Acquiring a complex cognitive skill reflects the human brain's remarkable capacity for long‑term plasticity, yet a fine‑grained, temporally dense map of the neural dynamics that unfold from novice to expert remains largely uncharted, limited by sparse sampling and group‑averaged designs. Leveraging longitudinal precision functional imaging, we tracked behavioral and whole‑brain functional dynamics in three individuals over a nine‑month period of real‑world abacus‑based mental calculation learning. This Precision Abacus‑based Representation Learning (PEARL) dataset comprises approximately 4,000 minutes of multimodal MRI data per participant, acquired across more than fifty scanning visits. We delineate a behavioral trajectory marked by initial practice gains, a transient cost of strategy switching, and eventual rapid, stable skill consolidation. Concomitant neuroimaging revealed continuous, individualized reorganization of functional brainwave networks, with each brain progressively diverging from its baseline before converging toward an expert‑like state. This study provides the first integrated, precision map of large‑scale brain plasticity during extended ecological skill learning. As an open‑access resource, the PEARL dataset offers an unprecedented opportunity to uncover the dynamic principles of learning‑induced plasticity, enabling the neuroscience community to explore and model the precise interplay between brain reorganization and behavioral mastery.

How does the learning brain give rise to emergent mental operations that enable skill internalization and generalization? Using five-year longitudinal tracking of children acquiring abacus-based mental calculation, we reveal how sustained practice progressively reconfigures whole-brain states. Learning induces nonlinear transitions from stable baseline configurations through a transient exploratory phase toward restabilized associative brain networks. This hierarchical remodeling cascades from perceptual systems to action and default networks, mediated by adaptive neural variability and selective connectivity reweighting, with the salience-parietal memory network acting as a dynamic relay. These brain-state transitions accompany an ordered cognitive transfer cascade, emerging within arithmetic and extending to visuospatial and executive functions. Our findings are reproducible and provide a systems-level account of how ecological learning sculpts large-scale networks to enable complex skill acquisition.

Abacus-based mental calculation (AMC) learning has been associated with improved arithmetic performance in children, but the underlying cognitive mechanisms remain unclear. This study examined whether domainspecific numerical processing and domain-general cognitive abilities account for this association. The AMC program, which involves visualizing and mentally manipulating an abacus to calculate, was implemented from the beginning of grade 1. A total of 256 children in grades 1-5 with 3-39 months of AMC learning and 232 control children participated in the study. Results showed that AMC learners outperformed controls in arithmetic, numerical processing, short-term memory, and attention. Mediation analyses revealed that numerical processing partially accounted for the arithmetic advantage in Grades 2-5, whereas attention and short-term memory showed mediating roles only in Grades 4-5. These findings suggest that the arithmetic advantage associated with AMC learning may be partly explained by both domain-specific and domain-general cognitive pathways, with their relative contributions varying across learning durations.

Cerebral asymmetry is a core principle of human brain organization, showing dynamic changes across the lifespan and alterations in brain disorders. However, it remains unclear whether lifespan trajectories of asymmetry differ across populations. We compared lifespan structural asymmetry normative charts of 221 cerebral imaging phenotypes from 43,037 Chinese and 56,339 Western participants aged 0–100 years. The two populations showed distinct lifespan asymmetry patterns in 26.2% of the phenotypes. Chinese-minus-Western asymmetry difference curves displayed distinct patterns across brain phenotypes: rightward (45.7%), leftward (26.2%), rightward-to-leftward (11.8%), leftward-to-rightward (10.0%), and unclassified (6.3%). Population-matched normative models outperformed population-unmatched normative models in capturing normal asymmetry variability among healthy individuals and in detecting abnormal asymmetry deviations in patients with Alzheimer’s disease, mild cognitive impairment, schizophrenia, and major depressive disorder. These findings indicate that population mismatch can bias chart-based individual-level asymmetry assessment and underscore the need for population-representative brain asymmetry normative charts.

