Characterizing the brain's dynamical response from scalp-level neural electrical signals: a review of methodology development

Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome

Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S-R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non-adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule-based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto-parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper-learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions.

Effects of an acute bout of cycling on different domains of cognitive function

The literature suggesting acute exercise benefits cognitive function has been largely confined to single cognitive domains and measures of reliant on measures of central tendencies. Furthermore, studies suggest cognitive intra-individual variability (IIV) to reflect cognitive efficiency and provide unique insights into cognitive function, but there is limited knowledge on the effects of acute exercise on IIV. To this end, this study examined the effects of acute exercise on three different cognitive domains, executive function, implicit learning, and hippocampal-dependent memory function using behavioral performance and event-related potentials (ERPs). Furthermore, this study also sought to explore the effects of an acute bout of exercise on IIV using the RIDE algorithm to separate signals into individuals components based on latency variability. Healthy adult participants (N=20; 26.3±4.8years) completed a randomized cross-over trial with seated rest or 30min of high intensity cycling. Before and after each condition, participants completed a cognitive battery consisting of the Eriksen Flanker task, implicit statistical learning task, and a spatial reconstruction task. While exercise did not affect Flanker or spatial reconstruction performance, there were exercise related decreases in accuracy (F=5.47; P=0.040), slowed reaction time (F=5.18; P=0.036), and decreased late parietal positivity (F=4.26; P=0.046). However, upon adjusting for performance and ERP variability, there were exercise related decreases in Flanker reaction time (F=24.00; P<0.001), and reduced N2 amplitudes (F=13.03; P=0.002), and slower P3 latencies (F=3.57; P=0.065) for incongruent trials. These findings suggest that acute exercise may impact cognitive IIV as an adaptation to maintain function following exercise.

Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time-Frequency Representation, and Phase Dynamics

Characterizing the brain's dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain's response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time-frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.

Perception-Action Integration Is Altered in Functional Movement Disorders

BACKGROUND

Although functional neurological movement disorders (FMD) are characterized by motor symptoms, sensory processing has also been shown to be disturbed. However, how the integration of perception and motor processes, essential for the control of goal-directed behavior, is altered in patients with FMD is less clear. A detailed investigation of these processes is crucial to foster a better understanding of the pathophysiology of FMD and can systematically be achieved in the framework of the theory of event coding (TEC).

OBJECTIVE

The aim was to investigate perception-action integration processes on a behavioral and neurophysiological level in patients with FMD.

METHODS

A total of 21 patients and 21 controls were investigated with a TEC-related task, including concomitant electroencephalogram (EEG) recording. We focused on EEG correlates established to reflect perception-action integration processes. Temporal decomposition allowed to distinguish between EEG codes reflecting sensory (S-cluster), motor (R-cluster), and integrated sensory-motor processing (C-cluster). We also applied source localization analyses.

RESULTS

Behaviorally, patients revealed stronger binding between perception and action, as evidenced by difficulties in reconfiguring previously established stimulus-response associations. Such hyperbinding was paralleled by a modulation of neuronal activity clusters, including reduced C-cluster modulations of the inferior parietal cortex and altered R-cluster modulations in the inferior frontal gyrus. Correlations of these modulations with symptom severity were also evident.

CONCLUSIONS

Our study shows that FMD is characterized by altered integration of sensory information with motor processes. Relations between clinical severity and both behavioral performance and neurophysiological abnormalities indicate that perception-action integration processes are central and a promising concept for the understanding of FMD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level

