Tracking cell-type-specific temporal dynamics in human and mouse brains.

Progenitor cells are critical in preserving organismal homeostasis, yet their diversity and dynamics in the aged brain remain underexplored. We introduced TrackerSci, a single-cell genomic method that combines newborn cell labeling and combinatorial indexing to characterize the transcriptome and chromatin landscape of proliferating progenitor cells in vivo. Using TrackerSci, we investigated the dynamics of newborn cells in mouse brains across various ages and in a mouse model of Alzheimer's disease. Our dataset revealed diverse progenitor cell types in the brain and their epigenetic signatures. We further quantified aging-associated shifts in cell-type-specific proliferation and differentiation and deciphered the associated molecular programs. Extending our study to the progenitor cells in the aged human brain, we identified conserved genetic signatures across species and pinpointed region-specific cellular dynamics, such as the reduced oligodendrogenesis in the cerebellum. We anticipate that TrackerSci will be broadly applicable to unveil cell-type-specific temporal dynamics in diverse systems.

Transient promoter interactions modulate developmental gene activation.

Transcriptional induction coincides with the formation of various chromatin topologies. Strong evidence supports that gene activation is accompanied by a general increase in promoter-enhancer interactions. However, it remains unclear how these topological changes are coordinated across time and space during transcriptional activation. Here, we combine chromatin conformation capture with transcription and chromatin profiling during an embryonic stem cell (ESC) differentiation time course to determine how 3D genome restructuring is related to transcriptional transitions. This approach allows us to identify distinct topological alterations that are associated with the magnitude of transcriptional induction. We detect transiently formed interactions and demonstrate by genetic deletions that associated distal regulatory elements (DREs), as well as appropriate formation and disruption of these interactions, can contribute to the transcriptional induction of linked genes. Together, our study links topological dynamics to the magnitude of transcriptional induction and detects an uncharacterized type of transcriptionally important DREs.

Different temporal dynamics of foveal and peripheral visual processing during fixation.

Humans explore visual scenes by alternating short fixations with saccades directing the fovea to points of interest. During fixation, the visual system not only examines the foveal stimulus at high resolution, but it also processes the extrafoveal input to plan the next saccade. Although foveal analysis and peripheral selection occur in parallel, little is known about the temporal dynamics of foveal and peripheral processing upon saccade landing, during fixation. Here we investigate whether the ability to localize changes across the visual field differs depending on when the change occurs during fixation, and on whether the change localization involves foveal, extrafoveal processing, or both. Our findings reveal that the ability to localize changes in peripheral areas of the visual field improves as a function of time after fixation onset, whereas localization accuracy for foveal stimuli remains approximately constant. Importantly, this pattern holds regardless of whether individuals monitor only foveal or peripheral stimuli, or both simultaneously. Altogether, these results show that the visual system is more attuned to the foveal input early on during fixation, whereas change localization for peripheral stimuli progressively improves throughout fixation, possibly as a consequence of an increased readiness to plan the next saccade.

Neural Correlates of Crowding in Macaque Area V4.

Visual crowding refers to the phenomenon where a target object that is easily identifiable in isolation becomes difficult to recognize when surrounded by other stimuli (distractors). Many psychophysical studies have investigated this phenomenon and proposed alternative models for the underlying mechanisms. One prominent hypothesis, albeit with mixed psychophysical support, posits that crowding arises from the loss of information due to pooled encoding of features from target and distractor stimuli in the early stages of cortical visual processing. However, neurophysiological studies have not rigorously tested this hypothesis. We studied the responses of single neurons in macaque (one male, one female) area V4, an intermediate stage of the object-processing pathway, to parametrically designed crowded displays and texture statistics-matched metameric counterparts. Our investigations reveal striking parallels between how crowding parameters-number, distance, and position of distractors-influence human psychophysical performance and V4 shape selectivity. Importantly, we also found that enhancing the salience of a target stimulus could alleviate crowding effects in highly cluttered scenes, and this could be temporally protracted reflecting a dynamical process. Thus, a pooled encoding of nearby stimuli cannot explain the observed responses, and we propose an alternative model where V4 neurons preferentially encode salient stimuli in crowded displays. Overall, we conclude that the magnitude of crowding effects is determined not just by the number of distractors and target-distractor separation but also by the relative salience of targets versus distractors based on their feature attributes-the similarity of distractors and the contrast between target and distractor stimuli.

Temporal Dynamics, Discovery, and Emergence of Human-Transmissible RNA Viruses.

