Biology subjects

Calhoun, V.

Publications and source records attributed to Calhoun, V..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Toward Granular Brain Intrinsic Connectivity Networks and Insights into Schizophrenia

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 brains 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.

neuroscience↗

bioRxiv2025

An information theory framework for capturing multi-connectivity via spatial network encoding reveals reduced population count (Hamming weight) localized to auditory, visual, and motor networks

1.The human brain operates as a complex system where functional networks evolve and interact across spatially distributed regions. In traditional neuroimaging analyses, functional connectivity (FC), based on pairwise correlations or statistical dependencies of temporal fluctuations in the BOLD signal, has been a primary method for exploring interactions between brain regions and decoding brain function. However, traditional FC methods often overlook the intricate, multi-way interplay among brain elements that emerge from the brains densely interconnected nature. To overcome these limitations, we introduce a novel voxel-centric framework that captures the multi-way interactions between voxels and networks identified via high-model order independent component analysis. This framework posits that individual voxels serve as critical mediators of multi-network communication, reflecting the brains complex functional architecture. By encoding voxel contributions from brain networks into binary representations and quantifying the population count at each voxel via Hamming weights, the proposed method prioritizes high-contribution voxels that facilitate inter-network interactions. This approach provides new insights into the brains functional organization, revealing previously unrecognized patterns of voxel-to-network entanglement. Specifically, in the context of schizophrenia, our method enables the identification of spatial patterns that may underpin the cognitive and perceptual disturbances characteristic of the disorder. This enhanced understanding could improve diagnostic precision and help tailor interventions that target specific dysfunctional networks, offering a pathway to more effective treatments and better patient outcomes in schizophrenia.

neuroscience↗

bioRxiv2025

Brain State Convergence and Divergence as Resting State FMRI Biomarkers: A Large-Scale Study of Continuous, Overlapping, Time-resolved States Differentiates Four Psychiatric Disorders

AO_SCPLOWBSTRACTC_SCPLOWBrain function is inherently dynamic, characterized by transient, overlapping functional states rather than static connectivity patterns. Current clustering-based dynamic functional network connectivity methods often fail to capture overlapping states; meanwhile, independent component analysis (ICA)-based methods typically rely on group-level analysis, limiting subject-specific accuracy. To address this gap, we introduce a novel analytical framework estimating individualized dynamic double functional independent primitives (ddFIP)-based states. Our methodological innovation includes: (1) a two-stage ICA combining spatially constrained ICA to define group-level intrinsic connectivity networks (ICNs), followed by constrained ICA to estimate subject-specific states and timecourses; (2) calibration ensuring derived states preserve original correlation scales, enabling meaningful cross-subject and group-level comparisons; and (3) novel metrics leveraging this calibrated representation, including amplitude convergence (uniformity of simultaneous state contributions), amplitude divergence (variability of states independent of state dominance), and dynamic state density (number of concurrently active states at any given time). Validating our framework on an extensive resting-state fMRI dataset (N > 5.5K) spanning four neuropsychiatric conditions revealed disorder-specific connectivity signatures: schizophrenia exhibited extensive variability (increased divergence), while autism displayed pronounced stability (increased convergence). In summary, our proposed method uniquely integrates subject-specific ICA estimation, unit-preserving calibration, and novel convergence-divergence metrics, providing data-driven biomarkers that differentiating psychiatric disorders.

neuroscience↗

bioRxiv2025

Dynamic fusion of structural and functional connectivity via joint connectivity matrix ICA

The integration of multimodal MRI images, including functional MRI (fMRI) and diffusion MRI (dMRI), depicts a key advancement in neuroimaging, since it offers a more comprehensive and better understanding of brain function and connections. FMRI captures brain functional activity while dMRI reveals structural connectivity via white matter bundles, each of which provide unique yet complementary insights; however combing these two modalities, particularly at a dynamic level, is challenging due to their drastically different data characteristics. This study introduces a novel framework named "dynamic fusion," which extends joint component analysis (cmICA) to integrate static structural connectivity (SC) with dynamic functional connectivity (FC). Our approach gauges the relationship between these joint components across various temporal states, aiming to discover both static and dynamic features of brain connectivity. We applied this approach to fMRI and dMRI data from the same set of control subjects, which we had previously studied to estimate joint parcellation and their structural and functional connections using a static model only, and also included a comparable number of individuals with schizophrenia from the same study. Our results reveal that dynamic fusion not only highlights diverse temporal dynamics in FC but, more importantly, also shows how SC patterns differ across dynamic functional states at different time frames, providing new insights into brain organization. Furthermore, it successfully detects joint structural-functional connectivity differences between individuals with schizophrenia and controls, demonstrating its potential for detecting hidden group differences. Overall, this study establishes our dynamic fusion as a powerful tool for integrating structural and dynamic functional connectivity data, enhancing our understanding of brain connectivity and offering new perspectives for studying neurological disorders.

