Future-oriented thought, a projection of the self into the future to pre-experience an event, has been linked to default mode network (DMN). Previous studies showed that the DMN was generally divided into two subsystems: anterior part (aDMN) and posterior part (pDMN). The former is mostly related to self-referential mental thought and latter engages in episodic memory retrieval and scene construction. However, functional contribution of these two subsystems and functional connectivity between them during future-oriented thought has rarely been reported. Here, we investigated these issues by using an experimental paradigm that allowed prospective, episodic decisions concerning one's future (Future Self) to be compared with self-referential decisions about one's immediate present state (Present Self). Additionally, two parallel control conditions that relied on non-personal semantic knowledge (Future Non-Self Control and Present Non-Self Control) were conducted. Our results revealed that the aDMN was preferentially activated when participants reflected on their present states, whereas the pDMN exhibited preferentially activation when participants reflected on their personal future. Intriguingly, significantly decreased aDMN-pDMN connectivity was observed when thinking about their future relative to other conditions. These results support the notion that activation within these subsystems and connectivity between them contribute differently to future-oriented thought.

Previous work suggests that aging is associated with changes in risk taking but less is known about their underlying neural basis, such as the potential age differences in the neural processing of value and risk. The goal of the present study was to investigate adult age differences in functional neural responses in a naturalistic risk-taking task. Twenty-six young adults and 27 healthy older adults completed the Balloon Analogue Risk Task while undergoing functional magnetic resonance imaging. Young and older adults showed similar overt risk-taking behavior. Group comparison of neural activity in response to risky vs. control stimuli revealed similar patterns of activation in the bilateral striatum, anterior insula (AI) and ventromedial prefrontal cortex (vmPFC). Group comparison of parametrically modulated activity in response to continued pumping similarly revealed comparable results for both age groups in the AI and, potentially, the striatum, yet differences emerged for regional activity in the vmPFC. At whole brain level, insular, striatal and vmPFC activation was predictive of behavioral risk taking for young but not older adults. The current results are interpreted and discussed as preserved neural tracking of risk and reward in the AI and striatum, respectively, but altered value coding in the vmPFC in the two age groups. The latter finding points toward older adults exhibiting differential vmPFC-related integration and value coding. Furthermore, neural activation holds differential predictive validity for behavioral risk taking in young and older adults.

Partial directed coherence (PDC) computed from multivariate autoregressive (MVAR) model coefficient is increasingly being used to study directed functional connectivity between brain regions in the frequency domain based on electroencephalography (EEG) source signals. However, directly fitting MVAR model to the high-dimensional source signals is difficult. Besides, although PDC measurement often shows good results for simulated data, it is not clear to what extent the results for real data are physiologically plausible.

Concurrent EEG/fMRI recordings represent multiple, simultaneously active, regionally overlapping neuronal mass responses. To address the problems caused by the overlapping nature of these responses, we propose a parallel framework for Spatial-Temporal EEG/fMRI Fusion (STEFF). This technique adopts Independent Component Analysis (ICA) to recover the time-course and spatial mapping components from EEG and fMRI separately. These components are then linked concurrently in the spatial and temporal domain using an Empirical Bayesian (EB) model. This approach enables information one modality to be utilized as priors for the other and hence improves the spatial (for EEG) or temporal (for fMRI) resolution of the other modality. Consequently, STEFF achieves flexible and sparse matching among EEG and fMRI components with common neuronal substrates. Simulations under realistic noise conditions indicated that STEFF is a feasible and physiologically reasonable hybrid approach for spatiotemporal mapping of cognitive processing in the human brain.

Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, and has been widely used in various research fields of brain science. However, during the fMRI of the brain, ballistocardiogram (BCG) artifacts can seriously contaminate the EEG. As an unpaired problem, BCG artifact removal now remains a considerable challenge. Aiming to provide a solution, this paper proposed a novel modular generative adversarial network (GAN) and corresponding training strategy to improve the network performance by optimizing the parameters of each module. In this manner, we hope to improve the local representation ability of the network model, thereby improving its overall performance and obtaining a reliable generator for BCG artifact removal. Moreover, the proposed method does not rely on additional reference signal or complex hardware equipment. Experimental results show that, compared with multiple methods, the technique presented in this paper can remove the BCG artifact more effectively while retaining essential EEG information.

The Coronavirus disease of 2019 (COVID-19) and measures to curb it created population-level changes in male-dominant impulsive and risky behaviors such as violent crimes and gambling. One possible explanation for this is that the pandemic has been stressful, and males, more so than females, tend to respond to stress by altering their focus on immediate versus delayed rewards, as reflected in their delay discounting rates. Delay discounting rates from healthy undergraduate students were collected twice during the pandemic. Discounting rates of males (n=190) but not of females (n=493) increased during the pandemic. Using machine learning, we show that prepandemic functional connectome predict increased discounting rates in males (n=88). Moreover, considering that delay discounting is associated with multiple psychiatric disorders, we found the same neural pattern that predicted increased discounting rates in this study, in secondary datasets of patients with major depression and schizophrenia. The findings point to sex-based differences in maladaptive delay discounting under real-world stress events, and to connectome-based neuromarkers of such effects. They can explain why there was a population-level increase in several impulsive and risky behaviors during the pandemic and point to intriguing questions about the shared underlying mechanisms of stress responses, psychiatric disorders and delay discounting.

Here we present a test-retest dataset of electroencephalogram (EEG) acquired at two resting (eyes open and eyes closed) and three subject-driven cognitive states (memory, music, subtraction) with both short-term (within 90 mins) and long-term (one-month apart) designs. 60 participants were recorded during three EEG sessions. Each session includes EEG and behavioral data along with rich samples of behavioral assessments testing demographic, sleep, emotion, mental health and the content of self-generated thoughts (mind wandering). This data enables the investigation of both intra- and inter-session variability not only limited to electrophysiological changes, but also including alterations in resting and cognitive states, at high temporal resolution. Also, this dataset is expected to add contributions to the reliability and validity of EEG measurements with open resource.

