High Variability Periods in the EEG Distinguish Cognitive Brain States
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PROVIDER: S-EPMC10669877 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature
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Project description:Increasing evidence suggests that brain signal variability is an important measure of brain function reflecting information processing capacity and functional integrity. In this study, we examined how maturation from childhood to adulthood affects the magnitude and spatial extent of state-to-state transitions in brain signal variability, and how this relates to cognitive performance. We looked at variability changes between resting-state and task (a symbol-matching task with three levels of difficulty), and within trial (fixation, post-stimulus, and post-response). We calculated variability with multiscale entropy (MSE), and additionally examined spectral power density (SPD) from electroencephalography (EEG) in children aged 8-14, and in adults aged 18-33. Our results suggest that maturation is characterized by increased local information processing (higher MSE at fine temporal scales) and decreased long-range interactions with other neural populations (lower MSE at coarse temporal scales). Children show MSE changes that are similar in magnitude, but greater in spatial extent when transitioning between internally- and externally-driven brain states. Additionally, we found that in children, greater changes in task difficulty were associated with greater magnitude of modulation in MSE. Our results suggest that the interplay between maturational and state-to-state changes in brain signal variability manifest across different spatial and temporal scales, and influence information processing capacity in the brain.
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Project description:Changes in motor activity are core symptoms of mood episodes in bipolar disorder. The manic state is characterized by increased variance, augmented complexity and irregular circadian rhythmicity when compared to healthy controls. No previous studies have compared mania to euthymia intra-individually in motor activity. The aim of this study was to characterize differences in motor activity when comparing manic patients to their euthymic selves. Motor activity was collected from 16 bipolar inpatients in mania and remission. 24-h recordings and 2-h time series in the morning and evening were analyzed for mean activity, variability and complexity. Lastly, the recordings were analyzed with the similarity graph algorithm and graph theory concepts such as edges, bridges, connected components and cliques. The similarity graph measures fluctuations in activity reasonably comparable to both variability and complexity measures. However, direct comparisons are difficult as most graph measures reveal variability in constricted time windows. Compared to sample entropy, the similarity graph is less sensitive to outliers. The little-understood estimate Bridges is possibly revealing underlying dynamics in the time series. When compared to euthymia, over the duration of approximately one circadian cycle, the manic state presented reduced variability, displayed by decreased standard deviation (p = 0.013) and augmented complexity shown by increased sample entropy (p = 0.025). During mania there were also fewer edges (p = 0.039) and more bridges (p = 0.026). Similar significant changes in variability and complexity were observed in the 2-h morning and evening sequences, mainly in the estimates of the similarity graph algorithm. Finally, augmented complexity was present in morning samples during mania, displayed by increased sample entropy (p = 0.015). In conclusion, the motor activity of mania is characterized by altered complexity and variability when compared within-subject to euthymia.
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Project description:Children with Attention Deficit Hyperactivity Disorder (ADHD) have prominent deficits in sustained attention that manifest as elevated intra-individual response variability and poor decision-making. Influential neurocognitive models have linked attentional fluctuations to aberrant brain dynamics, but these models have not been tested with computationally rigorous procedures. Here we use a Research Domain Criteria approach, drift-diffusion modeling of behavior, and a novel Bayesian Switching Dynamic System unsupervised learning algorithm, with ultrafast temporal resolution (490 ms) whole-brain task-fMRI data, to investigate latent brain state dynamics of salience, frontoparietal, and default mode networks and their relation to response variability, latent decision-making processes, and inattention. Our analyses revealed that occurrence of a task-optimal latent brain state predicted decreased intra-individual response variability and increased evidence accumulation related to decision-making. In contrast, occurrence and dwell time of a non-optimal latent brain state predicted inattention symptoms and furthermore, in a categorical analysis, distinguished children with ADHD from controls. Importantly, functional connectivity between salience and frontoparietal networks predicted rate of evidence accumulation to a decision threshold, whereas functional connectivity between salience and default mode networks predicted inattention. Taken together, our computational modeling reveals dissociable latent brain state features underlying response variability, impaired decision-making, and inattentional symptoms common to ADHD. Our findings provide novel insights into the neurobiology of attention deficits in children.
