EEG source-space synchrostate transitions and Markov modeling in the math-gifted brain during a long-chain reasoning task

To reveal transition dynamics of global neuronal networks of math‐gifted adolescents in handling long‐chain reasoning, this study explores momentary phase‐synchronized patterns, that is, electroencephalogram (EEG) synchrostates, of intracerebral sources sustained in successive 50 ms time windows during a reasoning task and non‐task idle process. Through agglomerative hierarchical clustering for functional connectivity graphs and nested iterative cosine similarity tests, this study identifies seven general and one reasoning‐specific prototypical functional connectivity patterns from all synchrostates. Markov modeling is performed for the time‐sequential synchrostates of each trial to characterize the interstate transitions. The analysis reveals that default mode network, central executive network (CEN), dorsal attention network, cingulo‐opercular network, left/right ventral frontoparietal network, and ventral visual network aperiodically recur over non‐task or reasoning process, exhibiting high predictability in interactively reachable transitions. Compared to non‐gifted subjects, math‐gifted adolescents show higher fractional occupancy and mean duration in CEN and reasoning‐triggered transient right frontotemporal network (rFTN) in the time course of the reasoning process. Statistical modeling of Markov chains reveals that there are more self‐loops in CEN and rFTN of the math‐gifted brain, suggesting robust state durability in temporally maintaining the topological structures. Besides, math‐gifted subjects show higher probabilities in switching from the other types of synchrostates to CEN and rFTN, which represents more adaptive reconfiguration of connectivity pattern in the large‐scale cortical network for focused task‐related information processing, which underlies superior executive functions in controlling goal‐directed persistence and high predictability of implementing imagination and creative thinking during long‐chain reasoning

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies.

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings

On the Reliability of the EEG Microstate Approach.

EEG microstates represent functional brain networks observable in resting EEG recordings that remain stable for 40-120ms before rapidly switching into another network. It is assumed that microstate characteristics (i.e., durations, occurrences, percentage coverage, and transitions) may serve as neural markers of mental and neurological disorders and psychosocial traits. However, robust data on their retest-reliability are needed to provide the basis for this assumption. Furthermore, researchers currently use different methodological approaches that need to be compared regarding their consistency and suitability to produce reliable results. Based on an extensive dataset largely representative of western societies (2 days with two resting EEG measures each; day one: n = 583; day two: n = 542) we found good to excellent short-term retest-reliability of microstate durations, occurrences, and coverages (average ICCs = 0.874-0.920). There was good overall long-term retest-reliability of these microstate characteristics (average ICCs = 0.671-0.852), even when the interval between measures was longer than half a year, supporting the longstanding notion that microstate durations, occurrences, and coverages represent stable neural traits. Findings were robust across different EEG systems (64 vs. 30 electrodes), recording lengths (3 vs. 2 min), and cognitive states (before vs. after experiment). However, we found poor retest-reliability of transitions. There was good to excellent consistency of microstate characteristics across clustering procedures (except for transitions), and both procedures produced reliable results. Grand-mean fitting yielded more reliable results compared to individual fitting. Overall, these findings provide robust evidence for the reliability of the microstate approach

It's about Time

The purpose of this review/opinion paper is to argue that human cognitive neuroscience has focused too little attention on how the brain may use time and time-based coding schemes to represent, process, and transfer information within and across brain regions. Instead, the majority of cognitive neuroscience studies rest on the assumption of functional localization. Although the functional localization approach has brought us a long way towards a basic characterization of brain functional organization, there are methodological and theoretical limitations of this approach. Further advances in our understanding of neurocognitive function may come from examining how the brain performs computations and forms transient functional neural networks using the rich multi-dimensional information available in time. This approach rests on the assumption that information is coded precisely in time but distributed in space; therefore, measures of rapid neuroelectrophysiological dynamics may provide insights into brain function that cannot be revealed using localization-based approaches and assumptions. Space is not an irrelevant dimension for brain organization; rather, a more complete understanding of how brain dynamics lead to behavior dynamics must incorporate how the brain uses time-based coding and processing schemes

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies

Copyright © The Author(s) 2023. Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by “eyeballing” of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.Open access funding provided by University of Bern

Dreams and the daydream retrieval hypothesis

© 2020 American Psychological Association. Dreams and daydreams are as beguiling as they are intangible. Both share many features, from neurobiology to the sensed experience. Nevertheless, the specific narrative relationship between both, if any, remains uncertain. Theories of dream origins are many: from the psychodynamic royal road, to biological theories including Hebbian-based memory consolidation and a unified quantum brain theory that extends to waking and dreaming alike. Both the ephemeral nature of dreams, and an inability to simultaneously study their content and biology, renders them difficult to research from a conventional biomedical perspective. This leaves agreement on the fundamental properties of dreams as ambiguous, and even the state of consciousness enjoyed during sleep is contested. What is known is that the qualia and neurophysiological signature of dreams and daydreaming share many features. We propose further, from a subjective experientialist position, that dream content is specifically derived from daydreaming or mindful wandering (subserved by the default mode network). If substantiated, this concept offers a new insight into the origin of dreams

Quantifying Cognitive Workload and Mental Capacity from EEG Signals under Complex Cognitive Activities

The objective of the present research is to quantify the changes in cognitive workload and mental capacity from EEG signals when people are conducting complex cognitive activities. Design activities are good examples of complex cognitive activities that require simultaneous involvements of multiple cognitive functions including problem understanding, analyzing, evaluating, and creating. As one of the fundamental human activities, design activities are where a designer’s mental effort is applied to create product descriptions (design solutions) from an initial design problem, which involves looping and jumping among design problems, design knowledge, and design solutions. Using design activities as a starting point, the present study conducted a series of theoretical analyses and literature reviews to identify the opportunities and challenges for applying EEG to quantify designers' cognitive changes, including cognitive workload and mental capacity. The research objectives were formulated based on my pilot studies in applying and extending the stress model, leading to the methodology of the present research. A new framework (tEEG framework) has been proposed to address the identified challenges as a result of our past research attempts and theoretical analyses, which also serves as the foundation for the present research. Afterward, the proposed tEEG framework was applied for quantitatively monitoring changes in people's cognitive workload and mental capacity within and beyond the context of design, where mental capacity was considered as the umbrella of numerous cognitive factors including cognitive control. Finally, my future research goal is to apply the quantification results on cognitive workload and mental capacity to improving human mental effort under complex cognitive activities, which corresponds to the second research objective of the present research. Along this direction, the present research proposes a quantitative approach to elaborate the impact of cognitive workload and mental capacity on mental effort that has been verified by simulation results. My future research will continue to test the approach in cognitive experiments including the ongoing N-back study, aiming to bridge the gap between most existing cognitive studies and their applications in real life

Human Mental Workload: A Survey and a Novel Inclusive Definition

Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved