Biology subjects

Calhoun, V. D.

Publications and source records attributed to Calhoun, V. D..

At least 19 recordsLinked to original sources

bioRxiv2026

From micro-architecture to networks: canonical neurobiological architecture of functional brain organization across the lifespan and in psychopathology

Understanding how macroscale functional network connectivity (FNC) across the lifespan and in psychopathology is spatially organized with respect to microscale neurobiological architecture remains a challenge. Here, we constructed a cross-omics mapping framework by linking resting-state fMRI data (5,349 individuals, 6-80 years) with 49 neurobiological markers (24 cell types, 19 neurotransmitters, 6 mitochondria). We show that FNC development and aging unfold along cellular and molecular gradients. Specifically, spatial correspondence between age-related FNC changes and neurobiological microprofile networks varied by life stage, peaking for cell types in adolescence, neurotransmitter systems in mid-young adulthood and mitochondrial phenotypes in early adulthood. Applying normative modeling to 5 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism, and attention-deficit/hyperactivity disorder), we identified disorder-specific and cross-disorder FNC phenotypes spatially associated with neurobiological architecture. Together, these findings provide a hypothesis-generating framework linking functional brain organization and dysfunction to canonical neurobiological architecture across the lifespan and psychiatric disorders.

neuroscience↗

bioRxiv2026

Alpha-range neural oscillations are associated with multiple dynamic brain networks

We asked whether alpha range (8-12 Hz) EEG neural oscillations, a robust indicator of cortical excitability, are associated with dynamic BOLD brain networks. Two independent EEG-fMRI resting-state datasets (discovery: n = 94 and replication: n = 33) show that low- and high-alpha periods are associated with dynamic BOLD coactivation patterns (dCAPS) involving multiple dynamic networks rather than a single static network. Unique BOLD dCAPs consisting of visual cortex activation along with sensorimotor and attention networks in addition to the posterior thalamus, were mainly associated with periods of low-alpha power. However, overlapping BOLD dCAPs consisting of default, control, and attention networks were associated with periods of both low- and high-alpha power. The results suggest low-alpha dCAPS contain unique temporal-dependent configurations related to visual, sensorimotor, and attention networks while both low- and high-alpha dCAPs consist of overlapping temporal-dependent configurations involving CON, DMN, and attention networks involved in internal and external cognition. These findings reconcile discrepancies across static connectivity EEG-fMRI findings by showing that distinct transient BOLD configurations are associated with similar patterns of alpha EEG modulation in visual cortex, providing a dynamic framework for the gating function of alpha oscillations.

neuroscience↗

bioRxiv2026

Dynamic Estimation of Spatially Interactive Networks (DESINE) Reveals Constrained Brain Repertoire in Schizophrenia Linked to Clinical and Cognitive Symptoms

BackgroundWhile resting-state fMRI demonstrated that brain networks are spatially dynamic (expanding, shrinking, and changing complexity over time), understanding the transient spatial network interactions that remain poorly characterized is critical for revealing the mechanisms underlying brain disorders. MethodsWe introduce DESINE (Dynamic Estimation of Spatially Interactive Networks), a novel framework using joint density distributions (2D histograms) of voxel-wise activity to quantify 4D spatial network interactions across sliding windows. We analysed transient deviations from the average functional state using root-mean-square error (RMSE) and mean absolute deviation (MAD), and characterized recurring interaction patterns using k-means clustering. We applied DESINE to 91 network pairs (14 networks) in a cohort of 508 subjects (315 healthy controls; 193 patients with schizophrenia, SZ). ResultsSZ is characterized by a significantly "constrained dynamic repertoire" of network interactions. SZ patients showed markedly lower means and standard deviations for both RMSE and MAD metrics across network pairs, particularly in regions of high activity, indicating systematic rigidity. Cluster analysis revealed significant alterations in state affinity metrics, suggesting a global breakdown in the brains capacity to preserve diverse, high-fidelity spatial configurations. Critically, these interaction metrics were associated with cognitive performance, symptom scores on the positive and negative syndrome scale, and chlorpromazine equivalent drug scores. ConclusionsThis work introduces DESINE as a global, voxel-agnostic framework for characterizing time-varying spatial interactions. Our findings highlight spatial rigidity as a fundamental feature of psychopathology, suggesting that the inability to express a diverse range of spatial interactions is a factor underlying cognitive deficits in schizophrenia.

neuroscience↗

bioRxiv2026

Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models

Functional magnetic resonance imaging (fMRI) data are inherently complex, characterized by high dimensionality, intricate inter-regional dependencies, and substantial individual variability across experimental paradigms. Traditional linear mixed models (LMMs) provide a principled framework that models population-level fixed effects while estimating variance components arising from subject-level random effects; however, they often fail to adequately capture nonlinear relationships inherent in neuroimaging data. To address these limitations, we introduce the nonlinear mixed model (NMM) approach, an innovative extension of the LMM framework that integrates neural networks to flexibly model complex fixed-effect relationships while preserving the random-effects structure to account for individual differences. NMM advances fMRI analysis by: (1) identifying robust functional connectivity (FC) patterns consistently observed across multiple paradigms; (2) leveraging SHapley Additive exPlanations (SHAP) analysis to provide post-hoc interpretability of the nonlinear fixed effects, quantifying how age, sex, and paradigm contribute to predicted FC and how these effects are distributed across large-scale brain networks; and (3) using subject-specific random effects as neural fingerprints that not only show systematic variability across attention and default mode systems but also predict standardized cognitive scores, demonstrating biological relevance. Applied to the Philadelphia Neurodevelopmental Cohort (PNC) across emotion, n-back, and resting-state paradigms, NMM achieved superior model fit relative to classical LMMs, as evidenced by lower mean squared error (MSE) in predicting FC. This framework offers a statistically rigorous and practically explainable approach for modeling large-scale FC from modest covariates while explicitly separating population-level effects from stable individual variability in functional brain organization.

bioengineering↗

bioRxiv2026

Building an open ecosystem for molecular neuroimaging: standards and tools from the OpenNeuroPET initiative

Molecular neuroimaging with positron emission tomography (PET) and single-photon emission computed tomography (SPECT) enables quantification of specific molecular targets in the living brain. Despite its scientific impact, molecular neuroimaging research has historically faced challenges due to high costs, small sample sizes, laboratory-specific analysis pipelines, and limited large-scale data sharing. These factors have hindered reproducibility and the broader reuse of valuable PET datasets. The OpenNeuroPET initiative was established to address these barriers by developing standards, infrastructure, and open-source tools currently focused on PET. Through collaborations across Europe and North America, OpenNeuroPET has supported the PET extension of the Brain Imaging Data Structure (PET-BIDS), providing a standardized framework for PET datasets and metadata. Building on PET-BIDS, tools such as PET2BIDS, ezBIDS, and BIDSCoin facilitate data conversion and curation. In parallel, OpenNeuro now hosts PET-BIDS datasets for open sharing, while complementary platforms such as PublicnEUro provide controlled-access pathways designed to support compliance with the European Union General Data Protection Regulation (GDPR). Emerging open-source workflows further support automated, reproducible PET analysis, promoting harmonization across centers. Together, these developments mark an important step toward an open molecular neuroimaging community in which datasets, software, and workflows can be transparently shared, reused, and scaled for collaborative research.

bioinformatics↗

bioRxiv2026

NeuroFLAME: A Scalable, Privacy-Preserving Federated Framework for Secure, Reproducible, and Multi-Site Neuroimaging Analysis

Federated analysis offers a scalable approach to multi-site neuroimaging research by enabling distributed statistical modeling, machine learning, decomposition, harmonization, and validation without exchanging sensitive individual-level data. However, a significant implementation gap exists, as the majority of federated clinical neuroimaging studies remain technical proofs of concept, struggling to fully navigate the rigorous data-sharing regulations and security constraints of real-world clinical and research environments. Compounding this, existing federated analysis frameworks are typically exposed as command-line tools and configuration files, imposing a considerable setup burden that demands specialized technical expertise from neuroscience researchers. To address these barriers, we present NeuroFLAME, an enterprise-grade, open-source federated neuroimaging platform built upon the NVIDIA FLARE (NVFlare) framework. NeuroFLAME couples a client-outbound-only communication framework with a graphical user interface specifically tailored for neuroscientists. Using certificate-based trust and containerized execution, the platform ensures reproducible analyses with baseline privacy guarantees through data localization and mTLS encryption; advanced protections such as differential privacy and homomorphic encryption are available as optional configurations. By ensuring that clients only need outbound communication and that raw data never leaves each site, NeuroFLAME is designed to support institutional data governance requirements and regulatory frameworks such as HIPAA and GDPR. We demonstrate the platform's utility through three federated analysis workflows, each implemented as a NeuroFLAME computation: multi-site federated closed form voxel-wise regression, decentralized constrained joint independent component analysis (dcjICA) and federated label-based dimensional prediction. Empirical validation of both workflows shows that NeuroFLAME achieves high consistency with established centralized approaches, effectively bridging the gap between experimental federated learning prototypes and production-ready collaborative tools for privacy-preserving, large-scale federated neuroimaging analysis.

