Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network.

Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz.

Resting state network changes induced by experimental inaudible infrasound exposure and associations with self-reported noise sensitivity and annoyance.

The effects of prolonged infrasound (IS) exposure on brain function and behavior are largely unknown, with only one prior study investigating functional connectivity (FC) changes. In a long-term randomized-controlled trial, 38 participants were exposed to inaudible airborne IS (6 Hz, 80-90 dB) or sham devices for four weeks (8 h/night). We assessed FC changes in resting-state networks (auditory, default mode (DMN), sensorimotor (SMN), and executive control (ECN)), and explored IS 'sensitivity' as a predictor of identified significant FC changes. We also examined correlations between somatic symptoms and FC. IS exposure led to decreased FC in the right precuneus (DMN) and increased FC in the Vermis IV and V (SMN). In the ECN, we observed increased FC in the right frontal middle gyrus (BA8) and the right inferior parietal lobe, and decreased FC in another region of the right frontal middle gyrus. Changes in the ECN (right inferior parietal lobe) were negatively associated with self-reported annoyance from IS/low-frequency noise. A significant negative association was found between FC changes in the DMN (right precuneus) and somatic symptoms. Our study is the first to investigate prolonged IS exposure effects on brain FC, revealing changes in the vDMN, SMN, and ECN, but not in the auditory network. Future studies should assess annoyance and sensitivity markers, fine-grained measures of somatic symptoms, and stratify samples by sensitivity to uncover individual differences in response to IS.

Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end-to-end frequency-specific dual-attention-based adversarial network (FDAA-Net) to extend the time series of existing blood oxygen level-dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency-dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial-temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA-Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA-Net effectively overcame linear frequency-specific challenges and outperformed other popular prediction models. Test-retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA-Net using short-term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.

Operatic voices engage the default mode network in professional opera singers.

Extensive research with musicians has shown that instrumental musical training can have a profound impact on how acoustic features are processed in the brain. However, less is known about the influence of singing training on neural activity during voice perception, particularly in response to salient acoustic features, such as the vocal vibrato in operatic singing. To address this gap, the present study employed functional magnetic resonance imaging (fMRI) to measure brain responses in trained opera singers and musically untrained controls listening to recordings of opera singers performing in two distinct styles: a full operatic voice with vibrato, and a straight voice without vibrato. Results indicated that for opera singers, perception of operatic voice led to differential fMRI activations in bilateral auditory cortical regions and the default mode network. In contrast, musically untrained controls exhibited differences only in bilateral auditory cortex. These results suggest that operatic singing training triggers experience-dependent neural changes in the brain that activate self-referential networks, possibly through embodiment of acoustic features associated with one's own singing style.

When sensory input meets spontaneous brain activity.

A recent study by Wu, Podvalny, and colleagues investigated how ongoing spontaneous brain activity interacts with sensory input and shapes conscious perception. It reports diverse effects of prestimulus activity in several key networks, revealing new roles of the prefrontal cortex and the default mode network in perception and consciousness.

Oscillatory mechanisms of intrinsic human brain networks.

Non-invasive neuroimaging has revealed specific network-based resting-state dynamics in the human brain, yet the underlying neurophysiological mechanism remains unclear. We employed intracranial electroencephalography to characterize local field potentials within the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in 42 participants. We identified stronger within-network phase coherence at low frequencies (θ and α band) within the DMN, and at high frequencies (γ band) within the FPN. Hidden Markov modeling indicated that the DMN exhibited preferential low frequency phase coupling. Phase-amplitude coupling (PAC) analysis revealed that the low-frequency phase in the DMN modulated the high-frequency amplitude envelopes of the FPN, suggesting frequency-dependent characterizations of intrinsic brain networks at rest. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way brain networks communicate at rest.

