Two views of brain function

Investigation of the transcriptome and metabolome of the cerebral cortex and testes in Cntnap4-deficient mice

BACKGROUND

Autism spectrum disorder (ASD) involves challenges in social interaction and communication and repetitive behaviours. CNTNAP4 is implicated in neuronal signalling, and its deficiency plays a role in ASD. Transcriptomic analyses revealed similar gene expression between the brain and in humans as well as in mice. However, the relationships between the brain and testicular gene expression profiles and metabolism in ASD remain unclear. In this study, the effects of Cntnap4 deletion on gene expression and metabolic profiles in the cerebral cortex and testes were investigated to better understand ASD pathogenesis.

METHODS

Cntnap4 knockout mice were used to explore transcriptomic and metabolomic alterations. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were employed to identify significantly altered pathways.

RESULTS

Cntnap4 deletion caused significant changes in both tissues. In the cerebral cortex, GO and KEGG analyses revealed differentially expressed genes (DEGs) related to mitochondrial energy production and synaptic signalling. Metabolomic analysis revealed altered levels of metabolites such as glutamic acid and glutamine. In the testes, 482 DEGs were linked to mitochondrial function and steroid biosynthesis. Additionally, commonly downregulated genes in both tissues highlighted disruptions in antioxidant activity and glutathione metabolism.

CONCLUSIONS

These findings suggest that Cntnap4 deletion impacts mitochondrial function, synaptic signalling, and metabolic processes, contributing to the ASD phenotype. By highlighting these mechanisms, this study provides insights into ASD pathogenesis and potential molecular targets for treatment and highlights the importance of the mitochondrial and synaptic pathways in the development of ASD associated with Cntnap4 deficiency.

Bridging the gap of brain and experience - Converging Neurophenomenology with Spatiotemporal Neuroscience

Neuroscience faces the challenge of connecting brain and mind, with the mind manifesting in first-person experience while the brain's neural activity can only be investigated in third-person perspective. To connect neural and mental states, Neurophenomenology provides a methodological toolkit for systematically linking first-person subjective experience with third-person objective observations of the brain's neural activity. However, beyond providing a systematic methodological strategy ('disciplined circularity'), it leaves open how neural activity and subjective experience are related among themselves, independent of our methodological strategy. The recently introduced Spatiotemporal Neuroscience suggests that neural activity and subjective experience share a commonly underlying feature as their "common currency", notably analogous spatiotemporal dynamics. Can Spatiotemporal Neuroscience inform Neurophenomenology to allow for a deeper and more substantiative connection of first-person experience and third-person neural activity? The goal of our paper is to show how Spatiotemporal Neuroscience and Neurophenomenology can be converged and integrated with each other to gain better understanding of the brain-mind connection. We describe their convergence on theoretical grounds which, subsequently, is illustrated by empirical examples like self, meditation, and depression. In conclusion, we propose that the integration of Neurophenomenology and Spatiotemporal Neuroscience can provide complementary insights, enrich both fields, allows for deeper understanding of brain-mind connection, and opens the door for developing novel methodological approaches in their empirical investigation.

Temporal Effects of Hypoxia Exposure at High Altitudes on Compensatory Brain Function: Evidence from Functional Connectivity of Resting-State EEG Brain Networks

Ren, Hong, Xi-Yue Yang, Rui Su, HaiLin Ma, and Hao Li. Temporal effects of hypoxia exposure at high altitudes on compensatory brain function: evidence from functional connectivity of resting-state EEG brain networks. High Alt Med Biol. 26:165-174, 2025. Background: The aim of this study was to investigate the effects of prolonged exposure to hypobaric hypoxia at high altitude on changes in brain function measured by electroencephalography (EEG), focusing specifically on the resting-state brain network functional connectivity and compensatory adaptations in brain function among individuals with varying durations of high altitude residency. Methods: In study I, 64 participants were divided into high-altitude group (HG) and low-altitude group (LG). Ninety-six long-term migrants residing at an altitude of 3,650 m were recruited for studyII and categorized into three groups based on their duration of stay at high altitude: group A (1-2 years), group B (8-10 years), and group C (18-20 years). Resting-state EEG data were collected from each participant, and functional connectivity analysis was conducted using Phase Locking Value. Results: Study I showed that participants with HG had stronger functional connectivity in the occipital lobe than those with LG (p < 0.05). The study II findings indicate that there were significant differences in functional connectivity strength among the frontal and occipital lobes in groups A, B, and C across the α, β, δ, and θ frequency bands. Specifically, the functional connectivity strength of the frontal lobe was significantly higher in group A compared with group B, and in group B compared with group C (p < 0.05). Additionally, the functional connectivity of the occipital lobe was significantly higher in group C compared with group B, and in group B compared with group A (p < 0.05). Conclusions: The consistent results of the whole frequency band suggest that the individual's occipital lobe function is enhanced to compensate for the damage of frontal lobe function, so as to better adapt to the extreme environment at high altitude.

Parietal cortex is recruited by frontal and cingulate areas to support action monitoring and updating during stopping

Recent evidence indicates that the intraparietal sulcus (IPS) may play a causal role in action stopping, potentially representing a novel neuromodulation target for inhibitory control dysfunctions. Here, we leverage intracranial recordings in human subjects to establish the timing and directionality of information flow between IPS and prefrontal and cingulate regions during action stopping. Prior to successful inhibition, information flows primarily from the inferior frontal gyrus (IFG), a critical inhibitory control node, to IPS. In contrast, during stopping errors the communication between IPS and IFG is lacking, and IPS is engaged by posterior cingulate cortex, an area outside of the classical inhibition network and typically associated with default mode. Anterior cingulate and orbitofrontal cortex also display performance-dependent connectivity with IPS. Our functional connectivity results provide direct electrophysiological evidence that IPS is recruited by frontal and anterior cingulate areas to support action plan monitoring and updating, and by posterior cingulate during control failures.

A timeline of cognitive costs in decision-making

Recent research from economics, psychology, cognitive science, computer science, and marketing is increasingly interested in the idea that people face cognitive costs when making decisions. Reviewing and synthesizing this research, we develop a framework of cognitive costs that organizes concepts along a temporal dimension and maps out when costs occur in the decision-making process and how they impact decisions. Our unifying framework broadens the scope of research on cognitive costs to a wider timeline of cognitive processing. We identify implications and recommendations emerging from our framework for intervening on behavior to tackle some of the most pressing issues of our day, from improving health and saving decisions to mitigating the consequences of climate change.

The benefit and neural mechanisms of computerized inhibitory control training for insomnia with short sleep duration phenotype: a rs-fMRI study

BACKGROUND

Inhibitory control (IC) impairment is characteristic of insomnia disorder with short sleep duration (ISSD), but not with normal sleep duration (INSD). IC is critical for sleep-wake regulation. This study evaluates whether computerized IC training can improve sleep in ISSD and explores related neural mechanisms using resting-state fMRI (rs-fMRI).

METHODS

Twenty ISSD patients participated in a three-week computerized IC training program (15 sessions), alongside a control group of 17 participants. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Insomnia Severity Index (ISI), complemented by objective measures from overnight EEG recordings. Neuroimaging analyses focused on changes in regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuations (fALFF), and functional connectivity (FC) in brain regions associated with IC.

