Biology subjects

Qin, S.

Publications and source records attributed to Qin, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Fast calculation of small-angle scattering profiles of dense protein solutions modeled at the all-atom level

The small-angle scattering profile of a dense protein solution contains rich information about interprotein interactions, but extracting this information has been extremely challenging. In previous studies, we developed a fast Fourier transform (FFT)-based method, FMAPB2, to compute protein pair interactions at the all-atom level. Here, we use FMAPB2 to precompute pair interaction energies and then use the latter to drive simulations of dense protein solutions. The resulting MAEPPI (many-protein atomistic energy from pre-computed pair interaction) enables simulations of atomistic proteins at the speed of Lennard-Jones particles. On snapshots sampled from the simulations, we directly calculate the small-angle scattering profile. The results reveal artifacts generated by widely used spherical models and support a significant dimer population of bovine serum albumin at high concentrations. Our approach promises to modernize the prediction and interpretation of small-angle scattering profiles of dense protein solutions.

biophysics↗

bioRxiv2025

Differences in external loads of different pitch types in Chinese male college baseball players

BackgroundIn baseball, monitoring player load solely through pitch count and innings overlooks the variability introduced by different pitch types. This study analyzed differences in external load, measured as Player Load, across various pitch types using a GPS-based wearable device. Additionally, the relationship between Player Load and ball velocity was examined. MethodsFour male collegiate Division I baseball players from China (age: 18.5 {+/-} 0.58 years, height: 181.00 {+/-} 6.83 cm, weight: 78.00 {+/-} 5.48 kg) participated in this study. External loads were collected for four pitch types, fastball, changeup, curveball, and slider, using a wearable sensor device that recorded six variables: Maximum Player Load (Max PL), Maximum Rotation (Max Rot), Pitching Hourly Velocity, and Percentage of Acceleration Change in the Three Axes (Up Load %, Side Load %, and Fwd Load %). A Kruskal-Wallis non-parametric test was used to assess differences across pitch types, while stepwise multiple regression analyzed the impact of Player Load on pitching speed. ResultsSignificant differences were observed among pitch types for Max PL, Max Rot, Hourly Speed, Up Load %, and Fwd Load % (p<0.05), while Side Load % did not show a significant difference (p>0.05). Stepwise multiple regression indicated that pitching speed was influenced by Max PL, with the equation: mph = 78.816 + 7.001 Max PL, R2 = 0.192, suggesting that Max PL accounted for 19.2% of the variability in pitching speed. DiscussionExternal training loads in pitching vary by pitch type, with variable-speed pitches generating higher peak external loads, whereas fastballs are associated with greater ball speeds.

scientific communication and education↗

bioRxiv2025

A genomic catalog of Earth's bacterial and archaeal symbionts

1Microbial symbiosis drives the functional and phylogenomic diversification of life on Earth, yet remains underexplored due to culturing challenges. This study employed machine learning (ML) to predict symbiotic lifestyles in hundreds of thousands of microbial genomes from diverse environmental metagenome samples and reference genomes. Predictions were performed using symclatron, a novel ML framework developed here to identify genomic signatures of symbionts. Predictions were deposited in a novel Symbiont Genomes catalog (SymGs). The results indicated that 15-23% of uncultivated microbes likely engage in symbiotic relationships with other organisms and are present in half of all known bacterial and archaeal phyla. We also identified genomic signatures of symbiotic lifestyles, including the loss of certain metabolic functions and the differential presence of others that may enable host-dependent living. The symclatron software and the SymGs catalog represent valuable resources for studying symbioses, facilitating future mechanistic investigations of host-microbe associations.

genomics↗

bioRxiv2025

DIRseq: a method for predicting drug-interacting residues of intrinsically disordered proteins from sequences

Intrinsically disordered proteins (IDPs) are now well-recognized as drug targets. Identifying drug-interacting residues is valuable for both optimizing compounds and elucidating the mechanism of action. Currently, NMR chemical shift perturbation and all-atom molecular dynamics (MD) simulations are the primary tools for this purpose. Here we present DIRseq, a fast method for predicting drug-interacting residues from the amino-acid sequence. All residues contribute to the propensity of a particular residue to be drug-interacting; the contributing factor of each residue has an amplitude that is determined by its amino-acid type and attenuates with increasing sequence distance from the particular residue. DIRseq predictions match well with drug-interacting residues identified by NMR chemical shift perturbation and other methods, including residues L22WK24 and Q52WFT55 in the tumor suppressor protein p53. These successes augur well for deciphering the sequence code for IDP-drug binding. DIRseq is available as a web server at https://zhougroup-uic.github.io/DIRseq/ and has many applications, such as virtual screening against IDPs and designing IDP fragments for in-depth NMR and MD studies.