Translation of monosynaptic circuits underlying amygdala fMRI neurofeedback training.

fMRI neurofeedback using autobiographical memory recall to upregulate the amygdala is associated with resting-state functional connectivity (rsFC) changes between the amygdala and the salience and default mode networks (SN and DMN, respectively). We hypothesize the existence of anatomical circuits underlying these rsFC changes. Using a cross-species brain parcellation, we identified in non-human primates locations homologous to the regions of interest (ROIs) from studies showing pre-to-post-neurofeedback changes in rsFC with the left amygdala. We injected bidirectional tracers in the basolateral, lateral, and central amygdala nuclei of adult macaques and used bright- and dark-field microscopy to identify cells and axon terminals in each ROI (SN: anterior cingulate, ventrolateral, and insular cortices; DMN: temporal pole, middle frontal gyrus, angular gyrus, precuneus, posterior cingulate cortex, parahippocampal gyrus, hippocampus, and thalamus). We also performed additional injections in specific ROIs to validate the results following amygdala injections and delineate potential disynaptic pathways. Finally, we used high-resolution diffusion MRI data from four post-mortem macaque brains and one in vivo human brain to translate our findings to the neuroimaging domain. Different amygdala nuclei had significant monosynaptic connections with all the SN and DMN ipsilateral ROIs. Amygdala connections with the DMN contralateral ROIs are disynaptic through the hippocampus and parahippocampal gyrus. Diffusion MRI in both species benefitted from using the ground-truth tracer data to validate its findings, as we identified false-negative ipsilateral and false-positive contralateral connectivity results. This study provides the foundation for future causal investigations of amygdala neurofeedback modulation of the SN and DMN through these anatomic connections.

"All the Stars Will Be Wells with a Rusty Pulley": Neural Processing of the Social and Pragmatic Content in a Narrative.

Making sense of natural language and narratives requires building and manipulating a situation model by adding incoming information to the model and using the context stored in the model to comprehend subsequent details and events. Situation model maintenance is supported by the default mode network (DMN), but comprehension of the individual moments in the narrative relies on access to the conceptual store within the semantic system. The present study examined how these systems are engaged by different narrative content to investigate whether highly informative, or semantic, content is a particularly strong driver of semantic system activation compared with contextually driven content that requires using the situation model, which might instead engage DMN regions. The study further investigated which subregions of the graded semantic hub in the left anterior temporal lobe (ATL) were engaged by the type of narrative content. To do this, we quantified the semantic, pragmatic, social, ambiguous, and emotional content for each sentence in a complete narrative, the English translation of The Little Prince. Increased activation in the transmodal hub in the ventral ATL was only observed for high semantic (i.e., informative) relative to low semantic sentences. Activation in the dorsolateral and ventrolateral ATL subregions was observed for both high relative to low semantic and social content sentences, but the ventrolateral ATL effects were more extensive in the social condition. There was high correspondence between the social and pragmatic content results, particularly in the ventrolateral ATL. We argue that the ventrolateral ATL may be particularly engaged by internal, or endogenous, processing demands, aided by functional connections between the anterior middle temporal gyrus and the DMN. Pragmatic and social content may have driven endogenous processing given the pervasive and plot-progressing nature of this content in the narrative. We put forward a revised account of how the semantic system is engaged in naturalistic contexts, a critical step toward better understanding real-world semantic and social processing.

Silence between words: Is solitude important for relatedness?

Chronic loneliness is a risk factor for physical and health problems, in part due to dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system. In contrast, temporary moments of positive solitude (passing good times alone and not feeling lonely) appear to have positive effects on mental health, social life, and creativity, and seems to be a buffer against loneliness. Herein, three ways of how solitude may have positive effects on health and relatedness are discussed, namely effects on enhancement of mind-wandering, interoceptive awareness, and spirituality. Solitude may facilitate (1) activation of the default mode network (DMN) underlying mind-wandering including daydreaming about other people; (2) activation of brain areas supporting interoceptive awareness; (3) deactivation of prefrontal cortex, or deactivation and decreased connectivity of the DMN, giving raise to susceptibility to spiritual experiences. The capacity to handle and enjoy solitude is a developmental process that may be difficult for many persons. Craving for social connections and external stimulation with digital technologies (e.g., internet, smartphones, social media) might be interfering with the development of the capacity for solitude and thereby increasing loneliness; this might be partly due to impaired interoceptive awareness and impaired functional mind-wandering (common in solitude). Congruently, overuse of digital technologies was associated with reduced activity, and reduced gray matter volume and density, in brain areas supporting interoceptive awareness, as well as with decreased connectivity of the DMN supporting creative insights. Solitude has been a relatively dismissed topic in neuroscience and health sciences, but a growing number of studies is highlighting its importance for well-being.

Large-scale meta-analyses and network analyses of neural substrates underlying human escalated aggression.

