Dynamic spatiotemporal brain analyses using high performance electrical neuroimaging: theoretical framework and validation

The path of dishonesty: identification of mental processes with electrical neuroimaging

Much research finds that lying takes longer than truth-telling. Yet, the source of this response time difference remains elusive. Here, we assessed the spatiotemporal evolution of electrical brain activity during honesty and dishonesty in 150 participants using a sophisticated electrical neuroimaging approach-the microstate approach. This uniquely positioned us to identify and contrast the entire chain of mental processes involved during honesty and dishonesty. Specifically, we find that the response time difference is the result of an additional late-occurring mental process, unique to dishonest decisions, interrupting the antecedent mental processing. We suggest that this process inhibits the activation of the truth, thus permitting the execution of the lie. These results advance our understanding of dishonesty and clarify existing theories about the role of increased cognitive load. More broadly, we demonstrate the vast potential of our approach to illuminate the temporal organization of mental processes involved in decision-making.

Weighted Blind Source Separation Can Decompose the Frequency Mismatch Response by Deviant Concatenation: An MEG Study

The mismatch response (MMR) is thought to be a neurophysiological measure of novel auditory detection that could serve as a translational biomarker of various neurological diseases. When recorded with electroencephalography (EEG) or magnetoencephalography (MEG), the MMR is traditionally extracted by subtracting the event-related potential/field (ERP/ERF) elicited in response to “deviant” sounds that occur randomly within a train of repetitive “standard” sounds. However, there are several problems with such a subtraction, which include increased noise and the neural adaptation problem. On the basis of the original theory underlying MMR (i.e., the memory-comparison process), the MMR should be present only in deviant epochs. Therefore, we proposed a novel method called weighted-BSST/k, which uses only the deviant response to derive the MMR. Deviant concatenation and weight assignment are the primary procedures of weighted-BSST/k, which maximize the benefits of time-delayed correlation. We hypothesized that this novel weighted-BSST/k method highlights responses related to the detection of the deviant stimulus and is more sensitive than independent component analysis (ICA). To test this hypothesis and the validity and efficacy of the weighted-BSST/k in comparison with ICA (infomax), we evaluated the methods in 12 healthy adults. Auditory stimuli were presented at a constant rate of 2 Hz. Frequency MMRs at a sensor level were obtained from the bilateral temporal lobes with the subtraction approach at 96–276 ms (the MMR time range), defined based on spatio-temporal cluster permutation analysis. In the application of the weighted-BSST/k, the deviant responses were given a constant weight using a rectangular window on the MMR time range. The ERF elicited by the weighted deviant responses demonstrated one or a few dominant components representing the MMR that fitted well with that of the sensor space analysis using the conventional subtraction approach. In contrast, infomax or weighted-infomax revealed many minor or pseudo components as constituents of the MMR. Our single-trial, contrast-free approach may assist in using the MMR in basic and clinical research, and it opens a new and potentially useful way to analyze event-related MEG/EEG data.

A novel time-delayed correlation method decomposes mismatch response without using subtraction

The mismatch response (MMR) is thought to be a neurophysiological measure of novel auditory detection that could serve as a translational biomarker of various neurological diseases. When recorded with electroencephalography (EEG) or magnetoencephalography (MEG), the MMR is traditionally extracted by subtracting the event-related potential/field (ERP/ERF) elicited in response to "deviant" sounds that occur randomly within a train of repetitive "standard" sounds. To overcome the limitations of this subtraction procedure, we propose a novel method which we call weighted-BSS_T/k, which uses only the deviant response to derive the MMR. We hypothesized that this novel weighted-BSS_T/k method highlights responses related to the detection of the deviant stimulus and is more sensitive than independent component analysis (ICA). To test this hypothesis and the validity and efficacy of the weighted-BSS_T/k in comparison with ICA (infomax), we evaluated the methods in 12 healthy adults. Auditory stimuli were presented at a constant rate of 2 Hz. Frequency MMRs at a sensor level were obtained from the bilateral temporal lobes with the subtraction approach at 96-276 ms (the MMR time range), defined on the basis of spatio-temporal cluster permutation analysis. In the application of the weighted-BSS_T/k, the deviant responses were given a constant weight on the MMR time range. The ERF elicited by the weighted deviant responses demonstrated one or a few dominant components representing the MMR with a high signal-to-noise ratio and similar topography to that of the sensor space analysis using the subtraction approach. In contrast, infomax or weighted-infomax revealed many minor or pseudo components as constituents of the MMR. Our new approach may assist in using the MMR in basic and clinical research.Clinical Relevance-Our proposed method opens a new and potentially useful way to analyze event-related MEG/EEG data.

