Hemodialysis (HD) is the predominant treatment for end-stage renal disease (ESRD). Despite the efficacy of HD, the neurobiological underpinnings underlying high-risk complications remain unclear. In this study, using unsupervised fusion of functional and structural MRI, we identified a longitudinally altered default mode network (DMN)-insula pattern in ESRD receiving HD over 1-year follow-up (n = 39). This pattern was associated with cognition, and its related genes were enriched in biological processes involving DNA damage and repair, energy metabolism, and cellular activation. The baseline DMN-insula pattern demonstrated potential predictive value for follow-up cognition in ESRD. More importantly, these brain-cognition associations were validated in independent high-risk complications cohorts, including major depressive disorder (n = 60), mild cognitive impairment (n = 291), and Alzheimer’s disease (n = 77) by extracting the corresponding brain features and assessing their correlations with cognition. Collectively, this study may help researchers better understand the underlying mechanisms of ESRD receiving HD from a multimodal neuroimaging and molecular perspective.

During the transition from childhood to adulthood, the human brain undergoes significant developmental changes that facilitate cognitive maturation, driven by a complex interplay of environmental and genetic factors. Despite abundant studies on neuroimaging for brain development, the specific genetic influences remain unclear. Using brain age estimation, we categorized individuals (N = 7,435, aged 9-10 years) from the Adolescent Brain and Cognitive Development (ABCD) cohort into groups exhibiting either accelerated or delayed brain maturation. This study examines brain imaging and genetic associations within this subpopulation to identify the genetic influences on brain development. We have compared linear association using Sparse Canonical Correlation Analysis (sCCA)-base model and non-linear association with Supervised Contrastive Joint Latent Representation Network (SC-JLRN). Developmentally relevant nonlinear latent patterns between genetic variants highly expressed in the cerebellum and brain regions significantly associated with brain age (cerebellar-hippocamp-frontal circuitry) were extracted.

BACKGROUND:The difference between neuroimaging-predicted brain age and chronological age, the predicted age difference (PAD), has been studied as a potential biomarker reflecting individual brain health. Although previous large-scale studies have shown that brain age deviations occur across multiple disorders, cross-disorder comparisons of PAD within a unified framework, together with identification of the neuroimaging features associated with these differences and their related gene expression profiles, remain limited. Our aims are to systematically compare brain aging across multiple common brain disorders and explore the brain patterns and biological processes underlying these differences. METHODS AND FINDINGS:In this study, structural MRI data from 45,900 healthy controls (HCs) and 2,698 patients with developmental disorders (attention-deficit/hyperactivity disorder [ADHD] and autism spectrum disorder [ASD]), addiction (alcohol use disorder [AUD], tobacco use disorder [TUD], and AUD&TUD-A&TUD), dementia (Alzheimer's disease [AD], and mild cognitive impairment [MCI]) or other psychiatric disorders (schizophrenia [SZ], bipolar disorder [BP], and major depressive disorder [MDD]), were collected to generate PAD, along with transcriptome data. Then, we calculated the PAD difference between patient and HC as Cohen's d effect sizes, derived from a linear model that accounted for age, age2, sex, and site, and further identified the interpretable brain patterns associated with the PAD difference for each diagnostic group. Finally, enrichment analyses was conducted to identify the biological function of genes relatively over- or underexpressed in association with these patterns. Results showed that while PAD was consistently greater across disorders, different brain disorders showed different degrees of abnormality, the highest effects in dementia (AD: d = 0.97, 95% confidence interval (CI) [0.82,1.13]; p < 0.001 and MCI: d = 0.45, 95% CI [0.34,0.56]; p < 0.001), followed by addiction (A&TUD: d = 0.84, 95% CI [0.44,1.23]; p < 0.001, TUD: d = 0.72, 95% CI [0.49,0.96]; p < 0.001, and AUD d = 0.62, 95% CI [0.39,0.84]; p < 0.001) and psychiatric disorders (SZ: d = 0.53, 95% CI [0.30,0.76]; p < 0.001, BP: d = 0.46, 95% CI [0.22,0.69]; p < 0.001 and MDD: d = 0.28, 95% CI [0.11,0.46]; p < 0.001), but not different from expected in developmental disorders (ASD: d = 0.06, 95% CI [-0.04,0.16]; p = 0.36) and ADHD: d = 0.01, 95% CI [-0.14,0.15]; p = 0.98). Furthermore, higher PAD values in patient groups were linked to specific spatial brain patterns, including the frontotemporal network in psychiatric disorders, default mode network-salience network-putamen-thalamus in addiction and fronto-occipital network in dementia. Prefrontal cortex involvement was common across disorders, and disorder-specific brain patterns associated genes were enriched in different biological processes. A limitation of our study is that psychiatric disorders and addiction have high comorbidity, and these potential confounders were not considered. CONCLUSIONS:In summary, the different brain aging patterns, each based around specific underlying circuits, may serve as neuroimaging biomarkers for understanding the neural aging mechanisms in commonly occurring brain disorders. Future studies should test whether these disorder-specific brain aging patterns can serve as useful biomarkers to guide critical clinical decision-making.