RESULTS

Computerized IC training led to significant improvements in both subjective and objective sleep quality, demonstrated by reductions in PSQI and ISI scores, as well as decreased wake time during sleep. Neuroimaging revealed increased ReHo in the left medial orbitofrontal cortex (MOFC), elevated fALFF in the right middle frontal gyrus (MFG), and enhanced FC between the MOFC and the right rectus gyrus (RG), which correlated with improvements in sleep measures.

CONCLUSION

Computerized IC training appears to be an effective intervention for improving sleep in ISSD, likely by inducing functional changes in prefrontal cortex regions. These findings underscore the potential of IC-targeted treatments for ISSD and highlight the need for future research to evaluate the long-term effects of such interventions.

TRIAL REGISTRATION

The study was prospectively registered on May 30, 2024, in Chinese Clinical Trials registry (ChiCTR2400085063).

The predictive nature of spontaneous brain activity across scales and species

Emerging research suggests the brain operates as a "prediction machine," continuously anticipating sensory, motor, and cognitive outcomes. Central to this capability is the brain's spontaneous activity-ongoing internal processes independent of external stimuli. Neuroimaging and computational studies support that this activity is integral to maintaining and refining mental models of our environment, body, and behaviors, akin to generative models in computation. During rest, spontaneous activity expands the variability of potential representations, enhancing the accuracy and adaptability of these models. When performing tasks, internal models direct brain regions to anticipate sensory and motor states, optimizing performance. This review synthesizes evidence from various species, from C. elegans to humans, highlighting three key aspects of spontaneous brain activity's role in prediction: the similarity between spontaneous and task-related activity, the encoding of behavioral and interoceptive priors, and the high metabolic cost of this activity, underscoring prediction as a fundamental function of brains across species.

Cognitive control and consciousness in open biological systems

Thermodynamically open biological systems not only sustain a life-supporting mutual relationship with their environment by exchanging matter and energy but also constantly seek information to navigate probabilistic changes in their surroundings. This work argues that cognition and conscious thought should not be viewed in isolation but rather as parts of an integral control of biological systems to identify and act upon meaningful, semantic information to sustain viability. Under this framework, the development of key cognitive control capacities in centralized nervous systems and the resulting behavior are categorized into distinct Markov decision processes: decision-making with partially observable sensory exteroceptive and interoceptive information, learning and memory, and symbolic communication. It is proposed that the state of conscious thought arises from a control mechanism for speech production resembling actuator control in engineered systems. Also known as the phonological loop, this feedback from the motor to the sensory cortex provides a third type of information flowing into the sensory cortex. The continuous, dissipative loop updates the fleeting working memory and provides humans with an advanced layer of control through a sense of self, agency and perception of flow in time. These capacities define distinct degrees of information fitness in the evolution of information-powered organisms.

Modulation Effect of Physical Activity on Sleep Quality and Mental Hyperactivity in Higher-Education Students

Objectives: The present study seeks to analyze the relationships between the intensity of physical activity, mental hyperactivity and sleep quality. A comparative, descriptive and exploratory study was carried out. Methods: A sample of 1907 university students belonging to the degree of Physiotherapy and Physical Activity and Sport Sciences was used. The International Physical Activity and Mental Hyperactivity Questionnaires were used. The scale used was the Pittsburgh sleep quality index. The proposed model analyzes the relationships of physical activity with mental hyperactivity and various sleep-related factors. Results: The following fit indices were evaluated: Chi-Square = 80.242; Degrees of Freedom = 3; Incremental Fit Index = 0.951, Comparative Fit Index = 0.977; Normed Fit Index = 0.946; Root Mean Square Error of Approximation = 0.071. The values obtained show the good fit of the theoretical model. Statistically significant differences are observed (p < 0.05) in the causal relationship of mental hyperactivity with the personal assessment of sleep as a function of the intensity of physical activity. A greater effect of light (β = 0.671) compared to moderate- (β = 0.428) or vigorous-intensity (β = 0.343) physical activity in personal sleep assessment is evident. Statistically significant differences were also observed in the causal relationship of mental hyperactivity with the time to fall asleep (p < 0.05). Light physical activity (β = 0.479) has a greater causal relationship with time to fall asleep than moderate- (β = 0.302) or vigorous-intensity (β = 0.413) physical activity. Conclusions: Based on the results obtained, it is concluded that the intensity with which physical activity is performed has a modulating effect on sleep quality and mental hyperactivity.

Simultaneously capturing excessive variations and smooth dynamics of the underlying neural activity using spatiotemporal basis expansion and multisubject fMRI data

In the last decade, dictionary learning (DL) has gained popularity over independent component analysis (ICA) within the blind source separation (BSS) framework for functional magnetic resonance imaging (fMRI) signals. Despite its rising popularity, a primary challenge in DL remains model fitting. It is susceptible to overfitting because the conventional loss function strives to correspond too closely to the training data. However, in the case of multi-subject (MS) analysis, it becomes imperative to overfit in order to acquire the source diversities across different brains. In this paper, an attempt has been made to resolve this predicament by concurrently preserving and mitigating the effect of high variance. A novel algorithm named joint analysis and synthesis DL (JASDL) has been proposed that simultaneously learns the overfitted trends to retain the data-centric cross-subject diversities and wellfitted trends by adequately regularizing the model complexity. This fusion was achieved by benefiting from modeling each subject's data in terms of both spatiotemporal (ST) prior information (PI) and MS-ST components. The PI consisted of biological priors derived from neuroscience knowledge, such as brain network templates, and mathematical priors derived from basis functions, such as three-dimensional (3D) cubic basis splines (B-splines). In contrast, MS-ST components were estimated using the computationally most parsimonious sparse ST blind source separation (ssBSS) method. Using the proposed analysis/synthesis cost function that exploits tri and quad-factorization for matrix approximation, the JASDL algorithm can model temporal smoothness and spatial reduction of false positives while retaining MS variations. Its efficacy was evaluated by comparing it with existing DL techniques using both experimental and synthetic fMRI datasets. Overall, the mean of correlation and F-score was found to be [Formula: see text] higher for the JASDL synthesis dictionary than the state-of-the-art subject-wise sequential DL (swsDL).

Resting-state EEG network variability predicts individual working memory behavior

Even during periods of rest, the brain exhibits spontaneous activity that dynamically fluctuates across spatially distributed regions in a globally coordinated manner, which has significant cognitive implications. However, the relationship between the temporal variability of resting-state networks and working memory (WM) remains largely unexplored. This study aims to address this gap by employing an EEG-based protocol combined with fuzzy entropy. First, we identified both flexible and robust patterns of dynamic resting-state networks. Subsequently, we observed a significant positive correlation between WM performance and network variability, particularly in connections associated with the frontal, right central, and right parietal lobes. Moreover, we found that the temporal variability of network properties was positively and significantly associated with WM performance. Additionally, distinct patterns of network variability were delineated, contributing to inter-individual differences in WM abilities, with these distinctions becoming more pronounced as task demands increased. Finally, using a multivariable predictive model based on these variability metrics, we effectively predicted individual WM performances. Notably, analogous analyses conducted in the source space validated the reproducibility of the temporal variability of resting-state networks in predicting individual WM behavior at higher spatial resolution, providing more precise anatomical localization of key brain regions. These results suggest that the temporal variability of resting-state networks reflects intrinsic dynamic changes in brain organization supporting WM and can serve as an objective predictor for individual WM behaviors.

