An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivation

Long-term risk of late-life depression in widowed elderly: a five-year follow-up study

BACKGROUND

Late-life depression (LLD) poses a significant health risk among the elderly, with widowhood as a prominent contributing factor. However, the mechanisms that render some widowed individuals susceptible to depression while others remain resilient remain poorly understood.

METHODS

In this five-year longitudinal study, we followed 203 cognitively healthy, widowed elderly individuals (mean age: 65.2 years, 100 women). The median follow-up time was 4.8 years. Brain structural networks were constructed via diffusion tensor imaging and analyzed using graph theory metrics. Logistic regression and Cox proportional hazards models were employed to assess the predictive role of structural network attributes in depression onset. Moderation models further examined the influence of psychosocial factors on depression risk.

RESULTS

During our follow-up, 22 participants developed LLD (mean age: 65.6 years, 12 women). Altered brain structural network properties, alongside key psychosocial factors, were observed in those at risk of developing depression prior to symptom emergence. Logistic and Cox regression models revealed that decreased rich-club connections, reduced nodal efficiency in the left hippocampus (HIP.L), and lower network modularity significantly predicted depression onset. Additionally, these network alterations correlated with greater depression severity at follow-up. Moderation analyses indicated that weekly exercise frequency and time spent with children notably mitigated the effects of network disruptions on depression severity.

CONCLUSIONS

Among cognitively healthy widowed elders, diminished rich-club connections, modularity, and HIP.L nodal efficiency are strong predictors of future depression risk. Furthermore, low physical activity and limited family interaction may amplify susceptibility within this high-risk group, suggesting that targeted early interventions could reduce depression risk in this vulnerable population.

CLINICAL TRIAL NUMBER

Not applicable.

Local connections enhancement as a neuroprotective strategy against depression recurrence: Insights from structural brain network analysis

BACKGROUND

Depression recurrence significantly impacts patients' well-being and presents a major clinical challenge. Identifying the risk of recurrence during remission could enable early intervention and prevent disease progression.

METHODS

This study included 115 patients in remission from their first depressive episode and 47 healthy controls (HCs). Participants underwent diffusion tensor imaging (DTI), neuropsychological assessments, and follow-up evaluations every three months over a two-year period. Structural brain networks were constructed using deterministic fiber tracking and graph theory analysis.

RESULTS

Non-recurrence patients exhibited significantly higher baseline local connections compared to the recurrence group (t = 8.148; P < 0.001), which emerged as a robust negative predictor of recurrence (AUC = 0.853 [95 % CI: 0.774-0.912]; OR = 0.594 [95 % CI: 0.489-0.722]; P < 0.001). Rich-club connections were inversely correlated with depression severity (r = -0.510; P < 0.001) and duration (r = -0.221; P = 0.018). Additionally, increases in local connections during remission correlated positively with subsequent rich-club connections (r = 0.540; P < 0.05).

CONCLUSION

Elevated local connections during remission after the first depressive episode significantly reduce the risk of recurrence. This suggests a compensatory neuroprotective mechanism, where enhanced local connections stabilize rich-club connections, thereby maintaining the integrity of the whole-brain network. These findings highlight local connections as a critical factor in preventing depression recurrence and as a potential target for early clinical intervention.

The Awakening Brain is Characterized by a Widespread and Spatiotemporally Heterogeneous Increase in High Frequencies

Morning awakening is part of everyday life. Surprisingly, information remains scarce on its underlying neurophysiological correlates. Here simultaneous polysomnography and stereo-electroencephalography recordings from 18 patients are used to assess the spectral and connectivity content of the process of awakening at a local level 15 min before and after the awakening. Awakenings from non-rapid eye movement sleep are accompanied by a widespread increase in ripple (>80 Hz) power in the fronto-temporal and parieto-insular regions, with connectivity showing an almost exclusive increase in the ripple band in the somatomotor, default, dorsal attention, and frontoparietal networks. Awakenings from rapid eye movement sleep are characterized by a widespread and almost exclusive increase in the ripple band in all available brain lobes, and connectivity increases mainly in the low ripple band in the limbic system as well as the default, dorsal attention, somatomotor, and frontoparietal networks.

The impact of total sleep deprivation on attentional networks and its neural mechanisms: Based on the Attention Network Test