neuroscience↗

bioRxiv2026

Environmental Enrichment Remodels Brain Structural and Behavioral Plasticity in Restricted and Repetitive C58 Mouse Models

Restricted and repetitive behaviors are characteristic of several neurodevelopmental disorders. While environmental enrichment has been shown to affect these behaviors, the underlying neural mechanisms remain poorly understood. In this study, we systematically explored the effects of environmental enrichment on brain structure and microstructure in C58 mice, a model of restricted and repetitive behaviors, compared to C57 control mice. Using structural magnetic resonance imaging and diffusion-weighted imaging, we assessed regional brain volumes and microstructural properties and examined their association with behavioral outcomes. Our results revealed significant reductions in total brain volume in C58 mice, with region-specific volumetric changes following environmental enrichment exposure. Importantly, environmental enrichment promoted microstructural plasticity in both strains, with significant alterations in fractional anisotropy and fiber density. These neuroanatomical changes were linked to reductions in restricted and repetitive behaviors, with strain- and sex-dependent effects. Overall, our findings suggest that environmental enrichment remodels brain plasticity at both structural and microstructural levels, as well as behavior, providing insights into potential therapeutic approaches through environmental enrichment for neurodevelopmental disorders.

neuroscience↗

bioRxiv2026

A residual-ratio framework for auditing transcriptomic gene signatures against background expression structure