Neurobiology of loneliness: a systematic review

Loneliness is associated with increased morbidity and mortality. Deeper understanding of neurobiological mechanisms underlying loneliness is needed to identify potential intervention targets. We did not find any systematic review of neurobiology of loneliness. Using MEDLINE and PsycINFO online databases, we conducted a search for peer-reviewed publications examining loneliness and neurobiology. We identified 41 studies (n = 16,771 participants) that had employed various methods including computer tomography (CT), structural magnetic resonance imaging (MRI), functional MRI (fMRI), electroencephalography (EEG), diffusion tensor imaging (DTI), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and post-mortem brain tissue RNA analysis or pathological analysis. Our synthesis of the published findings shows abnormal structure (gray matter volume or white matter integrity) and/or activity (response to pleasant versus stressful images in social versus nonsocial contexts) in the prefrontal cortex (especially medial and dorsolateral), insula (particularly anterior), amygdala, hippocampus, and posterior superior temporal cortex. The findings related to ventral striatum and cerebellum were mixed. fMRI studies reported links between loneliness and differential activation of attentional networks, visual networks, and default mode network. Loneliness was also related to biological markers associated with Alzheimer's disease (e.g., amyloid and tau burden). Although the published investigations have limitations, this review suggests relationships of loneliness with altered structure and function in specific brain regions and networks. We found a notable overlap in the regions involved in loneliness and compassion, the two personality traits that are inversely correlated in previous studies. We have offered recommendations for future research studies of neurobiology of loneliness.

Aging Modulates Prefrontal Plasticity Induced by Executive Control Training

While declines in inhibitory control, the capacity to suppress unwanted neurocognitive processes, represent a hallmark of healthy aging, whether this function is susceptible to training-induced plasticity in older populations remains largely unresolved. We addressed this question with a randomized controlled trial investigating the changes in behavior and electrical neuroimaging activity induced by a 3-week adaptive gamified Go/NoGo inhibitory control training (ICT). Performance improvements were accompanied by the development of more impulsive response strategies, but did not generalize to impulsivity traits nor quality of life. As compared with a 2-back working-memory training, the ICT in the older adults resulted in a purely quantitative reduction in the strength of the activity in a medial and ventrolateral prefrontal network over the 400 ms P3 inhibition-related event-related potentials component. However, as compared with young adults, the ICT induced distinct configurational modifications in older adults' 200 ms N2 conflict monitoring medial-frontal functional network. Hence, while older populations show preserved capacities for training-induced plasticity in executive control, aging interacts with the underlying plastic brain mechanisms. Training improves the efficiency of the inhibition process in older adults, but its effects differ from those in young adults at the level of the coping with inhibition demands.

Determination of the Time Window of Event-Related Potential Using Multiple-Set Consensus Clustering

