Biology subjects

Calhoun, V.

Publications and source records attributed to Calhoun, V..

At least 19 recordsLinked to original sources

bioRxiv2026

NeuroMark dFNC: Transferable Dynamic-State Templates for Subject-Specific and Frequency-Resolved Chronnectomics

Dynamic functional network connectivity (dFNC) commonly identifies recurring connectivity states from resting-state fMRI, but states derived independently in each dataset lack guaranteed correspondence across studies and cannot be directly estimated in new participants. We introduce NeuroMark dFNC, which extends the NeuroMark principle of prior-guided network estimation to dynamic connectivity by using reproducible reference states as priors for estimating corresponding subject-specific states. Reference-state templates were derived from UK Biobank using windowed functional network connectivity and time-frequency moments (TFMs), with split-sample reproducibility evaluated for k-means, independent component analysis (ICA), and principal component analysis (PCA) template-generation approaches. Constrained ICA then used the templates to estimate subject-specific state patterns and continuous loadings in independent FBIRN schizophrenia and ADNI Alzheimer's disease cohorts. ICA loadings were calibrated by their contribution to reconstruction of the original feature matrix. Fractional occupancy and conditional mean dwell time were calculated from the state with the largest absolute calibrated loading at each observation. The predefined templates avoided the need for dataset-specific state discovery and post-hoc state matching. In both clinical cohorts, the analysis identified state-dependent, distributed edgewise differences and differences in selected temporal summaries. Nominal frequency-resolved findings varied across states and moment-frequency bins and were treated as exploratory. NeuroMark dFNC provides a common state space for cross-cohort comparison while retaining subject-specific connectivity patterns and continuous state expression.

neuroscience↗

bioRxiv2026

A Chronnectogram-ICA Framework for Tracking Continuously Evolving and Overlapping Functional Brain States

The discrete-state model of brain dynamics has become the dominant paradigm in functional connectivity analysis, representing network activity as transitions among a fixed set of transiently recurring states while generally assuming that the states themselves do not evolve. We introduce a multilevel chronnectogram framework that relaxes this assumption and explicitly represents the higher-order temporal structure of brain dynamics, how distinct states couple and co-evolve over time, alongside the persistence of each state. The method integrates spatially guided independent component analysis (ICA) with subject-specific, state-guided dynamic ICA to capture evolving and overlapping connectivity patterns in a shared representational space. Validation on simulations with known ground truth shows recovery of continuous overlap and higher-order coupling structure invisible to discrete-state methods. Applied to resting-state functional magnetic resonance imaging from the FBIRN dataset (151 schizophrenia, 160 controls), the framework reveals that the same pair of brain states is shifted toward opposite extremes across patients, with strong synchrony in some and active anti-coupling in others, creating a redistribution of connectivity regimes invisible to conventional occupancy metrics, providing a substrate in which schizophrenia can appear both hyper- and hypo-volatile depending on the measure applied.

neuroscience↗

bioRxiv2026

Beyond High-Amplitude Events: Amplitude-Resolved Spatial and Temporal ICA Representations in fMRI

Functional MRI analyses commonly reduce high-dimensional brain activity using predefined spatial summaries, such as region-of-interest (ROI) averages, or temporally selective representations, such as co-activation patterns (CAPs) and high-amplitude events. These approaches provide useful summaries but do not explicitly characterize information distribution across the continuous magnitude of spatial and temporal network expression. We introduce an amplitude-resolved framework for systematically interrogating this continuum using independent component analysis (ICA). Resting-state fMRI data in the FBIRN dataset were decomposed using spatially-constrained ICA with NeuroMark. For the temporal representation, time points were stratified according to global ICA amplitude and frequency bands, with functional network connectivity (FNC) estimated for each amplitude range. For the spatial representation, we introduce an approach that stratifies voxels according to ICA loading magnitude and generates amplitude-specific spatial similarity matrices. Linear support vector machine (SVM) classification of schizophrenia (SZ) and healthy control (HC) participants was used to probe the information retained by each representation. Discriminative information varied non-monotonically across amplitude: intermediate ranges frequently matched or exceeded the highest amplitudes, while combinations of amplitude ranges further improved classification. These findings demonstrate that informative fMRI structure is distributed across the ICA expression continuum and motivate representations that complement fixed ROI summaries and high-amplitude approaches.

neuroscience↗

bioRxiv2026

Spatio-dynamical decomposition: Joint spatial and dynamical criteria for FMRI Blind source separation.

