Multiscale Entropy as a New Feature for EEG and fNIRS Analysis

The Physiologic Complexity of Prefrontal Oxygenation Dynamics Is Associated With Age and Executive Function: An Exploratory Study

BACKGROUND

The hemodynamics of prefrontal cortex (PFC) oxygenation are regulated by numerous processes operating over multiple temporal scales, producing complex patterns in its output fluctuations. Age may alter this multiscale regulation of PFC oxygenation, leading to diminished physiologic complexity of this important regulatory process. We aimed to characterize the effects of age on such complexity and its relationship to performance of an executive n-back task.

METHODS

Twenty-four younger (aged 28 ± 3 years) and 27 older (aged 78 ± 6 years) adults completed this study. Continuous oxygenation (HbO2) and deoxygenation (HHb) signals of PFC were recorded using functional near-infrared spectroscopy (fNIRS) while participants stood and watched a blank screen (blank), clicked a mouse when an X appeared (IdX), or when a letter was repeated from "2-back" in a sequence shown on a screen (2-back). We used multiscale entropy to quantify the HbO2 and HHb complexity of fNIRS signals.

RESULTS

Older adults exhibited lower HbO2 and HHb complexity compared to younger adults, regardless of task (p = .0005-.002). Both groups exhibited greater complexity during the IdX and 2-back than blank task (p = .02-.04). Across all participants, those with greater HbO2 and/or HHb complexity during the blank task exhibited faster IdX and 2-back reaction time (β = -0.56 to -0.6, p = .009-.02). Those demonstrating greater increase in HbO2 and/or HHb complexity from IdX to 2-back task had lower percent increase in reaction time from IdX to 2-back task (β = -0.41 to -0.37, p = .005-.01).

CONCLUSIONS

The complexity of fNIRS-measured PFC oxygenation fluctuations may capture the influence of aging on the regulation of prefrontal hemodynamics involved in executive-function-based task performance.

A simultaneous EEG-fNIRS dataset of the visual cognitive motivation study in healthy adults

This article described a publicly available dataset of the visual cognitive motivation study in healthy adults. To gain an in-depth understanding and insights into motivation, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were measured simultaneously at shared locations while participants performed a visual cognitive motivation task. The participants' choices in the cognitive motivation task were recorded. The effects of their motivation were identified in the recognition test afterward. This dataset comprised EEG and fNIRS data from sixteen healthy adults (age: 21- 37 years; 14 males and 2 females) during the cognitive motivation task with visual scenic stimuli. In addition, the motivation and the corresponding motivation effect were also provided. This dataset provides understanding and analyzing opportunities for the process of attention and decision while the brain undergoes an induced motivated state and its effect on the recognition performance.

Cortical thickness moderates intraindividual variability in prefrontal cortex activation patterns of older adults during walking

OBJECTIVE

Increased intraindividual variability (IIV) in behavioral and cognitive performance is a risk factor for adverse outcomes but research concerning hemodynamic signal IIV is limited. Cortical thinning occurs during aging and is associated with cognitive decline. Dual-task walking (DTW) performance in older adults has been related to cognition and neural integrity. We examined the hypothesis that reduced cortical thickness would be associated with greater increases in IIV in prefrontal cortex oxygenated hemoglobin (HbO2) from single tasks to DTW in healthy older adults while adjusting for behavioral performance.

METHOD

Participants were 55 healthy community-dwelling older adults (mean age = 74.84, standard deviation (SD) = 4.97). Structural MRI was used to quantify cortical thickness. Functional near-infrared spectroscopy (fNIRS) was used to assess changes in prefrontal cortex HbO2 during walking. HbO2 IIV was operationalized as the SD of HbO2 observations assessed during the first 30 seconds of each task. Linear mixed models were used to examine the moderation effect of cortical thickness throughout the cortex on HbO2 IIV across task conditions.

RESULTS

Analyses revealed that thinner cortex in several regions was associated with greater increases in HbO2 IIV from the single tasks to DTW (ps < .02).

CONCLUSIONS

Consistent with neural inefficiency, reduced cortical thickness in the PFC and throughout the cerebral cortex was associated with increases in HbO2 IIV from the single tasks to DTW without behavioral benefit. Reduced cortical thickness and greater IIV of prefrontal cortex HbO2 during DTW may be further investigated as risk factors for developing mobility impairments in aging.