Transmissibility, the ability to spread within host populations, is a prerequisite for a pathogen to have epidemic or pandemic potential. Here, we estimate the phylogenies of human infectivity and transmissibility using 1,408 genome sequences from 743 distinct RNA virus species/types in 59 genera. By repeating this analysis using data sets censored by virus discovery date, we explore how temporal changes in the known diversity of RNA viruses-especially recent increases in recognized nonhuman viruses-have altered these phylogenies. Over time, we find significant increases in the proportion of RNA virus genera estimated to have a nonhuman-infective ancestral state, in the fraction of distinct human virus lineages that are purely human-transmissible or strictly zoonotic (compared to mixed lineages), and in the number of human viruses with nearest relatives known not to infect humans. Our results are consistent with viruses that are capable of spreading in human populations commonly emerging from a nonhuman reservoir. This is more likely in lineages that already contain human-transmissible viruses but is rare in lineages that contain only strictly zoonotic viruses.

The evolution of ectomycorrhizal symbiosis and host-plant switches are the main drivers for diversification of Amanitaceae (Agaricales, Basidiomycota).

BACKGROUND: Evolutionary radiation is widely recognized as a mode of species diversification, but the drivers of the rapid diversification of fungi remain largely unknown. Here, we used Amanitaceae, one of the most diverse families of macro-fungi, to investigate the mechanism underlying its diversification. RESULTS: The ancestral state of the nutritional modes was assessed based on phylogenies obtained from fragments of 36 single-copy genes and stable isotope analyses of carbon and nitrogen. Moreover, a number of time-, trait-, and paleotemperature-dependent models were employed to investigate if the acquisition of ectomycorrhizal (ECM) symbiosis and climate changes promoted the diversification of Amanitaceae. The results indicate that the evolution of ECM symbiosis has a single evolutionary origin in Amanitaceae. The earliest increase in diversification coincided with the acquisition of the ECM symbiosis with angiosperms in the middle Cretaceous. The recent explosive diversification was primarily triggered by the host-plant switches from angiosperms to the mixed forests dominated by Fagaceae, Salicaceae, and Pinaceae or to Pinaceae. CONCLUSIONS: Our study provides a good example of integrating phylogeny, nutritional mode evolution, and ecological analyses for deciphering the mechanisms underlying fungal evolutionary diversification. This study also provides new insights into how the transition to ECM symbiosis has driven the diversification of fungi.

Seasonally Changing Interactions of Species Traits of Termites and Trees Promote Complementarity in Coarse Wood Decomposition.

Complementary resource use by functionally different species may accelerate ecosystem processes. However, how co-variation in plant traits and animal traits promotes complementarity through temporal plant-animal interactions is poorly understood, even less so in detrital systems, thereby hampering our fundamental understanding of decomposition and carbon turnover. We hypothesised that, in seasonal subtropical forests where termites are major deadwood decomposers, trait complementarity of both termite species and tree species should promote overall deadwood decomposition through different seasons and years. Findings from a four-year coarse wood decomposition experiment involving 27 tree and 5 termite species support this hypothesis. Phenological and mandibular traits of the two most abundant termite species controlled wood decomposition of tree species differing in wood traits, through the seasons over 4 years, thereby promoting overall deadwood decomposition rates. Our findings indicate that complementarity in functional trait co-variation in plants and animals plays an important role in carbon cycling.

Arrive and wait: Inactive bacterial taxa contribute to perceived soil microbiome resilience after a multidecadal press disturbance.

Long-term (press) disturbances like the climate crisis and other anthropogenic pressures are fundamentally altering ecosystems and their functions. Many critical ecosystem functions, such as biogeochemical cycling, are facilitated by microbial communities. Understanding the functional consequences of microbiome responses to press disturbances requires ongoing observations of the active populations that contribute to functions. This study leverages a 7-year time series of a 60-year-old coal seam fire (Centralia, Pennsylvania, USA) to examine the resilience of soil bacterial microbiomes to a press disturbance. Using 16S rRNA and 16S rRNA gene amplicon sequencing, we assessed the interannual dynamics of the active subset and the 'whole' bacterial community. Contrary to our hypothesis, the whole communities demonstrated greater resilience than active subsets, suggesting that inactive members contributed to overall structural resilience. Thus, in addition to selection mechanisms of active populations, perceived microbiome resilience is also supported by mechanisms of dispersal, persistence, and revival from the local dormant pool.

Wearable functional near-infrared spectroscopy for measuring dissociable activation dynamics of prefrontal cortex subregions during working memory.