Blind source separation (BSS) recovers latent sources from observed mixtures. In brain imaging, sources occur in two domains, spatial and temporal, so a criterion applied in one leaves the other unconstrained. Independent component analysis, the most used BSS tool, exploits diversity such as non-Gaussianity and sample dependence, but in one domain at a time. Methods that use both domains apply the same criterion to each and weight them against each other. We propose spatio-dynamical decomposition (SDD), scoring maps by non-Gaussianity and timecourses by dynamical independence: how much each source's dynamics depend on the others. We normalize each score by how much it can improve on the data, eliminating the need for a weight. Across simulations and 130 naturalistic fMRI runs, SDD recovers sources distinguished by either maps or dynamics and reproduces both across disjoint episodes, while baselines trade off the two; map and timescale together also better predict stimulus tracking, with the largest gain for SDD. Together, these results show that separation is strongest when spatial and temporal domains each carry their own criterion.

neuroscience↗

bioRxiv2026

SILICA-SNC: FROM STREAMLINES TO NETWORKS

Whole brain tractography contains rich trajectory-level information about white matter organization, yet conventional structural connectomes capture only part of it. We introduce Structural Network Coupling (SNC), a network representation enabled by SILICA (Streamline Independent Component Analysis). SILICA decomposes tractography into structural source components and their streamline-specific loading profiles, while SNC characterizes relationships among these components through their shared trajectory expression. The resulting component-level network retains direct correspondence to the underlying white-matter pathways and supports network visualization and graph-based analysis. SNC produced anatomically interpretable coupling among trajectory-resolved white-matter components and revealed a highly organized group architecture that independently recapitulated commissural, projection, and association systems. It also showed strong hemispheric segregation and structured cross-system coupling, and was highly stable across split-half samples. SNC extends SILICA from trajectory decomposition to network-level organization and provides a flexible framework for future structural and multimodal network analysis.

neuroscience↗

bioRxiv2026

Beyond Marginal Coverage: Class-Conditional Conformal Prediction in Multi-Class Psychiatric Neuroimaging

Conformal prediction is increasingly proposed for clinical decision support because it provides distribution-free coverage guarantees that hold for any model and any data distribution. The guarantee routinely reported, however, is marginal, and we show that in multi-class diagnostic classification it can be satisfied exactly while individual diagnoses are covered very unevenly. On a four-way mood and psychosis classification task over 1,520 subjects from three studies and 14 acquisition sites, marginal split-conformal calibration attained 0.9000 empirical coverage against a nominal 0.90, while healthy controls were covered at 0.941 and schizoaffective disorder at 0.818. The surplus and the deficit cancel, so the aggregate figure is uninformative about either. Class-conditional (Mondrian) calibration reduced this 12.3-point disparity to 0.4 points at a cost of 0.07 labels in mean set size, under 3%. Holding coverage fixed across diagnoses has a further consequence: the residual variation in set size can no longer be attributed to class prior or per-class accuracy, and becomes interpretable as a property of the subject. We show that the resulting prediction sets separate subjects into confident, boundary, ambiguous and unresolved strata, and that the proportion of subjects independently flagged as label-ambiguous by a structural-MRI model trained separately on the same cohort rises monotonically across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18). No schizoaffective subject and 0.9% of bipolar subjects reach the confident stratum, against 18.0% of controls and 13.4% of schizophrenia subjects. Set size at matched coverage also provides a comparison between representations that accuracy cannot. Applied to structural MRI, functional MRI and their fusion, it reveals a sign reversal that accuracy conceals: fusion reduces set size for bipolar (-0.25) and schizoaffective (-0.23) subjects and increases it for controls (+0.16), and the aggregate fusion benefit itself changes sign below alpha = 0.10, while top-1 accuracy rises monotonically from 0.495 to 0.578 to 0.611 across the three models. Reporting marginal coverage alone is insufficient whenever diagnostic classes are unbalanced.