Many mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking dFNC to genetics largely follow a paradigm to identify associations between one set of genetic factors and multiple sets of connectivity features from different dFNC states, ignoring the potential variability in genetic correlates across states. We propose a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned genetic manifolds. A sliding window approach was utilized to estimate four dFNC states and compute subject-level state-average dFNC (sa-dFNC) features. The sa-dFNC features of each state were combined with schizophrenia risk single nucleotide polymorphisms (SNPs) within a jICA fusion framework, resulting in four parallel fusions in 32,861 individuals of the UK Biobank cohort. The extracted four sets of joint SNP-dFNC components were further validated for clinical relevance in a combined schizophrenia cohort of 820 individuals (348 patients). The similarity of SNP-dFNC components across four parallel fusions was evaluated as a measure of state variability. We observed a mixture of "state-invariant" and "state-variant" components for SNP and dFNC modalities. Particularly, the schizophrenia-related state-variant SNP components, or manifolds, complemented each other by capturing different SNPs involved in the same biological functions, revealing a partition of genomic risk particularly elicited by the dynamics of brain function. By augmenting the SNP factors to state-variant manifolds, this dynamic fusion framework promises additional insights into the underlying genetic risk of disease-related alterations in dynamic brain function.

Background:Schizophrenia is a severe neuropsychiatric disorder. Efforts to describe the underlying biology and establish diagnostic markers through non-invasive neuroimaging methods are ongoing, resulting in a range of theoretical brain-based frameworks. Prominent frameworks for aberrant schizophrenia-associated functional connectivity in resting-state functional magnetic resonance imaging (rsfMRI) include the dysconnectivity hypothesis, theory of cognitive dysmetria, and triple network theory. Although informative, prior work can be improved by increasing sample size, avoiding confirmation bias, and accounting for individual variability and the effects of medication and chronicity. Methods:With these recommendations in mind, we employed a data-driven, whole-brain approach using a large multi-site rsfMRI dataset ( N = 2,656; schizophrenia = 1,248). We used reference-guided independent component analysis to generate subject-specific whole-brain functional network connectivity (FNC) and extract imaging markers of similarity to schizophrenia patterns. We modeled the relationship between medication dosage, age of onset, chronicity, symptom severity, and cognitive performance and FNC. Results:Our analysis identified a reliable schizophrenia-FNC signature characterized by aberrantly stronger negative cerebellothalamic and positive thalamocortical connectivity, implicating sensory, motor, and associative cortical circuits. While medication and chronicity were significantly associated with these signatures, the core cerebellothalamic disruptions remained a robust marker of schizophrenia. Conclusions:This work represents the largest schizophrenia-specific rsfMRI study to date, refines existing theoretical frameworks with a more nuanced map of how clinical variables interact with brain connectivity, and provides a high-fidelity template of schizophrenia-related connectivity. We have released this template as an open-access resource to facilitate reproducibility and accelerate the development of reliable rsfMRI-based schizophrenia biomarkers.