Escalated aggression represents a frequent and severe form of violence, sometimes manifesting as antisocial behavior. Driven by the pressures of modern life, escalated aggression is of particular concern due to its rising prevalence and its destructive impact on both individual well-being and socioeconomic stability. However, a consistent neural circuitry underpinning it remains to be definitively identified. Here, we addressed this issue by comparing brain alterations between individuals with escalated aggression and those without such behavioral manifestations. We first conducted a meta-analysis to synthesize previous neuroimaging studies on functional and structural alterations of escalated aggression (325 experiments, 2997 foci, 16,529 subjects). Following-up network and functional decoding analyses were conducted to provide quantitative characterizations of the identified brain regions. Our results revealed that brain regions constantly involved in escalated aggression were localized in the subcortical network (amygdala and lateral orbitofrontal cortex) associated with emotion processing, the default mode network (dorsal medial prefrontal cortex and middle temporal gyrus) associated with mentalizing, and the salience network (anterior cingulate cortex and anterior insula) associated with cognitive control. These findings were further supported by additional meta-analyses on emotion processing, mentalizing, and cognitive control, all of which showed conjunction with the brain regions identified in the escalated aggression. Together, these findings advance the understanding of the risk biomarkers of escalated aggressive populations and refine theoretical models of human aggression.

Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks.

Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six functional magnetic resonance imaging studies, resulting in a sample of $155$ ($77$ women, $25 \pm 5$ years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activity. Higher precision of fine-grained representations was related to greater engagement of attentional and control brain systems throughout the task trial, and the stronger deactivation of the default network at the time of stimulus encoding. In contrast, the use of categorical representations was associated with lower default network activity during encoding and higher frontoparietal network activation during maintenance. These results may indicate a greater need for attentional resources and protection against interference for fine-grained compared with categorical representations.

Complexity organization of resting-state functional-MRI networks.

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

Precision mapping of the default network reveals common and distinct (inter) activity for autobiographical memory and theory of mind.

The default network is widely implicated as a common neural substrate for self-generated thought, such as remembering one's past (autobiographical memory) and imagining the thoughts and feelings of others (theory of mind). Findings that the default network comprises subnetworks of regions, some commonly and some distinctly involved across processes, suggest that one's own experiences inform their understanding of others. With the advent of precision functional MRI (fMRI) methods, however, it is unclear if this shared substrate is observed instead due to traditional group analysis methods. We investigated this possibility using a novel combination of methodological strategies. Twenty-three participants underwent multi-echo resting-state and task fMRI. We used their resting-state scans to conduct cortical parcellation sensitive to individual variation while preserving our ability to conduct group analysis. Using multivariate analyses, we assessed the functional activation and connectivity profiles of default network regions while participants engaged in autobiographical memory, theory of mind, or a sensorimotor control condition. Across the default network, we observed stronger activity associated with both autobiographical memory and theory of mind compared to the control condition. Nonetheless, we also observed that some regions showed preferential activity to either experimental condition, in line with past work. The connectivity results similarly indicated shared and distinct functional profiles. Our results support that autobiographical memory and theory of mind, two theoretically important and widely studied domains of social cognition, evoke common and distinct aspects of the default network even when ensuring high fidelity to individual-specific characteristics.NEW & NOTEWORTHY We used cutting-edge precision functional MRI (fMRI) methods such as multi-echo fMRI acquisition and denoising, a robust experimental paradigm, and individualized cortical parcellation across 23 participants to provide evidence that remembering one's past experiences and imagining the thoughts and feelings of others share a common neural substrate. Evidence from activation and connectivity analyses indicate overlapping and distinct functional profiles of these widely studied episodic and social processes.

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.

Differences Between Resting-State fMRI BOLD Variability and Default Mode Network Connectivity in Healthy Older and Younger Adults.