biophysics↗

bioRxiv2025

Key genes and pathways in asparagine metabolism in Alzheimers Disease: a bioinformatics approach

BackgroundAsparagine (Asn) metabolism is essential for maintaining cellular homeostasis and supporting neuronal energy demands. Recent studies have suggested its dysregulation may contribute to Alzheimers disease (AD) pathogenesis; however, the specific genes and regulatory mechanisms involved remain incompletely understood. MethodsFour publicly available microarray datasets (GSE5281, GSE29378, GSE36980, and GSE138260) were utilized to investigate genes with differential expression between control and AD samples. Asparagine metabolism-related genes (AMGs) were retrieved from the GeneCards database, and their intersection with DEGs yielded candidate asparagine metabolism-related differentially expressed genes (AMG-DEGs). Functional enrichment analysis (Gene Set Enrichment Analysis, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes), protein-protein interaction (PPI) network analysis, and centrality scoring identified hub genes. Regulatory mechanisms were investigated through construction of competing endogenous RNA and transcription factor networks. Potential therapeutic compounds were predicted via drug-gene enrichment and evaluated using molecular docking simulations. ResultsThirty-nine AMG-DEGs were identified and found to be enriched in neurodevelopmental, synaptic transmission, and inflammatory signaling pathways. PPI analysis and centrality screening revealed seven hub genes (HPRT1, GAD2, TUBB3, GFAP, CD44, CCL2, and NFKBIA). Regulatory network analysis highlighted specific miRNAs, long non-coding RNAs, and transcription factors involved in their modulation. Drug screening and docking identified Bathocuproine disulfonate, DL-Mevalonic acid, and Phenethyl isothiocyanate as promising compounds with strong binding affinities to hub proteins. ConclusionThis study comprehensively maps the dysregulation of asparagine metabolism in Alzheimers disease and reveals a set of hub genes and regulatory elements potentially involved in disease progression. The predicted therapeutic compounds provide a foundation for further experimental validation and may contribute to the development of novel metabolism-targeted strategies for AD treatment.

neuroscience↗

bioRxiv2025

brain network dynamics during multi-task demands predict children math achievement

Dynamic reconfiguration of neural network and flexible information integration across multiple tasks has been considered critical to characterize individual difference in complex cognition and general intelligence. A promising and underexplored question is how these neurocognitive processes related to childrens academic achievements, a hallmark of high-order cognitive abilities that integrate attention, memory and problem-solving. By using of a multitasking paradigm which bridging outside- in and inside-out approaches, we investigated the dynamic neural mechanisms underlying two core domains of academic performance: math and reading. We first apply partial least squares regression (PLSR) to examine static neural patterns and find that the first latent component--reflecting a generalized brain functional system--predicts math achievement but not reading. The multiple-demand system and the somato-cognitive action network (SCAN) are consistently engaged across diverse task demands. Furthermore, we use a Hidden Markov Model (HMM) to examine dynamic features of brain activity and identify distinct integrated and segregated brain states. Notably, the segregated state--characterized by heightened cortical network segregation--is associated with better math performance. Information-theoretic analyses further reveal that greater complexity in the temporal sequence of the segregated brain functional networks, along with stronger cerebrocerebellar functional coupling, correlates with higher math achievement. By means of multitasks design, these findings suggest that flexible engagement of specialized brain network and automatic information processing is crucial for math learning in children.

neuroscience↗

bioRxiv2024

Loss of virulence of Botrytis cinerea mutants defective in phytotoxin production is restored by modifying inoculation medium