One hallmark of brain maturation in adolescence is increased myelination (fractional anisotropy [FA]) of the axons, although the epigenetic drivers of this stage of neurodevelopment are as yet poorly understood. Our previous study of a longitudinal cohort of normally developing adolescents, aged nine to fourteen, established the connections between changes in DNA methylation (DNAm) at seven cytosine-phosphate-guanine (CpG) sites in genes highly expressed in the brain to grey matter maturation as well as cognitive improvement. Continuing that work, we investigate the relationships between the changes in DNAm of these genes (GRIN2D, GABRB3, KCNC1, SLC12A9, CHD5, STXBP5, and NFASC), four networks of FA change, and scores from seven cognitive tests. The demethylation of the CpGs over time was significantly related to a brain network highlighting FA increases in regions associated with maturation of interhemispheric connectivity. Mediation analysis found that this same network mediated the relationship between decreases in DNAm of four of these genes and increases in overall cognitive performance. These relationships suggest that changes in DNAm of genes involved in myelination and the excitatory/inhibitory balance in the brain might be driving maturation of white matter, which in turn is implicated in the improved cognitive performance seen in adolescents.

The brain generates diverse cognitive states while maintaining a stable functional architecture, a duality that remains difficult to reconcile. Prevailing views assume that flexible cognition necessitates correspondingly flexible architecture, in which different tasks demand distinct reconfigurations of functional networks. Here we introduce the intrinsic network flow (INF) framework as a complementary view. This framework is built on temporally coordinated signal flows across brain networks that constitute a universal scaffold, stable across diverse cognitive states and common across individuals. We show that a wide range of task-evoked activation patterns can be reconstructed by modulating only the temporal phase alignment of these flows, whose fixed structure determines functional connectivity topology, gradients, and large-scale networks, thereby preserving these properties across task states. This situates resting-state and task-state dynamics within a unified framework and suggests a generative relationship from flow-like dynamics to the full landscape of resting-state and task-state phenomena. Crucially, phase information, which neither existing brain state analyses nor eigenmode decompositions can extract, outperforms amplitude or activation-based markers in distinguishing cognitive states. These findings reframe task-evoked activation and deactivation as constructive and destructive interference among concurrent flows, rather than selective engagement or disengagement. This reconceptualization implies that the primary control variable for cognition is when intrinsic dynamics align in time, not where or how much the brain activates. Together, these results demonstrate that flexible cognition can emerge from retiming without reconfiguring functional architecture, offering a new path toward understanding the principled link between intrinsic dynamics and task-evoked cognition.

Abstract Background Amyloid-beta (Aβ) and Tau are defining pathologies of Alzheimer’s disease, but their relationship varies substantially across the brain. Amyloid deposition is spatially widespread, whereas Tau shows greater regional heterogeneity and closer relationships with disease severity. Consequently, understanding the disease requires more than measuring the two pathologies independently or summarizing them within predefined regions. A major unresolved question is how Amyloid and Tau covary across the whole brain, where their spatial patterns converge or diverge, and whether their local combination carries distinct information about disease stage and cognition. Methods We analyzed paired florbetapir Amyloid PET and flortaucipir Tau PET from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), comprising 378 paired imaging sessions from 320 participants spanning cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s Dementia (AD). High-order joint independent component analysis was used to identify fine-grained, data-driven patterns of Amyloid-Tau covariance across individuals. The resulting Amyloid and Tau maps were characterized by their spatial similarity and correspondence with rsfMRI-derived intrinsic functional networks. We then used the same data-driven spatial regions to quantify, for each participant, the relative extent of Amyloid abnormality alone, Tau abnormality alone, and spatially overlapping Amyloid-Tau abnormality. Results Ninety-six non-artifactual Amyloid-Tau components were identified, of which 73 showed appreciable spatial correspondence between their paired Amyloid and Tau maps, while 17 were Tau-localized and 6 Amyloid-localized. Amyloid component maps more frequently corresponded with rsfMRI-derived intrinsic network organization than Tau maps. Expression of the joint components differed across diagnosis groups, demonstrating spatially heterogeneous disease-stage patterns. Within the same data-driven regions, isolated Amyloid, isolated Tau, and overlapping Amyloid-Tau abnormality showed markedly different disease-stage profiles: Amyloid-only abnormality was more prominent in the earlier CN-to-MCI contrast, Tau-only abnormality in contrasts involving AD, whereas spatially overlapping Amyloid-Tau abnormality showed the broadest differences across disease stages and the largest mean CN-to-AD expansion. Overlapping abnormality was also associated with cognition across the greatest number of components and showed the strongest overall association with ADAS13. APOE4-related diagnosis-stage differences were considerably more widespread for pathology measures containing Amyloid than for Tau-only abnormality.