Neurological Manifestations Associated with Exercise at Altitude

PURPOSE OF REVIEW

The effects that exercise at altitude has on the neurological system are diverse and still not well studied, and range from metabolic adaptations to modification of cerebral blood flow and neurotransmitters. In this review we summarise changes with exercise intensity, the implications of ascent, cognitive impairment, psychosis-like symptoms, the role of exercise in the development and prevention of AMS, and use of free radical scavengers to enhance sports performance and acclimatization.

RECENT FINDINGS

We discuss the impact of oxidative stress in hypobaric hypoxia and reactive oxygen species (ROS) production and its consequences, with special focus on exercise at altitude. Finally we consider how moderate intensity exercise could help prevent AMS, and the necessity of research on high intensity exercise with elevated rate of ascent, the development of specific tools of cognitive assessment, and the role of free-radical scavengers in the prevention of AMS and neurological symptoms.

Real-world goal-directed behavior reveals aberrant functional brain connectivity in children with ADHD

Functional connectomics is a popular approach to investigate the neural underpinnings of developmental disorders of which attention deficit hyperactivity disorder (ADHD) is one of the most prevalent. Nonetheless, neuronal mechanisms driving the aberrant functional connectivity resulting in ADHD symptoms remain largely unclear. Whereas resting state activity reflecting intrinsic tonic background activity is only vaguely connected to behavioral effects, naturalistic neuroscience has provided means to measure phasic brain dynamics associated with overt manifestation of the symptoms. Here we collected functional magnetic resonance imaging (fMRI) data in three experimental conditions, an active virtual reality (VR) task where the participants execute goal-directed behaviors, a passive naturalistic Video Viewing task, and a standard Resting State condition. Thirty-nine children with ADHD and thirty-seven typically developing (TD) children participated in this preregistered study. Functional connectivity was examined with network-based statistics (NBS) and graph theoretical metrics. During the naturalistic VR task, the ADHD group showed weaker task performance and stronger functional connectivity than the TD group. Group differences in functional connectivity were observed in widespread brain networks: particularly subcortical areas showed hyperconnectivity in ADHD. More restricted group differences in functional connectivity were observed during the Video Viewing, and there were no group differences in functional connectivity in the Resting State condition. These observations were consistent across NBS and graph theoretical analyses, although NBS revealed more pronounced group differences. Furthermore, during the VR task and Video Viewing, functional connectivity in TD controls was associated with task performance during the measurement, while Resting State activity in TD controls was correlated with ADHD symptoms rated over six months. We conclude that overt expression of the symptoms is correlated with aberrant brain connectivity in ADHD. Furthermore, naturalistic paradigms where clinical markers can be coupled with simultaneously occurring brain activity may further increase the interpretability of psychiatric neuroimaging findings.

Parietal cortex is recruited by frontal and cingulate areas to support action monitoring and updating during stopping

Recent evidence indicates that the intraparietal sulcus (IPS) may play a causal role in action stopping, potentially representing a novel neuromodulation target for inhibitory control dysfunctions. Here, we leverage intracranial recordings in human subjects to establish the timing and directionality of information flow between IPS and prefrontal and cingulate regions during action stopping. Prior to successful inhibition, information flows primarily from the inferior frontal gyrus (IFG), a critical inhibitory control node, to IPS. In contrast, during stopping errors the communication between IPS and IFG is lacking, and IPS is engaged by posterior cingulate cortex, an area outside of the classical inhibition network and typically associated with default mode. Anterior cingulate and orbitofrontal cortex also display performance-dependent connectivity with IPS. Our functional connectivity results provide direct electrophysiological evidence that IPS is recruited by frontal and anterior cingulate areas to support action plan monitoring/updating, and by posterior cingulate during control failures.

Modulation of Cerebellar-Cortical Connectivity Induced by Modafinil and Its Relationship With Receptor and Transporter Expression

BACKGROUND

Modafinil is primarily used to treat narcolepsy but is also used as an off-label cognitive enhancer. Functional magnetic resonance imaging studies indicate that modafinil modulates the connectivity of neocortical networks primarily involved in attention and executive functions. However, much less is known about the drug's effects on subcortical structures. Following preliminary findings, we evaluated modafinil's activity on the connectivity of distinct cerebellar regions with the neocortex. We assessed the spatial relationship of these effects with the expression of neurotransmitter receptors/transporters.

METHODS

Patterns of resting-state functional magnetic resonance imaging connectivity were estimated in 50 participants from scans acquired pre- and postadministration of a single (100 mg) dose of modafinil (n = 25) or placebo (n = 25). Using specific cerebellar regions as seeds for voxelwise analyses, we examined modafinil's modulation of cerebellar-neocortical connectivity. Next, we conducted a quantitative evaluation of the spatial overlap between the modulation of cerebellar-neocortical connectivity and the expression of neurotransmitter receptors/transporters obtained by publicly available databases.

RESULTS

Modafinil increased the connectivity of crus I and vermis IX with prefrontal regions. Crus I connectivity changes were associated with the expression of dopaminergic D2 receptors. The vermis I-II showed enhanced coupling with the dorsal anterior cingulate cortex and matched the expression of histaminergic H3 receptors. The vermis VII-VIII displayed increased connectivity with the visual cortex, an activity associated with dopaminergic and histaminergic neurotransmission.

CONCLUSIONS

Our study reveals modafinil's modulatory effects on cerebellar-neocortical connectivity. The modulation mainly involves crus I and the vermis and spatially overlaps the distribution of dopaminergic and histaminergic receptors.

Exploring the transmission of cognitive task information through optimal brain pathways

Understanding the large-scale information processing that underlies complex human cognition is the central goal of cognitive neuroscience. While emerging activity flow models demonstrate that cognitive task information is transferred by interregional functional or structural connectivity, graph-theory-based models typically assume that neural communication occurs via the shortest path of brain networks. However, whether the shortest path is the optimal route for empirical cognitive information transmission remains unclear. Based on a large-scale activity flow mapping framework, we found that the performance of activity flow prediction with the shortest path was significantly lower than that with the direct path. The shortest path routing was superior to other network communication strategies, including search information, path ensembles, and navigation. Intriguingly, the shortest path outperformed the direct path in activity flow prediction when the physical distance constraint and asymmetric routing contribution were simultaneously considered. This study not only challenges the shortest path assumption through empirical network models but also suggests that cognitive task information routing is constrained by the spatial and functional embedding of the brain network.

Bioelectronic Therapeutics: A Revolutionary Medical Practice in Health Care

The emerging field of bioelectronic therapeutics unfolds great opportunities for treating numerous neurological and inflammatory conditions by utilizing the amalgamation of molecular medicine, neuroscience, engineering, and computing. These innovative treatments leverage advanced technology to precisely identify, design, and regulate electrical signaling patterns in the nervous system, addressing multiple diseases. Modifying neural signaling patterns to produce therapeutic effects at a particular organ may blur the lines between conventional medical practices. These modify the neurological behavior using electrical, magnetic, optical, and ultrasonic pulses through closed-loop systems to optimize neural behavior. The Food and Drug Administration (FDA) has approved numerous invasive and noninvasive bioelectronic devices, in the treatment of various neuronal diseases and non-neuronal diseases. Furthermore, the FDA has approved many devices for clinical studies. The field of bioelectronics encounters challenges in integrating with the health care system, including incomplete understanding of human nervous anatomy, neuronal function, membrane potential, and technological limitations. This review aims to explore bioelectronics therapeutics, their role or action in challenges to growth and their solutions, and the prospects of bioelectronic therapeutics.