In the present study, new findings reveal the close association between graph theoretic global brain connectivity measures and cognitive abilities the ability to manage and regulate negative emotions in healthy adults. Functional brain connectivity measures have been estimated from both eyes-opened and eyes-closed resting-state EEG recordings in four groups including individuals who use opposite Emotion Regulation Strategies (ERS) as follow: While 20 individuals who frequently use two opposing strategies, such as rumination and cognitive distraction, are included in 1st group, 20 individuals who don't use these cognitive strategies are included in 2nd group. In 3rd and 4th groups, there are matched individuals who use both Expressive Suppression and Cognitive Reappraisal strategies together frequently and never use them, respectively. EEG measurements and psychometric scores of individuals were both downloaded from a public dataset LEMON. Since it is not sensitive to volume conduction, Directed Transfer Function has been applied to 62-channel recordings to obtain cortical connectivity estimations across the whole cortex. Regarding well defined threshold, connectivity estimations have been transformed into binary numbers for implementation of Brain Connectivity Toolbox. The groups are compared to each other through both statistical logistic regression models and deep learning models driven by frequency band specific network measures referring segregation, integration and modularity of the brain. Overall results show that high classification accuracies of 96.05% (1st vs 2nd) and 89.66% (3rd vs 4th) are obtained in analyzing full-band ( 0.5 - 45 H z ) EEG. In conclusion, negative strategies may upset the balance between segregation and integration. In particular, graphical results show that frequent use of rumination induces the decrease in assortativity referring network resilience. The psychometric scores are found to be highly correlated with brain network measures of global efficiency, local efficiency, clustering coefficient, transitivity and assortativity in even resting-state.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Multi-level decoding of task sets in neurophysiological data during cognitive flexibility

Cognitive flexibility is essential to achieve higher level goals. Cognitive theories assume that the activation/deactivation of goals and task rules is central to understand cognitive flexibility. However, how this activation/deactivation dynamic is implemented on a neurophysiological level is unclear. Using EEG-based multivariate pattern analysis (MVPA) methods, we show that activation of relevant information occurs parallel in time at multiple levels in the neurophysiological signal containing aspects of stimulus-related processing, response selection, and motor response execution, and relates to different brain regions. The intensity with which task sets are activated and processed dynamically decreases and increases. The temporal stability of these activations could, however, hardly explain behavioral performance. Instead, task set deactivation processes associated with left orbitofrontal regions and inferior parietal regions selectively acting on motor response task sets are relevant. The study shows how propositions from cognitive theories stressing the importance task set activation/deactivation during cognitive flexibility are implemented on a neurophysiological level.

•

Stimulus-related, motor, and response selection aspects of task set were decoded

•

Activation of task rule information occurs at multiple neurophysiological levels

•

Activation and deactivation of rule sets contributes to cognitive flexibility

Neurophysiological and functional neuroanatomical coding of statistical and deterministic rule information during sequence learning

Humans are capable of acquiring multiple types of information presented in the same information stream. It has been suggested that at least two parallel learning processes are important during learning of sequential patterns-statistical learning and rule-based learning. Yet, the neurophysiological underpinnings of these parallel learning processes are not fully understood. To differentiate between the simultaneous mechanisms at the single trial level, we apply a temporal EEG signal decomposition approach together with sLORETA source localization method to delineate whether distinct statistical and rule-based learning codes can be distinguished in EEG data and can be related to distinct functional neuroanatomical structures. We demonstrate that concomitant but distinct aspects of information coded in the N2 time window play a role in these mechanisms: mismatch detection and response control underlie statistical learning and rule-based learning, respectively, albeit with different levels of time-sensitivity. Moreover, the effects of the two learning mechanisms in the different temporally decomposed clusters of neural activity also differed from each other in neural sources. Importantly, the right inferior frontal cortex (BA44) was specifically implicated in visuomotor statistical learning, confirming its role in the acquisition of transitional probabilities. In contrast, visuomotor rule-based learning was associated with the prefrontal gyrus (BA6). The results show how simultaneous learning mechanisms operate at the neurophysiological level and are orchestrated by distinct prefrontal cortical areas. The current findings deepen our understanding on the mechanisms of how humans are capable of learning multiple types of information from the same stimulus stream in a parallel fashion.

Pushing to the Limits: What Processes during Cognitive Control are Enhanced by Reaction-Time Feedback?