Environmental exposures influence the risk of psychiatric disorders, yet the biological mechanisms by which such experiences become embedded in brain structure remain poorly understood. The human cerebral cortex is crucial for cognition and emotional regulation, and variation in cortical thickness (CT) and surface area (SA) is linked to various behavioural and psychiatric traits. Here, we present a large-scale epigenome-wide association study that combines peripheral blood DNA methylation (DNAm) with MRI-derived cortical measures in over 7,400 individuals across 20 cohorts within the ENIGMA consortium. We identify mostly non-overlapping DNAm signatures associated with CT and SA, consistent with their distinct developmental and regulatory architectures. CT-associated CpGs are replicated across independent cohorts and are enriched for environmentally responsive regulatory elements and pathways associated with stress, metabolism, and immune signalling. In contrast, SA-associated CpGs cluster within chromatin-regulatory regions involved in early cortical development. Phenome-wide and Mendelian randomisation analyses reveal pleiotropic associations between DNAm, cortical structure, and psychiatric and cognitive traits. These findings suggest that peripheral DNAm captures environmentally sensitive biological processes that link exposure, cortical organisation, and behavioural vulnerability.

Preterm birth disrupts early brain development, increasing the risk of cognitive difficulties. However, its long-term effects on functional brain network energy, a metric of large-scale network stability and adaptability, remain poorly understood. Using resting-state fMRI data from the Adolescent Brain Cognitive Development (ABCD) study, we examined how prematurity influences brain network energy across late childhood and early adolescence. Our sample included 818 preterm-born youth and 2,149 matched full-term controls. Compared to full-term adolescents, preterm individuals exhibited altered multiscale functional connectivity and persistent reductions in functional network energy, specifically within the sensorimotor (SM) and higher-cognitive temporoparietal (HC-TP) networks, at both baseline and 2-year follow-up. These findings suggest diminished functional flexibility and network integration in preterm-born individuals. To further contextualize these results, we incorporated data from 1,633 participants in the Human Connectome Project (HCP) and HCP-Development studies, confirming that network energy increases with age in typically developing youth and is consistently reduced in preterm individuals. Notably, lower energy in the HC-TP network was specifically associated with deficits in crystallized cognition, but not with behavioral problems. Meta-analytic decoding further linked the HC-TP network to language and social cognition, providing a mechanistic explanation for its relationship with crystallized cognitive abilities. These findings highlight the persistence of neurodevelopmental alterations in preterm individuals, particularly affecting higher-order cognitive functions, and position network energy as a potential marker of altered neurodevelopment following preterm birth.