Understanding the Link Between Functional Profiles and Intelligence Through Dimensionality Reduction and Graph Analysis

There is a growing interest in neuroscience for how individual-specific structural and functional features of the cortex relate to cognitive traits. This work builds on previous research which, by using classical high-dimensional approaches, has proven that the interindividual variability of functional connectivity (FC) profiles reflects differences in fluid intelligence. To provide an additional perspective into this relationship, the present study uses a recent framework for investigating cortical organization: functional gradients. This approach places local connectivity profiles within a common low-dimensional space whose axes are functionally interpretable dimensions. Specifically, this study uses a data-driven approach to model the association between FC variability and interindividual differences in intelligence. For one of these loci, in the right ventral-lateral prefrontal cortex (vlPFC), we describe an association between fluid intelligence and the relative functional distance of this area from sensory and high-cognition systems. Furthermore, the topological properties of this region indicate that, with decreasing functional affinity with high-cognition systems, vlPFC functional connections are more evenly distributed across all networks. Participating in multiple functional networks may reflect a better ability to coordinate sensory and high-order cognitive systems.

Dynamic changes in large-scale functional connectivity prior to stimulation determine performance in a multisensory task

Complex behavior and task execution require fast changes of local activity and functional connectivity in cortical networks at multiple scales. The roles that changes of power and connectivity play during these processes are still not well understood. Here, we study how fluctuations of functional cortical coupling across different brain areas determine performance in an audiovisual, lateralized detection task in the ferret. We hypothesized that dynamic variations in the network's state determine the animals' performance. We evaluated these by quantifying changes of local power and of phase coupling across visual, auditory and parietal regions. While power for hit and miss trials showed significant differences only during stimulus and response onset, phase coupling already differed before stimulus onset. An analysis of principal components in coupling at the single-trial level during this period allowed us to reveal the subnetworks that most strongly determined performance. Whereas higher global phase coupling of visual and auditory regions to parietal cortex was predictive of task performance, a second component revealed a reduction in coupling between subnetworks of different sensory modalities, probably to allow a better detection of the unimodal signals. Furthermore, we observed that long-range coupling became more predominant during the task period compared to the pre-stimulus baseline. Taken together, our results show that fluctuations in the network state, as reflected in large-scale coupling, are key determinants of the animals' behavior.

Altered spatiotemporal consistency and their genetic mechanisms in mild cognitive impairment: a combined neuroimaging and transcriptome study

The Four-dimensional (spatiotemporal) Consistency of local Neural Activities (FOCA) metric was utilized to assess spontaneous whole-brain activity. Despite its application, the genetic underpinnings of FOCA alterations in Alzheimer's Disease (AD)-related Mild Cognitive Impairment (MCI) remain largely unexplored. To elucidate these changes, we analyzed group FOCA differences in 41 MCI patients and 46 controls from the Alzheimer's Disease Neuroimaging Initiative database. Integrating the Allen Human Brain Atlas, we performed transcriptome-neuroimaging spatial association analyses to pinpoint genes correlating with MCI-related FOCA changes. We observed heightened FOCA in the frontal-parietal system and diminished FOCA in the temporal lobe and medium cingulate gyrus among MCI patients. These FOCA alterations were spatially linked to the expression of 384 genes, which were enriched in crucial molecular functions, biological processes, and cellular components of the cerebral cortex, as well as related pathways. These genes were specifically expressed in brain tissue and corticothalamic neurons, particularly during late cortical development. They also connected to various behavioral domains. Furthermore, these genes could form a protein-protein interaction network, supported by 34 hub genes. Our results suggest that local spatiotemporal consistency of spontaneous brain activity in MCI may stem from the complex interplay of a broad spectrum of genes with diverse functional features.

Links between brain structure and function in children with autism spectrum disorder by parallel independent component analysis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder accompanied by structural and functional changes in the brain. However, the relationship between brain structure and function in children with ASD remains largely obscure. In the current study, parallel independent component analysis (pICA) was performed to identify inter-modality associations by drawing on information from different modalities. Structural and resting-state functional magnetic resonance imaging data from 105 children with ASD and 102 typically developing children (obtained from the open-access Autism Brain Imaging Data Exchange database) were combined through the pICA framework. Features of structural and functional modalities were represented by the voxel-based morphometry (VBM) and amplitude of low-frequency fluctuations (ALFF), respectively. The relationship between the structural and functional components derived from the pICA was investigated by Pearson's correlation analysis, and between-group differences in these components were analyzed through the two-sample t-test. Finally, multivariate support vector regression analysis was used to analyze the relationship between the structural/functional components and Autism Diagnostic Observation Schedule (ADOS) subscores in the ASD group. This study found a significant association between VBM and ALFF components in ASD. Significant between-group differences were detected in the loading coefficients of the VBM component. Furthermore, the ALFF component loading coefficients predicted the subscores of communication and repetitive stereotypic behaviors of the ADOS. Likewise, the VBM component loading coefficients predicted the ADOS communication subscore in ASD. These findings provide evidence of a link between brain function and structure, yielding new insights into the neural mechanisms of ASD.

Whole-body networks: a holistic approach for studying aging

Aging is a multi-organ disease, yet the traditional approach has been to study each organ in isolation. Such organ-specific studies have provided invaluable information regarding its pathomechanisms. However, an overall picture of the whole-body network (WBN) during aging is still incomplete. In this study, we analyzed the functional magnetic resonance imaging blood-oxygen level-dependent, respiratory rate and heart rate time series of a young and an elderly group during eyes-open resting-state. We constructed WBNs by exploring the time-lagged coupling between the different organs. First, we showed that our analytical pipeline could identify regional differences in the networks of both cohorts, allowing us to proceed with the remaining analyses. The comparison of the WBNs revealed a complex relationship where some connections were stronger and some weaker in the elderly. Finally, the interconnectivity and segregation of the WBNs were negatively correlated with the short-term memory and verbal learning of the young participants. This study: i) validated our methodology, ii) identified differences in the WBNs of the two groups and iii) showed correlations of WBNs with behavioral measures. In conclusion, the concept of WBN shows great potential for the understanding of aging and age-related diseases.

The metabolic costs of cognition

Cognition and behavior are emergent properties of brain systems that seek to maximize complex and adaptive behaviors while minimizing energy utilization. Different species reconcile this trade-off in different ways, but in humans the outcome is biased towards complex behaviors and hence relatively high energy use. However, even in energy-intensive brains, numerous parsimonious processes operate to optimize energy use. We review how this balance manifests in both homeostatic processes and task-associated cognition. We also consider the perturbations and disruptions of metabolism in neurocognitive diseases.