The prefrontal cortex (PFC) has been extensively studied in relation to various cognitive abilities, including executive function, attention, and memory. Nevertheless, there is a gap in our scientific knowledge regarding the functionally dissociable neural dynamics across the PFC during a cognitive task and their individual differences in performance. Here, we explored this possibility using a delayed match-to-sample (DMTS) working memory (WM) task using NIRSIT, a high-density, wireless, wearable functional near-infrared spectroscopy (fNIRS) system. First, upon presentation of the sample stimulus, we observed an immediate signal increase in the ventral (orbitofrontal) region of the anterior PFC, followed by activity in the dorsolateral PFC. After the DMTS test stimulus appeared, the orbitofrontal cortex activated once again, while the rest of the PFC showed overall disengagement. Individuals with higher accuracy showed earlier and sustained activation of the PFC across the trial. Furthermore, higher network efficiency and functional connectivity in the PFC were correlated with individual WM performance. Our study sheds new light on the dynamics of PFC subregional activity during a cognitive task and its potential applicability in explaining individual differences in experimental, educational, or clinical populations. PRACTITIONER POINTS: Wearable functional near-infrared spectroscopy (fNIRS) captured dissociable temporal dynamics across prefrontal subregions during a delayed match-to-sample task. Anterior regions of the orbitofrontal cortex (OFC) activated first during the delay period, followed by the dorsolateral prefrontal cortex (PFC). PFC disengaged overall after the delay, but the OFC reactivated to the test stimulus. Earlier and sustained activation of PFC was associated with better accuracy. Functional connectivity and network efficiency also varied with task performance.

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis.

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Decoding changes in tumor-infiltrating leukocytes through dynamic experimental models and single-cell technologies.

The ability to characterize immune cells and explore the molecular interactions that govern their functions has never been greater, fueled in recent years by the revolutionary advance of single-cell analysis platforms. However, precisely how immune cells respond to different stimuli and where differentiation processes and effector functions operate remain incompletely understood. Inferring cellular fate within single-cell transcriptomic analyses is now omnipresent, despite the assumptions typically required in such analyses. Recently developed experimental models support dynamic analyses of the immune response, providing insights into the temporal changes that occur within cells and the tissues in which such transitions occur. Here we will review these approaches and discuss how these can be combined with single-cell technologies to develop a deeper understanding of the immune responses that should support the development of better therapeutic options for patients.

A methylation clock model of mild SARS-CoV-2 infection provides insight into immune dysregulation.

DNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics in humans following an acute perturbation, such as infection. We characterized the temporal trajectory of blood epigenetic remodeling in 133 participants in a prospective study of young adults before, during, and after asymptomatic and mildly symptomatic SARS-CoV-2 infection. The differential methylation caused by asymptomatic or mildly symptomatic infections was indistinguishable. While differential gene expression largely returned to baseline levels after the virus became undetectable, some differentially methylated sites persisted for months of follow-up, with a pattern resembling autoimmune or inflammatory disease. We leveraged these responses to construct methylation-based machine learning models that distinguished samples from pre-, during-, and postinfection time periods, and quantitatively predicted the time since infection. The clinical trajectory in the young adults and in a diverse cohort with more severe outcomes was predicted by the similarity of methylation before or early after SARS-CoV-2 infection to the model-defined postinfection state. Unlike the phenomenon of trained immunity, the postacute SARS-CoV-2 epigenetic landscape we identify is antiprotective.

Divergent temporal responses of native macroinvertebrate communities to biological invasions.

Biological invasions pose a major threat to biodiversity, ecosystem functioning, and human well-being. Non-native species can have severe ecological impacts that are transformative, affecting ecosystems across both short-term and long-term timescales. However, few studies have determined the temporal dynamics of impact between these scales, impeding future predictions as invasion rates continue to rise. Our study uses a meta-analytical approach to dissect the changing taxonomic and functional impacts of biological invasions on native macroinvertebrate populations and communities in freshwater ecosystems across Europe, using a recently collated European long-term time series spanning several decades. Our findings reveal a complex temporal pattern: while initial stages of invasions (i.e. five years after the first record of non-native species) often exhibited benign impacts on macroinvertebrate abundance, richness, or functional diversity, the long-term (i.e. the period following the early invasion) effects became predominantly negative. This pattern was consistent between taxonomic and functional metrics for impacts at both the population and species level, with taxonomic metrics initially positively affected by invasions and functional metrics being more stable before also declining. These results suggest that even initially benign or positively perceived impacts could be eventually superseded by negative consequences. Therefore, understanding the magnitude of invasion effects increasingly requires long-term studies spanning several years or decades to offer insights into effective conservation strategies prioritising immediate and future biodiversity protection efforts. These findings also highlight the importance of integrating multiple taxonomic, functional and temporal components to inform adaptive management approaches to mitigate the negative effects of current and future biological invasions.