Bullying is an adverse childhood experience affecting up to one third of the global population and linked to psychosis-like experiences (PLEs), which increase the risk of psychotic disorders. This study aimed to investigate the association between the severity and persistence of bullying and PLEs and the neurobiological pathways from bullying to psychosis like experiences by assessing multiscale brain functional network connectivity (msFNC). We used data from the ABCD Study, a large, ongoing, multisite, population based prospective cohort study following U.S. adolescents. We included adolescents with complete bullying and PLEs assessments at the 2 and 3year follow-ups (T1: n=10,939; T2: n=10,102). We examined the associations between bullying severity and temporal exposure and PLEs using linear mixed effects models. In a 2year rsfMRI subsample (n=5,280), we used a Neuromark framework to analyze whether msFNC mediated the pathway from bullying to PLEs. Higher PLEs were associated with the presence and severity of bullying (non-bullied vs. mild bullying: d=-0.19, CI: -0.39 to -0.19, p<0.0001; moderate vs. severe bullying: d=-0.49, CI: -0.69 to -0.56, p<0.0001). When bullying ceased, PLEs returned to non bullied levels (d=-0.13, CI:-0.20 to -0.05, p=0.16), whereas persistence over two years led to greater elevations (d=-0.36, CI:-0.43 to -0.29, p<0.0001). We observed similar patterns for non paranoid and hallucination like experiences and their distress. msFNC in paralimbic, default mode, central executive, somatomotor, temporoparietal, insulotemporal, and frontal networks mediated the association. Bullying is time and dose dependently associated with psychosis like outcomes. msFNC between functional brain networks is a novel neurobiological pathway that mediates the link from bullying to PLEs. ### Competing Interest Statement Celso Arango has been a consultant to or has received honoraria or grants from Abbot, Acadia, Ambrosetti, Angelini, Biogen, BMS, Boehringer, Carnot, Gedeon Richter, Janssen Cilag, Lundbeck, Medscape, Menarini, Minerva, Otsuka, Pfizer, Roche, Rovi, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion, Takeda and Teva. Covadonga M. Diaz-Caneja has received honoraria or travel support from Johnson & Johnson and Viatris. Pablo Andres Camazon, Ram Ballem, Jiayu Chen, Zening Fu, Godfrey Pearlson, Vince Calhoun and Armin Iraji report no financial relationships with commercial interests. ### Funding Statement Pablo Andres Camazon is supported by the Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Science and Innovation, Rio Hortega Program CM24/00125. Covadonga M Diaz Caneja has received grant support from Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation (PI20/00721, PI23/00625, JR19/00024), and the European Commission (grant 101057182, project Youth GEMs and grant 101156514, project YOUTHreach). Vince Calhoun has received grant support from the National Institutes of Health (R01MH123610). Armin Iraji and Jiayu Chen have received grant support from the National Institutes of Health (R01MH136665). Celso Arango was supported by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (ISCIII), cofinanced by the European Union, ERDF Funds from the European Commission, A way of making Europe, financed by the European Union NextGenerationEU PMP21/00051, PI19/0102, PI22/01824 CIBERSAM, Madrid Regional Government (B2017/BMD 3740 AGES-CM-2), European Union Structural Funds, European Union Seventh Framework Program, European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking: Project PRISM2 (Grant agreement No.101034377), Project AIMS2TRIALS (Grant agreement No 777394), Horizon Europe, the National Institute of Mental Health of the National Institutes of Health under Award Number 1U01MH124639 01 (Project ProNET) and Award Number 5P50MH115846 03 (project FEP CAUSAL), Fundacion Familia Alonso, and Fundacion Alicia Koplowitz. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of University of California, San Diego, gave centralized ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The storage and management of data, as well as access procedures, are overseen by the National Institute of Mental Health (NIMH) through the National Data Archive (NDA).

Background Adolescence is known to be a crucial development period for future adult mental health. During this very sensitive window, the interplay between environment and genetics informs behavioral development. Disruption of this process, either because of adverse experiences or genetic vulnerability, can lead to the emergence of precursors to adult mental illness, seen in internalizing and externalizing behaviors. Studies have shown that these behaviors share some common genetic vulnerability, quantified as polygenic risk scores (PRS), and are related to the severity of adverse childhood experiences (ACEs). As yet though, we lack an understanding of how the combined effect of environment and genetics during adolescence might impact the developmental trajectories towards internalizing and externalizing behaviors and how these might be affecting neuronal development. Methods A parallel process latent class growth model, leveraging the longitudinal cohort (N=11k, 4 timepoints, aged 9-12) of the Adolescent Brain Cognitive Development study, was used to model the dual developmental trajectories of internalizing and externalizing behaviors. Scores for these behaviors from subjects were used for the observations across time. Level of ACEs and four genetic risk measures were included as time-invariant predictors. Fractional anisotropy (FA) measures were calculated from subjects’ diffusion images and included in a multivariate analysis of covariance to explore differences in FA based on ppLCGA group membership. Results The fit indices showed a 4-class model best fit the dual trajectories in this cohort. Adolescents were classified into the following subgroups: Low Level (86%), Increasing (4%), Decreasing (5%), and High Level Behaviors (5%). Higher general genetic risk for mental illness was associated with rates of increasing internalizing behaviors (t=2.066, p < 0.039), while high genetic risk for ADHD and ASD was associated with the higher intercepts (t=4.629, p < 0.00001) of externalizing trajectories. High genetic risk for alcohol dependency was associated with the increases in the rates of internalizing (t=2.506, p < 0.012) and externalizing (t=2.392, p < 0.017) behaviors. Low levels of ACEs were significantly related to the low levels of internalizing (t=-3.998, p < 0.0001) and externalizing (t=-2.428, p < 0.015). The multivariate results revealed differences (f = 1.56, p < 0.0000001) in FA, highlighting consistent increased FA for subjects in the groups with high levels of problematic behaviors in regions such as the cerebellum, superior longitudinal fasciculus, and splenium of the corpus callosum. Discussion While a large percentage of this cohort were exhibiting low level behaviors across early adolescence, there were differentiated relationships with genetic vulnerability across disorders, as well as a significant contribution of childhood adversity to the dual development of these behavioral trajectories. High levels of genetic risk for various mental illnesses in childhood, as well as high levels of adversity, were strongly associated with membership in the high level of problematic behaviors group. This same group showed accelerated development of their white matter in regions of the brain responsible for emotional regulation, attention, and executive function compared to their peers in other behavior groups. These findings further support the stress acceleration hypothesis of advanced brain development in the face of genetic vulnerability and adversity.