Sleep deprivation, both daily and occupational, has become a prevalent issue in modern society, significantly affecting individuals' attention functions. Traditionally, attention was viewed as a singular, unified system, but advances in neuroscience have revealed it as a network involving coordinated interactions across multiple brain regions. Posner and Petersen's Attention Network Theory delineates three distinct subcomponents - alerting, orienting, and executive control - based on anatomical localization and neurobiochemical mechanisms. However, most studies on sleep deprivation often overlook these subcomponents, treating attention as a generalized process. This paper aims to address this gap by investigating the effects of total sleep deprivation (TSD) on these attentional subcomponents and their potential neural mechanisms focusing on both the general healthy population and specific occupational groups. Using the Attention Network Test (ANT) paradigm and its variants, the findings reveal that TSD differentially affects the three subcomponents of attentional networks, with occupation-specific differences. Notably, the impact of TSD on executive control exhibits greater variability. The state instability hypothesis and local sleep theory are proposed to explain these neural mechanisms, suggesting that TSD disrupts attentional networks through an interplay of top-down state instability and bottom-up local sleep processes. Future research should refine experimental paradigms related to attentional networks, integrate cognitive neuroscience methodologies and computational modeling approaches, and expand investigations into sleep restriction. Such advancements will provide a more comprehensive understanding of how TSD affects attentional networks and further elucidate the interplay between the state instability hypothesis and local sleep theory.

Individualized rTMS Intervention Targeting Sleep Deprivation-Induced Vigilance Decline: Task fMRI-Guided Approach

STUDY OBJECTIVES

Sleep deprivation (SD) is prevalent in our increasingly round-the-clock society. Optimal countermeasures such as ample recovery sleep are often unfeasible, and brief naps, while helpful, do not fully restore cognitive performance following SD. Thus, we propose that targeted interventions, such as repetitive transcranial magnetic stimulation (rTMS), may enhance cognitive performance recovery post-SD.

METHODS

We recruited 50 participants for two SD experiments. In the first experiment, participants performed a psychomotor vigilance task (PVT) under three conditions: normal sleep (resting wakefulness), after 24 h of SD, and following a subsequent 30-min nap. We analyzed dynamic changes in PVT outcomes and cerebral responses across conditions to identify the optimal stimulation target. Experiment 2 adopted the same protocol except that, after the nap, 10-Hz, sham-controlled, individualized rTMS was administrated. Then, an analysis of variance was conducted to investigate the ability of stimulation to improve the PVT reaction times.

RESULTS

Through task-related functional magnetic resonance imaging, we identified cerebral responses within the right middle frontal gyrus (MFG) as the optimal stimulation target. Subsequent application of individualized 10-Hz rTMS over the right MFG attenuated SD-induced deterioration of vigilance.

CONCLUSION

Our findings suggest that combining a brief nap with individualized rTMS can significantly aid the recovery of vigilance following SD. This approach, through modulating neural activity within functional brain networks, is a promising strategy to counteract the cognitive effects of SD.

Human brain state dynamics are highly reproducible and associated with neural and behavioral features

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatiotemporal features of co-activation patterns at the single-subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

NREM sleep brain networks modulate cognitive recovery from sleep deprivation

Decrease in cognitive performance after sleep deprivation followed by recovery after sleep suggests its key role, and especially non-rapid eye movement (NREM) sleep, in the maintenance of cognition. It remains unknown whether brain network reorganization in NREM sleep stages N2 and N3 can uniquely be mapped onto individual differences in cognitive performance after a recovery nap following sleep deprivation. Using resting state functional magnetic resonance imaging (fMRI), we quantified the integration and segregation of brain networks during NREM sleep stages N2 and N3 while participants took a 1-hour nap following 24-hour sleep deprivation, compared to well-rested wakefulness. Here, we advance a new analytic framework called the hierarchical segregation index (HSI) to quantify network segregation across spatial scales, from whole-brain to the voxel level, by identifying spatio-temporally overlapping large-scale networks and the corresponding voxel-to-region hierarchy. Our results show that network segregation increased in the default mode, dorsal attention and somatomotor networks during NREM sleep compared to wakefulness. Segregation within the visual, limbic, and executive control networks exhibited N2 versus N3 sleep-specific voxel-level patterns. More segregation during N3 was associated with worse recovery of working memory, executive attention, and psychomotor vigilance after the nap. The level of spatial resolution of network segregation varied among brain regions and was associated with the recovery of performance in distinct cognitive tasks. We demonstrated the sensitivity and reliability of voxel-level HSI to provide key insights into within-region variation, suggesting a mechanistic understanding of how NREM sleep replenishes cognition after sleep deprivation.

Daily sleepiness magnifies the relation between same-day passive and active suicide ideation

Disrupted sleep has been linked to suicidal thoughts and behavior. Less is known, however, about the underlying mechanisms of this relationship. A more nuanced understanding of the link between sleep and suicide may help inform treatment decisions and the development of prevention and intervention strategies. The present study examined daily average sleepiness as a moderator to the relation between same-day passive and active suicide ideation (SI). Fifty-nine young adults (mean age = 21.04; SD = 2.22) endorsing SI at least twice in the two weeks prior to baseline completed 3-5 daily surveys of sleepiness and SI over 2 weeks as part of a broader study. Across several indicators of sleepiness (desire to stay awake, desire to fall asleep), passive SI (desire to die, desire to live), and active SI (occurrence, intensity, duration, and controllability), the overall findings demonstrated that daily average sleepiness magnified the relation between same-day passive SI and active SI severity. These findings indicate that being sleepier than usual may increase the likelihood that passive SI transitions to active SI. Future research is needed to test the causal influence of sleepiness on this transition.