Regional-scale disturbances drive long-term decline of inshore coral reef fish assemblages in the Great Barrier Reef Marine Park.

Anthropogenic pressure is increasing the variety and frequency of environmental disturbance events, limiting recovery and leading to long-term declines in wild plant and animal populations. Coral reefs and associated fish assemblages are inherently dynamic due to their susceptibility to a host of disturbances, but regional-scale nuances in the drivers of long-term change frequently remain poorly resolved. Here, we examine the effects of multiple potential drivers of change in coral reef fish assemblages across 4 inshore regions of the Great Barrier Reef Marine Park (GBRMP), Australia, over 12-14 years (2007-2021). Each region had a unique disturbance history, in conjunction with long-term changes in physical and habitat variables. Phases of recovery were apparent in the years between disturbance events at all locations, but these were not long enough to prevent substantial declines in reef fish density (by 33%-72%) and species richness (by 41%-75%) throughout the study period. The main drivers of change in fish assemblages varied among regions; however, the most rapid changes followed cyclone and flood events. Limited recovery periods resulted in temporal shifts in fish species composition from typically coral-associated to algae-associated. Most trophic groups declined in density except farmers, grazers, omnivores and parrotfish. No-take marine reserves (NTMRs) had small and inconsistent effects on total fish assemblages, but delivered benefits for fishery-targeted piscivores. Our findings suggest that coral reef responses to local stressors and cumulative escalating climate change impacts are highly variable at regional scales, and that small NTMRs are unlikely to mitigate the impacts of increasingly frequent climatic disturbances. Nearshore coral reefs worldwide are high-value habitats that are either already degraded or vulnerable to degradation and the loss of important fish groups. Global efforts to reduce greenhouse gas emissions must be coupled with effective local management that can support the functioning and adaptive capacity of coral reefs.

Biomathematical modeling of fatigue due to sleep inertia.

Biomathematical models of fatigue capture the physiology of sleep/wake regulation and circadian rhythmicity to predict changes in neurobehavioral functioning over time. We used a biomathematical model of fatigue linked to the adenosinergic neuromodulator/receptor system in the brain as a framework to predict sleep inertia, that is, the transient neurobehavioral impairment experienced immediately after awakening. Based on evidence of an adenosinergic basis for sleep inertia, we expanded the biomathematical model with novel differential equations to predict the propensity for sleep inertia during sleep and its manifestation after awakening. Using datasets from large laboratory studies of sleep loss and circadian misalignment, we calibrated the model by fitting just two new parameters and then validated the model's predictions against independent data. The expanded model was found to predict the magnitude and time course of sleep inertia with generally high accuracy. Analysis of the model's dynamics revealed a bifurcation in the predicted manifestation of sleep inertia in sustained sleep restriction paradigms, which reflects the observed escalation of the magnitude of sleep inertia in scenarios with sleep restriction to less than âˆ¼ 4 h per day. Another emergent property of the model involves a rapid increase in the predicted propensity for sleep inertia in the early part of sleep followed by a gradual decline in the later part of the sleep period, which matches what would be expected based on the adenosinergic regulation of non-rapid eye movement (NREM) sleep and its known influence on sleep inertia. These dynamic behaviors provide confidence in the validity of our approach and underscore the predictive potential of the model. The expanded model provides a useful tool for predicting sleep inertia and managing impairment in 24/7 settings where people may need to perform critical tasks immediately after awakening, such as on-demand operations in safety and security, emergency response, and health care.

Rodents mediate the relationship between seed rain, seed bank, and plant community with increased grazing disturbance.