Clustering is a promising tool for grouping the sequence of similar time-points aimed to identify the attention blocks in spatiotemporal event-related potentials (ERPs) analysis. It is most likely to elicit the appropriate time window for ERP of interest if a suitable clustering method is applied to spatiotemporal ERP. However, how to reliably estimate a proper time window from entire individual subjects' data is still challenging. In this study, we developed a novel multiset consensus clustering method in which several clustering results of multiple subjects were combined to retrieve the best fitted clustering for all the subjects within a group. Then, the obtained clustering was processed by a newly proposed time-window detection method to determine the most suitable time window for identifying the ERP of interest in each condition/group. Applying the proposed method to the simulated ERP data and real data indicated that the brain responses from the individual subjects can be collected to determine a reliable time window for different conditions/groups. Our results revealed more precise time windows to identify N2 and P3 components in the simulated data compared to the state-of-the-art methods. Additionally, our proposed method achieved more robust performance and outperformed statistical analysis results in the real data for N300 and prospective positivity components. To conclude, the proposed method successfully estimates the time window for ERP of interest by processing the individual data, offering new venues for spatiotemporal ERP processing.

A novel method for extracting interictal epileptiform discharges in multi-channel MEG: Use of fractional type of blind source separation

OBJECTIVE

Visual inspection of interictal epileptiform discharges (IEDs) in multi-channel MEG requires a time-consuming evaluation process and often leads to inconsistent results due to variability of IED waveforms. Here, we propose a novel extraction method for IEDs using a T/k type of blind source separation (BSS_T/k).

METHODS

We applied BSS_T/k with seven patients with focal epilepsy to test the accuracy of identification of IEDs. We conducted comparisons of the results of BSS components with those obtained by visual inspection in sensor-space analysis.

RESULTS

BSS_T/k provided better signal estimation of IEDs compared with sensor-space analysis. Importantly, BSS_T/k was able to uncover IEDs that could not be detected by visual inspection. Furthermore, IED components were clearly extracted while preserving spike and wave morphology. Variable IED waveforms were decomposed into one dominant component.

CONCLUSIONS

BSS_T/k was able to visualize the spreading signals over multiple channels into a single component from a single epileptogenic zone. BSS_T/k can be applied to focal epilepsy with a simple parameter setting.

SIGNIFICANCE

Our novel method was able to highlight IEDs with increased accuracy for identification of IEDs from multi-channel MEG data.

Practice-induced functional plasticity in inhibitory control interacts with aging

Inhibitory control deficits represent a key aspect of the cognitive declines associated with aging. Practicing inhibitory control has thus been advanced as a potential approach to compensate for age-induced neurocognitive impairments. Yet, the functional brain changes associated with practicing inhibitory control tasks in older adults and whether they differ from those observed in young populations remains unresolved. We compared electrical neuroimaging analyses of ERPs recorded during a Go/NoGo practice session with a Group (Young; Older adults) by Session (Beginning; End of the practice) design to identify whether the practice of an inhibition task in older adults reinforces already implemented compensatory activity or reduce it by enhancing the functioning of the brain networks primarily involved in the tasks. We observed an equivalent small effect of practice on performance in the two age-groups. The topographic ERP analyses and source estimations revealed qualitatively different effects of the practice over the N2 and P3 ERP components, respectively driven by a decrease in supplementary motor area activity and an increase in left ventrolateral prefrontal activity in the older but not in the young adults with practice. Our results thus indicate that inhibition task practice in older adults increases age-related divergences in the underlying functional processes.

Source-Informed Segmentation: A Data-Driven Approach for the Temporal Segmentation of EEG

GOAL

Understanding the dynamics of brain function through non-invasive monitoring techniques requires the development of computational methods that can deal with the non-stationary properties of recorded activities. As a solution to this problem, a new data-driven segmentation method for recordings obtained through electroencephalography (EEG) is presented.

METHODS

The proposed method utilizes singular value decomposition (SVD) to identify the time intervals in the EEG recordings during which the spatial distribution of clusters of active cortical neurons remains quasi-stationary. Theoretical analysis shows that the spatial locality features of these clusters can be, asymptotically, captured by the most significant left singular subspace of the EEG data. A reference/sliding window approach is employed to dynamically extract this feature subspace, and the running projection error is monitored for significant changes using Kolmogorov-Smirnov test.