| S-EPMC8589642 | biostudies-literature
Project description:BackgroundPreoperative cognitive reserve and brain integrity may explain commonly observed intraoperative fluctuations seen on a standard anesthesia depth monitor used ubiquitously in operating rooms throughout the nation. Neurophysiological variability indicates compromised regulation and organization of neural networks. Based on theories of neuronal integrity changes that accompany aging, we assessed the relative contribution of: 1) premorbid cognitive reserve, 2) current brain integrity (gray and white matter markers of neurodegenerative disease), and 3) current cognition (specifically domains of processing speed/working memory, episodic memory, and motor function) on intraoperative neurophysiological variability as measured from a common intraoperative tool, the Bispectral Index Monitor (BIS).MethodsThis sub-study included participants from a parent study of non-demented older adults electing unilateral Total Knee Arthroplasty (TKA) with the same surgeon and anesthesia protocol, who also completed a preoperative neuropsychological assessment and preoperative 3T brain magnetic resonance imaging scan. Left frontal two-channel derived EEG via the BIS was acquired preoperatively (un-medicated and awake) and continuously intraoperatively with time from tourniquet up to tourniquet down. Data analyses used correlation and regression modeling.ResultsFifty-four participants met inclusion criteria for the sub-study. The mean (SD) age was 69.5 (7.4) years, 54% were male, 89% were white, and the mean (SD) American Society of Anesthesiologists score was 2.76 (0.47). We confirmed that brain integrity positively and significantly associated with each of the cognitive domains of interest. EEG intra-individual variability (squared deviation from the mean BIS value between tourniquet up and down) was significantly correlated with cognitive reserve (r = -.40, p = .003), brain integrity (r = -.37, p = .007), and a domain of processing speed/working memory (termed cognitive efficiency; r = -.31, p = .021). Hierarchical regression models that sequentially included age, propofol bolus dose, cognitive reserve, brain integrity, and cognitive efficiency found that intraoperative propofol bolus dose (p = .001), premorbid cognitive reserve (p = .008), and current brain integrity (p = .004) explained a significant portion of intraoperative intra-individual variability from the BIS monitor.ConclusionsOlder adults with higher premorbid reserve and less brain disease were more stable intraoperatively on a depth of anesthesia monitor. Researchers need to replicate findings within larger cohorts and other surgery types.
| S-EPMC6532861 | biostudies-literature
Project description:A common behavioral marker of optimal attention focus is faster responses or reduced response variability. Our previous study found two dominant brain states during sustained attention, and these states differed in their behavioral accuracy and reaction time (RT) variability. However, RT distributions are often positively skewed with a long tail (i.e., reflecting occasional slow responses). Therefore, a larger RT variance could also be explained by this long tail rather than the variance around an assumed normal distribution (i.e., reflecting pervasive response instability based on both faster and slower responses). Resolving this ambiguity is important for better understanding mechanisms of sustained attention. Here, using a large dataset of over 20,000 participants who performed a sustained attention task, we first demonstrated the utility of the exGuassian distribution that can decompose RTs into a strategy factor, a variance factor, and a long tail factor. We then investigated which factor(s) differed between the two brain states using fMRI. Across two independent datasets, results indicate unambiguously that the variance factor differs between the two dominant brain states. These findings indicate that 'suboptimal' is different from 'slow' at the behavior and neural level, and have implications for theoretically and methodologically guiding future sustained attention research.
| S-EPMC8295386 | biostudies-literature
Project description:The human brain is a complex dynamical system, and how cognition emerges from spatiotemporal patterns of regional brain activity remains an open question. As different regions dynamically interact to perform cognitive tasks, variable patterns of partial synchrony can be observed, forming chimera states. We propose that the spatial patterning of these states plays a fundamental role in the cognitive organization of the brain and present a cognitively informed, chimera-based framework to explore how large-scale brain architecture affects brain dynamics and function. Using personalized brain network models, we systematically study how regional brain stimulation produces different patterns of synchronization across predefined cognitive systems. We analyze these emergent patterns within our framework to understand the impact of subject-specific and region-specific structural variability on brain dynamics. Our results suggest a classification of cognitive systems into four groups with differing levels of subject and regional variability that reflect their different functional roles.