There is a growing interest in the study of signal processing and machine learning methods, which may make the brain computer interface (BCI) a new communication channel. A variety of classification methods have been utilized to convert the brain information into control commands. However, most of the methods only produce uncalibrated values and uncertain results.

Recent developments in graph theory have heightened the need for investigating the disruptions in the topological structure of functional brain network in major depressive disorder (MDD). In this study, we employed resting-state functional magnetic resonance imaging (fMRI) and graph theory to examine the whole-brain functional networks among 42 MDD patients and 42 healthy controls. Our results showed that compared with healthy controls, MDD patients showed higher local efficiency and modularity. Furthermore, MDD patients showed altered nodal centralities of many brain regions, including hippocampus, temporal cortex, anterior cingulate gyrus and dorsolateral prefrontal gyrus, mainly located in default mode network and cognitive control network. Together, our results suggested that MDD was associated with disruptions in the topological structure of functional brain networks, and provided new insights concerning the pathophysiological mechanisms of MDD.

Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) is considered as a powerful and non-invasive method that allows definition of the irritative zone. However, the complex interictal epileptic discharge (IED) may be present in some patients, and sometimes no active foci can be localized using General Linear Model (GLM) which is a widely adopted tool in EEG-fMRI study. The purpose of this study is to develop a new scheme to improve the detectability and localize the canonical HRF localizable foci.

A period of post-learning sleep benefits memory consolidation compared with an equal-length wake interval. However, whether this sleep-based memory consolidation changes as a function of age remains controversial. Here we report a meta-analysis that investigates the age differences in the sleep-based memory consolidation in two types of memory: declarative memory and procedural memory. The meta-analysis included 22 comparisons of the performance between young adults (N =640) and older adults (N =529) on behavioral tasks measuring sleep-based memory consolidation. Our results showed a significant overall sleep-based beneficial effect in young adults but not in older adults. However, further analyses suggested that the age differences were mainly manifested in sleep-based declarative memory consolidation but not in procedural memory consolidation. We discussed the possible underlying mechanisms for the age-related degradation in sleep-based memory consolidation. Further research is needed to determine the crucial components for sleep-related memory consolidation in older adults such as age-related changes in neurobiological and cardiovascular functions, which may play an important role in this context and have the potential to delineate the interrelationships between age-related changes in sleep and memory.

Sleep deprivation disrupted functional and structural brain areas which are associated with cognition and emotion in healthy participants. However, the effect of age on the structural changes after sleep restriction remains unclear. In the current study, gray matter volume was calculated in 43 young adults and 37 old adults before and after sleep restriction. Two-way mixed analysis of variance (between-subject factor: deprivation; within-subject factor: age) was then employed to investigate differences in gray matter volume changes between young and old adults. Gaussian random field theory was applied for multiple comparison correction. Results revealed that sleep restriction decreases gray matter volume in the right thalamus, left precuneus, and postcentral gyrus. More importantly, we found a significant deprivation × age interaction effect mainly in the right dorsal/ventral anterior insula where the gray matter volume increased in young adults after sleep restriction but showed no difference in old adults. These findings highlight the crucial role of the anterior insula in the neural mechanisms underlying sleep lose, especially among young adults. The current work provided structural evidence for describing emotional dysfunction and suggests the potential effect of age on functional and structural changes after sleep restriction.

Asthma exacerbations are associated with heightened asthma symptoms, which can result in hospitalization in severe cases. However, the molecular immunologic processes that determine the course of an exacerbation remain poorly understood, impeding the progression of development of effective therapies.

Recent studies have demonstrated that sleep not only facilitates memory consolidation but also benefits more complex cognitive skills such as decision-making in young adults. Older adults use different decision strategies compared with young adults, which leaves the role of sleep in older adults' decision-making unclear. We investigated the age-by-sleep effect on decision-making.

Characteristics from resting-state electroencephalography (rsEEG) provides relevant information about individual differences in cognitive tasks and personality traits. Due to its increasing application, it is crucial to know the reproducibility of several analysis measures of rsEEG.

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

Consciousness arises from the spatiotemporal neural dynamics, however, its relationship with neural flexibility and regional specialization remains elusive. We identified a consciousness-related signature marked by shifting spontaneous fluctuations along a unimodal-transmodal cortical axis. This simple signature is sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics and in psychosis. The hierarchical dynamic reflects brain state changes in global integration and connectome diversity under task-free conditions. Quasi-periodic pattern detection revealed that hierarchical heterogeneity as spatiotemporally propagating waves linking to arousal. A similar pattern can be observed in macaque electrocorticography. Furthermore, the spatial distribution of principal cortical gradient preferentially recapitulated the genetic transcription levels of the histaminergic system and that of the functional connectome mapping of the tuberomammillary nucleus, which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we propose that global consciousness is supported by efficient hierarchical processing constrained along a low-dimensional macroscale gradient.

Sleep deprivation (SD) has a negative influence on mood and emotion processing, and previous studies have elucidated the impaired coupling within the default network (DN) after SD. However, the dynamic characteristic with high temporal precision was rarely investigated in the DN after SD.

The global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined.

Chronotype is an appropriate variable to investigate sleep homeostatic and circadian rhythm. Based on functional MRI, the resting-state functional connectivity (rsFC) of insula-angular decrease with the increase in homeostatic sleep pressure (HSP). However, the distinct neural response of different chronotype remained to be clarified. Therefore, we investigated how HSP influenced insular-angular neural interaction of different chronotype.