neuroscience↗

bioRxiv2025

LINKING MULTI-SCALE BRAIN CONNECTIVITY WITH VIGILANCE, WORKING MEMORY, AND BEHAVIOR IN ADOLESCENTS

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).

neuroscience↗

bioRxiv2025

Linking Brain Entropy to Molecular and Cellular Architecture in Psychosis

Brain entropy reflects the complexity of intrinsic activity and has been linked to both cognitive function and psychiatric disorders. Yet its connection to the brains neurochemical and cellular architecture remains poorly understood. By integrating molecular imaging with neuroimaging datasets, we show that the spatial distribution of cell types, neurotransmitter systems, and mitochondrial phenotypes is systematically related to brain entropy. We observed significant differences in entropy correlations: between healthy controls and individuals with schizophrenia for the mu-opioid and dopamine D1 receptors, and between controls and individuals with bipolar disorder for the norepinephrine transporter (NAT) and N-methyl-D-aspartate (NMDA) receptor. No significant differences emerged between schizophrenia and bipolar disorder in the neurotransmitter domain. At the cellular and metabolic level, both control-schizophrenia and control-bipolar comparisons revealed widespread alterations involving most mitochondrial markers, glial cells, and inhibitory neurons. These patterns point to disruptions in energy metabolism, neuroinflammatory processes, and inhibitory regulation within the clinical groups. Overall, the findings indicate that brain entropy is not randomly distributed but is closely tied to specific neurochemical systems and cellular features. This systems-level view helps explain how the complexity of brain activity arises from molecular architecture, and how it is altered in psychiatric disorders. It also provides a biological foundation for understanding brain entropy in the context of psychosis.

neuroscience↗

bioRxiv2025

NeuroMark-HiFi: A Data-Driven Method for Detecting High-Spatial-Frequency Functional Brain Networks

ObjectiveThe Traditional functional neuroimaging approaches typically focus on low-frequency spatial structures, potentially overlooking critical fine-scale connectivity disruptions associated with brain disorders MethodsWe introduce NeuroMark-HiFi, a fully automated algorithm designed to enhance the detection of high-spatial-frequency functional brain network patterns. NeuroMark-HiFi systematically preserves and analyzes fine-grained network variations by integrating reference-informed independent component analysis (ICA), 3D high-frequency spatial filtering, and a frequency-informed ICA decomposition to extract high-frequency functional components with greater precision. ResultsSimulation studies and mathematical evaluations demonstrate that NeuroMark-HiFi significantly improves sensitivity to both individual and group differences driven by small local shifts in spatial patterns of intrinsic connectivity networks (ICNs). Compared to traditional methods, NeuroMark-HiFi revealed additional group differences between individuals with schizophrenia (SZ) and healthy controls (HC), particularly in the visual, sensorimotor, frontal, temporal, and insular networks. ConclusionNeuroMark-HiFi successfully captures biologically meaningful alterations in spatial network patterns that conventional approaches may miss. SignificanceBy improving sensitivity to subtle brain network alterations, NeuroMark-HiFi holds promise for early diagnosis, treatment monitoring, neurodevelopment studies, aging research, and multimodal biomarker discovery, advancing the goals of precision psychiatry and neuroscience.

bioengineering↗

bioRxiv2025

PET-derived amyloid patterns in gray and white matter across Alzheimer's disease: A high-model-order ICA

