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Evans, A.

Publications and source records attributed to Evans, A..

At least 19 recordsLinked to original sources

Functional importance and paralog-specificity of the intrinsically disordered RsmE C-terminus region in Pseudomonas fluorescens Pf0-1

RsmE and its homologs function as RNA-binding post-transcriptional regulators that control the production of diverse secondary metabolites. Pseudomonads harbor varying numbers of RsmE paralogs that are often generalized to overlap in function. However, recent studies indicate that these paralogs bind to both overlapping and unique sets of mRNA. In Pseudomonas fluorescens Pf0-1, RsmE exclusively regulates the production of multiple extracellular secretions, distinguishing it from its paralogs, RsmA and RsmI. While the majority of the three paralogs core sequences are highly conserved, each possesses a vastly different C-terminus region. The C-terminus tails are generalized to be functionless, as they are intrinsically disordered and devoid of known mRNA binding sites. Here, we challenge this notion through analyses of various naturally emergent RsmE variants that differentially impact repressive function solely from changes in the C-terminus tail. Engineered Rsm chimeras containing the various paralog cores and tails further confirmed the functional importance of the C-terminus tail, as neither replacing the RsmE tail with the RsmA tail nor attaching the RsmE tail to a different paralog core restored repression. However, the RsmI tail complemented function in the presence of the RsmE core, indicating that RsmEs functional specificity lies within the core despite the essentiality of the C-terminus. Bioinformatic analyses also revealed that the C-terminus tail composition of RsmA to be the most unique among the paralogs and each tail sequence is uniquely and highly conserved across diverse proteins, not only in bacteria, but also in eukaryotic species.

microbiology↗

A Patient-Derived Ex Vivo Tissue Model of Cholangiocarcinoma Using Precision-Cut Tissue Slices

Background & Aims: Cholangiocarcinoma (CCA) is an aggressive malignancy with poor five-year survival. Improving outcomes requires preclinical models that faithfully recapitulate the native tumour microenvironment. Precision-Cut Tissue Slices (PCTS) retain in-vivo architecture and represent a promising candidate. We aimed to establish a patient-derived PCTS model of CCA, characterise its response to ex-vivo culture, and assess its use as a platform for therapeutic testing. Methods: PCTS were generated from 25 patients undergoing curative-intent resection for presumed CCA (2022-2024) and cultured for up to 15 days. Viability and histological architecture were assessed serially. Quantitative proteomics (SWATH-DIA) was performed at Days 0, 3, 7 and 15. A subset were treated with staurosporine or clinically relevant chemotherapy (5'-deoxy-5-fluorouridine, gemcitabine {+/-} cisplatin). Results: PCTS viability was maintained to Day 15, with no significant reduction from baseline. PCTS retain tumour architecture, cytokeratin-19-positivity and resident CD3+/CD68+ immune cells during culture. Proteomic profiling quantified 4,578 proteins and identified a staged response, with differentially abundant proteins increasing from 8 (Day 3) to 127 (Day 7) and 180 (Day 15), stabilising thereafter. This comprised of an early loss of inflammatory and stromal proteins, sustained cellular stress responses and metabolic reprogramming. CCA subtype-specific proteomic signatures were preserved throughout culture. Staurosporine produced dose-dependent cytotoxicity whilst chemotherapy responses were variable. Conclusions: Patient-derived CCA PCTS maintain viability, tumour architecture and subtype identity for 15 days while undergoing a defined proteomic response to culture, and retain pharmacological responsiveness,

cancer biology↗

Heterogeneity of Brain Dynamics in Genetic and Psychiatric Conditions

Whether the heterogeneity of psychiatric conditions converges on shared neurophysiological alterations or translates into distinct signatures remains unclear. We assembled high-density electroencephalogram (hd-EEG) resting-state recordings from 4,812 individuals aged 5 months to 66 years across 11 psychiatric conditions, a broad spectrum of rare genetic variants, and typically developing (TD) individuals. We established normative developmental trajectories of source-space EEG across spectral organization, connectivity, and signal complexity. Psychiatric conditions showed small deviations, revealing a shared transdiagnostic profile. In contrast, single rare variants showed substantially larger, distinct and sometimes mirror-opposite signatures that collapsed toward the psychiatric profile when pooled. Autism Spectrum Disorder showed some of the smallest group-level effects yet the largest individual deviations, indicating substantial but directionally inconsistent alterations. EEG deviations followed a cortical gradient, with larger effects in sensorimotor regions. We demonstrate that sample sizes in the hundreds are required for robust associations with psychiatric diagnoses. This interactive open resource provides normative scores to benchmark future results.