To respond as quickly as possible in a given task is a widely used instruction in cognitive neuroscience; however, the neural processes modulated by this common experimental procedure remain largely elusive. We investigated the underlying neurophysiological processes combining electroencephalography (EEG) signal decomposition (residue iteration decomposition, RIDE) and source localization. We show that trial-based response speed instructions enhance behavioral performance in conflicting trials, but slightly impair performance in nonconflicting trials. The modulation seen in conflicting trials was found at several coding levels in EEG data using RIDE. In the S-cluster N2 time window, this modulation was associated with modulated activation in the posterior cingulate cortex and the superior frontal gyrus. Furthermore, in the C-cluster P3 time window, this modulation was associated with modulated activation in the middle frontal gyrus. Interestingly, in the R-cluster P3 time window, this modulation was strongest according to statistical effect sizes, associated with modulated activity in the primary motor cortex. Reaction-time feedback mainly modulates response motor execution processes, whereas attentional and response selection processes are less affected. The study underlines the importance of being aware of how experimental instructions influence the behavior and neurophysiological processes.

Neurophysiological mechanisms underlying motor feature binding processes and representations

Coherent, voluntary action requires an integrated representation of these actions and their defining features. Although theories delineate how action integration requiring binding between different action features may be accomplished, the underlying neurophysiological mechanisms are largely elusive. The present study examined the neurophysiological mechanisms underlying binding processes in actions. To this end, we conducted EEG recordings and applied standard event-related potential analyses, temporal EEG signal decomposition and multivariate pattern analyses (MVPA). According to the code occupation account, an overlap between a planned and a to-be-performed action impairs performance. The level, to which performance is attenuated depends on the strength of binding of action features. This binding process then determines the representation of them, the so-called action files. We show that code occupation and bindings between action features specifically modulate processes preceding motor execution as showed by the stimulus-locked lateralized readiness potential (LRP). Conversely, motor execution processes reflected by the response-locked LRP were not modulated by action file binding. The temporal decomposition of the EEG signal, further distinguished between action file related processes: the planned response determining code occupation was reflected in general (voluntary) response selection but not in involuntary (response priming-related) activation. Moreover, MVPA on temporally decomposed neural signals indicated that action files are represented as a continuous chain of activations. Within this chain, inhibitory and response re-activation patterns can be distinguished. Taken together, the neurophysiological correlates of action file binding suggest that parallel, stimulus- and response-related pre-motor processes are responsible for the code occupation in the human motor system.

Neurophysiology of embedded response plans: age effects in action execution but not in feature integration from preadolescence to adulthood

Performing a goal-directed movement consists of a chain of complex preparatory mechanisms. Such planning especially requires integration (or binding) of various action features, a process that has been conceptualized in the "theory of event coding." Theoretical considerations and empirical research suggest that these processes are subject to developmental effects from adolescence to adulthood. The aim of the present study was to investigate age-related modulations in action feature binding processes and to shed light on underlying neurophysiological development from preadolescence to early adulthood. We examined a group of healthy participants (n = 61) between 10 and 30 yr of age, who performed a task that requires a series of bimanual response selections in an embedded paradigm. For an in-depth analysis of the underlying neural correlates, we applied EEG signal decomposition together with source localization analyses. Behavioral results across the whole group did not show binding effects in reaction times but in intraindividual response variability. From age 10 to 30 yr, there was a decrease in reaction times and reaction time variability but no age-related effect in action file binding. The latter were corroborated by Bayesian data analyses. On the brain level, the developmental effects on response selection were associated with activation modulations in the superior parietal cortex (BA7). The results show that mechanisms of action execution and speed, but not those of action feature binding, are subject to age-related changes between the age of 10 and 30 yr.NEW & NOTEWORTHY Different aspects of an action need to be integrated to allow smooth unfolding of behavior. We examine developmental effects in these processes and show that mechanisms of action execution and speed, but not those of action feature binding, are subject to age-related changes between the age of 10 and 30 yr.