Resting-state functional abnormalities in ischemic stroke: a meta-analysis of fMRI studies

Ischemic stroke is a leading neurological cause of severe disabilities and death in the world and has a major negative impact on patients' quality of life. However, the neural mechanism of spontaneous fluctuating neuronal activity remains unclear. This meta-analysis explored brain activity during resting state in patients with ischemic stroke including 22 studies of regional homogeneity, amplitude of low-frequency fluctuation, and fractional amplitude of low-frequency fluctuation (692 patients with ischemic stroke, 620 healthy controls, age range 35-80 years, 41% female, 175 foci). Results showed decreased regional activity in the bilateral caudate and thalamus and increased regional activity in the left superior occipital gyrus and left default mode network (precuneus/posterior cingulate cortex). Meta-analysis of the amplitude of low-frequency fluctuation studies showed that increased activity in the left inferior frontal gyrus was reduced across the progression from acute to chronic phases. These findings may indicate that disruption of the subcortical areas and default mode network could be one of the core functional abnormalities in ischemic stroke. Altered brain activity in the inferior frontal gyrus could be the imaging indicator of brain recovery/plasticity after stroke damage, which offers potential insight into developing prediction models and therapeutic strategies for ischemic stroke rehabilitation and recovery.

Unveiling Frequency-Specific Microstate Correlates of Anxiety and Depression Symptoms

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with anxiety and depression symptoms. We conducted clustering on EEG topographies in decomposed frequency bands (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their ability to predict the severity of depression and anxiety symptoms were systematically identified by correlation, regression and classification analyses. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate E (coverage, occurrence, transitions from B to E) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < 0.01). Similar results were found in classification of participants with high and low anxiety symptom scores (68% accuracy in alpha vs. 52% in broadband). These results suggested the value of frequency-specific microstates in predicting emotional symptoms.

Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models

Brain tumors are among the most fatal and devastating diseases, and they often result in a significant reduction in life expectancy. The devising of treatment plans that can extend the lives of affected individuals hinges on an accurate diagnosis of these tumors. Identifying and analyzing large volumes of magnetic resonance imaging (MRI) data manually proves to be both challenging and time-consuming. As a result, there exists a pressing need for a reliable machine-learning approach to accurately diagnose brain tumors, and numerous methods have already been proposed over the last decade. In this paper, a novel, comprehensive approach is proposed for identifying and classifying a given MR brain image as abnormal. Three common brain diseases, namely glioma, meningioma, and pituitary tumor, are chosen as abnormal brains, and the Figshare MRI brain image dataset was collected from the Kaggle and IEEE websites. The proposed method is initiated by employing 1st-order statistics, 2nd-order statistics, and higher-order transformed (DWT) feature extraction to extract features from images. Then missing data is addressed and handled using KNNImputer, followed by the application of the ExtratreesClassifier and PCA feature selection methods to identify the most relevant features and reduce the dimensions of these features. Subsequently, the reduced features are submitted to seven machine learning models, namely RF, GB, CB, SVM, LGBM, DT, and LR. The strategy of k-fold cross-validation is utilized to enhance the performance of those models. Finally, the models are evaluated using XAI approaches, which ensure transparent decision-making processes and provide insights into the model's predictions. Remarkably, our approach achieves the highest accuracy, precision, recall, F1 score, MCC, Kappa, AUC-ROC, and R2, as well as the lowest loss, among the seven models evaluated, proving its effectiveness and applicability in multiple analytic applications relying on publicly available datasets.

The temporal asymmetry of cortical dynamics as a signature of brain states

The brain is a complex non-equilibrium system capable of expressing many different dynamics as well as the transitions between them. We hypothesized that the level of non-equilibrium can serve as a signature of a given brain state, which was quantified using the arrow of time (the level of irreversibility). Using this thermodynamic framework, the irreversibility of emergent cortical activity was quantified from local field potential recordings in male Lister-hooded rats at different anesthesia levels and during the sleep-wake cycle. This measure was carried out on five distinct brain states: slow-wave sleep, awake, deep anesthesia-slow waves, light anesthesia-slow waves, and microarousals. Low levels of irreversibility were associated with synchronous activity found both in deep anesthesia and slow-wave sleep states, suggesting that slow waves were the state closest to the thermodynamic equilibrium (maximum symmetry), thus requiring minimum energy. Higher levels of irreversibility were found when brain dynamics became more asynchronous, for example, in wakefulness. These changes were also reflected in the hierarchy of cortical dynamics across different cortical areas. The neural dynamics associated with different brain states were characterized by different degrees of irreversibility and hierarchy, also acting as markers of brain state transitions. This could open new routes to monitoring, controlling, and even changing brain states in health and disease.

Is frontal EEG gamma power a neural correlate of language in toddlerhood? An examination of late talking and expressive language ability

Few studies have examined neural correlates of late talking in toddlers, which could aid in understanding etiology and improving diagnosis of developmental language disorder (DLD). Greater frontal gamma activity has been linked to better language skills, but findings vary by risk for developmental disorders, and this has not been investigated in late talkers. This study examined whether frontal gamma power (30-50 Hz), from baseline-state electroencephalography (EEG), was related to DLD risk (categorical late talking status) and a continuous measure of expressive language in n = 124 toddlers. Frontal gamma power was significantly associated with late talker status when controlling for demographic factors and concurrent receptive language (β = 1.96, McFadden's Pseudo R2 = 0.21). Demographic factors and receptive language did not significantly moderate the association between frontal gamma power and late talker status. A continuous measure of expressive language ability was not significantly associated with gamma (r = -0.07). Findings suggest that frontal gamma power may be useful in discriminating between groups of children that differ in DLD risk, but not for expressive language along a continuous spectrum of ability.

Attentional bias in psoriasis: The role of processing time and emotional valence

PURPOSE

The present study explored whether people with psoriasis display an attentional bias towards disease-related threat words and whether this bias occurs relatively early during the phase of stimulus disengagement, or during a later maintained attention phase dominated by controlled strategic processes. We also explored the degree to which attentional bias is dependent on the emotional valence of control words.

METHODS

Individuals with psoriasis and matched controls took part in 4 online experiments. Participants completed a spatial cueing paradigm using disease-related threat words and control words as cues, in order to obtain reaction time estimates of attentional bias.

RESULTS

We did not observe evidence for attentional bias when control words were matched with threat words for emotional valence, regardless of whether processing time for the cues was limited (Experiment 1: SOA = 250 ms) or extended (Experiment 2: SOA = 1050 ms). We also did not observe evidence for attentional bias when control words of positive valence were used, but processing time was limited (Experiment 3). An attentional bias was only observed (p = .012, Cohen's d = .37) when sufficient processing time was available and positively-valanced control words were used (Experiment 4).

CONCLUSION

Rather than showing large and generalized AB effects as predicted by previous accounts, our results tentatively suggest that AB in psoriasis is restricted to situations where participants have ample processing time and threat words are easily distinguishable from control words on the basis of emotional valence. The pattern of results suggests that attentional bias in psoriasis is best characterized as a relatively slow strategic process.

The Human Connectome Project of adolescent anxiety and depression dataset

This article describes primary data and resources available from the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, a novel arm of the Human Connectome Project (HCP). Data were collected from 215 adolescents (14-17 years old), 152 of whom had current diagnoses of anxiety and/or depressive disorders at study intake. Data include cross-sectional structural (T1- and T2-weighted), functional (resting state and three tasks), and diffusion-weighted magnetic resonance images. Both unprocessed and HCP minimally-preprocessed imaging data are available within the data release packages. Adolescent and parent clinical interview data, as well as cognitive and neuropsychological data are also included within these packages. Release packages additionally provide data collected from self-report measures assessing key features of adolescent psychopathology, including: anxious and depressive symptom dimensions, behavioral inhibition/activation, exposure to stressful life events, and risk behaviors. Finally, the release packages include 6- and 12-month longitudinal data acquired from clinical measures. Data are publicly accessible through the National Institute of Mental Health Data Archive (ID: #2505).