neuroscience↗

SALRR: Scalable Analysis of Long-Read RNA-Seq Enables Comprehensive Transcriptome Profiling in Human Brain

Isoform-resolved transcriptomics is fundamental to decoding the molecular complexity of the human brain, yet population-scale long-read RNA sequencing has remained inaccessible due to labor-intensive library preparation, sensitivity to RNA degradation in postmortem tissue, and the absence of integrated, reproducible analysis pipelines. Here we present SALRR (Scalable Analysis of Long-Read RNA-seq), an integrated wet-lab and computational platform designed to overcome these barriers. Automated ONT long-read cDNA library preparation on the Hamilton Microlab NGS STAR platform reduces hands-on time by 67% and enables 24 libraries per operator per day while maintaining performance across RNA integrity values. A modular, Snakemake-based pipeline performs end-to-end processing from ONT signal data to isoform-level quantification, incorporating SIRV spike-in calibration, multi-stage quality control, and stringent isoform validation. Applied to 10 postmortem frontal cortex samples from the North American Brain Expression Consortium, SALRR identified 31,607 high-confidence isoforms from 10,075 genes, including 8,532 novel splice variants absent from GENCODE v49, and complex splicing events systematically missed by short-read sequencing at neurodegeneration-relevant loci, including GBA1, CCNF, CHCHD10, and TREM2. All protocols and code are openly available, providing a scalable, community-ready framework for isoform-resolved transcriptomics in neurodegeneration, aging, and complex brain disease.

genomics↗

Functional impact of a type VI secretion system on RsmE-mediated spatial structure formation in Pseudomonas fluorescens Pf0-1

Bacteria naturally form densely structured populations wherein both space and nutrients become locally limited. In densely populated Pseudomonas fluorescens Pf0-1 colonies, spatiogenetic patches with drastically reduced cellular density naturally emerge through spontaneous mutations in rsmE. RsmE is a posttranscriptional regulator that binds to specific mRNAs, including those that consequently repress the production of extracellular secretions. We have previously shown that {Delta}rsmE produces an extracellular polysaccharide (EPS) and biosurfactant that collectively function to define the spatiogenetic structure and locally outcompete the WT. Here, we identify additional RsmE-regulated secretions through a combination of RNA-sequencing and LC-MS/MS. In particular, a type VI secretion system (T6SS) was exclusively produced by {Delta}rsmE. Confocal microscopy imaging of WT and genetically modified {Delta}rsmE cocultures showed that the T6SS kills WT cells that invade the low- density spatiogenetic structure. However, its impact on competition with the WT was both quantifiably and spatially limited in the absence of the normally co-produced EPS and biosurfactant, largely due to the consequently altered spatial structure. These results highlight the importance of understanding both the individual and collective spatial functions of extracellular secretions, especially in the ecological context of densely structured microbial populations. IMPORTANCEIn crowded wild type (WT) Pseudomonas fluorescens Pf0-1 colonies, rsmE mutants spontaneously emerge by forming a low cellular density spatial structure that is devoid of WT cells. RsmE functions primarily to repress the production of various extracellular secretions, including an extracellular polysaccharide and biosurfactant, that collectively form the unique spatial structure. Here, we describe an RsmE-regulated type VI secretion system that kills the invading WT cells and protect the low-density structure. Importantly, individual RsmE-regulated secretions carry out unique functions, but their efficacies largely depend on other co-regulated products. Extracellular secretions that are mechanistically similar to those described here are co- produced across diverse biofilm-forming and virulent bacterial species, where they also likely play complex molecular and ecological roles.

microbiology↗

Demographic changes and behavioural responses shape vulnerability to infectious disease outbreaks