It has been widely observed that cognitive training can enhance the working memory capacity (WMC) of participants, yet the underlying mechanisms remain unexplained. Previous research has confirmed that abacus-based mental calculation (AMC) training can enhance the WMC of subjects and suggested its possible association with changes in functional connectivity. With fMRI data, we construct whole brain resting state connectivity of subjects who underwent long-term AMC training and other subjects from a control group. Their working memory capacity is simulated based on their whole brain resting state connectivity and reservoir computing. It is found that the AMC group has higher WMC than the control group, and especially the WMC involved in the frontoparietal network (FPN), visual network (VIS) and sensorimotor network (SMN) associated with the AMC training is even higher in the AMC group. However, the advantage of the AMC group disappears if the connection strengths between brain regions are neglected. The effects on WMC from the connection strength differences between the AMC and control groups are evaluated. The results show that the WMC of the control group is enhanced and achieved consistency with or even better than that the AMC group if the connection strength of the control group are weakened. And the advantage of FPN, VIS and SMN is reproduced too. In conclusion, our work reveals a correlation between reduction in functional connection strength and enhancements in the WMC of subjects undergoing cognitive training.

The global burden of pulmonary fibrosis is increasing. Recent studies have shown that some pulmonary fibrotic lesions caused by COVID-19 infection may persist for a long time. Emerging evidence suggested a critical association between gut microbiota and pulmonary fibrosis. In this study, the clinical follow-up data from post-COVID-19 patients indicated that those with higher CT image scores were older, had a significantly lower Blautia and Bifidobacterium to Streptococcus ratio (B/S index). We examined whether Bifidobacterium adolescentis could attenuate bleomycin-induced pulmonary fibrosis in mice, with particular attention in the aging mice. Aging mice exhibited more severe pulmonary fibrosis after BLM induction, while the intervention of B. adolescentis attenuated the degree of pulmonary fibrosis in aging mice to a state similar to that of young mice. B. adolescentis alleviated inflammatory responses by enhancing the gut barrier, and reduced fibrotic marker expression (TGF-β, IL-17, α-SMA, Collagen I/III) by modulating PPAR and Th17 signaling pathways. Furthermore, B. adolescentis stabilized gut microbiota and increased the abundance of Bifidobacterium, Turicibacter, and norank_f_Desulfovibrionaceae, thereby suppressed the prostaglandin E2 (PGE2) and affected collagen deposition. B. adolescentis alleviates pulmonary fibrosis through the gut-lung axis by regulating PGE2/PPAR/Th17 signaling, providing a promising therapeutic approach for pulmonary fibrosis management.

Gut microbiota dysbiosis has been observed in HBV-related cirrhosis, but its role in early-stage disease and its correlation with liver pathology remain unclear. Moreover, whether dysbiosis is a cause or consequence of liver cirrhosis is still debated. We recruited 20 treatment-naïve patients with chronic HBV infection, assessing liver injury via biopsy. Fecal metagenomic sequencing was used to analyze the correlation between gut microbiota and liver histology. To explore the causality, fecal samples from an HBV-related cirrhosis patient were transplanted into mice with CCl₄-induced liver fibrosis. Patients with significant histological damage exhibited reduced alpha diversity and greater microbial homogeneity. Species such as Eubacterium_sp_CAG_180, Gemmiger_formicilis, and Oscillibacter_sp_ER4 had decreased abundance, while Parabacteroides_distasonis, Bacteroides_dorei, and Bacteroides_finegoldii were enriched. Mice receiving fecal transplants from the cirrhotic patient showed aggravated liver fibrosis, with increased collagen deposition; elevated ALT, AST, and ALP levels; and heightened hepatic inflammatory gene expression. Additionally, abnormal bile acid profiles with elevated unconjugated bile acids (e.g., GCA and CA) were observed. Gut microbiota dysbiosis is closely associated with liver histological damage in chronic HBV infection and may drive fibrosis progression via microbial-bile acid interactions. These findings suggest potential for gut microbiota-based assessment and treatment strategies in chronic hepatitis B.IMPORTANCEThis study elucidates a significant association between gut microbiota dysbiosis and liver histological damage in patients with chronic hepatitis B (HBV), potentially exacerbating fibrosis progression through bile acid interactions. By analyzing patient gut microbiota and conducting fecal transplant experiments in mice, researchers have identified that gut microbiota dysbiosis contributes to hepatic fibrosis during chronic HBV infection. These findings underscore the importance of the gut-liver axis in HBV disease progression, indicating that monitoring or modulating gut bacteria may facilitate early diagnosis or therapeutic interventions. This research bridges the gap in understanding whether microbial alterations drive disease progression or result from it, providing a foundation for developing therapies targeting the microbiome to mitigate liver damage in chronic HBV infections.