neuroscience↗

bioRxiv2026

TE-aware state analysis and evolution in dynamic network connectivity

Multi-echo functional magnetic resonance imaging (fMRI) acquires signals with distinct contrast profiles across echo times (TEs). Empirical evidence suggests that different TEs capture distinct signal contributions with varying sensitivity to BOLD and non-BOLD processes from long to short TEs (Stroman, 2002; Krishnamurthy, 2023; Dong, 2024). Previous literature has not yet examined how brain states extracted from temporal dynamic functional connectivity (dFNC) evolve across TEs. Here we present a methodological and empirical study of TE-dependent evolution of dynamic connectivity states, with a phenomenological two-component model for interpretation. Multiple echoes were acquired with an echo planar time-resolved imaging (EPTI) sequence. TE-specific components were extracted via atlas-based group ICA (Neuromark 1.0), and brain connectivity states were generated via windowed dFNC and spatial ICA clustering of windows. Within-subject and group-level states evolved across TEs in network organization and variance. States exhibited TE-dependent variance profiles, with distinct peak echo times; and group-level points of state transition were independently mapped to TE-dependent transitions in variance. We modified a bi-exponential model to simulate and model changes in the dFNC time-course across TEs. The bi-exponential model revealed high consistency with subject states (R2 fit=0.86, res. error=0.55, 86% model convergence rate), suggesting a two-component signal system contributing to dynamic TE-dependent connectivity changes in state. A significant correlation between relative model terms was estimated on a cell-wise basis across states (r(24) = -0.43, p=0.03, 95% CI [-0.70, -0.04]). This suggests that specific bi-exponential signal parameters (i.e., short FNC1,TE=0, long FNC2,TE=0, and long FNC2,decay) were associated with the observed TE-dependent variations in connectivity states across TEs. In sum, our analyses provide downstream implications for TE-aware state analysis dependent on connectivity magnitude (FNCTE=0) and rate of connectivity decay (FNCdecay), in a two-component signal model, with future implications for TE-dependent state biomarkers.

neuroscience↗

bioRxiv2026

Concurrent Supervised-Unsupervised Representative Subspace Clustering (CSU-RSC)

The biological heterogeneity of psychotic disorders (PDs) has motivated the identification of psychosis imaging neurosubtypes (PINs), which aims to stratify the PD population into subgroups with similar neurobiological underpinnings. Yet the influence of sex, although well documented in the literature, has not been well counted in the existing subtyping paradigm. Here, we introduce concurrent supervised-unsupervised representative subspace clustering (CSU-RSC), a general functional neurosubtyping framework that jointly estimates cluster-specific subspaces and a partition while softly incorporating a supervised label, aiming to identify sex-dominant subgroups based on differences in the spatial organization of brain networks. This soft supervising design differentiates CSU-RSC from unsupervised approaches that completely ignore a label as well as from supervised approaches that exclusively analyze each label group separately. On simulated data, CSU-RSC recovered the ground truth of subgroups more accurately than its unsupervised counterpart. Applied to resting-state fMRI from 1,239 individuals with psychosis, CSU-RSC identified two sex-dominant PINs that replicated across discovery and validation sets, and showed significant differences in clinical characteristics, including cognitive impairment and symptom severity, with the most cognitively impaired subtype being female-dominant. Projecting controls onto the patient-derived subspaces showed that the female-dominant pattern did not persist in controls, suggesting psychosis-specific sex heterogeneity. Overall, by incorporating sex as an explicit source of information, CSU-RSC highlights the value of sex for resolving biological heterogeneity and provides a basis for more reproducible and biologically informative patient stratification.

