Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

2019 ◽  
Vol 31 (11) ◽  
pp. 2177-2211 ◽  
Author(s):  
Saurabh Bhaskar Shaw ◽  
Kiret Dhindsa ◽  
James P. Reilly ◽  
Suzanna Becker
Keyword(s):  
Systems Analysis ◽  
Temporal Dynamics ◽  
Field Potential ◽  
Stable States ◽  
Dynamical Systems Analysis ◽  
Eeg Data ◽  
Show Evidence ◽  
Winner Takes All ◽  
Microstate Analysis ◽  
The Brain

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

scholarly journals Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis

2021 ◽  
Author(s):  
Mohammad Khazaei ◽  
Khadijeh Raeisi ◽  
Pierpaolo Croce ◽  
Gabriella Tamburro ◽  
Anton Tokariev ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Temporal Dynamics ◽  
Functional Organization ◽  
Nrem Sleep ◽  
Quiet Sleep ◽  
Functional Dynamics ◽  
Spatio Temporal ◽  
Eeg Recordings ◽  
Microstate Analysis ◽  
The Brain

AbstractNeonates spend most of their life sleeping. During sleep, their brain experiences fast changes in its functional organization. Microstate analysis permits to capture the rapid dynamical changes occurring in the functional organization of the brain by representing the changing spatio-temporal features of the electroencephalogram (EEG) as a sequence of short-lasting scalp topographies—the microstates. In this study, we modeled the ongoing neonatal EEG into sequences of a limited number of microstates and investigated whether the extracted microstate features are altered in REM and NREM sleep (usually known as active and quiet sleep states—AS and QS—in the newborn) and depend on the EEG frequency band. 19-channel EEG recordings from 60 full-term healthy infants were analyzed using a modified version of the k-means clustering algorithm. The results show that ~ 70% of the variance in the datasets can be described using 7 dominant microstate templates. The mean duration and mean occurrence of the dominant microstates were significantly different in the two sleep states. Microstate syntax analysis demonstrated that the microstate sequences characterizing AS and QS had specific non-casual structures that differed in the two sleep states. Microstate analysis of the neonatal EEG in specific frequency bands showed a clear dependence of the explained variance on frequency. Overall, our findings demonstrate that (1) the spatio-temporal dynamics of the neonatal EEG can be described by non-casual sequences of a limited number of microstate templates; (2) the brain dynamics described by these microstate templates depends on frequency; (3) the features of the microstate sequences can well differentiate the physiological conditions characterizing AS and QS.

scholarly journals A study of changes in cognitive evoked potentials in persons with visual impairment

2021 ◽  
Vol 273 ◽  
pp. 10051
Author(s):  
Anton Selskii ◽  
Maksim Zhuravlev ◽  
Anastasiia Runnova ◽  
Elena Grinina ◽  
Marina Konovalova ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Evoked Potential ◽  
Temporal Dynamics ◽  
Wilcoxon Test ◽  
Brain Electrical Activity ◽  
Potential Analysis ◽  
Statistical Comparison ◽  
Cognitive Evoked Potentials ◽  
Eeg Data ◽  
The Brain

In this work we have used psychophysiological assessments of the human brain electrical activity according to the classical neurological method for calculating the evoked potential. The experiment was designed to extraction cognitive evoked potentials. Taking into account the characteristic components, the temporal dynamics of the EEG data channels was investigated. This approach allows one to consistently assess the distribution of all components of the evoked potential on the subject's head map. Based on the results of evoked potentials processing, a statistical comparison of the components of evoked potentials in subjects of different groups by channels was carried out in accordance with the Wilcoxon test. Demonstrated for which channels the results significantly differ between the two groups of subjects. The sequence of evoked potential analysis demonstrated in the article suits for adjusting the settings of the “brain-computer” systems for a particular subject and allows to select channels used in further BCI training efficiently.

scholarly journals Overlapping neural representations for the position of visible and imagined objects

2020 ◽  
Author(s):  
Amanda K. Robinson ◽  
Tijl Grootswagers ◽  
Sophia M. Shatek ◽  
Jack Gerboni ◽  
Alex O. Holcombe ◽  
...  
Keyword(s):  
Moving Objects ◽  
Temporal Dynamics ◽  
Visual Signals ◽  
Time Resolved ◽  
Attentional Processes ◽  
Visual Processes ◽  
Eeg Data ◽  
Tracking Model ◽  
Multivariate Pattern ◽  
The Brain