Spatial group independent component analysis (sgr-ICA) has become a crucial method to understand brain function in functional magnetic resonance imaging (fMRI) research, especially in resting-state fMRI (rsfMRI) studies. Early studies identified large-scale brain networks using sgr-ICA with lower order (e.g., 20 - 45 components); however, more recent studies have employed higher model orders (e.g., 200 components) to reveal more refined intrinsic connectivity networks (ICNs), offering a more detailed representation of functional brain architecture. This increased granularity has encouraged researchers to explore even higher model orders to better capture the brain's function. Although previous studies explored higher model orders, small datasets often limited them. In this study, we addressed this gap by assessing an sgr-ICA model with 500 components using a large rsfMRI dataset of over 100,000 subjects. This extensive data set allowed us to provide a robust estimation of fine-grained ICNs. We further assessed diagnostic effects and cognitive performance using whole-brain functional network connectivity (FNC) of 502 individuals with schizophrenia and 640 typical controls from these ICNs. We also compared the results with ICNs obtained using a lower-order, multi-spatial-scale template. Results demonstrate that our approach yields a large set of reliable and fine-grained ICNs, enhancing characterization of schizophrenia related dysconnectivity patterns. Specifically, we observed a relatively large number of ICNs within the cerebellar and paralimbic area. We detected significant hypoconnectivity between the cerebellar and subcortical domains, including the basal ganglia and thalamic regions. We also found hyperconnectivity between the cerebellar domain and the visual, sensorimotor, and higher cognitive domains, as well as between the sensorimotor and subcortical domains. Our finding revealed that granular ICNs can detect significant FNC differences between cohorts which are missed in larger scale ICNs. This work highlights the capability of higher model order ICA to capture distinct, fine-grained ICNs, enriching our understanding of FNC and serving as a valuable addition to current multiscale ICN templates. The ICNs derived from this study may serve as valuable references for future research, with the potential to improve the clinical utility of rsfMRI and advance the study of psychiatric disorders.

Psychosis is a functionally disruptive condition, however little is known about the varying connectivity between different sexes and the extent of its variability. Resting-state fMRI (rsfMRI) data from the BSNIP consortium involving 626 controls (CON) and 1127 psychosis (PSY) participants (873 Females and 719 Males) were analyzed. Multiscale functional network connectivity (msFNC) for each subject was extracted by applying multivariate objective optimization ICA with Neuromark 2.2 data-driven reference template. The principal component space of PSY msFNC was then identified, within which sex specificity was uncovered. Our analysis revealed 4 statistically significant sex biotypes, 2 female dominant and 2 male dominant ones with unique connectivity characteristics. Psychosis dysconnectivity along cerebellar-subcortical regions was more prominent in males than females. These biotype findings highlight the manifestations of psychosis across different sexes with varying connectivity deficits relative to CON.