Botrydial, botcinic acid and their derivatives are major phytotoxic metabolites produced by the necrotrophic fungal pathogen Botrytis cinerea. These phytotoxins are able to induce programmed cell death in the host and thereby promote plant susceptibility to B. cinerea. We observed that a{Delta} bot2{Delta}boa6 double mutant strain, which synthesizes neither botrydial nor botcinic acid, was almost avirulent on tomato leaves when the disease assay was performed using synthetic minimal Gamborg B5 medium. However, virulence of this mutant was restored when the inoculation medium was supplemented with yeast extract. Further virulence assays which compared the double mutant with other multiple mutants using both inoculation media, revealed a prominent contribution of botrydial and botcinic acid to the full virulence of B. cinerea. Therefore, we performed an RNA-sequencing experiment to identify B. cinerea genes that contribute to the phenotypic switch from an "incompatible" to a "compatible" interaction between tomato and the{Delta} bot2{Delta}boa6 double mutant. Four genes encoding cell death-inducing effector proteins were upregulated in B. cinerea by the addition of yeast extract, and their transcript profiles grouped within a co-expression module that was positively correlated with the compatible interaction. Functional analyses of these effector genes were performed by overexpressing them individually in the{Delta} bot2{Delta}boa6 background, followed by disease assays with the Gamborg B5 medium without yeast extract. ImportanceThe grey mould fungus Botrytis cinerea is a model for necrotrophic plant pathogens due to its wide host range, economic impact, well-assembled genome, and versatile mechanisms for inducing host cell death during colonization. Botrydial and botcinic acid have previously been characterized as major phytotoxins produced by B. cinerea. However, studies from different groups reported variable results regarding the contributions of these phytotoxins to fungal virulence. Here we demonstrate that botrydial and botcinic acid make a prominent contribution to the full virulence of B. cinerea, by performing infection assays with mutants that are defective in phytotoxin production and/or multiple cell death-inducing proteins using different inoculation media. This work highlights the pivotal roles of these phytotoxins as compared to other virulence factors, as well as the significant impact of inoculation conditions on compatible and incompatible interactions between the fungus and its hosts.

microbiology↗

bioRxiv2024

Refined Myocardium Segmentation from CT Using a Hybrid-Fusion Transformer

1.Accurate segmentation of the left ventricle (LV) in cardiac CT images is crucial for assessing ventricular function and diagnosing cardiovascular diseases. Creating a sufficiently large training set with accurate manual labels of LV can be cumbersome. More efficient semi-automatic segmentation, however, often includes unwanted structures, such as papillary muscles, due to low contrast between the LV wall and surrounding tissues. This study introduces a two-input-channel method within a Hybrid-Fusion Transformer deep-learning framework to produce refined LV labels from a combination of CT images and semi-automatic rough labels, effectively removing papillary muscles. By leveraging the efficiency of semi-automatic LV segmentation, we train an automatic refined segmentation model on a small set of images with both refined manual and rough semi-automatic labels. Evaluated through quantitative cross-validation, our method outperformed models that used only either CT images or rough masks as input.

bioengineering↗

bioRxiv2024

Neural representational precision of distance predicts arithmetic performance

Focusing on the distance between magnitudes as the start point to investigate the mechanism of relationship detecting and its contribution to mathematical thinking, this study explores the precision of neural representations of numerical distance and their impact on arithmetic performance. By employing neural decoding techniques and representational similarity analysis, the present study investigates how accurately the brain represents numerical distances and how this precision relates to arithmetic skills. Thirty-two children participated, completing a dot number comparison task during fMRI scanning and an arithmetic fluency test. Results indicated that neural activation patterns in the intra-parietal sulcus decoded the distance between the presented pair of dots, and higher precision in neural distance representation correlates with better arithmetic performance. These findings suggest that the accuracy of neural decoding can serve as an index of the neural representation precision and that the ability to precisely encode numerical distances in the brain is a key factor in mathematical abilities. This provides new insights into the neural basis of mathematical cognition and learning. HighlightsO_LIUtilizing representational similarity analysis and neural decoding techniques, the research proposes that the accuracy of neural decoding serve as an index of neural representation precision. C_LIO_LIThe precision of neural representation of numerical distances in the brain predicts task performance and math arithmetic proficiency in children. C_LIO_LIThese findings imply the potential significance of relational information in general cognition and learning beyond mathematical learning. C_LI

neuroscience↗

bioRxiv2024

The continuous differentiation of multiscale structural gradients from childhood to adolescence correlates with the maturation of cortical morphology and functional specialization