neuroscience↗

bioRxiv2026

Multiscale Functional Dysconnectivity Reveals Sex-Dominant Neurosubtypes and Predicts Unique Neurocognitive Pathways in Psychosis

Psychotic disorders are severe mental conditions whose biological underpinnings remain elusive. This is in part due to marked heterogeneity, which may have precluded advances in precise understanding and treatment of the disorders. Previous work has tried to elucidate this heterogeneity; nevertheless, sex, as an important biological variable, has not been well accounted for despite established sex differences. Using resting-state fMRI data from the BSNIP consortium (N=1753; 64.3% probands with psychosis include 571 females and 556 males; 35.7% of controls includes 369 females and 257 males), we extracted multiscale functional network connectivity and employed unsupervised learning methods to identify potential subgroups with distinct neurobiological profiles, characterized by an overrepresentation of males or females (termed here as sex-dominant Psychosis Imaging Neurosubtypes, PINs). Four sex-dominant neurosubtypes emerged, two female-dominant (fPIN-1, fPIN-2) and two male-dominant (mPIN-1, mPIN-2). Each PIN exhibited distinct, replicable dysconnectivity patterns that uniquely contributed to cognitive deficits. Brain-Predicted Cognitive performance derived from these PIN-specific dysconnectivity, prominent among the triple network (default mode and salience), subcortical-basal ganglia, higher cognition-insular temporal, and visual-occipitotemporal subdomains, correlated with measured cognitive performance and showed PIN-specific reductions relative to controls, confirming neurosubtype-congruent associations. Moreover, fPIN-2's distinct dysconnectivity also predicted positive and negative symptom scores. Our findings lend support for sex-dominant neurobiological heterogeneity, with sex-dominant PINs presenting unique dysconnectivity patterns that contribute to cognitive and symptom outcomes, even though neurosubtypes appear clinically similar. These results establish sex as a critical biological variable in deconstructing psychosis heterogeneity, which holds promise in revealing more precise biomarkers for disease characterization and guiding personalized treatment strategies.

neuroscience↗

bioRxiv2026

A dominant frontoparietal beta oscillatory brain state in the days after psilocybin and 5-MeO-DMT

Background. Serotonergic psychedelics show promise for treating psychiatric disorders, with symptom improvements lasting for weeks after a single dose. Clarifying the neural basis of these effects would benefit from an identification of empirical biomarkers of such lasting shifts in brain function. Resting state EEG offers a rapid (<5 minute), low-cost window into functional brain networks. However, connectivity is not static, but cycles between recurring semi-stable patterns that vary across frequency bands. Here we employed a dynamic function connectivity (dFC) framework to identify frequency-specific connectivity states and examine how they change in the weeks following psychedelic use. Methods. We collected resting-state EEG from individuals who had used one of two serotonergic psychedelic subclasses within the prior three weeks, psilocybin/LSD (typical; n=14) or 5-MeO-DMT (atypical; n=12), and age- and sex-matched controls (n=16). Frequency-band-specific spatial connectivity states (phase-lag index) were estimated across the full sample (5 per band). Groups were compared on proportion and dwell-time (per state) and state-to-state transitions. Results. A right frontoparietally-distributed beta synchrony state was dominant after both typical and atypical psychedelics use (proportion/dwell-time). This effect correlated with the number of days since using psychedelics. A globally-distributed theta-band state was prominent in recent users of typical psychedelics but occurred less often in atypical users. In contrast, neural entropy (Lempel-Ziv complexity; known to increase acutely during psychedelic dosing) was not altered in recent users of either subclass. Conclusion. These results reveal a beta-band signature of altered neural dynamics in the week following a psychedelic dose, consistent with a relaxation of brain network hierarchy after psychedelics.

neuroscience↗

bioRxiv2026

Recovering directional brain networks under temporal undersampling: an application to schizophrenia