INTRODUCTIONAlzheimers Disease (AD) is a neurodegenerative disorder marked by gray matter (GM) changes driven by amyloid-beta (A{beta}) plaques and neurofibrillary tangles. While GM alterations are well documented, spatially distinct patterns of homogeneous A{beta} uptake and white matter (WM) involvement remain underexplored. METHODSWe applied high-order independent component analysis (ICA) to 716 [18F]Florbetapir PET scans, identifying 80 GM and 13 WM networks. Diagnostic and cognitive associations were evaluated via statistical modeling. RESULTSIdentified networks delineated a progression trajectory, with mild cognitive impairment (MCI) profiles in temporoparietal and frontal subdomains more closely aligned with AD than cognitively normal (CN) profiles. GM networks, including the hippocampal-entorhinal complex and precuneus, and WM networks, including the retrolenticular internal capsule, demonstrated robust associations with cognitive performance. DISCUSSIONOur findings highlight the utility of high-order ICA in identifying reproducible A{beta} networks and the contribution of WM networks, such as the posterior corpus callosum, in the early pathological landscape of AD.

neuroscience↗

bioRxiv2025

Mapping Dynamic Metabolic Energy Distribution in Brain Networks using fMRI: A Novel Dynamic Time Warping Framework

Schizophrenia has long been linked to impaired coordination of brain activity, yet most frameworks overlook two key dimensions: the amplitude of brain signals and the differing timescales on which regions operate. These factors are critical in disorders where neural activity is exaggerated and slowed. In healthy adults, networks compensate for mismatched processing speeds to maintain proportionate activity, but this process is poorly understood in schizophrenia. We developed a timescale-aligned, time-resolved framework that separates temporal distortions from genuine amplitude differences, enabling measurement of amplitude balance between networks across timescales. This approach was applied to large-scale fMRI datasets, including the Human Connectome Project and a multi-site schizophrenia cohort. Patients with schizophrenia showed greater amplitude imbalance, especially during fast fluctuations, along with more frequent re-entry into unbalanced states and slower recovery to stable coordination. We further identified a flexible intermediate state that patients occupied more often, and that predicted better working-memory performance. Across cohorts, amplitude imbalance was associated with greater symptom severity and poorer reasoning ability. These findings provide a new mechanistic view of dyscoordination in schizophrenia grounded in timescale-normalized amplitude dynamics, highlight aberrant recovery of amplitude balance as a core feature of the illness, and suggest that timescale-aligned amplitude imbalance may serve as a promising target for biomarker development.

neuroscience↗

bioRxiv2025

Beyond Pairwise Connections in Complex Systems: Insights into the Human Multiscale Psychotic Brain

Complex biological systems, like the brain, exhibit intricate multiway and multiscale interactions that drive emergent behaviors. In psychiatry, neural processes extend beyond pairwise connectivity, involving higher-order interactions critical for understanding mental disorders. Conventional brain network studies focus on pairwise links, offering insights into basic connectivity but failing to capture the complexity of neural dysfunction in psychiatric conditions. This study aims to bridge this gap by applying a matrix-based entropy functional to estimate total correlation, a mathematical framework that incorporates multivariate information measures extending beyond pairwise interactions. We apply this framework to fMRI-ICA-derived multiscale brain networks, enabling the investigation of interactions beyond pairwise relationships in the human multiscale brain. Additionally, this approach holds promise for psychiatric studies, providing a new lens through which to explore beyond pairwise brain network interactions. By examining both triple interactions and the latent factors underlying the triadic relationships among intrinsic brain connectivity networks through tensor decomposition, our study presents a novel approach to understanding higher-order brain dynamics. This framework not only enhances our understanding of complex brain functions but also offers new opportunities for investigating pathophysiology, potentially informing more targeted diagnostic and therapeutic strategies. Moreover, the methodology of analyzing multiway interactions beyond pairwise connections can be applied to any signal analysis. In this study, we specifically explore its application to neural signals, demonstrating its power in uncovering complex multiway interaction patterns of brain activity, which provide a window to explore connectivity beyond pairwise interactions in the multiscale functionality of the brain.

neuroscience↗

bioRxiv2025

Physical Activity and Depressive Mood Shared the Structural Connectivity between Motor and Reward Network: a population-based study from the UK Biobank