Structural brain networks underpin complex brain dynamics and cognitive functions, but abnormality in the network structures may disrupt neural activity in schizophrenia (SCHZ). The mechanism how structural alterations in brain networks lead to abnormal dynamics remain unclear. In this work, we construct a whole-brain dynamic model to simulate resting-state brain activity, using Aihara chaotic neurons to represent brain regions and structural connection strengths from diffusion spectrum magnetic resonance imaging as coupling weights across 83 regions defined by a standard atlas. Our simulations reveal oscillatory patterns shared across groups, highlighting beta-frequency activity in the default mode, basal ganglia, and somatomotor networks. In SCHZ patients, four right-hemisphere regions, the posterior cingulate, isthmus cingulate, temporal pole and amygdala, show transitions from uncertain states in the control group to stable beta-wave activity, consistent with prior EEG findings of elevated beta waves in SCHZ. Graph theory analysis reveals increased local connection strength in the four regions of the SCHZ group, suggesting that the structural changes promote pathological beta activity. Our results illuminate how region-specific alterations in structural brain networks disrupt its dynamics, offering insights into the neural mechanism driving functional abnormalities in SCHZ.

Pulmonary sequelae associated with COVID-19 are an increasing concern. Emerging evidence suggests that gut dysbiosis may contribute to post-acute COVID-19 syndromes; yet, the specific link between gut mycobiota and pulmonary sequelae remains unclear. We conducted a prospective study with 46 COVID-19 patients and 37 healthy controls. Longitudinal assessments of gut mycobiota were performed at admission, 6 months, 1 year, and 2 years post-discharge using internal transcribed spacer (ITS) 3-4 sequencing. At the 2-year follow-up, pulmonary function tests and chest computed tomography (CT) scans were also performed and correlated with gut mycobiota. While pulmonary function remained well-preserved, radiographic abnormalities persisted in a small subset of patients at 2 years. During the acute phase, fungal richness, as indicated by Chao1 and Shannon indices, was significantly lower than in controls, but gradually returned to normal levels after 6 months of recovery. Genus Candida, elevated during the acute phase, reverted to control levels after 6 months. At the 2-year follow-up, there was an increased presence of Hanseniaspora, Issatchenkia, and Saturnispora and a decreased presence of Aspergillus, Penicillium, and Rhodotorula. In addition, Pichia, Saccharomycopsis, and Cladosporium were especially enriched in patients without residual CT abnormalities at 2 years. At 2 years post-discharge, significant correlations were observed between pulmonary function and gut mycobiota, including negative correlations between Hanseniaspora and PEF, as well as negative correlations between Saturnispora and DLCO. This study reveals the longitudinal shifts in gut mycobiota over 2 years post-discharge in COVID-19 patients. Significant correlations were observed between pulmonary function and the gut mycobiota, suggesting a potential detrimental effect on lung function, offering potential insights into the pathogenesis of post-acute COVID-19 syndromes. IMPORTANCE:This article elucidates the intricate process of gut mycobiota reconstitution within a 2-year timeframe following COVID-19 infection and establishes a significant link between the gut mycobiota and the recuperation of pulmonary function. Our research suggests that the gut mycobiota could be utilized as diagnostic indicators for post-acute COVID-19 syndromes and may offer avenues for developing therapeutic interventions.

The brain has great plasticity. Cognitive training can change the functional connectivity of brain networks and then improve trainees’ cognitive ability. Most cognitive training studies have focused on revealing the correlation between cognitive ability improvement and structural changes in functional brain networks. The attention to change in brain dynamics induced by cognitive training is relatively less. No report was found for the alteration of state distributions in phase spaces. Abacus-based mental calculation (AMC) training is one of cognitive training methods, often used to enhance the cognitive ability of subjects. We construct a dynamical model of the brain based on the Aihara chaotic neuron model and resting-state functional brain networks. The differences on phase space distributions between the AMC group and the control group are investigated. Compared to the control group, the state distributions in the phase space of brain regions associated with memory function exhibite aggregation in the AMC group, while the state trajetories of AMC group are diffused in larger area in the phase spaces with bain regions related to the somatomotor network. Our results indicate that AMC training can promote the memory function of participants and the functional segregation of community structures in the brain. Furthermore, our work suggests that greater improvements in memory function of the right cerebral than that of the left cerebral hemisphere in AMC training.