Two decades of functional connectivity research have established schizophrenia as a disorder of distributed dysconnectivity, with a robust thalamocortical signature: reduced prefrontal and increased sensory coupling. A fundamental issue is that functional connectivity is undirected, operates at a single slow timescale, and cannot reveal causal direction. Moreover, the mismatch between BOLD sampling speed and neural dynamics can hide edges, fabricate spurious ones, and reverse the apparent orientation of causal relationships. To overcome these limitations, we introduce a general framework for estimating directed causal graphs from fMRI that explicitly accounts for temporal undersampling. We apply RnR, a causal discovery method built on the rate-agnostic RASL framework, to resting-state fMRI from the multi-site FBIRN cohort. Rather than returning a single directed graph, RnR recovers an equivalence class of directed graphs consistent with the observed data, each annotated with the sampling rate that would produce it and classifies each estimated orientation by its stability across inferred rates. This provides a principled basis for distinguishing directed interpretations that are safe to trust from those that are timescale-contingent. Benchmarking against five single-timescale estimators on schizophrenia data, RnR recovered substantially more group-differentiating directed edges, reproducing the fields most replicated finding in directed form: a sensory-to-visual hyperconnectivity hub oriented from the post-central gyrus component to primary visual cortex. Through simulation, we show that coarse spatial resolution has shielded single-timescale methods from the full effects of undersampling, whereas finer parcellations will require explicit undersampling modeling. This work reframes fMRI effective connectivity estimation by treating undersampling as a fundamental property of the measurement, enabling directed interpretations that are grounded in the data-generating process.

neuroscience↗

bioRxiv2026

SILICA: Streamline Independent Component Analysis for Trajectory-Resolved White Matter Decomposition

Whole-brain tractography reconstructs the major white matter pathways as millions of individual streamlines, offering an exceptionally rich description of neural geometry. Yet the statistical methods used to compare these reconstructions across individuals inevitably discard key information. Voxel-based analyses sacrifice pathway continuity, trajectory-based methods rarely support population-level statistical decomposition, and connectome models largely abstract away the underlying geometry. No existing framework jointly characterizes the population-level statistical organization of white matter and the three-dimensional geometry of the pathways from which that organization is expressed. We introduce streamline independent component analysis (SILICA), a framework that links group-level voxel-space statistical decomposition to subject-specific trajectories through a sparse streamline-by-voxel fingerprint. Each streamline is represented by its physical path length within a common anatomical voxel grid while retaining an explicit index-level link to its original trajectory. A two-stage dimensionality reduction reconciles tractograms of differing size and enables continuous component loadings to be back-reconstructed for every original streamline. These subject-specific loadings support weighted trajectory visualization and can be projected into voxel space to generate track-weighted component maps for conventional image-based visualization and future voxel-wise analysis. Separately, the learned group spatial components can be expressed on an independently reconstructed representative whole-brain tractogram to generate a compact trajectory-resolved atlas for group-level visualization. SILICA is a single decomposition expressed simultaneously in statistical and geometric form. SILICA was evaluated in diffusion MRI tractograms from 30 healthy adults. The recovered spatial patterns correspond to recognizable commissural, projection, and association systems. Back-reconstructions preserved individual trajectory variation while isolating components shared across the group, and their projection into voxel and trajectory space yielded interpretable maps and atlases. As a proof of concept, SILICA has not yet been validated against anatomical reference standards or evaluated for reproducibility and performance relative to established methods. Nevertheless, these results establish a coherent foundation for analyzing white matter in a framework that jointly represents population-level statistical structure and streamline geometry.

neuroscience↗

bioRxiv2026

Comparing Nonlinear Trajectories Across Brain Networks: A Key to Understanding Complex Brain Dynamics