RESULTS

Simulation results, for a wide range of possible scenarios regarding the spatial distribution of active cortical neurons, show that the algorithm is successful in accurately detecting the segmental structure of the simulated EEG data. The algorithm is also applied to experimental EEG recordings of a modified visual oddball task. Results identify a unique sequence of dynamic patterns in the event-related potential (ERP) response to each of the three involved stimuli.

CONCLUSION

The proposed method, without using source localization methods or scalp topographical maps, is able to identify intervals of quasi-stationarity in the EEG recordings.

SIGNIFICANCE

The proposed segmentation technique can offer new insights on the dynamics of functional organization of the brain in action.

Spatiotemporal Phase Synchronization in Adaptive Reconfiguration from Action Observation Network to Mentalizing Network for Understanding Other's Action Intention

In action intention understanding, the mirror system is involved in perception-action matching process and the mentalizing system underlies higher-level intention inference. By analyzing the dynamic functional connectivity in α (8-12 Hz) and β (12-30 Hz) frequency bands over a "hand-cup interaction" observation task, this study investigates the topological transition from the action observation network (AON) to the mentalizing network (MZN), and estimates their functional relevance for intention identification from other's different action kinematics. Sequential brain microstates were extracted based on event-related potentials (ERPs), in which significantly differing neuronal responses were found in N170-P200 related to perceptually matching kinematic profiles and P400-700 involved in goal inference. Inter-electrode weighted phase lag index analysis on the ERP microstates revealed a shift of hub centrality salient in α frequency band, from the AON dominated by left-lateral frontal-premotor-temporal and temporal-parietooccipital synchronizations to the MZN consisting of more bilateral frontal-parietal and temporal-parietal synchronizations. As compared with usual actions, intention identification of unintelligible actions induces weaker synchronizations in the AON but dramatically increased connectivity in right frontal-temporal-parietal regions of the MZN, indicating a spatiotemporally complementary effect between the functional network configurations involved in mirror and mentalizing processes. Perceptual processing in observing usual/unintelligible actions decreases/increases requirements for intention inference, which would induce less/greater functional network reorganization on the way to mentalization. From the comparison, our study suggests that the adaptive topological changes from the AON to the MZN indicate implicit causal association between the mirror and mentalizing systems for decoding others' intentionality.

Spatio-temporal dynamics of the mirror neuron system during social intentions

Previous research has shown that specific goals and intentions influence a person's allocation of social attention. From a neural viewpoint, a growing body of evidence suggests that the inferior fronto-parietal network, including the mirror neuron system, plays a role in the planning and the understanding of motor intentions. However, it is unclear whether and when the mirror neuron system plays a role in social intentions. Combining a behavioral task with electrical neuroimaging in 22 healthy male participants, the current study investigates whether the temporal brain dynamic of the mirror neuron system differs during two types of social intentions i.e., lust vs. romantic intentions. Our results showed that 62% of the stimuli evoking lustful intentions also evoked romantic intentions, and both intentions were sustained by similar activations of the inferior frontal gyrus and the inferior parietal lobule/angular gyrus for the first 432 ms after stimulus onset. Intentions to not love or not lust, on the other hand, were characterized by earlier differential activations of the inferior fronto-parietal network i.e., as early as 244 ms after stimulus onset. These results suggest that the mirror neuron system may not only code for the motor correlates of intentions, but also for the social meaning of intentions and its valence at both early/automatic and later/more elaborative stages of information processing.

Dynamic spatiotemporal brain analyses of the visual checkerboard task: Similarities and differences between passive and active viewing conditions