BackgroundExercise has been revealed to leave a positive effect on alleviating depressive symptoms by various existing studies. However, the neural basis behind this phenomenon remains unknown, as well as its underlying biological mechanism.AimsUsing a large neuroimaging cohort from UK Biobank, this study aimed to identify structural connectivity (SC) patterns simultaneously linked with physical activity and depression, as well as its biological interpretation.Method492 participants with major depression disorder (MDD) and 535 healthy controls (HC) were extracted from the UK Biobank dataset. Partial least squares regression was first utilized to explore a replicable SC pattern simultaneously associated with physical activity and depressive mood. The neuromaps toolbox was applied to interpret the biological ontologies of the identified SC pattern. Neuroimaging-transcriptome association and enrichment analyses were conducted to explore its underlying genetic basis, pathways and cell types. The reproducibility and generalizability were tested on another independent MDD dataset (N=3,496) and bipolar disorder (BD) dataset (N=81).ResultsA SC pattern linked with exercise was identified to be both significantly correlated with depressive mood and group discriminative between MDDs and HCs, which primarily located between the motor-related regions and reward-related regions. This pattern was associated with multiple neurotransmitter receptors, such as serotonin and GABA receptors, and enriched in pathways like synaptic signaling and astrocytes cell type. The SC pattern and genetic results were also replicated in another independent MDD dataset and present commonalities with BD.ConclusionsThis study not only initially identified a reproducible shared SC pattern between physical activity and depressive mood, but also elucidated the underlying biological mechanisms, which enhanced our understanding of how exercise helps alleviate depression and may inform the development of novel neuromodulation targets.

neuroscience↗

bioRxiv2025

Topologically Optimized Intrinsic Brain Networks

The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this challenge, researchers have developed group-inference frameworks that leverage robust group-level estimations as a common reference point to infer corresponding subject-level networks. Generally, existing approaches either leverage the common reference as a strict, voxel-wise spatial constraint (i.e., strong constraints at the voxel level) or impose no constraints. Here, we propose a targeted approach that harnesses the topological information of group-level networks to encode a high-level representation of spatial properties to be used as constraints, which we refer to as Topologically Optimized Intrinsic Brain Networks (TOIBN). Consequently, our method inherits the significant advantages of constraint-based approaches, such as enhancing estimation efficacy in noisy data or small sample sizes. On the other hand, our method provides a softer constraint than voxel-wise penalties, which can result in the loss of individual variation, increased susceptibility to model biases, and potentially missing important subject-specific information. Our analyses show that the subject maps from our method are less noisy and true to the group networks while promoting subject variability that can be lost from strict constraints. We also find that the topological properties resulting from the TOIBN maps are more expressive of differences between individuals with schizophrenia and controls in the default mode, subcortical, and visual networks.

neuroscience↗

bioRxiv2025

Building Multivariate Molecular Imaging Brain Atlases Using the NeuroMark PET Independent Component Analysis Framework

Molecular imaging analyses using positron emission tomography (PET) data often rely on macro-anatomical regions of interest (ROI), which may not align with chemo-architectural boundaries and obscure functional distinctions. While methods such as independent component analysis (ICA) have been useful to address this limitation, the fully data-driven nature can make it challenging to compare results across studies. Here, we introduce the NeuroMark PET approach, utilizing spatially constrained independent component analysis to define overlapping regions that may reflect the brains molecular architecture. We first generate an ICA template for the PET radiotracer florbetapir (FBP), targeting amyloid-{beta} (A{beta}) accumulation in the brain, using blind ICA on large datasets to identify replicable independent components. Only components that targeted A{beta} were included in this study, defined as A{beta} networks (A{beta}Ns), by omitting components targeting myelin or other non-A{beta} targets. Next, we use the A{beta}Ns as priors for spatially constrained ICA, resulting in a fully automated ICA pipeline called NeuroMark PET. This NeuroMark pipeline, including its A{beta}Ns, was validated against a standard neuroanatomical PET atlas, using data from the Alzheimers Disease Neuroimaging Initiative (ADNI). The study included 296 cognitively normal participants with FBP PET scans and 173 with florbetaben (FBB) PET scans, an analogue radiotracer also targeting A{beta} accumulation. Our results show that NeuroMark PET captures biologically meaningful, participant-specific features, such as subject specific loading values, consistent across individuals, and also shows higher sensitivity and power for detecting age-related changes compared to traditional atlas-based ROIs. Using this framework, we also highlight some of the advantages of using ICA analysis for PET data. In this study, an A{beta}N consists of weighted voxels and forms a pattern throughout the entire brain. For example, components may have weighted values at every voxel and can overlap with one another, enabling the separation of artifacts which may coincide with the A{beta}Ns of interest. In addition, this approach allows for the differentiation, separating white matter components, which may overlap in complex ways with the A{beta}Ns, mainly residing in the neighboring gray matter. Results also showed that the most age associated A{beta}N (representing the cognitive control network, CC1) exhibited a stronger association with age compared with macro-anatomical regions of interest. This may suggest that each NeuroMark FBP A{beta}N represents a spatial network following chemo-architectural uptake with greater biological relevance compared with anatomical ROIs. In summary, the proposed NeuroMark PET approach offers a fully automated framework, providing accurate and reproducible brain A{beta}Ns. This approach enhances our ability to investigate the molecular underpinnings of brain function and pathology, offering an alternative to traditional ROI-based analyses.