From childhood to adolescence, the structural organization of the human brain undergoes dynamic and regionally heterogeneous changes across multiple scales, from synaptic pruning to the reorganization of large-scale anatomical wiring. However, during this period, the developmental process of multiscale structural architecture, its association with cortical morphological changes, and its role in the maturation of functional organization remain largely unknown. Here, we utilized a longitudinal multimodal imaging dataset including 276 children aged 6 to 14 years to investigate the developmental process of multiscale cortical wiring. We used an in vivo model of cortical wiring that combines features of white matter tractography, cortico-cortical proximity, and microstructural similarity to construct a multiscale brain structural connectome. By employing the gradient mapping method, the gradient space derived from the multiscale structural connectome effectively recapitulated the sensory-association axis and anterior-posterior axis. Our findings revealed a continuous expansion of the multiscale structural gradient space during development, with the principal gradient increasingly distinguishing between primary and transmodal regions. This age-related differentiation coincided with regionally heterogeneous changes in cortical morphology. Furthermore, our study revealed that developmental changes in coupling between multiscale structural and functional connectivity were correlated with functional specialization refinement, as evidenced by changes in the participation coefficient. We also found that the differentiation of the principal multiscale structural gradient was associated with improved cognitive abilities, such as enhanced working memory and attention performance, and potentially supported by molecular processes related to synaptic functions. These findings advance our understanding of the intricate maturation process of brain structural organization and its implications for cognitive performance.

neuroscience↗

bioRxiv2024

Realigning representational drift in mouse visual cortex by flexible brain-machine interfaces

The ability to stably decode brain activity is crucial for brain-machine interfaces (BMIs), which are often compromised by recording instability due to immune responses and probe drifting. In addition, many brain regions undergo intrinsic dynamics such as "representational drift", in which neural activities associated with stable sensation and action continually change over time. In this study, we employed tissue-like flexible electrode arrays for recording visual stimulus-dependent single-unit action potentials in the mouse visual cortex. The flexible electrode array enabled us to record action potentials from the same neurons over extended periods under visual stimuli, allowing us to characterize the representational drift during these stimuli. With this approach, we tested hypotheses about the origins and mechanisms of representational drift, tracked latent dynamics transformation, and modeled these dynamics with affine transformation. Our findings enabled the construction of a single, long-term stable, high-performance visual information decoder that accounts for representational drift, potentiating chronically stable flexible BMIs in brain regions experiencing representational drifts.

bioengineering↗

bioRxiv2024

Development of the overlapping network modules in the human brain

Developmental connectomic studies have shown that the modular organization of functional networks in the human brain undergoes substantial reorganization with age to support cognitive growth. However, these studies implicitly assume that each brain region belongs to one and only one specific network module, ignoring the potential spatial overlap between functional modules. How the overlapping functional modular architecture develops and whether this development is related to structural signatures remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6-14 years), we investigated the development of the overlapping modular architecture of functional networks, and further explored their structural associations. Specifically, an edge-centric network model was used to identify the overlapping functional modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a remarkable regional inhomogeneity in module overlap in children, with higher entropy in the ventral attention, somatomotor, and subcortical networks and lower entropy in the visual and default-mode networks. Furthermore, the overlapping modules developed in a linear, spatially dissociable manner from childhood to adolescence, with significantly reduced entropy in the prefrontal cortex and putamen and increased entropy in the parietal lobules. Personalized overlapping modular patterns capture individual brain maturity as characterized by brain age. Finally, the overlapping functional modules can be significantly predicted by integrating gray matter morphology and white matter network properties. Our findings highlight the maturation of overlapping network modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

neuroscience↗

bioRxiv2024

Large Language Models to process, analyze, and synthesize biomedical texts - a scoping review

The advent of large language models (LLMs) such as BERT and, more recently, GPT, is transforming our approach of analyzing and understanding biomedical texts. To stay informed about the latest advancements in this area, there is a need for up-to-date summaries on the role of LLM in Natural Language Processing (NLP) of biomedical texts. Thus, this scoping review aims to provide a detailed overview of the current state of biomedical NLP research and its applications, with a special focus on the evolving role of LLMs. We conducted a systematic search of PubMed, EMBASE, and Google Scholar for studies and conference proceedings published from 2017 to December 19, 2023, that develop or utilize LLMs for NLP tasks in biomedicine. LLMs are being applied to a wide array of tasks in the biomedical field, including knowledge management, text mining, drug discovery, and evidence synthesis. Prominent among these tasks are text classification, relation extraction, and named entity recognition. Although BERT-based models remain prevalent, the use of GPT-based models has substantially increased since 2023.

bioinformatics↗

bioRxiv2024

A postnatal molecular switch drives the activity-dependent maturation of parvalbumin interneurons