Demographic shifts are reshaping population age structures worldwide, with implications for infectious disease dynamics. Since contact patterns, susceptibility and infectiousness often vary by age, the risk that pathogen introductions initiate a substantial outbreak depends on the population's age distribution and associated behavioural characteristics. We develop an age-structured mathematical model to estimate the risk that a single pathogen introduction leads to sustained transmission (the probability of a major outbreak) under long-term demographic transitions, incorporating changes in age-specific contact patterns and behavioural adaptation (specifically, extended workforce participation in an ageing society). Using the Republic of Korea (projected to become the world's oldest population by 2050) as a case study, we show that population ageing may reduce the probability of a major outbreak due to older individuals' lower contact rates. However, this effect is attenuated for pathogens with increasing susceptibility or infectiousness with age, and by factors such as later retirement that increase contacts among older individuals. These findings demonstrate that, while outbreak risks are affected by demographic changes, they are further modified by associated behavioural responses, highlighting the importance of accounting for demographic and socio-behavioural context when assessing future infectious disease outbreak risks.

ecology↗

Visual Cortical Response Variability in Infants at High Familial Likelihood for Autism

Visual processing undergoes rapid development in the first year of life, supporting the emergence of higher-order cognitive, language, and motor functions. Visual evoked potentials (VEPs) provide a non-invasive measure of visual system maturation that may shed light on heterogeneous developmental trajectories among infants at high familial likelihood for autism. Infants with an older sibling with autism spectrum disorder (N = 177 at 6 months; N = 132 at 12 months) participated in the Infant Brain Imaging Study-Early Prediction (IBIS-EP) study. Pattern-reversal VEPs were recorded at 6 and 12 months, and developmental skills were assessed at 24 months using the Bayley Scales of Infant and Toddler Development (Bayley-4). VEP components (P1 and N1) were characterized by their amplitude and latency, as well as trial-to-trial variability in these measures. Associations with 24-month cognitive, language, and motor scores were examined using general linear models controlling for age, site, sex, and trial count. Robust VEPs were observed at both time points, with age-appropriate morphology and expected developmental changes, including decreases in P1 latency and amplitude from 6 to 12 months. Greater trial-to-trial variability in P1 latency at both time points was associated with higher cognitive and language scores at 24 months. In contrast, conventional measures of mean P1 latency and amplitude were not associated with developmental outcomes. These findings suggest that temporal variability in early visual responses may index adaptive sensory-circuit flexibility during a period of rapid experience-dependent development. VEP response-timing variability may therefore provide an early mechanistic marker of sensory-circuit organization relevant to later developmental trajectories. Research HighlightsO_LIGreater trial-to-trial variability in visual cortical response timing was associated with higher cognitive and language scores in infants at high familial likelihood for autism. C_LIO_LIConventional average VEP measures were unrelated to outcomes, suggesting temporal variability may more sensitively capture relevant neural-circuit differences in this population. C_LIO_LIGreater variability in early visual responses may reflect adaptive sensory-circuit flexibility during a critical period of neurodevelopment. C_LIO_LIVEP response-timing measures provide an early mechanistic window into sensory-circuit organization and its relation to later developmental trajectories. C_LI

neuroscience↗

RNA foundation models enable generalizable endometriosis disease classification and stable gene-level interpretation

Endometriosis is a chronic inflammatory condition with significant diagnostic delays impacting one in ten reproductive age women worldwide. While machine learning (ML) models trained on transcriptomic data show promise for disease prediction, limited generalizability across independent patient cohorts has hindered clinical translation. Foundations models (FMs) pretrained on large-scale transcriptomic data offer promise to learn transferrable, biologically meaningful representations that could support cross-cohort predictions. We assembled a 12-cohort bulk RNA-seq benchmark (334 samples) and developed a computationally efficient pipeline to test whether FMs improve endometriosis classification, an approach not previously applied to this disease. Using AutoXAI4Omics with cohort-aware validation, we compared embeddings derived from five state-of-the-art RNA FMs against TPM baselines. In cross-cohort prediction, FM embeddings significantly improved performance, achieving a weighted F1-score of 0.83 vs. 0.68 for the baseline. To allow gene-level interpretation of FM embedding models, we introduce classified-aligned integrated gradients (CA-IG), an interpretability approach aligning gene-level attributions to the downstream classifier without end-to-end finetuning. CA-IG revealed a conserved set of predictive genes from FM embeddings across cohort-validation regimes, contrasting with unstable baseline explainability, suggesting that FM embeddings prioritized transferable disease-related signal over cohort-specific effects. These genes include novel candidates that converge on biologically plausible pathways for endometriosis.

