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.

Dynamic coupling between fMRI local connectivity and interictal EEG in focal epilepsy: A wavelet analysis approach.

Simultaneous scalp EEG-fMRI recording is a noninvasive neuroimaging technique for combining electrophysiological and hemodynamic aspects of brain function. Despite the time-varying nature of both measurements, their relationship is usually considered as time-invariant. The aim of this study was to detect direct associations between scalp-recorded EEG and regional changes of hemodynamic brain connectivity in focal epilepsy through a time-frequency paradigm. To do so, we developed a voxel-wise framework that analyses wavelet coherence between dynamic regional phase synchrony (DRePS, calculated from fMRI) and band amplitude fluctuation (BAF) of a target EEG electrode with dominant interictal epileptiform discharges (IEDs). As a proof of concept, we applied this framework to seven patients with focal epilepsy. The analysis produced patient-specific spatial maps of DRePS-BAF coupling, which highlight regions with a strong link between EEG power and local fMRI connectivity. Although we observed DRePS-BAF coupling proximate to the suspected seizure onset zone in some patients, our results suggest that DRePS-BAF is more likely to identify wider 'epileptic networks'. We also compared DRePS-BAF with standard EEG-fMRI analysis based on general linear modelling (GLM). There was, in general, little overlap between the DRePS-BAF maps and GLM maps. However, in some subjects the spatial clusters revealed by these two analyses appeared to be adjacent, particularly in medial posterior cortices. Our findings suggest that (1) there is a strong time-varying relationship between local fMRI connectivity and interictal EEG power in focal epilepsy, and (2) that DRePS-BAF reflect different aspects of epileptic network activity than standard EEG-fMRI analysis. These two techniques, therefore, appear to be complementary. Hum Brain Mapp 38:5356-5374, 2017. © 2017 Wiley Periodicals, Inc.

The dynamics of functional connectivity in neocortical focal epilepsy.

Focal epilepsy is characterised by paroxysmal events, reflecting changes in underlying local brain networks. To capture brain network activity at the maximal temporal resolution of the acquired functional magnetic resonance imaging (fMRI) data, we have previously developed a novel analysis framework called Dynamic Regional Phase Synchrony (DRePS). DRePS measures instantaneous mean phase coherence within neighbourhoods of brain voxels. We use it here to examine how the dynamics of the functional connections of regional brain networks are altered in neocortical focal epilepsy. Using task-free fMRI data from 21 subjects with focal epilepsy and 21 healthy controls, we calculated the power spectral density of DRePS, which is a measure of signal variability in local connectivity estimates. Whole-brain averaged power spectral density of DRePS, or signal variability of local connectivity, was significantly higher in epilepsy subjects compared to healthy controls. Maximal increase in DRePS spectral power was seen in bilateral inferior frontal cortices, ipsilateral mid-cingulate gyrus, superior temporal gyrus, caudate head, and contralateral cerebellum. Our results provide further evidence of common brain abnormalities across people with focal epilepsy. We postulate that dynamic changes in specific cortical brain areas may help maintain brain function in the presence of pathological epileptiform network activity in neocortical focal epilepsy.

Spatiotemporal mapping of epileptic spikes using simultaneous EEG-functional MRI.

