scholarly journals Stability of spectral estimates in resting-state magnetoencephalography: recommendations for minimal data duration with neuroanatomical specificity

2021 ◽  
Author(s):  
Alex I Wiesman ◽  
Jason da Silva Castanheira ◽  
Sylvain Baillet
Keyword(s):  
Resting State ◽  
Complex Dynamics ◽  
Brain Activity ◽  
Special Populations ◽  
Data Repositories ◽  
Spectral Estimates ◽  
Signal Features ◽  
Cortical Regions ◽  
The Stability ◽  
Leave One Out

The principle of resting-state paradigms is appealing and practical for collecting data from impaired patients and special populations, especially if data collection times can be minimized. To achieve this goal, researchers need to ensure estimated signal features of interest are robust. In electro- and magnetoencephalography (EEG, MEG) we are not aware of studies of the minimal length of recording required to yield a robust one-session snapshot of the frequency-spectrum derivatives that are typically used to characterize the complex dynamics of the brain's resting-state. We aimed to fill this knowledge gap by studying the stability of common spectral measures of resting-state MEG source time series obtained from large samples of single-session recordings from shared data repositories featuring different recording conditions and instrument technologies (OMEGA: N = 107; Cam-CAN: N = 50). We discovered that the rhythmic and arrhythmic spectral properties of intrinsic brain activity can be robustly estimated in most cortical regions when derived from relatively short recordings of 30-s to 120-s of resting-state data, regardless of instrument technology and resting-state paradigm. Using an adapted leave-one-out approach and Bayesian analysis, we also provide evidence that the stability of spectral features over time is unaffected by age, sex, handedness, and general cognitive function. In summary, short MEG sessions are sufficient to yield robust estimates of frequency-defined brain activity during resting-state. This study may help guide future empirical designs in the field, particularly when recording times need to be minimized, such as with patient or special populations.

scholarly journals Brief segments of neurophysiological activity enable individual differentiation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jason da Silva Castanheira ◽  
Hector Domingo Orozco Perez ◽  
Bratislav Misic ◽  
Sylvain Baillet
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Complex Dynamics ◽  
Brain Activity ◽  
Resting State Functional Connectivity ◽  
Neural Data ◽  
Individual Traits ◽  
Spectral Signal ◽  
The Rich ◽  
Individual Differentiation

AbstractLarge, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual’s neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.

Brain Activity During Resting State in Relation to Academic Performance

2014 ◽  
Vol 28 (2) ◽  
pp. 47-53 ◽  
Author(s):  
Mei-chun Cheung ◽  
Agnes S. Chan ◽  
Yvonne M. Han ◽  
Sophia L. Sze
Keyword(s):  
Academic Performance ◽  
Resting State ◽  
Brain Activity ◽  
Power Spectra ◽  
Eeg Coherence ◽  
Grade Point ◽  
Eyes Open ◽  
Neural Efficiency ◽  
Cortical Regions ◽  
The Relationship

EEG coherence has been widely used to investigate brain activity and learning. However, relatively little is known about the relationship between resting-state EEG coherence and academic performance. The present study investigated this relationship with 140 healthy, normal participants. EEG was recorded during resting periods, with eyes open for 3 min, and the recordings were analyzed for 64 electrode positions in the theta (4–8 Hz), alpha (8–12 Hz), and beta (12–25 Hz) frequency bands. Coherence, defined as the spectral cross-correlation between two signals normalized by their power spectra, was calculated. Short- and long-range intrahemispheric coherence within each hemisphere and interhemispheric coherence across the left and right hemispheres were then computed and compared for each of the theta, alpha, and beta bands. The results showed that academic performance, as measured by grade point average (GPA), was negatively correlated with short-range intrahemispheric alpha and beta coherences in both hemispheres and with interhemispheric alpha and beta coherences in the temporal cortical regions. Therefore, better academic performers demonstrated more decoupling of brain areas when resting with eyes open. This is consistent with a model that relates decreased coherence to neural efficiency.

scholarly journals MEG, myself, and I: individual identification from neurophysiological brain activity

2021 ◽  
Author(s):  
Jason Da Silva Castanheira ◽  
Hector D Orozco ◽  
Bratislav Misic ◽  
Sylvain Baillet
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Complex Dynamics ◽  
Brain Activity ◽  
Individual Identification ◽  
Resting State Functional Connectivity ◽  
Individual Traits ◽  
Spectral Signal ◽  
The Rich ◽  
Individual Differentiation

AbstractLarge, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. This variability challenges the sensitivity and specificity of analysis methods. Yet, recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully identify individuals within a cohort and predict their individual traits, yielding the notion of a neural fingerprint. Here, we aimed to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of magnetoencephalography (MEG) resting-state brain activity in 158 participants. The resulting neurophysiological functional connectomes enabled the identification of individuals with similar identifiability rates to fMRI. We also show that individual identification was equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data indicate that identifiability can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: individuals remain identifiable from recordings performed weeks after their baseline reference data was collected. We can anticipate a vast range of diverse applications in personalized, clinical and basic neuroscience of individual differentiation from large-scale neural electrophysiology, in future longitudinal and cross-section studies.

scholarly journals Jumping over baselines with new methods to predict activation maps from resting-state fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Lacosse ◽  
Klaus Scheffler ◽  
Gabriele Lohmann ◽  
Georg Martius
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Behavioral Differences ◽  
Large Variability ◽  
Single Vertex ◽  
Spontaneous Brain Activity ◽  
New Methods ◽  
Group Averaging ◽  
Proposed Modification

AbstractCognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

scholarly journals The impact of hyperoxia on brain activity: A resting-state and task-evoked electroencephalography (EEG) study

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0176610 ◽  
Author(s):  
Min Sheng ◽  
Peiying Liu ◽  
Deng Mao ◽  
Yulin Ge ◽  
Hanzhang Lu
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
The Impact ◽  
And Task

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

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