bioinformatics↗

Nicotine-driven hyperactivation of larval locomotion

Balance between excitatory and inhibitory activity is essential for nervous system function. Neuroactive substances like nicotine can disrupt this balance and hyperactivate dopaminergic circuits, with combined effects on behavior and neural activity. We use Drosophila larvae to examine nicotine-induced, linked behavioral changes over multiple time scales by integrating high-resolution locomotor analysis with genetic and pharmacological manipulations. Acute nicotine exposure produces concentration-dependent hyperactivity. Manipulations of the dopaminergic system establish dopamine as the main mediator for motor responses, and further exploration establishes the{gamma} lobe of the mushroom body as a key site for nicotine integration. Experiments with long-term and repetitive nicotine exposure suggest sustained circuit excitability. Finally, nicotine exposure history induces nicotine preference, highlighting experience-dependent plasticity contributing to addiction-like behaviors. These results help establish Drosophila larvae as a model organism to elucidate how neuroactive substances reconfigure neural circuits and behavior.

neuroscience↗

Modeling Individualized Adolescent Mental Health through a Multifactorial Causal Framework to Identify Potential Intervention Strategies

Associations between psychopathology, cognition, and personality are well established. However, these associations unfold within a broader developmental context shaped by brain maturation, environmental exposures and pubertal transitions, whose influences remain poorly understood. Here, we characterized how cognition and personality relate to adolescent mental health within this developmental context. Using data from the Adolescent Brain Cognitive Development Study (N = 4501), we simultaneously modeled 16610 brain structural, environmental, and pubertal measures to examine 165 behavioral pairs comprising cognitive and personality traits with mental health symptoms. Using an integrated analytic pipeline combining multifactorial mediation and longitudinal interaction-inclusive models, we evaluated whether developmental changes in cognition and personality relate to mental health at the individual level, and whether these associations vary across brain, environmental, and pubertal profiles. Most behavioral pairs showing significant mediation effects likewise exhibited significant longitudinal effects. These pairs were primarily influenced by neurobiological and environmental measures; cortical frontal and temporal regions relevant for cognitive control and emotional regulation as well as family and lifestyle contexts were most prominently implicated. Finally, after accounting for inter-individual heterogeneity, personality traits emerged as the main behavior associated with mental health. These findings characterize developmental associations linking cognition and personality with adolescent mental health, and show that these associations vary across brain, environmental, and pubertal profiles. The study advances understanding of behavioral development and provides a framework for identifying targets for psychopathology prevention.

neuroscience↗

A multisensory, bidirectional, valence encoder guides behavioral decisions

A key function of the brain is to categorize sensory cues as repulsive or attractive and respond accordingly. While we have some understanding of how sensory information is processed in the sensory periphery, the classification of cues according to valence in central brain circuits is less well understood. Here, we addressed this question in the Drosophila larva, where we could leverage the synaptic resolution connectome to determine where innate and learnt information from distinct aversive and appetitive sensory modalities converges; and combine this with imaging and manipulation of neural activity to determine how valence information is encoded and used to guide navigation. We found that information from multiple innately aversive and attractive sensory modalities converges onto a common output neuron of the learning circuit, specifically the Mushroom Body output neuron, MBON-m1. We discovered that this neuron is required for navigating both attractive odor and aversive temperature cues and is activated by attractive cues, such as food odour and sugar, and inhibited by different aversive cues, such as cooling, salt, or non-food odors. Together, our study reveals a neuron that bi-directionally encodes valence and controls actions.

neuroscience↗

Transcriptional Circuitry in HGSOC: A Dynamic Three-State Model Informed by a Living Biobank of Purified Tumour Fractions

High-grade serous ovarian cancer (HGSOC) is a heterogeneous disease, but efforts to define transcriptional subtypes using bulk RNA sequencing have been confounded by the presence of non-malignant cells. As a result, it remains unclear whether tumour-cell-intrinsic states exist, and whether these represent stable disease subtypes or are dynamically remodelled during disease progression and treatment. Here, we address this question using a living biobank of patient-derived ovarian cancer models (OCMs) cultured as purified tumour-cell populations under uniform conditions. RNA sequencing followed by unsupervised non-negative matrix factorisation (NMF) revealed a robust, hierarchical architecture comprising three core tumour-cell-intrinsic subtypes: the Alpha cluster, marked by cell-cycle deregulation and E2F-driven replication stress; the Beta cluster, defined by tumour-cell-intrinsic immune mimicry and inflammatory signalling; and the Gamma cluster, characterised by epithelial identity, extracellular matrix engagement, and metabolic adaptation. At higher clustering resolution, a fourth cluster, Delta, emerged as a Gamma sub-lineage distinguished by a vesicle-oriented, neuronal-like secretory programme. By projecting cluster labels onto a subset of matched longitudinal OCMs using non-negative least squares, we show that while some tumours retain stable subtype identities, others display transcriptional plasticity, including transitions from epithelial-like Gamma states to more proliferative or secretory phenotypes. Together, these findings define the core architecture and dynamic potential of tumour-cell-intrinsic transcriptional states within HGSOC, thereby bridging legacy bulk classifications with emerging single-cell insights, establishing a framework for more precise patient stratification.