Epileptic spikes occur on the sub-second timescale and are known to involve not only epileptic foci but also large-scale distributed brain networks. There is likely to be a sequence of neural activity in multiple brain regions that occurs within the duration of a single spike, but standard electroencephalography-functional magnetic resonance imaging analyses, which use only the timing of the spikes to model the functional magnetic resonance imaging data, cannot determine the sequence of these activations. Our aim in this study is to temporally resolve these spatial activations to observe the spatiotemporal dynamics of the spike-related neural activity at a sub-second timescale. We studied eight focal epilepsy patients (age 11-42 years, six female) and used amplitude features of the electroencephalogram specific to different spike components (early and late peaks and troughs) to encode temporal information into our functional magnetic resonance imaging models. This enables us to associate each activation with a specific model of each of the spike components to infer the temporal order of these spike-related spatial activations. In seven of eight patients the distributed networks were associated with the late spike component. The focal activations were more variably coupled with time epochs, but tended to precede the distributed network effects. We also found that incorporating electroencephalogram features into the models increased sensitivity and in six patients revealed additional regions unseen in the standard analysis result. This included strong bilateral thalamus activation in two patients. We demonstrate the clinical utility of this approach in a patient who recently underwent a successful surgical resection of the region where we saw enhanced activation using electroencephalogram amplitude information specific to the early spike component. This focal cluster of activation was larger and more precisely tracked the anatomy compared to what was seen using the standard timing-based analysis. Our novel electroencephalography-functional magnetic resonance imaging data fusion approach, which utilizes information based on the single spike variability across all electroencephalogram channels, has the potential to help us better understand epileptic networks and aid in the interpretation of functional magnetic resonance imaging activation maps during treatment planning.

Spontaneous brain network activity: Analysis of its temporal complexity.

The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain's complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the "cliquiness" of a node) and participation coefficients (the between-module connectivity of a node) were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1) Entropy is higher for the participation coefficient than for the clustering coefficient. (2) The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3) The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy.

Dynamic regional phase synchrony (DRePS): An Instantaneous Measure of Local fMRI Connectivity Within Spatially Clustered Brain Areas.

Dynamic functional brain connectivity analysis is a fast expanding field in computational neuroscience research with the promise of elucidating brain network interactions. Sliding temporal window based approaches are commonly used in order to explore dynamic behavior of brain networks in task-free functional magnetic resonance imaging (fMRI) data. However, the low effective temporal resolution of sliding window methods fail to capture the full dynamics of brain activity at each time point. These also require subjective decisions regarding window size and window overlap. In this study, we introduce dynamic regional phase synchrony (DRePS), a novel analysis approach that measures mean local instantaneous phase coherence within adjacent fMRI voxels. We evaluate the DRePS framework on simulated data showing that the proposed measure is able to estimate synchrony at higher temporal resolution than sliding windows of local connectivity. We applied DRePS analysis to task-free fMRI data of 20 control subjects, revealing ultra-slow dynamics of local connectivity in different brain areas. Spatial clustering based on the DRePS feature time series reveals biologically congruent local phase synchrony networks (LPSNs). Taken together, our results demonstrate three main findings. Firstly, DRePS has increased temporal sensitivity compared to sliding window correlation analysis in capturing locally synchronous events. Secondly, DRePS of task-free fMRI reveals ultra-slow fluctuations of ∼0.002-0.02 Hz. Lastly, LPSNs provide plausible spatial information about time-varying brain local phase synchrony. With the DRePS method, we introduce a framework for interrogating brain local connectivity, which can potentially provide biomarkers of human brain function in health and disease. Hum Brain Mapp 37:1970-1985, 2016. © 2016 Wiley Periodicals, Inc.

Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding.

Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

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.

Your eyes give you away: prestimulus changes in pupil diameter correlate with poststimulus task-related EEG dynamics.