neuroscience↗

bioRxiv2025

Identifying Neurobiological Psychosis Biotypes Using Multi-Scale Functional Network Connectivity and its Latent Independent Subspace

This study aims to identify Psychosis Imaging Neurosubtypes (PINs)-- homogeneous subgroups of individuals with psychosis characterized by distinct neurobiology derived from imaging features. Specifically, we utilized resting-state fMRI data from 2103 B-SNIP 1&2 participants (1127 with psychosis, 350 relatives, 626 controls) to compute subject-specific multiscale functional network connectivity (msFNC). We then derived a low-dimensional neurobiological subspace, termed Latent Network Connectivity (LNC), which captured system-wide interconnected multiscale information across three components (cognitive-related, typical, psychosis-related). Projections of psychosis participants msFNC onto this subspace revealed three PINs through unsupervised learning, each with distinct cognitive, clinical, and connectivity profiles, spanning all DSM diagnoses (Schizophrenia, Bipolar, Schizoaffective). PIN-1, the most cognitively impaired, showed Cerebellar-Subcortical and Visual-Sensorimotor hypoconnectivity, alongside Visual-Subcortical hyperconnectivity. Most cognitively preserved PIN-2 showed Visual-Subcortical, Subcortical-Sensorimotor, and Subcortical-Higher Cognition hypoconnectivity. PIN-3 exhibited intermediate cognitive function, showing Cerebellar-Subcortical hypoconnectivity alongside Cerebellar-Sensorimotor and Subcortical-Sensorimotor hyperconnectivity. Notably, 55% of relatives aligned with the same neurosubtype as their affected family members--a significantly higher rate than random chance (p-valueRelatives-to-PIN-1 < 0.001, p-valueRelatives-to-PIN-2 < 0.05, p-valueRelatives-to-PIN-3 < 0.001) compared to a non-significant 37% DSM-based classification, supporting a biological basis of these neurosubtypes. Cognitive performance reliably aligns with distinct brain connectivity patterns, which are also evident in relatives, supporting their construct validity. Our PINs differed from original B-SNIP Biotypes, which were determined from electrophysiological, cognitive, and oculomotor data. These findings underscore the limitations of DSM-based classifications in capturing the biological complexity of psychotic disorders and highlight the potential of imaging-based neurosubtypes to enhance our understanding of the psychosis spectrum.

neuroscience↗

bioRxiv2025

Altered Functional Network Energy Across Multiscale Brain Networks in Preterm vs. Full-Term Subjects: Insights from the Adolescent Brain Cognitive Development (ABCD) Study

Infants born prematurely, or preterm, can experience altered brain connectivity, due in part to incomplete brain development at the time of parturition. Research has also shown structural and functional differences in the brain that persist in these individuals as they enter adolescence when compared to peers who were fully mature at birth. In this study, we examined functional network energy across multiscale functional connectivity in approximately 4600 adolescents from the Adolescent Brain Cognitive Development (ABCD) study who were either preterm or full term at birth. We identified three key brain networks that show significant differences in network energy between preterm and full-term subjects. These networks include the visual network (comprising the occipitotemporal and occipital subnetworks), the sensorimotor network, and the high cognitive network (including the temporoparietal and frontal subnetworks). Additionally, it was demonstrated that full-term subjects exhibit greater instability, leading to more dynamic reconfiguration of functional brain information and increased flexibility across the three identified canonical brain networks compared to preterm subjects. In contrast, those born prematurely show more stable networks but less dynamic and flexible organization of functional brain information within these key canonical networks. In summary, measuring multiscale functional network energy offered insights into the stability of canonical brain networks associated with subjects born prematurely. These findings enhance our understanding of how early birth impacts brain development.