cancer biology↗

Accurate DNA Methylation Predictor for C9orf72 Repeat Expansion Alleles in the Pathogenic Range

The hexanucleotide (G4C2) repeat expansion in the promoter region of C9orf72 is the most frequent genetic cause of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). In this study, we conducted a genome-wide DNA methylation (DNAm) analysis using EPIC version 2 (EPICv2) arrays on an FTD cohort comprising 27 carriers and 250 non-carriers of the pathogenic C9orf72 repeat expansion from the Amsterdam Dementia Cohort. We identified differentially methylated CpGs probes associated with the pathogenic C9orf72 expansion and used these findings to create a DNAm Least Absolute Shrinkage and Selection Operator (LASSO) predictor to identify repeat expansion carriers. Eight CpG sites at the C9orf72 locus were significantly differentially hypermethylated in repeat expansion carriers compared to non-carriers. The LASSO model predicted repeat expansion status with an average accuracy of 98.6%. The LASSO predictor was further validated in an independent cohort of 2,548 subjects with available EPICv2 data, identifying four C9orf72 repeat expansion carriers, subsequently confirmed by repeat-primed PCR. This result not only illustrates the accuracy of the DNAm predictor of C9orf72 repeat expansion carriers but also suggests that repeat expansion carriers may be more prevalent than expected. The identification of a highly accurate DNAm biomarker for a repeat expansion locus associated with neurodegenerative disorders may provide great value for studying this locus. The approach holds significant promise for investigating this and other repeat expansion loci, particularly given the growing interest in epigenetic epidemiological studies involving large cohorts with available DNAm data. Graphical abstract (optional) O_FIG O_LINKSMALLFIG WIDTH=155 HEIGHT=200 SRC="FIGDIR/small/643775v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@9d7879org.highwire.dtl.DTLVardef@505560org.highwire.dtl.DTLVardef@154720org.highwire.dtl.DTLVardef@f0c063_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

Early cell cycle genes in cortical organoid progenitors predict interindividual variability in infant brain growth trajectories

Human induced pluripotent stem cell (iPSC) derived cortical organoids (hCOs) model neurogenesis on an individuals genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from which they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who have undergone longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of cortical hem cells were associated with in vivo cortical growth rates. Cell cycle associated genes expression in early progenitors at the crux of fate decision trajectories were correlated with cortical growth rate from 6-12 months of age, and were enriched in microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types influencing infant cortical surface area expansion.

neuroscience↗

Association between Adolescent Brain Morphometry and Cognitive Function: Insights from a Cross-Sectional Analysis of ABCD Data from 9 to 15 Years Old

Significant changes occur in brain structure and cognitive abilities during adolescence. Investigating their association can provide insight into brain-based cognitive development, yet previous studies were limited by narrow brain measures, small samples, and lacking focus on age-related variation. Here, we analyzed a large cohort (N = 8,534, age 9-15) from the Adolescent Brain Cognitive Development dataset. Using structural MRI and diffusion imaging, we derived 16 regional structural measures and integrated them via morphometric similarity networks to characterize 16,563 regional, connectivity, and hub features. We applied large-scale computational models to investigate their associations with performance on seven cognitive subtests and general intelligence (g), as well as age-related changes. Brain areas most strongly associated with cognitive ability also showed the greatest age-related variability in these associations, located primarily in the frontal, temporal, and occipital lobes. Structural MRI measures exhibited stronger associations with cognition and greater age-related variability than diffusion-derived metrics, while global hub measures showed stronger and more variable associations than local measures. Overall, our study provides a comprehensive and reliable understanding of brain structure-cognition associations during adolescence.