We introduce a new analytic technique for the microsegmentation of high-density EEG to identify the discrete brain microstates evoked by the visual reversal checkerboard task. To test the sensitivity of the present analytic approach to differences in evoked brain microstates across experimental conditions, subjects were instructed to (a) passively view the reversals of the checkerboard (passive viewing condition), or (b) actively search for a target stimulus that may appear at the fixation point, and they were offered a monetary reward if they correctly detected the stimulus (active viewing condition). Results revealed that, within the first 168 ms of a checkerboard presentation, the same four brain microstates were evoked in the passive and active viewing conditions, whereas the brain microstates evoked after 168 ms differed between these two conditions, with more brain microstates elicited in the active than in the passive viewing condition. Additionally, distinctions were found in the active condition between a change in a scalp configuration that reflects a change in microstate and a change in scalp configuration that reflects a change in the level of activation of the same microstate. Finally, the bootstrapping procedure identified that two microstates lacked robustness even though statistical significance thresholds were met, suggesting these microstates should be replicated prior to placing weight on their generalizability across individuals. These results illustrate the utility of the analytic approach and provide new information about the spatiotemporal dynamics of the brain states underlying passive and active viewing in the visual checkerboard task.

Spatiotemporal Brain Dynamics of Empathy for Pain and Happiness in Friendship

Background: Although a large number of functional magnetic resonance imaging (fMRI) studies have investigated the neural bases of empathy, little is known about its spatiotemporal dynamics or its modulation by the level of friendship between the observer and the agent who is being hurt. Moreover, most of the previous studies on empathy have focused on empathy for pain rather than empathy for positive emotions, such as happiness. In the present study, we addressed this question by investigating the spatiotemporal brain dynamics of two different kinds of empathy (empathy for pain, empathy for happiness) with a behavioral priming empathy task involving two different level of primes (a close friend, a stranger).

Method/Principal Findings: Electrical brain activity and behavioral data were analyzed from 30 subjects (12 males and 18 females). Half of the subjects performed a behavioral task on empathy for pain task (EPT), while the other half performed a behavioral task on empathy for happiness task (EHT). In each task, participants viewed prime photographs of either: (1) a stranger; or (2) a close friend (primes) followed by target photographs showing either a hand being hurt (or not; targets in the EPT), or a hand in happy circumstances (or not; targets in the EHT). In each task, participants were asked to judge the target situation and report whether they could feel the pain (in EPT) or the happiness (in the EHT), as a function of the primes i.e., either from the close friend’s or from the stranger’s perspective. Although our behavioral results didn’t reveal any explicit differences among the different types of primes within each task, our electrophysiological results showed variations as a function of the primes. First, a early smaller N110 amplitude for pain was observed in the anterior prefrontal cortex during the friend prime condition compared to the stranger prime condition. No similar early effects were found for happiness. On the other hand, both empathy for happiness (EHT) and empathy for pain (EPT) elicited later differences. In the EPT, the friend prime elicited a larger late positive potential (LPP) than the stranger prime. In the EHT, the friend prime elicited a larger N250, a smaller P300, and a smaller LPP than the stranger prime.

Conclusions: Taking the perspective of a close friend (as a prime stimulus) does have a dual-stage effect on empathy that is characterized by an early modulation for pain and later modulations for both pain and happiness. The early differences between friend and stranger primes for pain (but not for happiness) suggest that empathy for pain is an automatic process that has been socially learned and passed among friends. On the other hand, the later differences observed between stranger and friend prime suggest that additional cognitive appraisal take place for both pain and happiness. Our results suggest that it takes more cognitive attentional efforts to judge a stranger’s happiness than a friend’s happiness, whereas the opposite is true for pain. These findings open new avenues toward a better understanding of the empathic mind.

Clocking the social mind by identifying mental processes in the IAT with electrical neuroimaging

Why do people take longer to associate the word "love" with outgroup words (incongruent condition) than with ingroup words (congruent condition)? Despite the widespread use of the implicit association test (IAT), it has remained unclear whether this IAT effect is due to additional mental processes in the incongruent condition, or due to longer duration of the same processes. Here, we addressed this previously insoluble issue by assessing the spatiotemporal evolution of brain electrical activity in 83 participants. From stimulus presentation until response production, we identified seven processes. Crucially, all seven processes occurred in the same temporal sequence in both conditions, but participants needed more time to perform one early occurring process (perceptual processing) and one late occurring process (implementing cognitive control to select the motor response) in the incongruent compared with the congruent condition. We also found that the latter process contributed to individual differences in implicit bias. These results advance understanding of the neural mechanics of response time differences in the IAT: They speak against theories that explain the IAT effect as due to additional processes in the incongruent condition and speak in favor of theories that assume a longer duration of specific processes in the incongruent condition. More broadly, our data analysis approach illustrates the potential of electrical neuroimaging to illuminate the temporal organization of mental processes involved in social cognition.