The precise relationship between brain structure and function has been investigated through a multitude of lenses, but one detail that is held constant across most neuroimaging studies in this space is the identification of a singular structural basis set of the brain, upon which functional activation signals can be reconstructed to examine the linkage between structure and function. Such basis sets can be considered "functionally independent", as they are derived through structural data alone and have no explicit association to functional data. Recent work in multimodal fusion has facilitated a more integrated view of structure-function linkages by enabling the equal contribution of both modalities to the joint decomposition, resulting in components that are independent within modality but co-vary closely across modalities. These existing symmetric fusion approaches thus identify structural bases given an associated functional context. In this work, we consider an additional layer of precision to the investigation of structure-function coupling by studying these context-dependent linkages in a time-resolved manner. In other words, we ask which features of brain structure become (or remain) salient given the dynamically changing functional contexts (i.e., dynamic functional connectivity states, task structure, etc.) the brain may pass through during a given fMRI scan. We introduce "dynamic fusion", an ICA-based symmetric fusion approach that enables flexible, time-resolved linkages between brain structure and dynamic brain function. We show evidence that temporally resolved and functionally contextualized structural basis sets can accurately reflect dynamic functional processes and capture diagnostically relevant structure-functional coupling while detecting nuanced functionally driven structural components that cannot be captured with traditionally computed structural bases. Lastly, differential analysis of component stability across repeated scans from a control cohort reveals that the organization of static and dynamic structure-function coupling falls along unimodal/transmodal hierarchical lines.

The widespread occurrence of non-laying hens in commercial poultry flocks significantly affects the management and efficiency of egg production. The current identification of non-laying hens in commercial layer operations relies predominantly on manual empirical methods involving visual inspection and physical touch. These labor-intensive and time-consuming processes not only lack standardized protocols for objective assessment but also induce physiological stress in poultry populations, thereby contradicting contemporary animal welfare standards. This study proposes an intelligent detection framework for non-laying hens by leveraging a proprietary dataset comprising 977 images of chickens that capture multi-perspective views and behavioral patterns. We systematically compared the feature extraction capabilities of five CNN architectures (ResNet50, ResNeXt50, ResNeXt101, EfficientNet, and ConvNeXt) for non-laying hen identification, analyzed the performance enhancement effects of channel attention and self-attention mechanisms, employed Grad-CAM visualizations to interpret deep feature representations, systematically investigated the contribution of distinct regions to the recognition results, and ultimately established an objective classification framework for non-laying hens. Experimental results demonstrated that ConvNeXt achieved superior classification performance for non-laying hens, with accuracy, precision, recall, and F1 scores of 94.81 %, 93.48 %, 97.37 %, and 95.16 %, respectively. The integration of squeeze-and-excitation (SE) attention mechanisms further enhanced ConvNeXt's classification metrics with accuracy, precision, recall, and F1 scores of 1.29 %, 7.12 %, 2.37 %, and 4.68 %. Although Vision Transformers (ViTs) and Swin Transformers exhibit constrained performances due to dataset scale limitations, they demonstrate potential recognition capabilities. Heatmap visualization analysis revealed that the head features contributed most significantly to identification, followed sequentially by the feet, tail, wings, abdomen, and back regions, suggesting that feather distribution patterns and phenotypic characteristics in these areas may serve as critical biomarkers for non-laying hen classification.

Spatial group independent component analysis (sgr-ICA) is widely used in resting-state fMRI to identify intrinsic connectivity networks (ICNs). While lower-order decompositions reveal large-scale networks, higher-order models provide finer granularity but have been limited by small sample sizes. In this study, we applied sgr-ICA with 500 components to more than 100,000 subjects with rsfMRI to generate a robust fine-grained ICN template. Using this template, we examined whole brain functional network connectivity (FNC) in 502 individuals with schizophrenia and 640 typical controls and compared the findings with a lower order multiscale template. The 500-component template yielded a large set of reliable ICNs, particularly in the cerebellar and paralimbic regions, and revealed schizophrenia-related dysconnectivity patterns that were not detected at larger spatial scales. Specifically, we observed hypoconnectivity between the cerebellar and subcortical domains (basal ganglia and thalamus) and hyperconnectivity between the cerebellar domain and the visual, sensorimotor and higher cognitive domains. These results demonstrate that very high-order ICA can capture distinct fine-grained ICNs, improving the detection of disease-related connectivity differences and enriching current multiscale ICN templates. The derived ICNs can serve as a valuable reference for future studies and potentially enhance the clinical utility of rsfMRI in psychiatric research.