| S-EPMC6447382 | biostudies-literature
Project description:IntroductionWe investigated the between-subject variability of EEG (electroencephalography) electrode placement from a simultaneously recorded EEG-fMRI (functional magnetic resonance imaging) dataset.MethodsNeuro-navigation software was used to localize electrode positions, made possible by the gel artifacts present in the structural magnetic resonance images. To assess variation in the brain regions directly underneath electrodes we used MNI coordinates, their associated Brodmann areas, and labels from the Harvard-Oxford Cortical Atlas. We outline this relatively simple pipeline with accompanying analysis code.ResultsIn a sample of 20 participants, the mean standard deviation of electrode placement was 3.94 mm in x, 5.55 mm in y, and 7.17 mm in z, with the largest variation in parietal and occipital electrodes. In addition, the brain regions covered by electrode pairs were not always consistent; for example, the mean location of electrode PO7 was mapped to BA18 (secondary visual cortex), whereas PO8 was closer to BA19 (visual association cortex). Further, electrode C1 was mapped to BA4 (primary motor cortex), whereas C2 was closer to BA6 (premotor cortex).ConclusionsOverall, the results emphasize the variation in electrode positioning that can be found even in a fixed cap. This may be particularly important to consider when using EEG positioning systems to inform non-invasive neurostimulation.
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Project description:This work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and their rapid reconfigurations at sub-second time scale in different conditions, (naming/spelling/rest) and modalities, (auditory/visual) and compare them to each other, (ii) validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG, such as the open question of optimal number of channels and number of regions of interest and (iii) allow the reproducibility of results obtained so far using HD-EEG. We hope that delivering these datasets will lead to the development of new methods that can be used to estimate brain cortical networks and to better understand the general functioning of the brain during rest and task. Data are freely available from https://openneuro.org .
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Project description:BackgroundAlthough blood pressure variability (BPV) has emerged as a novel risk factor for Alzheimer's disease, few studies have examined the effects of night BPV on brain structure and function. This study investigated the association of night BPV with brain atrophy and cognitive function changes.MethodsThe analysis included 1,398 participants with valid ambulatory blood pressure (BP) monitoring at baseline and both baseline and 4-year follow-up brain magnetic resonance images who were recruited from the Korean Genome and Epidemiology Study. Participants underwent a comprehensive neuropsychological test battery. BPV was derived from ambulatory BP monitoring and calculated as a standard deviation (SD) of 24-h and daytime and nighttime BP.ResultsDuring the median follow-up of 4.3 years, increased SD of night systolic or diastolic BP was an indicator of total brain volume reduction, while daytime BPV or night average BP was not associated with total brain volume changes. High SD of night systolic BP was associated with reduced gray matter (GM) volume, independent of average night BP, and use of antihypertensive drugs. It also was associated with a reduction of temporal GM volume, mostly driven by atrophy in the left entorhinal cortex and the right fusiform gyrus. In cognitive performance, high variability of night systolic BP was associated with a decrease in visual delayed recall memory and verbal fluency for the category.ConclusionIncreased night BPV, rather than night mean BP, was associated with reduced brain volume and cognitive decline. High night BPV could be an independent predictor for rapid brain aging in a middle-aged population.
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Project description:With wearable, relatively unobtrusive health monitors and smartphone sensors, it is increasingly easy to collect continuously streaming physiological data in a passive mode without placing much burden on participants. At the same time, smartphones provide the ability to survey participants to provide "ground-truth" reporting on psychological states, although this comes at an increased cost in participant burden. In this paper, we examined how analytical approaches from the field of machine learning could allow us to distill the collected physiological data into actionable decision rules about each individual's psychological state, with the eventual goal of identifying important psychological states (e.g., risk moments) without the need for ongoing burdensome active assessment (e.g., self-report). As a first step towards this goal, we compared two methods: (1) a k-nearest neighbor classifier that uses dynamic time warping distance, and (2) a random forests classifier to predict low and high states of affective arousal states based on features extracted using the tsfresh python package. Then, we compared random-forest-based predictive models tailored for the individual with individual-general models. Results showed that the individual-specific model outperformed the general one. Our results support the feasibility of using passively collected wearable data to predict psychological states, suggesting that by relying on both types of data, the active collection can be reduced or eliminated.
| S-EPMC8982753 | biostudies-literature