regions to the frontal lobe. Measuring this cascade is key to developing an understanding of brain function. Here we report on a novel methodology that employs multi-modal imaging for inferring this cascade in humans at unprecedented spatiotemporal resolution. Specifically, we develop an encoding model to link simultaneously measured electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) signals to infer high-resolution spatiotemporal brain dynamics during a perceptual decision. After demonstrating replication of results from the literature, we report previously unobserved sequential reactivation of a substantial fraction of the pre-response network whose magnitude correlates with a proxy for decision confidence. Our encoding model, which temporally tags BOLD activations using time localized EEG variability, identifies a coordinated and spatially distributed neural cascade that is associated with a perceptual decision. In general the methodology illuminates complex brain dynamics that would otherwise be unobservable using fMRI or EEG acquired separately.

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.

Perceptual Salience and Reward Both Influence Feedback-Related Neural Activity Arising from Choice.

For day-to-day decisions, multiple factors influence our choice between alternatives. Two dimensions of decision making that substantially affect choice are the objective perceptual properties of the stimulus (e.g., salience) and its subjective value. Here we measure EEGs in human subjects to relate their feedback-evoked EEG responses to estimates of prediction error given a neurally derived expected value for each trial. Unlike in traditional reinforcement learning paradigms, in our experiment the reward itself is not probabilistic; rather, it is a fixed value, which, when combined with the variable stimulus salience, yields uncertainty in the choice. We find that feedback-evoked event-related potentials (ERPs), specifically those classically termed feedback-related negativity, are modulated by both the reward level and stimulus salience. Using single-trial analysis of the EEG, we show stimulus-locked EEG components reflecting perceived stimulus salience can be combined with the level of reward to create an estimate of expected reward. This expected reward is used to form a prediction error that correlates with the trial-by-trial variability of the feedback ERPs for negative, but not positive, feedback. This suggests that the valence of prediction error is more important than the valence of the actual feedback, since only positive rewards were delivered in the experiment (no penalty or loss). Finally, we show that these subjectively defined prediction errors are informative of the riskiness of the subject's choice on the subsequent trial. In summary, our work shows that neural correlates of stimulus salience interact with value information to yield neural representations of subjective expected reward. Significance statement: How we make perceptual decisions depends on sensory evidence and the value of our options. These two factors often interact to yield subjective decisions; i.e., individuals integrate sensory evidence and value to form their own estimates of expected reward. Here, we use electroencephelography to identify trial-by-trial neural activity of perceived stimulus salience, showing that this activity can be combined with the value of choice options to form a representation of expected reward. Our results provide insight into the neural processing governing the interaction between salience and value and the formation of subjective expected reward and prediction error. This work is potentially important for identifying neural markers of abnormal sensory/value processing, as is seen in some cases of psychiatric illnesses.

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.

Human scalp potentials reflect a mixture of decision-related signals during perceptual choices.

Single-unit animal studies have consistently reported decision-related activity mirroring a process of temporal accumulation of sensory evidence to a fixed internal decision boundary. To date, our understanding of how response patterns seen in single-unit data manifest themselves at the macroscopic level of brain activity obtained from human neuroimaging data remains limited. Here, we use single-trial analysis of human electroencephalography data to show that population responses on the scalp can capture choice-predictive activity that builds up gradually over time with a rate proportional to the amount of sensory evidence, consistent with the properties of a drift-diffusion-like process as characterized by computational modeling. Interestingly, at time of choice, scalp potentials continue to appear parametrically modulated by the amount of sensory evidence rather than converging to a fixed decision boundary as predicted by our model. We show that trial-to-trial fluctuations in these response-locked signals exert independent leverage on behavior compared with the rate of evidence accumulation earlier in the trial. These results suggest that in addition to accumulator signals, population responses on the scalp reflect the influence of other decision-related signals that continue to covary with the amount of evidence at time of choice.

Knowing when not to swing: EEG evidence that enhanced perception-action coupling underlies baseball batter expertise.

Given a decision that requires less than half a second for evaluating the characteristics of the incoming pitch and generating a motor response, hitting a baseball potentially requires unique perception-action coupling to achieve high performance. We designed a rapid perceptual decision-making experiment modeled as a Go/No-Go task yet tailored to reflect a real scenario confronted by a baseball hitter. For groups of experts (Division I baseball players) and novices (non-players), we recorded electroencephalography (EEG) while they performed the task. We analyzed evoked EEG single-trial variability, contingent negative variation (CNV), and pre-stimulus alpha power with respect to the expert vs. novice groups. We found strong evidence for differences in inhibitory processes between the two groups, specifically differential activity in supplementary motor areas (SMA), indicative of enhanced inhibitory control in the expert (baseball player) group. We also found selective activity in the fusiform gyrus (FG) and orbital gyrus in the expert group, suggesting an enhanced perception-action coupling in baseball players that differentiates them from matched controls. In sum, our results show that EEG correlates of decision formation can be used to identify neural markers of high-performance athletes.