IntroductionTypical adolescent neurodevelopment is marked by decreases in grey matter (GM) volume, increases in myelination, measured by fractional anisotropy (FA), and improvement in cognitive performance.MethodsTo understand how epigenetic changes, methylation (DNAm) in particular, may be involved during this phase of development, we studied cognitive assessments, DNAm from saliva, and neuroimaging data from a longitudinal cohort of normally developing adolescents, aged nine to fourteen. We extracted networks of methylation with patterns of correlated change using a weighted gene correlation network analysis (WCGNA). Modules from these analyses, consisting of co-methylation networks, were then used in multivariate analyses with GM, FA, and cognitive measures to assess the nature of their relationships with cognitive improvement and brain development in adolescence.ResultsThis longitudinal exploration of co-methylated networks revealed an increase in correlated epigenetic changes as subjects progressed into adolescence. Co-methylation networks enriched for pathways involved in neuronal systems, potassium channels, neurexins and neuroligins were both conserved across time as well as associated with maturation patterns in GM, FA, and cognition.DiscussionOur research shows that correlated changes in the DNAm of genes in neuronal processes involved in adolescent brain development that were both conserved across time and related to typical cognitive and brain maturation, revealing possible epigenetic mechanisms driving this stage of development.

This study examines how multi-scale intrinsic connectivity networks (ICNs) relate to cognitive and behavioral functions in adolescents, focusing on attention/vigilance, working memory, and behavioral regulation. Leveraging the NeuroMark 2.2 multi-scale ICN template obtained from over 100,000 subjects, we obtained multi-scale ICNs from baseline resting-state fMRI data from the ABCD Study. For this study, we are interested in “the fronto-thalamo-cerebellar (FTC) circuitry” and choose the subdomains of Neuromark 2.2 that cover it: Cerebellar (CB), Subcortical - Extended Thalamic (SC-ET), Higher Cognition - Insular Temporal (HC-IT), and Higher Cognition - Frontal (HC-FR), previously identified as relevant to cognitive and behavioral functions. Employing a multivariate approach combining principal component analysis (PCA) and canonical correlation analysis (CCA), we examined associations between these multi-scale ICNs and cognitive-behavioral outcomes. Our findings revealed significant associations, particularly for one of the estimated canonical components, linking multi-scale ICNs to cognitive and behavioral measures across both discovery and replication sets. This connectivity pattern may serve as a potential marker for attention, working memory, and behavioral regulation, offering new insights into a wide spectrum of neurodevelopmental disorders including Attention-Deficit/Hyperactivity Disorder (ADHD).

Background:Many mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking dFNC to genetics largely follow a paradigm to identify associations between one set of genetic factors and multiple sets of connectivity features from different dFNC states, ignoring the potential variability in genetic correlates across states. Methods:We propose a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically-tuned genetic manifolds. A sliding window approach was utilized to estimate four dFNC states and compute subject-level state-average dFNC (sa-dFNC) features. The sa-dFNC features of each state were combined with schizophrenia risk SNPs within a jICA fusion framework, resulting in four parallel fusions in 32861 individuals of the UK Biobank cohort. The extracted four sets of joint SNP-dFNC components were further validated for clinical relevance in a combined schizophrenia cohort of 1237 individuals (528 patients). The similarity of SNP-dFNC components across four parallel fusions was evaluated as a measure of state variability. Results:We observed a mixture of "state-invariant" and "state-variant" components for SNP and dFNC modalities. Particularly, the schizophrenia-related state-variant SNP components, or manifolds, complemented each other by capturing different genes involved in the same biological functions, revealing a partition of genomic risk particularly elicited by the dynamics of brain function. Conclusions:By augmenting the SNP factors to state-variant manifolds, this dynamic fusion framework promises additional insights into underlying genetic risk of disease-related alterations in dynamic brain function.