We present a dataset of healthy children (N = 99) to explore the impact of long-term cognitive training on multimodal brain structure, function, and cognitive performance. At the start of primary school, participants were randomly assigned to an experimental group (n = 53), which received five years of structured abacus-based mental calculation (AMC) training from Grade 1 to Grade 5, or a control group (n = 46) without additional training. Neuroimaging data, including resting-state functional MRI and T1-weighted structural MRI, were collected after the first training year (at the start of Grade 2). Behavioral data, including standardized mathematical ability tests and psychological assessments, were collected longitudinally across Grades 2 to 5. To promote open access, the Brain Imaging Data Structure (BIDS) formatted data and corresponding quality control reports are available on the Science Data Bank. It offers a unique opportunity to deepen our understanding of how long-term training shapes neural and behavioral development in childhood.

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine 1-7. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.

The cognitive differentiation of executive function (EF) and mathematical ability during child development, characterized by their decreasing correlation, is well established. However, the impact of long-term cognitive training on this developmental effect remains largely unexplored. The present study investigated this by analyzing behavioral and neuroimaging data from schoolchildren who participated in five years of abacus training. The findings indicate that, compared to the control group, the training group exhibits cognitive dedifferentiation, characterized by stronger correlations between EF and mathematical abilities, accompanied by lower inter-individual variability. These observations are consistent with the discovery of greater overlap in behavior-associated brain connectivity patterns and more uniform connectivity profiles across individuals. Furthermore, the individual-to-group similarity in connectivity pattern is significantly associated with EF and mathematical performance, suggesting a shared cognitive strategy shaped by prolonged training. The findings provide empirical evidence in support of neurocognitive plasticity, highlighting the capacity of targeted cognitive training to functionally reshape brain networks and modulate the developmental trajectory of cognitive traits.

Gut microbiota dysbiosis plays a role in the pathogenesis of post-acute coronavirus disease (COVID-19); however, the long-term recovery of the gut microbiota following SARS-CoV-2 infection remains insufficiently understood. In this study, 239 fecal samples were collected from 87 COVID-19 patients during the acute phase, and at 6 months, 1 year, and 2 years post-discharge. An additional 48 fecal samples from non-COVID-19 controls were also analyzed. Gut enterotypes were determined through 16S rRNA sequencing, and dynamic changes from the acute phase through recovery were assessed. Correlations between enterotypes and clinical characteristics were also examined. Two distinct enterotypes were identified: a Blautia-dominated enterotype (Enterotype-B) and a Streptococcus-dominated enterotype (Enterotype-S). Species diversity and richness were significantly higher in Enterotype-B. Enterotype-S, associated with inflammation, was more prevalent during the acute phase. Six months post-discharge, the ratio of Enterotype-B to Enterotype-S approached normal levels. Patients with Enterotype-S at admission had a higher incidence of severe cases during hospitalization and a longer duration of nasopharyngeal viral shedding compared with those with Enterotype-B. Furthermore, at 6 months post-discharge, residual pulmonary Computed Tomography (CT) abnormalities were more common in patients with Enterotype-S (55%) than in those with Enterotype-B (20%, P = 0.046). An index, B/S, representing the ratio of Blautia and Bifidobacterium to Streptococcus, was introduced and found to correlate closely with clinical characteristics. The Streptococcus-dominated enterotype is associated with inflammation and appears to influence both the severity of illness during the acute phase and cardiopulmonary recovery. IMPORTANCE:This study sheds new light on the intricate process of rehabilitating the gut microbiota following disruptions caused by COVID-19. Our approach, which examines the dynamics from the vantage point of enterotypes, reveals a more rapid recovery than previously reported, with the majority of the microbiota rebounding within a 6-month timeframe. Furthermore, our findings underscore the importance of the Blautia-dominated enterotype as a marker of gut health, which plays a pivotal role in mitigating the risk of severe progression and lingering effects post-SARS-CoV-2 infection. By scrutinizing these enterotypes, we can now foresee the potential severity and aftermath of COVID-19, offering a valuable tool for prognosis and intervention.