This study explores the nonlinear developmental trajectories of brain networks in neurotypically developing infants during their first six months. Using a longitudinal dataset of 137 resting-state functional MRI scans from 74 infants, we analyzed five spatial metrics across 13 intrinsic connectivity networks, including motor, visual, subcortical, and prefrontal networks. A cubic model was specifically employed to capture distinct linear and non-linear trends in the networks developmental patterns, allowing for the identification of significant differences in cubic, quadratic, and linear slope parameters across networks. This model choice was driven by the need to examine how specific non-linear components (e.g., inflection points and acceleration rates) uniquely characterize each networks trajectory, which a generalized approach might smooth out without pinpointing such network-specific features. Notably, the subcortical network exhibited a distinct cubic growth pattern, while secondary motor and visual networks showed pronounced quadratic variations, suggesting network-specific shifts in spatial organization and connectivity. These findings highlight the unique maturation timelines and interactions between functional systems, such as early sensory-motor coordination and later cognitive integration. The results underscore the importance of network-specific growth patterns, providing deeper insights into how infant brain networks evolve and interact to support emerging cognitive and behavioral functions.

neuroscience↗

bioRxiv2026

Age Alters the Relationship between Post-Encoding Sleep Quality and Context Memory Neural Reinstatement

Sleep supports episodic memory consolidation, yet it remains unclear how naturalistic post-encoding sleep quality relates to the neural reinstatement of episodic representations across adulthood. The present study examined whether sleep discontinuity during the retention interval predicted delayed context memory and encoding-retrieval similarity (ERS) of EEG in younger and older adults. Participants completed an object-scene context memory task with immediate and delayed retrieval, while EEG was recorded during encoding and retrieval. Actigraphy was used to measure sleep across the post-encoding retention period, and principal component analysis identified sleep discontinuity and sleep time components. Behavioral results showed that greater post-encoding sleep discontinuity, but not sleep time, was associated with poorer delayed memory accuracy for mismatching object-context pairs across age. ERS analyses further showed that greater sleep discontinuity was associated with reduced ERS for correctly rejected mismatching pairs across frontal and posterior spatiotemporal clusters. Age moderated sleep-ERS associations: greater sleep discontinuity was generally related to lower ERS in younger adults, whereas some spatiotemporal clusters showed positive associations in older adults, potentially reflecting compensatory or effortful retrieval-related processing in poorer sleepers. Together, these findings suggest that sleep continuity during the post-encoding retention interval is important for preserving high-fidelity episodic representations needed for later context discrimination. More broadly, the results demonstrate that naturalistic sleep fragmentation is linked to both behavioral memory outcomes and neural reinstatement across adults.

neuroscience↗

bioRxiv2026

Large-scale brain dynamics are organized by a directional coordination hierarchy

The cortical hierarchy is a central organizing feature of brain structure and function, yet whether it is continuously expressed in spontaneous neural activity remains unresolved. Here, we show that resting-state brain dynamics are organized along a directional coordination axis that recapitulates hierarchical information flow. Focusing on the directional dynamics of interregional coordination, we identify three recurrent coordination regimes: a feedback-dominated mode in which transmodal cortex leads sensory systems, a feedforward-dominated mode reflecting the reverse, and an integrative mode of balanced bidirectional exchange. These modes define a low-dimensional coordination landscape that replicates across four independent cohorts, establishing directional structure as a stable property of the adult brain. In schizophrenia, this landscape is selectively reorganized: feedback-dominated coordination becomes less persistent, integrative coordination becomes more persistent, and global dynamics shift toward faster convergence and reduced entropy, reflecting a loss of directional constraint and dynamical flexibility. These alterations track symptom severity, cognitive performance, and medication exposure. Mediation analyses suggest that medication-related reductions in integrative coordination are statistically routed through feedforward dynamics, consistent with dopaminergic modulation of recurrent cortical loops as a candidate mechanism. Together, these findings identify directional functional dynamics as a fundamental and clinically informative axis of large-scale brain organization.

neuroscience↗

bioRxiv2026

Comparative Mapping of Functional and Structural Homologies in the Pig and Human Brain