BackgroundTranscriptomic gene signatures are widely used to infer pathway activity and biological mechanism from bulk cancer expression data, yet current evaluation strategies primarily emphasize internal coherence, predictive performance, or scoring robustness. A quantitative framework for assessing how much signature variation remains independent of background expression structure has been lacking. ResultsUnlike existing single-number signature-quality metrics such as Berglund uniqueness, residual-ratio auditing reports a trajectory across null-model richness: for each signature we compute the residual ratio [Formula] at progressively enriched expression-PC subspaces, together with an inverse-participation-ratio (IPR) concentration diagnostic that reports the effective number of axes absorbing each signature. Applied to a curated 17-entry benchmark, all 50 MSigDB Hallmark gene sets, and 1,181 Reactome pathways across 8 TCGA cancer types (4,462 samples), with external validation in METABRIC, the framework produces two complementary readouts. First, the curated panel is absorbed into the ExprPC50 subspace at residual ratios 18-43% below size-matched random 30-gene baselines in every cancer (curated mean r{perp} range 0.109-0.177 vs. random mean 0.182-0.288), providing the frameworks central quantitative discrimination between biologically coherent signatures and arbitrary gene combinations. Second, within the curated panel the ExprPC50 residual ratio is negatively correlated with the top-5 absorption concentration in every cancer (Spearman{rho} from -0.59 in PRAD to -0.89 in SKCM, median -0.71; all 8 significant at p < 0.05, most at p < 10-3); we report this correlation as a descriptive geometric property of the null-model coordinate system rather than as a biological law, because 1,000 random 30-gene draws projected through the same top-50 expression-PC basis reproduce the same pan-cancer median{rho} (-0.73; Supplementary Table S16), and it is robust to compositional nuisance: after rebuilding the null basis as immune-PC1 {oplus} stromal-PC1 {oplus} proliferation-PC1 plus 47 residual PCs, the per-cancer{rho} becomes more negative rather than shallower (median -0.86; Supplementary Table S17), ruling out tumor purity, immune infiltrate, and stromal fraction as drivers of the pattern. Because absorption at ExprPC50 is a geometric property of how any signature direction sits in expression-PC space, tier-level distributional structure at this operating point is not separable beyond the low-vs-upper band split: a Kruskal-Wallis omnibus is significant (p = 4.9 x 10-13), but pairwise Dunns post-hoc tests show that Tiers 1, 4, and 5 are not separable (pBH > 0.2). The trajectory shape itself is empirically bootstrap-invariant: across 200 sample-level fixed-basis bootstrap resamples of the 17 curated entries in BRCA, the mean pairwise Pearson correlation of trajectory-shape vectors is 0.999, and individual cell-level 95% bootstrap CI half-widths at B= 1,000 resamples are in the range 0.002-0.053. External replication in the METABRIC breast cancer cohort (nsamples = 1,980, microarray) showed moderate-to-strong rank-ordering concordance with TCGA-BRCA across the 17 curated entries (Spearman{rho} = 0.72 on the 17-signature ordering, 95% Fisher-z CI 0.37-0.89, p = 0.001). Under an upper-bound sensitivity analysis, 45 of 50 Hallmark gene sets and 992 of 1,181 Reactome pathways had ExprPC200 residual ratios below the mean of their size-matched random baselines--a descriptive statistic reflecting axis alignment under rich null models, not a failure rate. In causal DAG simulations (nrep = 100 replicates), a signature driven entirely by a latent confounder retained r{perp} = 0.233 at ExprPC50, numerically comparable to Tier 1 validated drivers, so a single-point residual ratio cannot adjudicate confounder-independence. The frameworks load-bearing signals are therefore the trajectory shape (statistically invariant under sample-level resampling) and the magnitude gap between the curated panel and its random 30-gene baseline (the curated-vs-random discrimination), read jointly--not the value of r{perp} at any single null-model dimensionality. O_TBL View this table: org.highwire.dtl.DTLVardef@1b56848org.highwire.dtl.DTLVardef@d18636org.highwire.dtl.DTLVardef@1c26db4org.highwire.dtl.DTLVardef@1b0620corg.highwire.dtl.DTLVardef@f507d2_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable S16:C_FLOATNO O_TABLECAPTIONRandom-gene-set null for the ExprPC50 r{perp}-vs-c(5) correlation, and curated-panel absolute gap vs random baselines. For each of the 8 primary-analysis TCGA cancer cohorts, we drew B = 1,000 random 30-gene sets from the gene universe of the preprocessed expression matrix and computed, for each draw, the residual ratio r{perp}(k = 50) and the top-5 absorption concentration c(5) under the same top-50 sample-space PC basis used for the curated benchmark (Methods [§]Statistical analysis; reference implementation accompanies the project repository as script 35). Column "Empirical curated{rho} " repeats the 17-signature Spearman{rho} between r{perp} and c(5) reported in the main text and in Supplementary Table S10; column "Null-A{rho} (random 30-gene)" gives the Spearman{rho} across the 1,000 random-draw (r{perp}, c(5)) pairs per cancer; column "{Delta} (emp null-A)" reports the difference. For reference, column "Null-B{rho} " gives the corresponding Spearman{rho} across 1,000 iid Gaussian unit vectors h (0, IN) in sample space (a uniform random direction that does not inherit the expression covariance geometry). Rightmost columns compare the curated 17-entry panels mean r{perp} at ExprPC50 to the random 30-gene baseline mean, both in absolute units and as a percentage gap; this magnitude gap is the quantitative discrimination between curated biological signatures and arbitrary gene combinations on which the frameworks central claim rests. C_TABLECAPTION C_TBL O_TBL View this table: org.highwire.dtl.DTLVardef@d4a1d3org.highwire.dtl.DTLVardef@1cc3baaorg.highwire.dtl.DTLVardef@1611aecorg.highwire.dtl.DTLVardef@2e9c76org.highwire.dtl.DTLVardef@220711_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable S17:C_FLOATNO O_TABLECAPTIONPurity-aware null for the curated-panel ExprPC50 r{perp}-vs-c(5) correlation. For each cancer we rebuild a rank-50 sample-space basis as follows. Columns 1-3 are the PC1 directions of: an immune-infiltrate proxy panel (CD3D, CD3E, CD4, CD8A, CD8B, CD19, CD68, PTPRC, FOXP3, IFNG); a stromal/fibroblast proxy panel (COL1A1, COL1A2, COL3A1, VIM, FN1, ACTA2, PDGFRA, PDGFRB); and the 50 proliferation markers already used in the null-model hierarchy ([§]Null model hierarchy). Columns 4-50 are the top-47 PCs of the residual expression matrix Y - QbioQ Y after QR-orthonormalization of the 3-column biological block; the full 50-column basis is re-orthonormalized by a final QR pass. The 17 curated benchmark entries are then re-scored under this purity-aware basis, and the per-cancer Spearman{rho} between r{perp} and c(5) is recomputed. Column "Standard{rho} " is the value reported in the main text (same as Supplementary Table S16, column "Empirical curated"); column "Purity-adjusted{rho} " is the value under the new basis; column "{Delta}" is the difference. This test was pre-specified as a check on whether the observed{rho} (r{perp}, c(5)) could be driven by tumor-purity, immune, or stromal composition artifacts that a standard top-50 PC basis would implicitly absorb. C_TABLECAPTION C_TBL ConclusionsResidual-ratio auditing provides an interpretable and practical framework for quantifying how much of a transcriptomic gene signatures variance remains orthogonal to a chosen background-expression model. The two statistically reliable quantities it reports are (i) the shape of the trajectory r{perp}(k) across null-model richness, which is bootstrap-invariant across sample-level resamples, and (ii) the magnitude gap between the curated panels residual ratio and size-matched random 30-gene baselines at a fixed operating point, which is 18-43% in all 8 TCGA cancers and survives a purity-aware null-model construction. The negative correlation between r{perp} and the top-5 absorption concentration c (curated-panel median{rho} = -0.71) is reproduced by random 30-gene sets under the same basis (random-draw median{rho} = -0.73) and is therefore best read as a descriptive geometric property of the null-model coordinate system rather than a biological discovery about curated signatures. Any single operating-point residual ratio carries materially wider cell-level uncertainty than the trajectory shape and cannot, on its own, adjudicate confounder-independence. The frameworks outputs describe a signatures geometric relationship to modeled background expression structure and do not evaluate clinical utility: a signature with a low residual ratio may still be clinically valuable when that low value reflects alignment with a strong prognostic or actionable program such as proliferation, immune infiltration, or cell cycle, and the framework is not a substitute for calibrated prognostic or predictive classifiers. All findings are based on bulk RNA-seq (TCGA PanCancer Atlas, 8 cancer types) and microarray (METABRIC) data; transfer to single-cell, single-nucleus, or spatial transcriptomics is out of scope and not claimed. Used within this scope--reading the trajectory shape and the magnitude-gap signal jointly, rather than the value of r{perp} at any one k--the framework adds a complementary audit layer to existing pathway-scoring and experimental-validation workflows, and supports more calibrated interpretation, comparison, and reporting of transcriptomic gene signatures in cancer studies.