AbstractHumans can covertly track the position of an object, even if the object is temporarily occluded. What are the neural mechanisms underlying our capacity to track moving objects when there is no physical stimulus for the brain to track? One possibility is that the brain “fills-in” information about imagined objects using internally generated representations similar to those generated by feed-forward perceptual mechanisms. Alternatively, the brain might deploy a higher order mechanism, for example using an object tracking model that integrates visual signals and motion dynamics (Kwon et al., 2015). In the present study, we used electroencephalography (EEG) and time-resolved multivariate pattern analyses to investigate the spatial processing of visible and imagined objects. Participants tracked an object that moved in discrete steps around fixation, occupying six consecutive locations. They were asked to imagine that the object continued on the same trajectory after it disappeared and move their attention to the corresponding positions. Time-resolved decoding of EEG data revealed that the location of the visible stimuli could be decoded shortly after image onset, consistent with early retinotopic visual processes. For processing of unseen/imagined positions, the patterns of neural activity resembled stimulus-driven mid-level visual processes, but were detected earlier than perceptual mechanisms, implicating an anticipatory and more variable tracking mechanism. Encoding models revealed that spatial representations were much weaker for imagined than visible stimuli. Monitoring the position of imagined objects thus utilises similar perceptual and attentional processes as monitoring objects that are actually present, but with different temporal dynamics. These results indicate that internally generated representations rely on top-down processes, and their timing is influenced by the predictability of the stimulus. All data and analysis code for this study are available at https://osf.io/8v47t/.

scholarly journals Stability and flexibility in multisensory sampling: insights from perceptual illusions

2019 ◽  
Vol 121 (5) ◽  
pp. 1588-1590 ◽  
Author(s):  
Luca Casartelli
Keyword(s):  
Temporal Dynamics ◽  
Multisensory Processing ◽  
General Idea ◽  
Perceptual Illusions ◽  
The Brain ◽  
Multisensory Processes

Neural, oscillatory, and computational counterparts of multisensory processing remain a crucial challenge for neuroscientists. Converging evidence underlines a certain efficiency in balancing stability and flexibility of sensory sampling, supporting the general idea that multiple parallel and hierarchically organized processing stages in the brain contribute to our understanding of the (sensory/perceptual) world. Intriguingly, how temporal dynamics impact and modulate multisensory processes in our brain can be investigated benefiting from studies on perceptual illusions.

scholarly journals The brain timewise: how timing shapes and supports brain function

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Generative Models ◽  
Network Activity ◽  
Brain Diseases ◽  
Specific Information ◽  
Non Invasive ◽  
The Brain

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals Uncovering the spatio-temporal dynamics of value-based decision-making in the human brain: a combined fMRI–EEG study

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130473 ◽  
Author(s):  
Tobias Larsen ◽  
John P. O'Doherty
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Temporal Dynamics ◽  
Brain Regions ◽  
Choice Task ◽  
Binary Choice ◽  
Intraparietal Sulcus ◽  
Trial Onset ◽  
Eeg Data ◽  
Spatio Temporal

While there is a growing body of functional magnetic resonance imaging (fMRI) evidence implicating a corpus of brain regions in value-based decision-making in humans, the limited temporal resolution of fMRI cannot address the relative temporal precedence of different brain regions in decision-making. To address this question, we adopted a computational model-based approach to electroencephalography (EEG) data acquired during a simple binary choice task. fMRI data were also acquired from the same participants for source localization. Post-decision value signals emerged 200 ms post-stimulus in a predominantly posterior source in the vicinity of the intraparietal sulcus and posterior temporal lobe cortex, alongside a weaker anterior locus. The signal then shifted to a predominantly anterior locus 850 ms following the trial onset, localized to the ventromedial prefrontal cortex and lateral prefrontal cortex. Comparison signals between unchosen and chosen options emerged late in the trial at 1050 ms in dorsomedial prefrontal cortex, suggesting that such comparison signals may not be directly associated with the decision itself but rather may play a role in post-decision action selection. Taken together, these results provide us new insights into the temporal dynamics of decision-making in the brain, suggesting that for a simple binary choice task, decisions may be encoded predominantly in posterior areas such as intraparietal sulcus, before shifting anteriorly.

scholarly journals Using the past to estimate sensory uncertainty

2019 ◽  
Author(s):  
Ulrik Beierholm ◽  
Tim Rohe ◽  
Ambra Ferrari ◽  
Oliver Stegle ◽  
Uta Noppeney
Keyword(s):  
Temporal Dynamics ◽  
Visual Signals ◽  
Perceptual Inference ◽  
Reliability Estimates ◽  
Uncertainty Estimates ◽  
Classical Models ◽  
Past Experiences ◽  
Combine Information ◽  
Sensory Uncertainty ◽  
The Brain

AbstractTo form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discountingOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

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