Dynamic spatiotemporal brain analyses using high-performance electrical neuroimaging, Part II: A step-by-step tutorial

Our recently published analytic toolbox (Cacioppo et al., 2014), running under MATLAB environment and Brainstorm, offered a theoretical framework and set of validation studies for the automatic detection of event-related changes in the global pattern and global field power of electrical brain activity. Here, we provide a step-by-step tutorial of this toolbox along with a detailed description of analytical plans (aka the Chicago Electrical Neuroimaging Analytics, CENA) for the statistical analysis of brain microstate configuration and global field power in within and between-subject designs. Available CENA functions include: (1) a difference wave function; (2) a high-performance microsegmentation suite (HPMS), which consists of three specific analytic tools: (i) a root mean square error (RMSE) metric for identifying stable states and transition states across discrete event-related brain microstates; (ii) a similarity metric based on cosine distance in n dimensional sensor space to determine whether template maps for successive brain microstates differ in configuration of brain activity, and (iii) global field power (GFP) metrics for identifying changes in the overall level of activation of the brain; (3) a bootstrapping function for assessing the extent to which the solutions identified in the HPMS are robust (reliable, generalizable) and for empirically deriving additional experimental hypotheses; and (4) step-by-step procedures for performing a priori contrasts for data analysis. CENA is freely available for brain data spatiotemporal analyses at https://hpenlaboratory.uchicago.edu/page/cena, with sample data, user tutorial videos, and documentation.

Microscale spatiotemporal dynamics during neocortical propagation of human focal seizures

Some of the most clinically consequential aspects of focal epilepsy, e.g. loss of consciousness, arise from the generalization or propagation of seizures through local and large-scale neocortical networks. Yet, the dynamics of such neocortical propagation remain poorly understood. Here, we studied the microdynamics of focal seizure propagation in neocortical patches (4×4 mm) recorded via high-density microelectrode arrays (MEAs) implanted in people with pharmacologically resistant epilepsy. Our main findings are threefold: (1) a newly developed stage segmentation method, applied to local field potentials (LFPs) and multiunit activity (MUA), revealed a succession of discrete seizure stages, each lasting several seconds. These different stages showed characteristic evolutions in overall activity and spatial patterns, which were relatively consistent across seizures within each of the 5 patients studied. Interestingly, segmented seizure stages based on LFPs or MUA showed a dissociation of their spatiotemporal dynamics, likely reflecting different contributions of non-local synaptic inputs and local network activity. (2) As previously reported, some of the seizures showed a peak in MUA that happened several seconds after local seizure onset and slowly propagated across the MEA. However, other seizures had a more complex structure characterized by, for example, several MUA peaks, more consistent with the succession of discrete stages than the slow propagation of a simple wavefront of increased MUA. In both cases, nevertheless, seizures characterized by spike-wave discharges (SWDs, ~2-3 Hz) eventually evolved into patterns of phase-locked MUA and LFPs. (3) Individual SWDs or gamma oscillation cycles (25-60 Hz), characteristic of two different types of recorded seizures, tended to propagate with varying degrees of directionality, directions of propagation and speeds, depending on the identified seizure stage. However, no clear relationship was observed between the MUA peak onset time (in seizures where such peak onset occurred) and changes in MUA or LFP propagation patterns. Overall, our findings indicate that the recruitment of neocortical territories into ictal activity undergoes complex spatiotemporal dynamics evolving in slow discrete states, which are consistent across seizures within each patient. Furthermore, ictal states at finer spatiotemporal scales (individual SWDs or gamma oscillations) are organized by slower time scale network dynamics evolving through these discrete stages.