bioinformatics↗

bioRxiv2026

Spectral Graph Features for Reference-free RNA 3D Quality Assessment

MotivationExisting RNA 3D structure quality assessment (QA) methods rely on local geometric descriptors or statistical potentials that evaluate atomic-level contacts but are blind to global topological coherence. This creates a critical failure mode--structures that are "locally correct but globally wrong"--where well-formed local helices mask misplaced domains and incorrect overall packing. ResultsWe introduce SpecRNA-QA, a lightweight RNA QA method based on multi-scale graph-Laplacian features of inter-nucleotide contact networks. In CASP16 leave-one-out cross-validation, it achieves median per-target Spearman{rho} = 0.69 (target-clustered bootstrap 95% CI [0.64, 0.73]) versus 0.47 for an internal geometry baseline--a +0.22 gap that is significant at p = 1.2 x 10-10 (one-sided Wilcoxon signed-rank) and reflects a per-target win rate of 93%. The gain is concentrated on large, multi-domain RNAs, where global coherence is poorly captured by local descriptors. In a contextual comparison with established statistical potentials, local energy-based scores remain strongest on compact RNAs, while SpecRNA-QA yields the strongest signal we observed on targets longer than 200 nt; within the single-threaded runtime budget used here, the strongest local-energy comparator, rsRNASP, timed out on 22 of 26 large targets, and we report an explicit paired head-to-head on the four commonly scored targets in Section 4.2. A training-free heuristic variant further shows that the spectral prior carries intrinsic quality information even in the absence of labeled QA data. AvailabilitySpecRNA-QA is available as a Python package at https://github.com/yudabitrends/specrnaq. Contactybi3@gsu.edu Supplementary informationSupplementary data are available online. Key PointsO_LISpecRNA-QA uses multi-scale graph-Laplacian spectra to score global RNA fold coherence that local geometric descriptors and local statistical potentials can miss. C_LIO_LIThe method uncovers a size-dependent division of labor: on compact RNAs that can be scored exhaustively, atom-level statistical potentials such as rsRNASP remain strongest, whereas on >200 nt RNAs--where the strongest local comparator times out on most targets under the single-threaded runtime budget used here--SpecRNA-QA provides the strongest signal we observed. C_LIO_LIHeat-kernel traces at intermediate diffusion times emerge as the most discriminative spectral features and form an interpretable bridge between local packing and long-range tertiary organization. C_LIO_LIA training-free heuristic variant of SpecRNA-QA retains informative spectral signal without any labeled QA data, supporting the interpretation of the learned model as amplifying a real structural signal rather than overfitting one. C_LI