Comparative mapping of functional and structural homologies across humans, small animals, and nonhuman primates has been extensively pursued due to its strong translational relevance. However, these experimental models possess inherent limitations in fully recapitulating the complexity of human cortical organization. The porcine model has recently emerged as a promising alternative, given its neuroanatomical and physiological similarities to the human brain. Despite these advantages, systematic cross-species characterization of functional and structural homologies between humans and pigs remains largely understudied. In the present study, we acquired resting-state functional MRI and diffusion MRI data from pigs and analyzed them alongside corresponding human datasets to investigate cross-species correspondence in large-scale brain organization. First, to enhance functional network alignment across species, group independent component analysis was performed separately within each species to identify intrinsic large-scale functional networks. Our results demonstrated that multiple canonical human resting-state networks are represented in the porcine brain, including sensorimotor, default mode, cerebellar, frontal, and central executive networks. Moreover, we observed significant cross-species concordance in intrinsic functional architecture across multiple distributed networks, both in spatial distribution and temporal patterns, indicating homologous large-scale brain organization between pigs and humans. Second, we conducted comparative structural analyses using diffusion MRI-based fiber tractography, color-encoded fractional anisotropy maps, and structural connectivity analyses to examine and compare white matter organization in pigs and humans. Cross-species comparison revealed substantial similarities in major white matter pathways and their spatial organization, supporting structural correspondence at the level of tract geometry. Together, these findings underscore the translational value of the porcine model as a robust and neurobiologically relevant platform for investigating human brain function, structural organization, and related neurological disorders.

neuroscience↗

bioRxiv2026

Functional Connectivity Changes in Major Depressive Disorder

Major depressive disorder (MDD) is associated with brain-wide network disruptions. This study investigates a large resting-state functional magnetic resonance imaging dataset (N = 519) to analyze static and dynamic functional network connectivity (FNC). Using independent component analysis, our analysis revealed hyperconnectivity within sensorimotor and temporal subdomains, hypoconnectivity from higher cognitive networks, and hyperconnectivity from the default mode and sensorimotor domains in MDD. A novel frequency-sensitive dynamic approach identified disruptions in the temporal synchrony of brain states engaging the default mode-paralimbic, sensorimotor, and frontal regions, as well as the subcortical limbic, frontal, and salience regions. Overall, the findings highlight the utility of combining static and dynamic approaches in large neuroimaging datasets to elucidate the neural underpinnings of MDD pathology.

neuroscience↗

bioRxiv2026

Conditioned Graph Reconstruction of Brain Functional Network Connectivity Reveals Interpretable Latent Axes of Sex and Fluid Intelligence

In studying the brains functional connectivity and its associations with clinically observed assessments, novel learning frameworks modeling its network properties in conjunction with assessment variables are crucial to uncover variable-specific patterns via meaningful encoding and reconstruction. We present a generative framework for modeling human brain functional connectivity features while retaining key network metrics and differences associated with demographic and cognitive variables. A conditional graph variational autoencoder is employed to encode static functional network connectivity (sFNC) features into a latent representation, which is then utilized for the dual purpose of reconstructing sFNC data conditioned on variables such as biological sex or fluid intelligence, and identifying discriminative connectivity features associated with the conditioning variables in the latent space. Using over 20,000 subjects from the UK Biobank, our model demonstrates high-fidelity reconstructions that preserve condition-specific network patterns, while the latent space captures interpretable patterns associated with these variables. The group differences in latent space are highlighted by one-hot probing of the latent dimensions and forward mapping to connectivity patterns. This approach provides a scalable, network-informed framework for studying brain functional connectivity and its associations with individual differences, offering potential applications in characterizing functional signatures for mental health conditions via clinically observed assessment variables. AUTHOR SUMMARYTo enable the modeling of the brain functional connectivity network for encoding and reconstructing assessment-specific differences, we propose a conditional graph-based generative framework for modeling human brain functional connectivity while accounting for demographic and cognitive differences. Using a conditional graph variational autoencoder, our approach learns interpretable latent representations of functional connectivity networks derived from fMRI data. Evaluated on over 20,000 UK Biobank subjects, the model accurately reconstructs connectivity patterns outperforming baseline architectures and preserves differences associated with biological sex and fluid intelligence. By probing the latent space and mapping latent dimensions back to brain networks, we identify condition-specific connectivity features in an interpretable manner. This work provides a scalable, network-informed approach for studying individual differences in functional brain organization.

neuroscience↗