Pupillary measures have been linked to arousal and attention as well as activity in the brainstem's locus coeruleus norepinephrine (LC-NE) system. Similarly, there is evidence that evoked EEG responses, such as the P3, might have LC-NE activity as their basis. Since it is not feasible to record electrophysiological data directly from the LC in humans due to its location in the brainstem, an open question has been whether pupillary measures and EEG variability can be linked in a meaningful way to shed light on the nature of the LC-NE role in attention and arousal. We used an auditory oddball task with a data-driven approach to learn task-relevant projections of the EEG, for windows of data spanning the entire trial. We investigated linear and quadratic relationships between the evoked EEG along these projections and both prestimulus (baseline) and poststimulus (evoked dilation) pupil diameter measurements. We found that baseline pupil diameter correlates with early (175-200 ms) and late (350-400 ms) EEG component variability, suggesting a linear relationship between baseline (tonic) LC-NE activity and evoked EEG. We found no relationships between evoked EEG and evoked pupil dilation, which is often associated with evoked (phasic) LC activity. After regressing out reaction time (RT), the correlation between EEG variability and baseline pupil diameter remained, suggesting that such correlation is not explainable by RT variability. We also investigated the relationship between these pupil measures and prestimulus EEG alpha activity, which has been reported as a marker of attentional state, and found a negative linear relationship with evoked pupil dilation. In summary, our results demonstrate significant relationships between prestimulus and poststimulus neural and pupillary measures, and they provide further evidence for tight coupling between attentional state and evoked neural activity and for the role of cortical and subcortical networks underlying the process of target detection.

Simultaneous EEG-fMRI reveals a temporal cascade of task-related and default-mode activations during a simple target detection task.

Focused attention continuously and inevitably fluctuates, and to completely understand the mechanisms responsible for these modulations it is necessary to localize the brain regions involved. During a simple visual oddball task, neural responses measured by electroencephalography (EEG) modulate primarily with attention, but source localization of the correlates is a challenge. In this study we use single-trial analysis of simultaneously-acquired scalp EEG and functional magnetic resonance image (fMRI) data to investigate the blood oxygen level dependent (BOLD) correlates of modulations in task-related attention, and we unravel the temporal cascade of these transient activations. We hypothesize that activity in brain regions associated with various task-related cognitive processes modulates with attention, and that their involvements occur transiently in a specific order. We analyze the fMRI BOLD signal by first regressing out the variance linked to observed stimulus and behavioral events. We then correlate the residual variance with the trial-to-trial variation of EEG discriminating components for identical stimuli, estimated at a sequence of times during a trial. Post-stimulus and early in the trial, we find activations in right-lateralized frontal regions and lateral occipital cortex, areas that are often linked to task-dependent processes, such as attentional orienting, and decision certainty. After the behavioral response we see correlates in areas often associated with the default-mode network and introspective processing, including precuneus, angular gyri, and posterior cingulate cortex. Our results demonstrate that during simple tasks both task-dependent and default-mode networks are transiently engaged, with a distinct temporal ordering and millisecond timescale.

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.

Fast bootstrapping and permutation testing for assessing reproducibility and interpretability of multivariate fMRI decoding models.

Multivariate decoding models are increasingly being applied to functional magnetic imaging (fMRI) data to interpret the distributed neural activity in the human brain. These models are typically formulated to optimize an objective function that maximizes decoding accuracy. For decoding models trained on full-brain data, this can result in multiple models that yield the same classification accuracy, though some may be more reproducible than others--i.e. small changes to the training set may result in very different voxels being selected. This issue of reproducibility can be partially controlled by regularizing the decoding model. Regularization, along with the cross-validation used to estimate decoding accuracy, typically requires retraining many (often on the order of thousands) of related decoding models. In this paper we describe an approach that uses a combination of bootstrapping and permutation testing to construct both a measure of cross-validated prediction accuracy and model reproducibility of the learned brain maps. This requires re-training our classification method on many re-sampled versions of the fMRI data. Given the size of fMRI datasets, this is normally a time-consuming process. Our approach leverages an algorithm called fast simultaneous training of generalized linear models (FaSTGLZ) to create a family of classifiers in the space of accuracy vs. reproducibility. The convex hull of this family of classifiers can be used to identify a subset of Pareto optimal classifiers, with a single-optimal classifier selectable based on the relative cost of accuracy vs. reproducibility. We demonstrate our approach using full-brain analysis of elastic-net classifiers trained to discriminate stimulus type in an auditory and visual oddball event-related fMRI design. Our approach and results argue for a computational approach to fMRI decoding models in which the value of the interpretation of the decoding model ultimately depends upon optimizing a joint space of accuracy and reproducibility.