Motivation-related influences on fNIRS signals during walking exercise: a permutation entropy approach

Cortical activity is typically indexed by analyzing functional near-infrared spectroscopy (fNIRS) signals in terms of the mean (e.g., mean oxygenated hemoglobin; HbO). Entropy approaches have been proposed as useful complementary methods for analyzing fNIRS signals. Entropy methods consider the regularity of a time series, and in doing so, may provide additional insights into the underlying dynamics of brain activity. Recent research using fNIRS found that non-disabled adults exhibit widespread increases in cortical activity and walk faster when under "extra motivation" conditions (e.g., verbal encouragement, lap timer) compared to trials without such motivators ("standard motivation"). This ancillary analysis of that study aimed to assess the extent to which fNIRS permutation entropy (PE) was affected by motivational conditions and explained variance in self-reported motivation. No regional PE differences were found between different motivational conditions. However, a greater difference in PE between motivational conditions (higher in standard, lower in extra motivation) in the anterior prefrontal cortex (aPFC) was associated with greater self-determined motivation. PE was also higher (less regular) in the primary sensorimotor cortex lower limb area compared to all other cortical areas analyzed, except the dorsal premotor cortex, regardless of motivational condition. This study provides early evidence to suggest that while different motivational environments during walking activity influence the magnitude of fNIRS signals, they may not influence the regularity of cortical signals. However, the magnitude of PE difference between motivational conditions was related to self-determined motivation in the aPFC, and this is an area warranting further investigation.

EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs

Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.

Decreased frontal lobe complexity in left-behind children during joint attention: a fNIRS study with multivariable and multiscale sample entropy analysis

Human brain development is shaped by experiences, especially during preschool, the critical period for cognitive and socioemotional development. This study employed the functional Near-Infrared Spectroscopy technique to explore the neural differences between left-behind children (LBC) and non-left-behind children (NLBC) on joint attention. Through collecting brain image data of 50 children (26 boys, aged 65.08 ± 6.28 months) and conducting multivariable and multiscale sample entropy (MMSE) analysis, the present study found that: (i) LBC showed lower brain complexity than NLBC in right prefrontal cortex; (ii) all participants demonstrated higher brain complexity in responding to joint attention conditions, compared to initiating joint attention ones; (iii) their brain complexity during joint attention was negatively associated with their emotional abilities. The findings advance our understanding of early brain development in LBC by providing evidence for the neural process characteristics of joint attention. Implications for early intervention to promote their brain development are also addressed.

Sensory-Processing Sensitivity Is Associated with Increased Neural Entropy

BACKGROUND

This study aimed at answering the following research questions: (1) Does the self-reported level of sensory-processing sensitivity (SPS) correlate with complexity, or criticality features of the electroencephalogram (EEG)? (2) Are there significant EEG differences comparing individuals with high and low levels of SPS?

METHODS

One hundred fifteen participants were measured with 64-channel EEG during a task-free resting state. The data were analyzed using criticality theory tools (detrended fluctuation analysis, neuronal avalanche analysis) and complexity measures (sample entropy, Higuchi's fractal dimension). Correlations with the 'Highly Sensitive Person Scale' (HSPS-G) scores were determined. Then, the cohort's lowest and the highest 30% were contrasted as opposites. EEG features were compared between the two groups by applying a Wilcoxon signed-rank test.

RESULTS

During resting with eyes open, HSPS-G scores correlated significantly positively with the sample entropy and Higuchi's fractal dimension (Spearman's ρ = 0.22, p < 0.05). The highly sensitive group revealed higher sample entropy values (1.83 ± 0.10 vs. 1.77 ± 0.13, p = 0.031). The increased sample entropy in the highly sensitive group was most pronounced in the central, temporal, and parietal regions.

CONCLUSION

For the first time, neurophysiological complexity features associated with SPS during a task-free resting state were demonstrated. Evidence is provided that neural processes differ between low- and highly-sensitive persons, whereby the latter displayed increased neural entropy. The findings support the central theoretical assumption of enhanced information processing and could be important for developing biomarkers for clinical diagnostics.

Modelling mouse auditory response dynamics along a continuum of consciousness using a deep recurrent neural network

Objective Understanding neurophysiological changes that accompany transitions between anaesthetized and conscious states is a key objective of anesthesiology and consciousness science. This study aimed to characterize the dynamics of auditory-evoked potential morphology in mice along a continuum of consciousness. Approach Epidural field potentials were recorded from above the primary auditory cortices of two groups of laboratory mice: urethane-anaesthetized (A, n = 14) and conscious (C, n = 17). Both groups received auditory stimulation in the form of a repeated pure-tone stimulus, before and after receiving 10 mg/kg i.p. ketamine (AK and CK). Evoked responses were then ordered by ascending sample entropy into AK, A, CK, and C, considered to reflect physiological correlates of awareness. These data were used to train a recurrent neural network (RNN) with an input parameter encoding state. Model outputs were compared with grand-average event-related potential (ERP) waveforms. Subsequently, the state parameter was varied to simulate changes in the ERP that occur during transitions between states, and relationships with dominant peak amplitudes were quantified. Main results The RNN synthesized output waveforms that were in close agreement with grand-average ERPs for each group (r2 > 0.9, p < 0.0001). Varying the input state parameter generated model outputs reflecting changes in ERP morphology predicted to occur between states. Positive peak amplitudes within 25 to 50 ms, and negative peak amplitudes within 50 to 75 ms post-stimulus-onset, were found to display a sigmoidal characteristic during the transition from anaesthetized to conscious states. In contrast, negative peak amplitudes within 0 to 25 ms displayed greater linearity. Significance This study demonstrates a method for modelling changes in ERP morphology that accompany transitions between states of consciousness using a RNN. In future studies, this approach may be applied to human data to support the clinical use of ERPs to predict transition to consciousness.

Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate 'danger' and 'safety' units

The ability to respond appropriately to sensory information received from the external environment is among the most fundamental capabilities of central nervous systems. In the auditory domain, processes underlying this behaviour are studied by measuring auditory‐evoked electrophysiology during sequences of sounds with predetermined regularities. Identifying neural correlates of ensuing auditory novelty responses is supported by research in experimental animals. In the present study, we reanalysed epidural field potential recordings from the auditory cortex of anaesthetised mice during frequency and intensity oddball stimulation. Multivariate pattern analysis (MVPA) and hierarchical recurrent neural network (RNN) modelling were adopted to explore these data with greater resolution than previously considered using conventional methods. Time‐wise and generalised temporal decoding MVPA approaches revealed previously underestimated asymmetry between responses to sound‐level transitions in the intensity oddball paradigm, in contrast with tone frequency changes. After training, the cross‐validated RNN model architecture with four hidden layers produced output waveforms in response to simulated auditory inputs that were strongly correlated with grand‐average auditory‐evoked potential waveforms (r² > .9). Units in hidden layers were classified based on their temporal response properties and characterised using principal component analysis and sample entropy. These demonstrated spontaneous alpha rhythms, sound onset and offset responses and putative ‘safety’ and ‘danger’ units activated by relatively inconspicuous and salient changes in auditory inputs, respectively. The hypothesised existence of corresponding biological neural sources is naturally derived from this model. If proven, this could have significant implications for prevailing theories of auditory processing.

The Application of Complexity Analysis in Brain Blood-Oxygen Signal

One of the daunting features of the brain is its physiology complexity, which arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various cognitive functions. As a reflection of the complexity of brain physiology, the complexity of brain blood-oxygen signal has been frequently studied in recent years. This paper reviews previous literature regarding the following three aspects: (1) whether the complexity of the brain blood-oxygen signal can serve as a reliable biomarker for distinguishing different patient populations; (2) which is the best algorithm for complexity measure? And (3) how to select the optimal parameters for complexity measures. We then discuss future directions for blood-oxygen signal complexity analysis, including improving complexity measurement based on the characteristics of both spatial patterns of brain blood-oxygen signal and latency of complexity itself. In conclusion, the current review helps to better understand complexity analysis in brain blood-oxygen signal analysis and provide useful information for future studies.

Altered complexity in resting-state fNIRS signal in autism: a multiscale entropy approach

Objective.Feature extraction and recognition in brain signal processing is of great significance for understanding the neurological mechanism of autism spectrum disorder (ASD). Resting-state (RS) functional near-infrared spectroscopy measurement provides a way to investigate the possible alteration in ASD-related complexity of resting-state (RS) functional near-infrared spectroscopy (fNIRS) signals and to explore the relationship between brain functional connectivity and complexity.Approach.Using the multiscale entropy (MSE) of fNIRS signals recorded from the bilateral temporal lobes (TLs) on 25 children with ASD and 22 typical development (TD) children, the pattern of brain complexity was assessed for both the ASD and TD groups.Main results.The quantitative analysis of MSE revealed the increased complexity in RS-fNIRS in children with ASD, particularly in the left temporal lobe. The complexity in the RS signal and resting state functional connectivity (RSFC) were also observed to exhibit negative correlation in the medium magnitude.Significance.These results indicated that the MSE might serve as a novel measure for RS-fNIRS signals in characterizing and understanding ASD.

Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence.

Dynamical diversity of mitochondrial BK channels located in different cell types

Mitochondrial large-conductance voltage- and Ca²⁺-activated potassium channels (mitoBK) exhibit substantial similarities in their physiology regardless of the channel's location. Nevertheless, depending on the cell type, composition of membranes can vary, and mitoBK channels can be expressed in different splice variants as well as they can be co-assembled with different types of auxiliary β subunits. These factors can modulate their voltage- and Ca²⁺-sensitivity, and single-channel current kinetics. It is still an open question to what extent the mentioned factors can affect the complexity of the conformational dynamics of the mitoBK channel gating. In this work the dynamical diversity of mitoBK channels from different cell types was unraveled by the use of nonlinear methods of analysis: multifractal detrended fluctuation analysis (MFDFA) and multiscale entropy (MSE). These techniques were applied to the experimental series of single channel currents. It turns out that the differences in the mitoBK expression systems influence gating machinery by changing the scheme of switching between the stable channel conformations, and affecting the average number of available channel conformations (this effect is visible for mitoBK channels in glioblastoma cells). The obtained results suggest also that a pathological dynamics can be represented by signals of relatively low complexity (low MSE of the mitoBK channel gating in glioblastoma).