Individual peak alpha frequency does not index individual differences in inhibitory cognitive control

Previous work has indicated that individual differences in cognitive performance can be predicted by characteristics of resting state oscillations, such as individual peak alpha frequency (IAF). Although IAF has previously been correlated with cognitive functions, such as memory, attention, or mental speed, its link to cognitive conflict processing remains unexplored. The current work investigated the relationship between IAF and inhibitory cognitive control in two well-established conflict tasks, Stroop and Navon task, while also controlling for alpha power, theta power, and the 1/f offset of aperiodic broadband activity. In Bayesian analyses on a large sample of 127 healthy participants, we found substantial evidence against the assumption that IAF predicts individual abilities to spontaneously exert cognitive control. Similarly, our findings yielded substantial evidence against links between cognitive control and resting state power in the alpha and theta bands or between cognitive control and aperiodic 1/f offset. In sum, our results challenge frameworks suggesting that an individual's ability to spontaneously engage attentional control networks may be mirrored in resting state EEG characteristics.

Psilocybin desynchronizes the human brain

A single dose of psilocybin, a psychedelic that acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials1-4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus5-8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.

Neural and behavioral markers of inhibitory control predict symptom improvement during internet-delivered cognitive behavioral therapy for depression

Poor inhibitory control contributes to deficits in emotion regulation, which are often targeted by treatments for major depressive disorder (MDD), including cognitive behavioral therapy (CBT). Brain regions that contribute to inhibitory control and emotion regulation overlap; thus, inhibitory control might relate to response to CBT. In this study, we examined whether baseline inhibitory control and resting state functional connectivity (rsFC) within overlapping emotion regulation-inhibitory control regions predicted treatment response to internet-based CBT (iCBT). Participants with MDD were randomly assigned to iCBT (N = 30) or a monitored attention control (MAC) condition (N = 30). Elastic net regression was used to predict post-treatment Patient Health Questionnaire-9 (PHQ-9) scores from baseline variables, including demographic variables, PHQ-9 scores, Flanker effects (interference, sequential dependency, post-error slowing), and rsFC between the dorsal anterior cingulate cortex, bilateral anterior insula (AI), and right temporoparietal junction (TPJ). Essential prognostic predictor variables retained in the elastic net regression included treatment group, gender, Flanker interference response time (RT), right AI-TPJ rsFC, and left AI-right AI rsFC. Prescriptive predictor variables retained included interactions between treatment group and baseline PHQ-9 scores, age, gender, Flanker RT, sequential dependency effects on accuracy, post-error accuracy, right AI-TPJ rsFC, and left AI-right AI rsFC. Inhibitory control and rsFC within inhibitory control-emotion regulation regions predicted reduced symptom severity following iCBT, and these effects were stronger in the iCBT group than in the MAC group. These findings contribute to a growing literature indicating that stronger inhibitory control at baseline predicts better outcomes to psychotherapy, including iCBT.

Development and validation of a mental hyperactivity questionnaire for the evaluation of chronic stress in higher education

BACKGROUND

Examination and understanding of neural hyperactivity are some of the greatest scientific challenges faced in the present day. For this reason, the present study aimed to examine this phenomenon in the context of higher education.

METHOD

Likewise, this work will enable an instrument to be created to appropriately and reliably estimate neural hyperactivity associated with chronic stress in university students undertaking a Physiotherapy degree.

RESULTS

Analysis of content validity was carried out according to agreement and consensus between nineteen experts with Education Science or Psychology degrees, via the Delphi method. On the other hand, face validity was established by administering the questionnaire to a sample of 194 university students aged between 18 and 45 years (M = 30.48%; SD = 13.152).

CONCLUSION

The final self-report measure, denominated mental hyperactivity, was composed of 10 items which showed adequate fit with regards to face and content validity (α = 0.775). Confirmatory factor analysis confirmed that the questionnaire was unidimensional.

Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain

PURPOSE

The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach.

METHODS

51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions.

RESULTS

Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning.

CONCLUSIONS

Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

Rapid Mental Workload Detection of Air Traffic Controllers with Three EEG Sensors

Air traffic controllers' mental workload significantly impacts their operational efficiency and safety. Detecting their mental workload rapidly and accurately is crucial for preventing aviation accidents. This study introduces a mental workload detection model for controllers based on power spectrum features related to gamma waves. The model selects the feature with the highest classification accuracy, β + θ + α + γ, and utilizes the mRMR (Max-Relevance and Min-Redundancy) algorithm for channel selection. Furthermore, the channels that were less affected by ICA processing were identified, and the reliability of this result was demonstrated by artifact analysis brought about by EMG, ECG, etc. Finally, a model for rapid mental workload detection for controllers was developed and the detection rate for the 34 subjects reached 1, and the accuracy for the remaining subjects was as low as 0.986. In conclusion, we validated the usability of the mRMR algorithm in channel selection and proposed a rapid method for detecting mental workload in air traffic controllers using only three EEG channels. By reducing the number of EEG channels and shortening the data processing time, this approach simplifies equipment application and maintains detection accuracy, enhancing practical usability.

Predicting creative behavior using resting-state electroencephalography

Neuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model's predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity.

Brain Structural and Functional Damage Network Localization of Suicide

BACKGROUND

Extensive neuroimaging research on brain structural and functional correlates of suicide has produced inconsistent results. Despite increasing recognition that damage in multiple different brain locations that causes the same symptom can map to a common brain network, there is still a paucity of research investigating network localization of suicide.

METHODS

To clarify this issue, we initially identified brain structural and functional damage locations in relation to suicide from 63 published studies with 2135 suicidal and 2606 nonsuicidal individuals. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 suicide brain damage networks corresponding to different imaging modalities.

RESULTS

The suicide gray matter volume damage network comprised widely distributed brain areas primarily involving the dorsal default mode, basal ganglia, and anterior salience networks. The suicide task-induced activation damage network was similar to but less extensive than the gray matter volume damage network, predominantly implicating the same canonical networks. The suicide resting-state activity damage network manifested as a localized set of brain regions encompassing the orbitofrontal cortex and middle cingulate cortex.

CONCLUSIONS

Our findings not only may help reconcile prior heterogeneous neuroimaging results, but also may provide insights into the neurobiological mechanisms of suicide from a network perspective, which may ultimately inform more targeted and effective strategies to prevent suicide.

Towards personalized precision functional mapping in infancy

The precise network topology of functional brain systems is highly specific to individuals and undergoes dramatic changes during critical periods of development. Large amounts of high-quality resting state data are required to investigate these individual differences, but are difficult to obtain in early infancy. Using the template matching method, we generated a set of infant network templates to use as priors for individualized functional resting-state network mapping in two independent neonatal datasets with extended acquisition of resting-state functional MRI (fMRI) data. We show that template matching detects all major adult resting-state networks in individual infants and that the topology of these resting-state network maps is individual-specific. Interestingly, there was no plateau in within-subject network map similarity with up to 25 minutes of resting-state data, suggesting that the amount and/or quality of infant data required to achieve stable or high-precision network maps is higher than adults. These findings are a critical step towards personalized precision functional brain mapping in infants, which opens new avenues for clinical applicability of resting-state fMRI and potential for robust prediction of how early functional connectivity patterns relate to subsequent behavioral phenotypes and health outcomes.