Background: Resting-state fMRI analyses have been used to examine functional connectivity in the aging brain. Recently, fluctuations in the fMRI BOLD signal have been used as a potential marker of integrity in neural systems. Despite its increasing popularity, the results of BOLD variability analyses and traditional seed-based functional connectivity analyses have rarely been compared. The current study examined fMRI BOLD signal variability and default mode network seed-based analyses in healthy older and younger adults to better understand the unique contributions of these methodological approaches. Methods: Thirty-four healthy participants were separated into a younger adult group (age 25-35, n = 17) and an older adult group (age 65+, n = 17). For each participant, a map of the standard deviation of the BOLD signal (SDBOLD) was derived. Group comparisons examined differences in resting-state SDBOLD in younger versus older adults. Seed-based analyses were used to examine differences between younger and older adults in the default mode network. Results: Between-group comparisons revealed significantly greater BOLD variability in widespread brain regions in older relative to younger adults. There were no significant differences between younger and older adults in the default mode network connectivity. Conclusion: The current findings align with an increasing number of studies reporting greater BOLD variability in older relative to younger adults. The current results also suggest that the traditional resting state examination methods may not detect nuanced age-related differences. Further large-scale studies in an adult lifespan sample are needed to better understand the functional relevance of the BOLD variability in normative aging.

Shared and distinctive brain networks underlying trait and state rumination.

Although trait and state rumination play a central role in the exacerbation of negative affect, evidence suggests that they are weakly correlated and exert distinct influences on emotional reactivity to stressors. Whether trait and state rumination share a common or exhibit distinct neural substrate remains unclear. In this study, we utilized functional near-infrared spectroscopy (fNIRS) combined with connectome-based predictive modeling (CPM) to identify neural fingerprints associated with trait and state rumination. CPM identified distinctive functional connectivity (FC) profiles that contribute to the prediction of trait rumination, primarily involving FC within the default mode network (DMN) and the dorsal attention network (DAN) as well as FC between the DMN, control network (CN), DAN, and salience network (SN). Conversely, state rumination was predominantly associated with FC between the DMN and CN. Furthermore, the predictive features of trait rumination can be robustly generalized to predict state rumination, and vice versa. In conclusion, this study illuminates the importance of both DMN and non-DMN systems in the emergence and persistence of rumination. While trait rumination was associated with stronger and broader FC than state rumination, the generalizability of the predictive features underscores the presence of shared neural mechanisms between the two forms of rumination. These identified connectivity fingerprints may hold promise as targets for innovative therapeutic interventions aimed at mitigating rumination-related negative affect.

Functional integration and segregation during semantic cognition: Evidence across age groups.

Semantic cognition is underpinned by ventral anterior temporal lobe (vATL) which encodes knowledge representations and inferior frontal gyrus (IFG), which controls activation of knowledge based on the needs of the current context. This core semantic network has been validated in substantial empirical findings in the past. However, it remains unclear how these core semantic areas dynamically communicate with each other, and with other neural networks, to achieve successful semantic processing. Here, we investigated this question by testing functional connectivity in the core semantic network during semantic tasks and whether these connections were affected by cognitive ageing. Compared to a non-semantic task, semantic tasks increased the connectivity between left and right IFGs, indicating a bilateral semantic control system. Strengthened connectivity was also found between left IFG and left vATL, and this effect was stronger in the young group. At a whole-brain scale, IFG and vATL increased their coupling with multiple-demand regions during semantic tasks, even though these areas were deactivated relative to non-semantic tasks. This suggests that the domain-general executive network contributes to semantic processing. In contrast, IFG and vATL decreased their interaction with default mode network (DMN) areas during semantic tasks, even though these areas were positively activated by the task. This suggests that DMN areas do not contribute to all semantic tasks: their activation may sometimes reflect automatic retrieval of task-irrelevant memories and associations. Taken together, our study characterizes a dynamic connectivity mechanism supporting semantic cognition within and beyond core semantic regions.

A synergistic workspace for human consciousness revealed by Integrated Information Decomposition.

How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.

The human brain consists of billions of neurons which process sensory inputs, such as sight and sound, and combines them with information already stored in the brain. This integration of information guides our decisions, thoughts, and movements, and is hypothesized to be integral to consciousness. However, it is poorly understood how the brain regions responsible for processing this integration are organized in the brain. To investigate this question, Luppi et al. employed a mathematical framework called Partial Information Decomposition (PID) which can distinguish different types of information: redundancy (available from many regions) and synergy (which reflects genuine integration). The team applied the PID framework to the brain scans of 100 individuals. This allowed them to identify which brain regions combine information from across the brain (known as gateways), and which ones transmit it back to the rest of the brain (known as broadcasters). Next, Luppi et al. set out to find how these regions compared in unconscious and conscious individuals. To do this, they studied 15 healthy volunteers whose brains were scanned (using a technique called functional MRI) before, during, and after anaesthesia. This revealed that the brain integrated less information when unconscious, and that this reduction happens predominantly in gateway rather than broadcaster regions. The same effect was also observed in the brains of individuals who were permanently unconscious due to brain injuries. These findings provide a way of understanding how information is organised in the brain. They also suggest that loss of consciousness due to brain injuries and anaesthesia involve similar brain circuits. This means it may be possible to gain insights about disorders of consciousness from studying how people emerge from anaesthesia.