Seed rain and the soil seed bank represent the dispersal of seeds in space and time, respectively, and can be important sources of recruitment of new individuals during plant community regeneration. However, the temporal dynamics of seed rain and the mechanisms by which the seed rain and soil seed bank may play a role in plant community regeneration with increased grazing disturbance remain unclear. Seed rain, soil seed bank, aboveground vegetation, and rodent density were sampled along a grazing gradient in an alpine marsh on the eastern Tibetan Plateau. We described the temporal dynamics of seed dispersal using Bayesian generalized mixed models, and nonmetric multidimensional scaling and the structural equation model were used to examine the effects of grazing disturbance on the relative role of seed rain and soil seed bank on aboveground plant community regeneration. The temporal dynamics of seed rain changed from a unimodal to a bimodal pattern with increased grazing disturbance. Both species diversity and seed density of the seed rain and seed bank increased significantly with increased grazing disturbance. Increased grazing disturbance indirectly increased the similarity of composition between seed rain, seed bank, and aboveground plant community by directly increasing species diversity and abundance of aboveground plant community. However, increased grazing disturbance also indirectly decreased the similarity of seed rain, soil seed bank, and aboveground plant community by directly increasing rodent density. The similarity between seed rain and aboveground plant community was greater than that of the soil seed bank and aboveground plant community with increased grazing disturbance. Grazing disturbance spreads the risk of seed germination and seedling establishment by changing the temporal dynamics of seed dispersal. Plants (positive) and rodents (negative) mediated the role of seed rain and soil seed bank in plant community regeneration. The role of seed rain in plant community regeneration is higher than the seed bank in disturbed alpine marshes. Our findings increase our understanding of the regeneration process of the plant community, and they provide valuable information for the conservation and restoration of alpine marsh ecosystems.

Temporal Variations in Fire Impacts on Characteristics and Composition of Soil-Derived Dissolved Organic Matter at Qipan Mountain, China.

Dissolved organic matter (DOM), the most reactive fraction of forest soil organic matter, is increasingly impacted by wildfires worldwide. However, few studies have quantified the temporal changes in soil DOM quantity and quality after fire. Here, soil samples were collected after the Qipan Mountain Fire (3-36 months) from pairs of burned and unburned sites. DOM contents and characteristics were analyzed using carbon quantification and various spectroscopic and spectrometric techniques. Compared with the unburned sites, burned sites showed higher contents of bulk DOM and most DOM components 3 months after the fire but lower contents of them 6-36 months after the fire. During the sharp drop of DOM from 3 to 6 months after the fire, carboxyl-rich alicyclic molecule-like and highly unsaturated compounds had greater losses than condensed aromatics. Notably, the burned sites had consistently higher abundances of oxygen-poor dissolved black nitrogen and fluorescent DOM 3-36 months after the fire, particularly the abundance of pyrogenic C2 (excitation/emission maxima of <250/∼400 nm) that increased by 150% before gradually declining. This study advances the understanding of temporal variations in the effects of fire on different soil DOM components, which is crucial for future postfire environmental management.

Resting-State EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia.

Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.

Normative Intercorrelations Between EEG Microstate Characteristics.

EEG microstates are brief, recurring periods of stable brain activity that reflect the activation of large-scale neural networks. The temporal characteristics of these microstates, including their average duration, number of occurrences, and percentage contribution have been shown to serve as biomarkers of mental and neurological disorders. However, little is known about how microstate characteristics of prototypical network types relate to each other. Normative intercorrelations among these parameters are necessary to help researchers better understand the functions and interactions of underlying networks, interpret and relate results, and generate new hypotheses. Here, we present a systematic analysis of intercorrelations between EEG microstate characteristics in a large sample representative of western working populations (n = 583). Notably, we find that microstate duration is a general characteristic that varies across microstate types. Further, microstate A and B show mutual reinforcement, indicating a relationship between auditory and visual sensory processing at rest. Microstate C appears to play a special role, as it is associated with longer durations of all other microstate types and increased global field power, suggesting a relationship of these parameters with the anterior default mode network. All findings could be confirmed using independent EEG recordings from a retest-session (n = 542).

Temporal dynamic patterns of the ventromedial prefrontal cortex underlie the association between rumination and depression.

As a major contributor to the development of depression, rumination has proven linked with aberrant default-mode network (DMN) activity. However, it remains unclear how the spontaneous spatial and temporal activity of DMN underlie the association between rumination and depression. To illustrate this issue, behavioral measures and resting-state functional magnetic resonance images were connected in 2 independent samples (NSample1 = 100, NSample2 = 95). Fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) were used to assess spatial characteristic patterns, while voxel-wise functional concordance (across time windows) (VC) and Hurst exponent (HE) were used to assess temporal dynamic patterns of brain activity. Results from both samples consistently show that temporal dynamics but not spatial patterns of DMN are associated with rumination. Specifically, rumination is positively correlated with HE and VC (but not fALFF and ReHo) values, reflecting more consistent and regular temporal dynamic patterns in DMN. Moreover, subregion analyses indicate that temporal dynamics of the ventromedial prefrontal cortex (VMPFC) reliably predict rumination scores. Furthermore, mediation analyses show that HE and VC of VMPFC mediate the association between rumination and depression. These findings shed light on neural mechanisms of individual differences in rumination and corresponding risk for depression.