Cognitive impairments in narcolepsy type 1 (NT1) significantly compromise daily functioning, but their neural mechanisms remain unclear. This study employed multimodal electroencephalography (EEG) analyses to investigate electrophysiological substrates of attention and inhibition deficits in NT1 and their association with clinical characteristics, particularly orexin deficiency. High-density EEG recordings were acquired during a Go/NoGo task from 39 NT1 patients and 41 age-/sex-matched healthy controls. Behavioral analyses revealed that compared to controls, NT1 patients exhibited significantly prolonged reaction times and increased errors across both Go and NoGo conditions. Electrophysiological analyses demonstrated that NT1 patients showed: (1) delayed Go-P3 latencies, meaning impaired response preparation; (2) reduced NoGo-P3 amplitudes, reflecting deficient inhibitory control; and (3) attenuated theta-band power and inter-trial phase consistency across conditions. Notably, decreased theta-band power correlated with both lower orexin levels and slower reaction times. This suggests that altered theta-band activity may represent a core neural substrate linking the underlying pathophysiology (i.e., orexin deficiency) to its clinical cognitive manifestations. Thus, we propose theta-band oscillations as a potential clinically translatable biomarker for NT1-related cognitive deficits, with promising implications for objective monitoring of disease progression and developing EEG-targeted neuromodulation therapies.

Quantifying individual deviations in brain morphology from normative references is useful for understanding neurodiversity and facilitating personalized management of brain health. Here we report Chinese brain normative references using morphological imaging scans of 24,061 healthy volunteers from 105 sites, revealing later peak ages of lifespan neurodevelopmental milestones (1.2-8.9 years) than European/North American populations. We model individual brain deviation scores in 3,932 individuals with different neurological disorders from population references to evaluate three key aspects of brain health assessment using machine learning approaches: estimating disease propensity, predicting cognitive and physical outcomes and assessing treatment effects with distinct disability progression. The norm-deviation scores outperformed raw structural measures in these evaluations. Chinese-specific normative brain references may foster personalized diagnosis and prognosis in neurological diseases, enabling clinically applicable assessments of brain health.

The human brain is highly plastic. Cognitive training is usually used to modify functional connectivity of brain networks. Moreover, the structures of brain networks may determine its dynamic behavior which is related to human cognitive abilities. To study the effect of functional connectivity on the brain dynamics, the dynamic model based on functional connections of the brain and the Hindmarsh-Rose model is utilized in this work. The resting-state fMRI data from the experimental group undergoing abacus-based mental calculation (AMC) training and from the control group are used to construct the functional brain networks. The dynamic behavior of brain at the resting and task states for the AMC group and the control group are simulated with the above-mentioned dynamic model. In the resting state, there are the differences of brain activation between the AMC group and the control group, and more brain regions are inspired in the AMC group. A stimulus with sinusoidal signals to brain networks is introduced to simulate the brain dynamics in the task states. The dynamic characteristics are extracted by the excitation rates, the response intensities and the state distributions. The change in the functional connectivity of brain networks with the AMC training would in turn improve the brain response to external stimulus, and make the brain more efficient in processing tasks.

Abacus-based mental calculation (AMC) is a widely used educational tool for enhancing math learning, offering an accessible and cost-effective method for classroom implementation. Despite its universal appeal, the neurocognitive mechanisms that drive the efficacy of AMC training remain poorly understood. Notably, although abacus training relies heavily on the rapid recall of number positions and sequences, the role of memory systems in driving long-term AMC learning remains unknown. Here, we sought to address this gap by investigating the role of the medial temporal lobe (MTL) memory system in predicting long-term AMC training gains in second-grade children, who were longitudinally assessed up to fifth grade. Leveraging multimodal neuroimaging data, we tested the hypothesis that MTL systems, known for their involvement in associative memory, are instrumental in facilitating AMC-induced improvements in math skills. We found that gray matter volume in bilateral MTL, along with functional connectivity between the MTL and frontal and ventral temporal-occipital cortices, significantly predicted learning gains. Intriguingly, greater gray matter volume but weaker connectivity of the posterior parietal cortex predicted better learning outcomes, offering a more nuanced view of brain systems at play in AMC training. Our findings not only underscore the critical role of the MTL memory system in AMC training but also illuminate the neurobiological factors contributing to individual differences in cognitive skill acquisition. A video abstract of this article can be viewed at https://youtu.be/StVooNRc7T8. RESEARCH HIGHLIGHTS: We investigated the role of medial temporal lobe (MTL) memory system in driving children's math learning following abacus-based mental calculation (AMC) training. AMC training improved math skills in elementary school children across their second and fifth grade. MTL structural integrity and functional connectivity with prefrontal and ventral temporal-occipital cortices predicted long-term AMC training-related gains.