Human brain state dynamics reflect individual neuro-phenotypes

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.

Sleep deprivation changes frequency-specific functional organization of the resting human brain

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

A multidimensional investigation of sleep and biopsychosocial profiles with associated neural signatures

Sleep is essential for optimal functioning and health. Interconnected to multiple biological, psychological and socio-environmental factors (i.e., biopsychosocial factors), the multidimensional nature of sleep is rarely capitalized on in research. Here, we deployed a data-driven approach to identify sleep-biopsychosocial profiles that linked self-reported sleep patterns to inter-individual variability in health, cognition, and lifestyle factors in 770 healthy young adults. We uncovered five profiles, including two profiles reflecting general psychopathology associated with either reports of general poor sleep or an absence of sleep complaints (i.e., sleep resilience) respectively. The three other profiles were driven by sedative-hypnotics-use and social satisfaction, sleep duration and cognitive performance, and sleep disturbance linked to cognition and mental health. Furthermore, identified sleep-biopsychosocial profiles displayed unique patterns of brain network organization. In particular, somatomotor network connectivity alterations were involved in the relationships between sleep and biopsychosocial factors. These profiles can potentially untangle the interplay between individuals' variability in sleep, health, cognition and lifestyle - equipping research and clinical settings to better support individual's well-being.

Alteration of default mode network: association with executive dysfunction in frontal glioma patients

OBJECTIVE

Patients with frontal gliomas often experience executive dysfunction (EF-D) before surgery, and the changes in brain plasticity underlying this effect remain obscure. In this study, the authors aimed to assess whole-brain structural and functional alterations by using structural MRI and resting-state functional MRI (rs-fMRI) in frontal glioma patients with or without EF-D.

METHODS

Fifty-seven patients with frontal gliomas were admitted prospectively to the authors' institution and assigned to one of two groups: 1) the normal executive function (EF-N) group and 2) the EF-D group, based on patient results for the Trail Making Test, Part B and Stroop Color-Word Test, Part C. Twenty-nine baseline-matched healthy controls were also recruited. All participants underwent multimodal MRI examination. Cortical surface thickness, surface-based resting-state activity (fractional amplitude of low-frequency fluctuation [fALFF] and regional homogeneity [ReHo]), and edge-based network functional connectivity (FC) were measured with FreeSurfer and fMRIPrep. The correlation between altered MRI parameters and executive function (EF) was assessed using Pearson correlation and receiver operating characteristic (ROC) analysis.

RESULTS

Demographic characteristics (sex, age, and education level) and clinical characteristics (location, volume, grade of tumor, and preoperative epilepsy) were not significantly different between the groups, but the Karnofsky Performance Scale score was worse in the EF-D group. There was no significant difference in cortical surface thickness between the EF-D and EF-N groups. In both low-grade and high-grade glioma patients the fALFF value (permutation test + threshold-free cluster enhancement, p value after family-wise error correction < 0.05) and ReHo value (t-test, p < 0.001) of the left precuneus cortex in the EF-D group were greater than those in the EF-N group, which were negatively correlated with EF (p < 0.05) and enabled prediction of EF (area under the ROC curve 0.826 for fALFF and 0.855 for ReHo, p < 0.001). Compared with the EF-N group, the FCs between the default mode network (DMN) from DMN node to DMN node (DMN-DMN) and from the DMN to the central executive network (DMN-CEN) in the EF-D group were increased significantly (network-based statistics corrected p < 0.05) and negatively correlated with EF (Pearson correlation, p < 0.05).

CONCLUSIONS

Apart from local disruption, the abnormally activated DMN in the resting state is related to EF-D in frontal glioma patients. DMN activity should be considered during preoperative planning and postoperative neurorehabilitation for frontal glioma patients to preserve EF. Clinical trial registration no.: NCT03087838 (ClinicalTrials.gov).

Arousal impacts distributed hubs modulating the integration of brain functional connectivity

Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.

The Mind After Midnight: Nocturnal Wakefulness, Behavioral Dysregulation, and Psychopathology

Sufficient sleep with minimal interruption during the circadian/biological night supports daytime cognition and emotional regulation. Conversely, disrupted sleep involving significant nocturnal wakefulness leads to cognitive and behavioral dysregulation. Most studies to-date have examined how fragmented or insufficient sleep affects next-day functioning, but recent work highlights changes in cognition and behavior that occur when someone is awake during the night. This review summarizes the evidence for day-night alterations in maladaptive behaviors, including suicide, violent crime, and substance use, and examines how mood, reward processing, and executive function differ during nocturnal wakefulness. Based on this evidence, we propose the Mind after Midnight hypothesis in which attentional biases, negative affect, altered reward processing, and prefrontal disinhibition interact to promote behavioral dysregulation and psychiatric disorders.