Pre-stimulus functional networks modulate task performance in time-pressured evidence gathering and decision-making.

Rapid perceptual decision-making is believed to depend upon efficient allocation of neural resources to the processing of transient stimuli within task-relevant contexts. Given decision-making under severe time pressure, it is reasonable to posit that the brain configures itself, prior to processing stimulus information, in a way that depends upon prior beliefs and/or anticipation. However, relatively little is known about such configuration processes, how they might be manifested in the human brain, or ultimately how they mediate task performance. Here we show that network configuration, defined via pre-stimulus functional connectivity measures estimated from functional magnetic resonance imaging (fMRI) data, is predictive of performance in a time-pressured Go/No-Go task. Specifically, using connectivity measures to summarize network properties, we show that pre-stimulus brain state can be used to discriminate behaviorally correct and incorrect trials, as well as behaviorally correct commission and omission trial categories. More broadly, our results show that pre-stimulus functional configurations of cortical and sub-cortical networks can be a major determiner of task performance.

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

EEG alpha-band activity is generally thought to represent an inhibitory state related to decreased attention and play a role in suppression of task-irrelevant stimulus processing, but a competing hypothesis suggests an active role in processing task-relevant information - one in which phase dynamics are involved. Here we used simultaneous EEG-fMRI and a whole-brain analysis to investigate the effects of prestimulus alpha activity on the event-related BOLD response during an auditory oddball task. We separately investigated the effects of the posterior alpha rhythm's power and phase on activity related to task-relevant stimulus processing and also investigated higher-level decision-related processing. We found stronger decision-related BOLD activity in areas late in the processing stream when subjects were in the high alpha power state prior to stimulus onset, but did not detect any effect in primary sensory regions. Our phase analysis revealed correlates in the bilateral thalamus, providing support for a thalamo-cortical loop in attentional modulations and suggesting that the cortical alpha rhythm acts as a cyclic modulator of task-related responses very early in the processing stream. Our results help to reconcile the competing inhibition and active-processing hypotheses for ongoing alpha oscillations and begin to tease apart the distinct roles and mechanisms underlying their power and phase.

Simultaneous EEG-fMRI reveals temporal evolution of coupling between supramodal cortical attention networks and the brainstem.

Cortical and subcortical networks have been identified that are commonly associated with attention and task engagement, along with theories regarding their functional interaction. However, a link between these systems has not yet been demonstrated in healthy humans, primarily because of data acquisition and analysis limitations. We recorded simultaneous EEG-fMRI while subjects performed auditory and visual oddball tasks and used these data to investigate the BOLD correlates of single-trial EEG variability at latencies spanning the trial. We focused on variability along task-relevant dimensions in the EEG for identical stimuli and then combined auditory and visual data at the subject level to spatially and temporally localize brain regions involved in endogenous attentional modulations. Specifically, we found that anterior cingulate cortex (ACC) correlates strongly with both early and late EEG components, whereas brainstem, right middle frontal gyrus (rMFG), and right orbitofrontal cortex (rOFC) correlate significantly only with late components. By orthogonalizing with respect to event-related activity, we found that variability in insula and temporoparietal junction is reflected in reaction time variability, rOFC and brainstem correlate with residual EEG variability, and ACC and rMFG are significantly correlated with both. To investigate interactions between these correlates of temporally specific EEG variability, we performed dynamic causal modeling (DCM) on the fMRI data. We found strong evidence for reciprocal effective connections between the brainstem and cortical regions. Our results support the adaptive gain theory of locus ceruleus-norepinephrine (LC-NE) function and the proposed functional relationship between the LC-NE system, right-hemisphere ventral attention network, and P300 EEG response.

Prestimulus alpha power predicts fidelity of sensory encoding in perceptual decision making.

Pre-stimulus α power has been shown to correlate with the behavioral accuracy of perceptual decisions. In most cases, these correlations have been observed by comparing α power for different behavioral outcomes (e.g. correct vs incorrect trials). In this paper we investigate such covariation within the context of behaviorally-latent fluctuations in task-relevant post-stimulus neural activity. Specially we consider variations of pre-stimulus α power with post-stimulus EEG components in a two alternative forced choice visual discrimination task. EEG components, discriminative of stimulus class, are identified using a linear multivariate classifier and only the variability of the components for correct trials (regardless of stimulus class, and for nominally identical stimuli) are correlated with the corresponding pre-stimulus α power. We find a significant relationship between the mean and variance of the pre-stimulus α power and the variation of the trial-to-trial magnitude of an early post-stimulus EEG component. This relationship is not seen for a later EEG component that is also discriminative of stimulus class and which has been previously linked to the quality of evidence driving the decision process. Our results suggest that early perceptual representations, rather than temporally later neural correlates of the perceptual decision, are modulated by pre-stimulus state.