bioinformatics↗

bioRxiv2026

Explicitly nonlinear fMRI networks reveal hidden trajectories of infant brain development

Nonlinearity is a hallmark of brain complexity at multiple scales. However, existing functional magnetic resonance imaging (fMRI) functional connectivity studies typically utilize linear methods. Therefore, links between nonlinear fMRI connectivity patterns and the development of the human brain during critical periods such as infancy remain unclear. To address this gap in knowledge, we developed a data-driven approach to capture brain intrinsic connectivity networks from explicitly nonlinear resting-state fMRI connectivity and profiled their developmental associations in a cohort of typically developing human infants. We identified neurobiologically structured nonlinear fMRI connectivity patterns during early postnatal life, indicating that macroscopic brain ensembles systematically participate in nonlinear relationships at birth. Furthermore, we found that linear and explicitly nonlinear network counterparts are linked to partially overlapping but complementary developmental profiles during this period of rapid brain maturation, with the explicitly nonlinear approach unveiling insights into the development of networks that have been associated with sensorimotor capacities, default mode processes, executive functioning, language production, and stimulus saliency. Our study marks the first comprehensive developmental investigation of whole-brain nonlinear fMRI networks in human infants and deepens contemporary perspectives on neuroimaging data discovery by emphasizing the informational richness of nonlinear relationships at fMRI scales of observation.

neuroscience↗

bioRxiv2026

Cognition emerges from phase dynamics of intrinsic coordination

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 with different tasks demanding distinct reconfigurations, leaving the coexistence of stability and flexibility unexplained. 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 and reconciling flexibility with stability. 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 achieved 89.9% accuracy in classifying 23 cognitive tasks, outperforming amplitude- or activation-based markers. These findings reframe task-evoked activation and deactivation as interference among concurrent flows. Together, our work indicates that a key variable for cognition is when intrinsic dynamics align in time, not where or how much the brain activates.

neuroscience↗

bioRxiv2026

Beyond Neural Noise: Critical Dynamics Predict Slower Reaction Times in Adults With and Without ADHD

Historically, neural variability observed during task was interpreted as "noise," assumed to obscure meaningful signal and thus something to be minimized both analytically by researchers and functionally by the brain. Changes to this signal-to-noise ratio have been proposed as a possible neural mechanism behind the increased reaction-time variability (RTV) in attention deficit hyperactivity disorder (ADHD). However, not all variability is the same - in some cases, variability can have some underlying "statistical structure" that can be beneficial to information processing. The challenge lies in distinguishing meaningful variability from random noise. The edge-of-synchrony critical point, which describes a system poised between synchronous and asynchronous regimes, could be a good theoretical framework to study these different types of neural variability. In this study, we investigate whether changes in criticality and oscillatory dynamics preceded slower behavioral responses during a bimodal continuous performance task in ADHD. We find evidence that, prior to slower responses, neural dynamics shift toward criticality in both ADHD and control groups, suggesting that increase variability in ADHD and during attention lapses are related to structured variability and not necessarily random noise. Notably, these findings run counter predictions based on the proposed model and previous literature on neural noise in this population, challenging predictions of edge-of-synchrony criticality as a unifying account of neural variability and behavioral performance. Furthermore, this effect did not emerge at the between-subject level, underscoring the limitations of relying on between-subject correlations to infer neural mechanisms. Impact StatementOur findings add new perspective to the hypothesis that links neural variability to reaction time variability in adults with and without ADHD. We found that neural dynamics shift towards criticality prior to slow reaction times in adults with and without ADHD, but in ADHD, dynamics lie closer to criticality regardless of response type, suggesting a different "attractor" state.