[Cerebral oxygen metabolism and brain electrical activity of healthy full-term neonates in high-altitude areas: a multicenter clinical research protocol]

Further evidence is needed to explore the impact of high-altitude environments on the neurologic function of neonates. Non-invasive techniques such as cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography can provide data on cerebral oxygenation and brain electrical activity. This study will conduct multiple cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography monitoring sessions at various time points within the first 3 days postpartum for healthy full-term neonates at different altitudes. The obtained data on cerebral oxygenation and brain electrical activity will be compared between different altitudes, and corresponding reference ranges will be established. The study involves 6 participating centers in the Chinese High Altitude Neonatal Medicine Alliance, with altitude gradients divided into 4 categories: 800 m, 1 900 m, 2 400 m, and 3 500 m, with an anticipated sample size of 170 neonates per altitude gradient. This multicenter prospective cohort study aims to provide evidence supporting the impact of high-altitude environments on early brain function and metabolism in neonates.

The neuroimmune pathway of high-altitude adaptation: influence of erythrocytes on attention networks through inflammation and the autonomic nervous system

Introduction

Many studies have shown that the functional adaptation of immigrants to high-altitude is closely related to oxygen transport, inflammatory response and autonomic nervous system. However, it remains unclear how human attention changes in response to hypoxia-induced neurophysiological activity during high-altitude exposure.

Methods

In the present study, we analyzed the relationship between hypoxic-induced neurophysiological responses and attention networks in 116 immigrants (3,680 m) using an attention network test to simultaneously record electroencephalogram and electrocardiogram in combination with specific routine blood markers.

Results

Our analysis revealed that red blood cells exert an indirect influence on the three attention networks, mediated through inflammatory processes and heart rate variability.

Discussion

The present study provides experimental evidence for the role of a neuroimmune pathway in determining human attention performance at high- altitude. Our findings have implications for understanding the complex interactions between physiological and neurocognitive processes in immigrants adapting to hypoxic environments.

Dynamic Changes in Local Brain Connectivity and Activity: A Longitudinal Study in Adolescent Anorexia Nervosa

BACKGROUND

Resting-state functional connectivity analysis has been used to study disruptions in neural circuitries underlying eating disorder symptoms. Research has shown resting-state functional connectivity to be altered during the acute phase of anorexia nervosa (AN), but little is known about the biological mechanisms underlying neural changes associated with weight restoration. The goal of the current study was to investigate longitudinal changes in regional homogeneity (ReHo) among neighboring voxels, degree centrality (DC) (a voxelwise whole brain correlation coefficient), voxel-mirrored homotopic connectivity (VMHC) (measuring the synchronization between hemispheres), and the fractional amplitude of low-frequency fluctuations associated with weight gain during AN treatment.

METHODS

Resting-state functional connectivity data were acquired and analyzed from a sample of 174 female volunteers: 87 underweight patients with AN that were scanned before treatment and again after at least 12% body mass index increase, as well as 87 age-matched healthy control participants.

RESULTS

Longitudinal changes in ReHo, DC, VMHC, and the fractional amplitude of low-frequency fluctuations were observed in most regions identified to differ between patients with AN before treatment and healthy control participants. However, the degree of normalization varied for each parameter, ranging from 9% of all clusters in DC to 66% in VMHC. Longitudinal changes in ReHo and VMHC showed a linear association weight gain.

CONCLUSIONS

Resting-state functional magnetic resonance imaging measures, including ReHo, DC, VMHC, and the fractional amplitude of low-frequency fluctuations, show varying degrees of recovery after short-term weight restoration. Although only some of these changes were related to weight gain, our results provide an overall positive message, suggesting that weight restoration is associated with changes in functional brain measures that point toward normalization.

Similarities and differences of dynamic and static spontaneous brain activity between left and right temporal lobe epilepsy

To comprehensively investigate the potential temporal dynamic and static abnormalities of spontaneous brain activity (SBA) in left temporal lobe epilepsy (LTLE) and right temporal lobe epilepsy (RTLE) and to detect whether these alterations correlate with cognition. Twelve SBA metrics, including ALFF, dALFF, fALFF, dfALFF, ReHo, dReHo, DC, dDC, GSCorr, dGSCorr, VMHC, and dVMHC, in 46 LTLE patients, 43 RTLE patients, and 53 healthy volunteers were compared in the voxel-wise analysis. Correlation analyses between metrics in regions showing statistic differences and epilepsy duration, epilepsy severity, and cognition scores were also performed. Compared with the healthy volunteers, the alteration of SBA was identified both in LTLE and RTLE patients. The ALFF, fALFF, and dALFF values in LTLE, as well as the fALFF values in RTLE, increased in the bilateral thalamus, basal ganglia, mesial temporal lobe, cerebellum, and vermis. Increased dfALFF in the bilateral basal ganglia, increased ReHo and dReHo in the bilateral thalamus in the LTLE group, increased ALFF and dALFF in the pons, and increased ReHo and dReHo in the right hippocampus in the RTLE group were also detected. However, the majority of deactivation clusters were in the ipsilateral lateral temporal lobe. For LTLE, the fALFF, DC, dDC, and GSCorr values in the left lateral temporal lobe and the ReHo and VMHC values in the bilateral lateral temporal lobe all decreased. For RTLE, the ALFF, fALFF, dfALFF, ReHo, dReHo, and DC values in the right lateral temporal lobe and the VMHC values in the bilateral lateral temporal lobe all decreased. Moreover, for both the LTLE and RTLE groups, the dVMHC values decreased in the calcarine cortex. The most significant difference between LTLE and RTLE was the higher activation in the cerebellum of the LTLE group. The alterations of many SBA metrics were correlated with cognition and epilepsy duration. The patterns of change in SBA abnormalities in the LTLE and RTLE patients were generally similar. The integrated application of temporal dynamic and static SBA metrics might aid in the investigation of the propagation and suppression pathways of seizure activity as well as the cognitive impairment mechanisms in TLE.

Breathing Right… or Left! The Effects of Unilateral Nostril Breathing on Psychological and Cognitive Wellbeing: A Pilot Study

The impact of controlled breathing on cognitive and affective processing has been recognized since ancient times, giving rise to multiple practices aimed at achieving different psychophysical states, mostly related to mental clarity and focus, stress reduction, and relaxation. Previous scientific research explored the effects of forced unilateral nostril breathing (UNB) on brain activity and emotional and cognitive functions. Some evidence concluded that it had a contralateral effect, while other studies presented controversial results, making it difficult to come to an unambiguous interpretation. Also, a few studies specifically addressed wellbeing. In the present study, we invited a pilot sample of 20 participants to take part in an 8-day training program for breathing, and each person was assigned to either a unilateral right nostril (URNB) or left nostril breathing condition (ULNB). Then, each day, we assessed the participants' wellbeing indices using their moods and mind wandering scales. The results revealed that, after the daily practice, both groups reported improved wellbeing perception. However, the effect was specifically related to the nostril involved. URNB produced more benefits in terms of stress reduction and relaxation, while ULNB significantly and increasingly reduced mind-wandering occurrences over time. Our results suggest that UNB can be effectively used to increase wellbeing in the general population. Additionally, they support the idea that understanding the effects of unilateral breathing on wellbeing and cognition requires a complex interpretive model with multiple brain networks to address bottom-up and top-down processes.