Cortical neurons are specified during embryonic development but often only acquire their mature properties at relatively late stages of postnatal development. This delay in terminal differentiation is particularly prominent for fast-spiking parvalbumin-expressing (PV+) interneurons, which play critical roles in regulating the function of the cerebral cortex. We found that the maturation of PV+ interneurons is triggered by neuronal activity and mediated by the transcriptional cofactor peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC-1). Developmental loss of PGC-1 prevents PV+ interneurons from acquiring unique structural, electrophysiological, synaptic, and metabolic features and disrupts their diversification into distinct subtypes. PGC-1 exerts its function as a master regulator of the differentiation of PV+ interneurons by directly controlling gene expression through a transcriptional complex that includes ERR{gamma} and Mef2c. Our results uncover a molecular switch that translates neural activity into a specific transcriptional program promoting the maturation of PV+ interneurons at the appropriate developmental stage.

neuroscience↗

bioRxiv2024

Blended Genome Exome (BGE) as a Cost Efficient Alternative to Deep Whole Genomes or Arrays

Genomic scientists have long been promised cheaper DNA sequencing, but deep whole genomes are still costly, especially when considered for large cohorts in population-level studies. More affordable options include microarrays + imputation, whole exome sequencing (WES), or low-pass whole genome sequencing (WGS) + imputation. WES + array + imputation has recently been shown to yield 99% of association signals detected by WGS. However, a method free from ascertainment biases of arrays or the need for merging different data types that still benefits from deeper exome coverage to enhance novel coding variant detection does not exist. We developed a new, combined, "Blended Genome Exome" (BGE) in which a whole genome library is generated, an aliquot of that genome is amplified by PCR, the exome regions are selected and enriched, and the genome and exome libraries are combined back into a single tube for sequencing (33% exome, 67% genome). This creates a single CRAM with a low-coverage whole genome (2-3x) combined with a higher coverage exome (30-40x). This BGE can be used for imputing common variants throughout the genome as well as for calling rare coding variants. We tested this new method and observed >99% r2 concordance between imputed BGE data and existing 30x WGS data for exome and genome variants. BGE can serve as a useful and cost-efficient alternative sequencing product for genomic researchers, requiring ten-fold less sequencing compared to 30x WGS without the need for complicated harmonization of array and sequencing data.

genomics↗

bioRxiv2024

Calculating Structure Factors of Protein Solutions by Atomistic Modeling of Protein-Protein Interactions

We present a method, FMAPS(q), for calculating the structure factor, S(q), of a protein solution, by extending our fast Fourier transform-based modeling of atomistic protein-protein interactions (FMAP) approach. The interaction energy consists of steric, nonpolar attractive, and electrostatic terms that are additive among all pairs of atoms between two protein molecules. In the present version, we invoke the free-rotation approximation, such that the structure factor is given by the Fourier transform of the protein center-center distribution function gC(R). At low protein concentrations, gC(R) can be approximated as e-{beta}W(R), where W(R) is the potential of mean force along the center-center distance R. We calculate W(R) using FMAPB2, a member of the FMAP class of methods that is specialized for the second virial coefficient [Qin and Zhou, J Phys Chem B 123 (2019) 8203-8215]. For higher protein concentrations, we obtain S(q) by a modified random-phase approximation, which is a perturbation around the steric-only energy function. Without adjusting any parameters, the calculated structure factors for lysozyme and bovine serum albumin at various ionic strengths, temperatures, and protein concentrations are all in reasonable agreement with those measured by small-angle X-ray or neutron scattering. This initial success motivates further developments, including removing approximations and parameterizing the interaction energy function.

biophysics↗

bioRxiv2024

Normative growth modeling of brain morphology reveals neuroanatomical heterogeneity and biological subtypes in children with ADHD