neuroscience↗

bioRxiv2026

NeuroMark-SZ: A Holistic Resting-State-fMRI-Based Model for Divergent Functional Circuitry in Schizophrenia

BackgroundSchizophrenia 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. MethodsWith 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. ResultsOur 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. ConclusionsThis 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.

neuroscience↗

bioRxiv2026

Time-Varying Directed Interactions in Functional Brain Networks: Modeling and Validation

Brain function depends on time-resolved directional organization: influence is continuously redistributed across regions, shaping who drives whom, when, and for how long. Yet dynamic neuroimaging has largely emphasized undirected fluctuations in functional connectivity, limiting biological interpretation of asymmetric interactions. Here we show that directed influence can be resolved into two biologically distinct signatures: strength, reflecting the efficacy of influence, and duration, reflecting its temporal extent. To test this principle, we developed sliding-window prediction correlation (SWpC), a framework for estimating these signatures in fMRI and related signals. Across simultaneous rat local field potential (LFP)-fMRI and human motor-task fMRI, strength more closely tracks neural-BOLD coupling and task-evoked reconfiguration, whereas duration captures temporally extended BOLD patterns more susceptible to hemodynamic timing. In post-concussion vestibular dysfunction, altered strength-duration profiles define reproducible vestibular-multisensory states and improve patient-control discrimination. Together, these findings support a general principle of dynamic brain organization spanning neurophysiology, behavior, and disease.

bioengineering↗

bioRxiv2026

When Randomness Becomes Rigid: Dynamic Connectivity Entropy and Symptom-Linked Network Dysfunction in Schizophrenia

High dimensionality of dynamic functional connectivity (dFNC) data representation complicates clinical interpretation and biomarker discovery. We propose a new complementary analytical framework based on dynamic inter-network connectivity entropy (DICE) and its potential use as a biomarker of mental illness. Our framework shows that DICE features extend beyond patient-control discrimination, revealing distinct pathophysiological signatures and differential associations with symptom dimensions. Using resting-state fMRI data from 311 participants, 160 controls, 151 schizophrenia (SZ) patients, we identified 53 intrinsic networks, computed DICE and derived three families of DICE-based metrics: (i) entropy level and range, (ii) distributional shape and temporal organization, (iii) entropy-state repertoire and occupancy. These measures revealed a multidimensional signature of altered entropy dynamics in SZ: (1) elevated baseline entropy with reduced fluctuation magnitude and reduced entropy acceleration; (2) reduced temporal persistence of entropy excursions and entropy distributions closer to Gaussian; and (3) a narrowed repertoire of entropy states, prolonged time in near-baseline entropy configurations. The DICE-based metrics within the SZ group show different associations with symptom dimensions. Reduced fluctuation magnitude and acceleration were associated with greater PANSS general symptom severity (disturbance of volition and preoccupation). Reduced deviation from Gaussianity was associated with higher PANSS positive severity (delusions and hallucinations). Reduced temporal persistence was associated with multiple PANSS positive, negative, and general symptoms. Reduced entropy-state diversity and prolonged dwell time in near-baseline states were associated with depression and PANSS positive/general severity, respectively. The multidimensional pathophysiology revealed through the different entropy patterns may potentially guide biomarker development and personalized treatments.

neuroscience↗

bioRxiv2025

Diminished spatial dynamics and maladaptive spatial complexity link resting brain network disruption to cognition in schizophrenia