Evolving brain network dynamics in early childhood: Insights from modular graph metrics.

Modular dynamic graph theory metrics effectively capture the patterns of dynamic information interaction during human brain development. While existing research has employed modular algorithms to examine the overall impact of dynamic changes in community structure throughout development, there is a notable gap in understanding the cross-community dynamic changes within different functional networks during early childhood and their potential contributions to the efficiency of brain information transmission. This study seeks to address this gap by tracing the trajectories of cross-community structural changes within early childhood functional networks and modeling their contributions to information transmission efficiency. We analyzed 194 functional imaging scans from 83 children aged 2 to 8 years, who participated in passive viewing functional magnetic resonance imaging sessions. Utilizing sliding windows and modular algorithms, we evaluated three spatiotemporal metrics-temporal flexibility, spatiotemporal diversity, and within-community spatiotemporal diversity-and four centrality metrics: within-community degree centrality, eigenvector centrality, between-community degree centrality, and between-community eigenvector centrality. Mixed-effects linear models revealed significant age-related increases in the temporal flexibility of the default mode network (DMN), executive control network (ECN), and salience network (SN), indicating frequent adjustments in community structure within these networks during early childhood. Additionally, the spatiotemporal diversity of the SN also displayed significant age-related increases, highlighting its broad pattern of cross-community dynamic interactions. Conversely, within-community spatiotemporal diversity in the language network exhibited significant age-related decreases, reflecting the network's gradual functional specialization. Furthermore, our findings indicated significant age-related increases in between-community degree centrality across the DMN, ECN, SN, language network, and dorsal attention network, while between-community eigenvector centrality also increased significantly for the DMN, ECN, and SN. However, within-community eigenvector centrality remained stable across all functional networks during early childhood. These results suggest that while centrality of cross-community interactions in early childhood functional networks increases, centrality within communities remains stable. Finally, mediation analysis was conducted to explore the relationships between age, brain dynamic graph metrics, and both global and local efficiency based on community structure. The results indicated that the dynamic graph metrics of the SN primarily mediated the relationship between age and the decrease in global efficiency, while those of the DMN, language network, ECN, dorsal attention network, and SN primarily mediated the relationship between age and the increase in local efficiency. This pattern suggests a developmental trajectory in early childhood from global information integration to local information segregation, with the SN playing a pivotal role in this transformation. This study provides novel insights into the mechanisms by which early childhood brain functional development impacts information transmission efficiency through cross-community adjustments in functional networks.

The Relationship between Event Boundary Strength and Pattern Shifts across the Cortical Hierarchy during Naturalistic Movie-viewing.

Our continuous experience is spontaneously segmented by the brain into discrete events. However, the beginning of a new event (an event boundary) is not always sharply identifiable: Phenomenologically, event boundaries vary in salience. How are the response profiles of cortical areas at event boundaries modulated by boundary strength during complex, naturalistic movie-viewing? Do cortical responses scale in a graded manner with boundary strength, or do they merely detect boundaries in a binary fashion? We measured "cortical boundary shifts" as transient changes in multivoxel patterns at event boundaries with different strengths (weak, moderate, and strong), determined by across-participant agreement. Cortical regions with different processing timescales were examined. In auditory areas, which have short timescales, cortical boundary shifts exhibited a clearly graded profile in both group-level and individual-level analyses. In cortical areas with long timescales, including the default mode network, boundary strength modulated pattern shift magnitude at the individual participant level. We also observed a positive relationship between boundary strength and the extent of temporal alignment of boundary shifts across different levels of the cortical hierarchy. In addition, hippocampal activity was highest at event boundaries for which cortical boundary shifts were most aligned across hierarchical levels. Overall, we found that event boundary strength modulated cortical pattern shifts strongly in sensory areas and more weakly in higher-level areas and that stronger boundaries were associated with greater alignment of these shifts across the cortical hierarchy.