neuroscience↗

bioRxiv2025

Neuromark dFNC Patterns: A fully automated pipeline to estimate subject-specific states from rs-fMRI data via constrained ICA of dFNC in +100k Subjects

Resting-state functional MRI (rs-fMRI) provides valuable insights into brain function during rest, but faces challenges in clinical applications due to individual differences in functional connectivity. While Independent Component Analysis (ICA) is commonly used, it struggles to balance individual variations with inter-subject information. To address this, constrained ICA (cICA) approaches have been developed using templates from multiple datasets to improve accuracy and comparability. In this study, we collected rs-fMRI data from 100,517 individuals across diverse datasets. Data were preprocessed through a standard fMRI pipeline. Our method first used replicable fMRI component templates as priors in constrained ICA (the NeuroMark pipeline), then estimated dynamic functional network connectivity (dFNC). Through clustering analysis, we generated replicable dFNC states, which were then used as priors in constrained ICA to automatically estimate subject-specific states from new subjects.This approach provides a robust framework for analyzing individual rs-fMRI data while maintaining consistency across large datasets, potentially advancing clinical applications of rs-fMRI.

bioengineering↗

bioRxiv2025

Deciphering Multiway Multiscale Brain Network Connectivity:Insights from Birth to 6 Months

Converging evidence suggests that understanding the human brain requires more than just examining pairwise functional brain interactions. The human brain is a complex, nonlinear system, and focusing solely on linear pairwise functional connectivity often overlooks important nonlinear and higher-order relationships. Infancy is a critical period marked by significant brain development that could contribute to future learning, health, and life success. Exploring higher-order functional relationships in the brain can provide insight into brain function and development. To the best of our knowledge, there is no existing research on multiway, multiscale brain network interactions in infants. In this study, we comprehensively investigate the interactions among brain intrinsic connectivity networks (ICNs), including both pairwise (pair-FNC) and triple relationships (tri-FNC). We focused on an infant dataset collected between birth and six months, a critical period for brain maturation. Our results revealed significant hierarchical, multiway, multiscale brain functional network interactions in the infant brain. These findings suggest that tri-FNC provide additional insights beyond what pairwise interactions reveal during early brain development. The tri-FNC predominantly involve the default mode, sensorimotor, visual, limbic, language, salience, and central executive domains. Notably, these triplet networks align with the classical triple network model of the human brain, which includes the default mode network, the salience network, and the central executive network. This suggests that the brain network system might already be initially established during the first six months of infancy. Interestingly, tri-FNC in the default mode and salience domains showed significantly stronger nonlinear interactions with age compared to pair-FNC. We also found that pair-FNC were less effective at detecting these networks. The present study suggests that exploring tri-FNC can offer additional insights beyond pair-FNC by capturing higher-order nonlinear interactions, potentially yielding more reliable biomarkers to characterize developmental trajectories.

neuroscience↗

bioRxiv2025

Conditional Denoising Diffusion Probabilistic Models with Attention for Subject-Specific Brain Network Synthesis

The development of diffusion models, such as Glide, DALLE 2, Imagen, and Stable Diffusion, marks a significant advancement in generative AI for image synthesis. In this paper, we introduce a novel framework for synthesizing intrinsic connectivity networks (ICNs) by utilizing the nonlinear capabilities of denoising diffusion probabilistic models (DDPMs). This approach builds upon and extends traditional linear methods, such as independent component analysis (ICA), which are commonly used in neuroimaging studies. A central contribution of our work is the integration of attention mechanisms into conditional DDPMs, enabling the generation of subject-specific 3D ICNs. Conditioning the resting-state fMRI (rs-fMRI) data on the corresponding ICNs enables the extraction of individualized brain connectivity patterns, effectively capturing within-subject and between-subject variability. Unlike prior models limited to 2D visualization, this framework generates 3D representations, providing a more comprehensive depiction of ICNs. The models performance is validated on an external dataset to prevent over-fitting and for overall generalizability. Furthermore, comparative evaluations also demonstrate that the proposed DDPM-based approach outperforms state-of-the-art generative models in producing more detailed and accurate ICNs, as validated through qualitative assessments.

neuroscience↗