Reconfiguration of the costly punishment network architecture in punishment decision-making

Human costly punishment is rooted in multiple regions across large-scale functional systems, a collection of which constitutes the costly punishment network (CPN). Our previous study found that the CPN is intrinsically organized in an optimized and reliable manner to support individual costly punishment propensity. However, it remains unknown how the CPN is reconfigured in response to external cognitive demands in punishment decision-making. Here, we combined resting-state and task-functional magnetic resonance imaging to examine the task-related reconfigurations of intrinsic organizations of the CPN when participants made decisions of costly punishment in the Ultimatum Game. Although a strong consistency was observed in the overall pattern and each nodal profile between the intrinsic (task-free) and extrinsic (task-evoked) functional connectivity of the CPN, condition-general and condition-specific reconfigurations were also evident. Specifically, both unfair and fair conditions induced increases in functional connectivity between a few specific pairs of regions, and the unfair condition additionally induced increases in network efficiency of the CPN. Intriguingly, the specific changes in global efficiency of the CPN in the unfair condition were associated with individual differences in costly punishment after adjusting for the corresponding results in the fair condition, which were further identified for females but not for males. These findings were largely reproducible on independent samples. Collectively, our findings provide novel insights into how the CPN adaptively reconfigures its network architecture to support costly punishment.

The Commonality and Individuality of Human Brains When Performing Tasks

It is imperative to study individual brain functioning toward understanding the neural bases responsible for individual behavioral and clinical traits. The complex and dynamic brain activity varies from area to area and from time to time across the entire brain, and BOLD-fMRI measures this spatiotemporal activity at large-scale systems level. We present a novel method to investigate task-evoked whole brain activity that varies not only from person to person but also from task trial to trial within each task type, offering a means of characterizing the individuality of human brains when performing tasks. For each task trial, the temporal correlation of task-evoked ideal time signal with the time signal of every point in the brain yields a full spatial map that characterizes the whole brain's functional co-activity (FC) relative to the task-evoked ideal response. For any two task trials, regardless of whether they are the same task or not, the spatial correlation of their corresponding two FC maps over the entire brain quantifies the similarity between these two maps, offering a means of investigating the variation in the whole brain activity trial to trial. The results demonstrated a substantially varied whole brain activity from trial to trial for each task category. The degree of this variation was task type-dependent and varied from subject to subject, showing a remarkable individuality of human brains when performing tasks. It demonstrates the potential of using the presented method to investigate the relationship of the whole brain activity with individual behavioral and clinical traits.

Prediction of individual brain age using movie and resting-state fMRI

Brain age is a promising biomarker for predicting chronological age based on brain imaging data. Although movie and resting-state functional MRI techniques have attracted much research interest for the investigation of brain function, whether the 2 different imaging paradigms show similarities and differences in terms of their capabilities and properties for predicting brain age remains largely unexplored. Here, we used movie and resting-state functional MRI data from 528 participants aged from 18 to 87 years old in the Cambridge Centre for Ageing and Neuroscience data set for functional network construction and further used elastic net for age prediction model building. The connectivity properties of movie and resting-state functional MRI were evaluated based on the connections supporting predictive model building. We found comparable predictive abilities of movie and resting-state connectivity in estimating brain age of individuals, as evidenced by correlation coefficients of 0.868 and 0.862 between actual and predicted age, respectively. Despite some similarities, notable differences in connectivity properties were observed between the predictive models using movie and resting-state functional MRI data, primarily involving components of the default mode network. Our results highlight that both movie and resting-state functional MRI are effective and promising techniques for predicting brain age. Leveraging its data acquisition advantages, such as improved child and patient compliance resulting in reduced motion artifacts, movie functional MRI is emerging as an important paradigm for studying brain function in pediatric and clinical populations.

Astrocytes Drive Divergent Metabolic Gene Expression in Humans and Chimpanzees

The human brain utilizes ∼20% of all of the body's metabolic resources, while chimpanzee brains use <10%. Although previous work shows significant differences in metabolic gene expression between the brains of primates, we have yet to fully resolve the contribution of distinct brain cell types. To investigate cell type-specific interspecies differences in brain gene expression, we conducted RNA-seq on neural progenitor cells, neurons, and astrocytes generated from induced pluripotent stem cells from humans and chimpanzees. Interspecies differential expression analyses revealed that twice as many genes exhibit differential expression in astrocytes (12.2% of all genes expressed) than neurons (5.8%). Pathway enrichment analyses determined that astrocytes, rather than neurons, diverged in expression of glucose and lactate transmembrane transport, as well as pyruvate processing and oxidative phosphorylation. These findings suggest that astrocytes may have contributed significantly to the evolution of greater brain glucose metabolism with proximity to humans.

Analysis of Resting-State Functional Magnetic Resonance Imaging in Alzheimer's Disease

Alzheimer's disease (AD) has been characterized by widespread network disconnection among brain regions, widely overlapping with the hallmarks of the disease. Functional connectivity has been studied with an upward trend in the last two decades, predominantly in AD among other neuropsychiatric disorders, and presents a potential biomarker with various features that might provide unique contributions to foster our understanding of neural mechanisms of AD. The resting-state functional MRI (rs-fMRI) is usually used to measure the blood-oxygen-level-dependent signals that reflect the brain's functional connectivity. Nevertheless, the rs-fMRI is still underutilized, which might be due to the fairly complex acquisition and analytic methodology. In this chapter, we presented the common methods that have been applied in rs-fMRI literature, focusing on the studies on individuals in the continuum of AD. The key methodological aspects will be addressed that comprise acquiring, processing, and interpreting rs-fMRI data. More, we discussed the current and potential implications of rs-fMRI in AD.

Postural supporting cervical traction workstation to improve resting state brain activity in digital device users: EEG study

Objective

This study aimed to determine the effect of postural support workstation on inducing effective brain activity during rest.

Methods

Thirty-five healthy digital overusers were recruited as participants. We conducted two interventions of head weight support traction (ST) and conventional traction (CT) strength on all participants in random order. Participants' arousal levels and psychological comfort were assessed. In addition, changes in brain activity caused by traction were confirmed by measuring changes in resting state brain activity using an electroencephalogram (EEG).

Results

Under the ST condition, psychological comfort improved while alert levels were maintained. In addition the resting brain activity of EEG was characterized by strong focused attention and relaxed activity, as evidenced by increased alpha waves throughout the brain. By contrast, in the CT condition, no significant improvement in comfort was observed. Furthermore, high-frequency brain activity, such as beta 3 and gamma waves, was observed across the entire brain regions.

Conclusion

In this study, the ST workstation was shown to effectively improve resting attention and psychological comfort in individuals who excessively use digital devices by inducing resting state alpha activity without stimulating high-frequency brain waves, while maintaining an upright posture with appropriate traction.