neuroscience↗

AutoXAI4Omics: an Automated Explainable AI tool for Omics and tabular data

Machine learning (ML) methods have the potential of detailed insights of complex biological systems and today are increasingly used to analyse omics data for tasks such as the discovery of novel biomarkers and phenotype prediction. It can be extremely beneficial and powerful for scientists, domain experts, to easily run sophisticated, robust, and interpretable ML pipelines without the need for an in depth understanding of the code needed to train, tune, optimise ML algorithms. They can then focus on the biological interpretation and validation of the results and insights generated by ML models. Here, we present an entirely automated open-source explainable AI tool, AutoXAI4Omics, that performs classification and regression tasks from omics and tabular numerical data. AutoXAI4Omics accelerates scientific discovery by automating processes and decisions made by AI experts, e.g., selection of the best feature set, hyper-tuning of different ML algorithms and selection of the best ML model for a specific task and dataset. Prior to ML analysis AutoXAI4Omics incorporates feature filtering options that are tailored to specific omic data types. Moreover, the insights into the predictions that are provided by the tool through explainability analysis highlight associations between omic feature values and the targets under investigation e.g., predicted phenotypes, facilitating the discovery of actionable insights. AutoXAI4Omics is at: https://github.com/IBM/AutoXAI4Omics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/586460v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@366327org.highwire.dtl.DTLVardef@a7b559org.highwire.dtl.DTLVardef@7319aforg.highwire.dtl.DTLVardef@9b5030_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Mimicking an in cellulo environment for enzyme-free paper-based nucleic acid tests at the point of care

Point of care (PoC) nucleic acid amplification tests (NAATs) are a cornerstone of public health, providing the earliest and most accurate diagnostic method for many communicable diseases, such as HIV, in the same location the patient receives treatment. Communicable diseases disproportionately impact low-resource communities where NAATs are often unobtainable due to the resource intensive enzymes that drive the tests. Enzyme-free nucleic acid detection methods, such as hybridization chain reaction (HCR), use DNA secondary structures for self-driven amplification schemes producing large DNA nanostructures and capable of single molecule detection in cellulo. These thermodynamically driven DNA-based tests have struggled to penetrate the PoC diagnostic field due to their inadequate limits of detection or complex workflows. Here we present a proof-of-concept NAAT that combines HCR-based amplification of a target nucleic acid sequence with paper-based nucleic acid filtration and enrichment capable of detecting sub pM levels of synthetic DNA. We reconstruct the favorable hybridization conditions of an in cellulo reaction in vitro by incubating HCR in an evaporating, microvolume environment containing poly(ethylene glycol) as a crowding agent. We demonstrate that the kinetics and thermodynamics of DNA-DNA and DNA-RNA hybridization is enhanced by the dynamic evaporating environment and inclusion of crowding agents, bringing HCR closer to meeting PoC NAAT needs.

biophysics↗

Exploring the Brain Characteristics of Structure-informed Functional Connectivity through Graph Attention Network

Independent brain regions in neuroanatomy achieve a specific function through connections. As one of the significant morphological features of the cerebral cortex, previous studies have found significant differences in the structure and function of the cerebral gyri and sulci, which provides a basis for us to study the functional connectivity differences between these two anatomic parts. Previous studies using fully connected functional connectivity (FC) and structural connectivity (SC) matrices found significant differences in the perspective of region or connection in gyri and sulci. However, a clear issue is that previous studies have only analyzed the differences through either FC or SC, without effectively integrating both. Meanwhile, another nonnegligible issue is that the subcortical areas, involved in various tasks, have not been systematically explored with cortical regions. Due to the strong coupling between FC and SC, we use SC-informed FC to systematically explore the functional characteristics of gyri/sulci and subcortical regions by combining deep learning method with magnetic resonance imaging (MRI) technology. Specifically, we use graph attention network (GAT) to explore the important connections in the SC-informed FC through the Human Connectome Project (HCP) dataset. With high classification results of above 99%, we have successfully discovered important connections under different tasks. We have successfully explored the importance of different types of connections. In low threshold, gyri-gyri are the most important connections. With the threshold increasing, sub-sub become the most important. Gyri have a higher importance in functional connectivity than sulci. In the seven task states, these connections are mainly distributed among the front, subcortical, and occipital. This study provides a novel way to explore the characteristics of functional connectivity at the whole brain scale.

bioinformatics↗