Resting-state fMRI studies increasingly emphasize the dynamic nature of brain networks. While most approaches examine temporal fluctuations in connectivity, we focus on the spatial dynamics and complexity at voxel level - how networks expand and contract, and change their structural complexity over time. Using dynamic independent component analysis (ICA), we investigate the hierarchical structure of the resulting time-varying spatial networks, from their broad periphery to their most active core. We combine this with fractal dimension (FrD) as a measure of a networks spatial complexity and analyze temporal changes (dynamic flexibility) in a network and synchronized fluctuations between network pairs (fractal dimension coupling, FrDC). We refer to this approach as "dynamic spatial network complexity and connectivity (dSNCC)". Using a combined cohort of 508 subjects (315 healthy controls, 193 schizophrenia patients), we found that schizophrenia is associated with higher mean FrD in several networks, suggesting more irregular patterns/boundaries and a disorganized network structure. Critically, patients showed significantly reduced dynamic flexibility, indicating their networks are "stuck" in a less adaptable state. This robust finding is evidenced by a synergistic loss of temporal standard deviations in both network volume and FrD across multiple networks and activity thresholds. This maladaptive complexity was associated with cognitive impairment, with several dSNCC measures showing significant associations with subject scores for processing speed, visual learning, and verbal learning. Higher complexity in these networks and more significantly, their reduced dynamic flexibility as seen in patients, were particularly associated with impaired performance. Furthermore, we found aberrant connectivity (FrDC) in schizophrenia, with certain network pairs exhibiting overly synchronized complexity changes. Our results demonstrate that dSNCC is a powerful tool for characterizing network dynamics and may potentially provide a measurable mechanism for maladaptation in schizophrenia, where the brains inability to fluidly change its complexity may contribute to cognitive deficits and symptoms like disorganized thought. These findings highlight the importance of studying the intrinsic spatial dynamic properties to reveal the fundamental principles of brain network organization in health and disease. Our work represents a significant leap in complex systems neuroscience and provides a novel, quantifiable biomarker framework highly relevant for understanding and targeting other complex disorders characterized by network dysfunction, such as Alzheimers disease, autism, or other mental health conditions.

neuroscience↗

bioRxiv2025

Diagnosis-Optimized Dynamic Feature Learning Reveals Altered Default Mode Network Connectivity in Schizophrenia

Schizophrenia (SZ) is characterized by widespread neural dysconnectivity, with particularly pronounced alterations in the default mode network (DMN)--a set of brain regions involved in self-referential thought and mind-wandering. We investigated dynamic functional connectivity within the DMN in SZ by analyzing resting-state fMRI data from two independent cohorts (FBIRN and COBRE). Using a novel iterative feature-removal clustering approach focusing on DMN independent components, we identified distinct recurring connectivity states while iteratively removing dominant connectivity features to reveal subtler network patterns. This approach iteratively refines the set of features considered, ensuring a more balanced representation and facilitating the identification of significant interactions that would otherwise be overlooked. Cluster centroids for the four connectivity states were highly similar across both datasets, reflecting stable shared DMN patterns. State occupancy was compared between participants with SZ and healthy controls, and associations with clinical symptom severity were examined. The SZ group exhibited significant alterations in DMN connectivity dynamics, spending more time than controls in certain connectivity states characterized by atypical DMN coupling and less time in a state reflecting a normative DMN configuration. A comparison between the FBIRN and COBRE datasets reveals both similarities and differences in occupancy rate (OCR) states. Overall, the pattern of group effects converged while state-specific divergences likely reflected protocol/sample differences rather than conflicting biology. Crucially, the explainability pipeline yielded a highly similar feature-removal order across datasets, indicating that the same DMN edges drive the diagnostic signal in both cohorts. Several OCR effects also tracked symptom ratings, linking abnormal DMN dynamics to clinical expression. Our findings suggest that SZ is characterized by reproducible disturbances in the temporal organization of DMN connectivity. These dynamic DMN features may serve as potential biomarkers for SZ, offering diagnostic and clinical utility by capturing network dysfunction that static connectivity measures could overlook.

neuroscience↗

bioRxiv2025

Frequency-Aware Interpretable Deep Learning Framework for Alzheimer's Disease Classification Using rs-fMRI

Gaining insight into the spectral and temporal alterations in brain connectivity associated with Alzheimers disease (AD) may offer pathways toward more informative biomarkers and a deeper understanding of disease mechanisms. We propose FINE (Frequency-aware Interpretable Neural Encoder), a novel deep learning model designed to capture multi-scale temporal and frequency-specific patterns in dynamic functional network connectivity (dFNC) derived from resting-state fMRI. FINE integrates multiple expert branches, including convolutional layers, learnable wavelet layers, transformers, and static encoders, enabling the joint modeling of temporal evolution and spectral content of brain networks in an end-to-end framework. Beyond classification, FINE supports frequency-wise interpretability by aligning gradient-based saliency maps with statistical group differences, revealing potential robust, biologically meaningful biomarkers of AD. Evaluated on the large OASIS-3 dataset (856 subjects), FINE achieves AD classification performance (ROC-AUC 0.769) and provides insights into frequency-specific connectivity disruptions, particularly within subcortical, sensorimotor, and cerebellar networks. Our results demonstrate that incorporating frequency-aware modeling and interpretable architectures can advance both disease classification and underlying functional disruption of AD-related brain dynamics.

neuroscience↗