BackgroundNeuroimaging studies suggest substantial individual heterogeneity in brain phenotypes in attention-deficit/hyperactivity disorder (ADHD). However, how these individual-level brain phenotypes contribute to the identification of ADHD biotypes and whether these biotypes have different treatment outcomes and neurobiological underpinnings remain largely unknown. MethodsWe collected multisite, high-quality structural magnetic resonance imaging data from 1,006 children aged 6-14 years, including 351 children with ADHD and 655 typically developing children. Normative growth models of cortical thickness were established for 219 regions in the typically developing children. Individual-level deviations from these normal references were quantified and clustered to identify ADHD biotypes. We validated the replicability and generalizability of the ADHD biotypes using two independent datasets and evaluated the associations of the biotypes with symptomatic, cognitive, and gene expression profiles, as well as follow-up treatment outcomes. FindingsNo more than 10% of children with ADHD had extreme deviations in cortical thickness in a single region, suggesting high heterogeneity among individuals with ADHD. On the basis of the brain deviation maps, we discovered two robust ADHD biotypes, an infra-normal subtype with cortical thinning associated with ADHD symptoms and a supranormal subtype with cortical thickening associated with cognition. Patients with the infra-normal subtype responded better to methylphenidate than to atomoxetine, although both subtypes showed treatment efficacy. Brain deviations in the infra-normal subtype were explained by the expression levels of genes enriched in presynaptic and axonal development and polygenic risk of ADHD. InterpretationWe identified anatomically distinct, clinically valuable, and biologically informed ADHD subtypes, providing insight into the neurobiological basis of clinical heterogeneity and facilitating a personalized medication strategy for ADHD patients. Panel: Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSubstantial individual heterogeneity in brain phenotypes in attention-deficit/hyperactivity disorder (ADHD) motivates the need to discover homogeneous biotypes. We searched PubMed for research articles on ADHD biotypes using brain MRI published before December 1, 2023, using the search terms ((attention deficit hyperactivity disorder [Title/Abstract]) OR (ADHD [Title/Abstract])) AND ((subtypes [Title/Abstract]) OR (subgroups [Title/Abstract]) OR (subtyping [Title/Abstract])) AND ((MRI [Title/Abstract]) OR (neuroimaging [Title/Abstract]) OR (brain [Title/Abstract])) without language restrictions. Of the eight included studies, two identified ADHD biotypes using structural morphology, four used functional activity, and two used multimodal features. However, none of these studies considered the developmental effect of the brain phenotypes, examined treatment response, or investigated the genetic correlates of the biotypes. Added value of this studyThis study is the first to use individualized brain measures extracted from normative models to investigate ADHD biotypes in a large sample of more than 1,000 children. We identified two reproducible ADHD biotypes, characterized by distinct symptomatic, cognitive, and gene expression profiles, as well as differential treatment responses. This study advances our understanding of the neurobiological basis underlying the clinical heterogeneity of ADHD and highlights the critical need to discover ADHD biotypes using an unbiased and individualized approach. Implications of all the available evidenceThis study revealed remarkable neuroanatomical heterogeneity in ADHD patients and identified anatomically distinct, clinically valuable, and biologically informed ADHD biotypes. Our findings have potential value for the investigation of data-driven biotypes to evaluate treatment efficacy and facilitate personalized treatment. We also highlight the need for future studies to move beyond the understanding of ADHD solely based on the "average patient" perspective.

neuroscience↗

bioRxiv2024

Brain preparedness: The cortisol awakening response proacts dynamic organization of large-scale brain networks across emotional and executive functions

Emotion and cognition involve an intricate crosstalk of neural and endocrine systems that support allostatic processes for maintenance of dynamic equilibrium and rapid adaptation for upcoming challenges. As a hallmark of human endocrine activity, the cortisol awakening response (CAR) is recognized to play a critical role in modulating emotional and executive functions. Yet, the underlying mechanisms of such effects remain elusive. By leveraging pharmacological neuroimaging technique and Hidden Markov Modeling of brain state dynamics, we show that the CAR proactively modulates rapid reconfigurations (state) of large-scale brain networks across multi-task demands. Behaviorally, suppression of CAR proactively and selectively impaired accuracy for emotional discrimination task but not for working memory (WM). In parallel, suppressed CAR led to a decrease in the occurrence rate of brain state dominant to emotional processing, but an increase in brain state linking to executive control under high WM demand. Further energy-based analyses revealed an increase in transition frequency and sequence complexity along with an increased entropy during emotional tasks when suppressed CAR, suggesting a decreased energy supply. Moreover, an increased transition frequency was observed when shifting from neutral to emotional conditions, but an opposite pattern during WM task, with n decreased transition frequency shifts from low to high-executive demands. Our findings establish a causal link between CAR and dynamic allocation of neural resources for emotional and executive functions, suggesting a cognitive neuroendocrine account for CAR-mediated proactive effects and human allostasis.

neuroscience↗