Null models in network neuroscience

Nature reviews. Neuroscience, Journal Year: 2022, Volume and Issue: 23(8), P. 493 - 504

Published: May 31, 2022

Language: Английский

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Macroscale coupling between structural and effective connectivity in the mouse brain

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 7, 2024

Abstract Exploring how the emergent functional connectivity (FC) relates to underlying anatomy (structural connectivity, SC) is one of major goals modern neuroscience. At macroscale level, no one-to-one correspondence between structural and links seems exist. And we posit that better understand their coupling, two key aspects should be considered: directionality connectome limitations in explaining networks functions through an undirected measure such as FC. Here, employed accurate directed SC mouse brain acquired viral tracers compared it with single-subject effective (EC) matrices derived from a dynamic causal model (DCM) applied whole-brain resting-state fMRI data. We analyzed deviates EC quantified respective couplings by conditioning on strongest links. found when links, obtained coupling follows unimodal-transmodal hierarchy. Whereas reverse not true, there are strong within high-order cortical areas corresponding This mismatch even more clear across networks; only sensory motor did observe connections align terms both strength.

Language: Английский

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Benchmarking methods for mapping functional connectivity in the brain

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 8, 2024

The networked architecture of the brain promotes synchrony among neuronal populations and emergence coherent dynamics. These communication patterns can be comprehensively mapped using noninvasive functional imaging, resulting in connectivity (FC) networks. Despite its popularity, FC is a statistical construct operational definition arbitrary. While most studies use zero-lag Pearson's correlations by default, there exist hundreds pairwise interaction statistics broader scientific literature that used to estimate FC. How organization matrix varies with choice statistic fundamental methodological question affects all this rapidly growing field. Here we benchmark topological geometric organization, neurobiological associations, cognitive-behavioral relevance matrices computed large library 239 statistics. We investigate how canonical features networks vary statistic, including (1) hub mapping, (2) weight-distance trade-offs, (3) structure-function coupling, (4) correspondence other neurophysiological networks, (5) individual fingerprinting, (6) brain-behavior prediction. find substantial quantitative qualitative variation across methods. Throughout, observe measures such as covariance (full correlation), precision (partial correlation) distance display multiple desirable properties, close structural connectivity, capacity differentiate individuals predict differences behavior. Using information flow decomposition, methods may arise from differential sensitivity underlying mechanisms inter-regional communication, some more sensitive redundant synergistic flow. In summary, our report highlights importance tailoring specific mechanism research question, providing blueprint for future optimize their method.

Language: Английский

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mGNN-bw: Multi-scale Graph Neural Network Based on Biased Random Walk Path Aggregation for ASD Diagnosis

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2025, Volume and Issue: 33, P. 900 - 910

Published: Jan. 1, 2025

In recent years, computationally assisted diagnosis for classifying autism spectrum disorder (ASD) and typically developing (TD) individuals based on neuroimaging data, such as functional magnetic resonance imaging (fMRI), has garnered significant attention. Studies have shown that long-range connectivity patterns in ASD patients exhibit abnormalities, individual brain networks display considerable heterogeneity. However, current graph neural (GNNs) used research failed to adequately capture overlooked differences. To address these limitations, this study proposes a novel multi-scale network biased random walks (mGNN-bw). The model introduces co-optimization strategy between sub-models the main model, leveraging node pooling scores from guide walks, effectively capturing connectivity. By constructing high-order through path encoding aggregation, integrating them with low-order Pearson correlation, achieves robust feature representation. Experimental results publicly available ABIDE I dataset demonstrate superior performance of our approach, achieving accuracy rates 74.8% 73.2% using CC200 AAL atlases, respectively, outperforming existing methods. Additionally, identifies key ASD-associated regions, including frontal lobe, insula, cingulate, calcarine, supported by research. proposed method significantly contributes clinical ASD.

Language: Английский

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Macroscale coupling between structural and effective connectivity in the mouse brain

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Feb. 22, 2023

Abstract How the emergent functional connectivity (FC) relates to underlying anatomy (structural connectivity, SC) is one of biggest questions modern neuroscience. At macro-scale level, no one-to-one correspondence between structural and links seems exist. And we posit that better understand their coupling, two key aspects should be taken into account: directionality connectome limitations describing network functions in terms FC. Here, employed an accurate directed SC mouse brain obtained by means viral tracers, related it with single-subject effective (EC) matrices computed applying a recently developed DCM whole-brain resting-state fMRI data. We analyzed how deviates from EC quantified couplings conditioning both on strongest links. found when links, coupling follows unimodal-transmodal hierarchy. Whereas reverse not true, as there are strong within high-order cortical areas corresponding This mismatch even more clear across networks. Only connections sensory motor networks align strength.

Language: Английский

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Abnormal brain regional activity in acute thyrotoxic myopathy assessed by resting-state functional MRI

Acta Radiologica, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 24, 2024

Background The neurophysiological mechanisms underlying manifestations of bulbar paralysis in acute thyrotoxic myopathy (ATM) and the afflicted brain areas are unclear. Purpose We used resting-state functional magnetic resonance imaging (rs-fMRI) to evaluate regional activities patients with ATM. Material Methods In total, 16 ATM, hyperthyroidism without healthy controls underwent MRI scans. By calculating fractional amplitude low-frequency fluctuation (fALFF), homogeneity (ReHo), connectivity (FC), we assessed variations cerebral activity. correlation between indexes clinical assessments was also explored. Results Compared hyperthyroid patients, ATM had stronger ReHo left precentral gyrus, reduced orbitofrontal gyrus (OFG), decreased FC gyri, superior frontal (SFG), middle (MFG). Patients showed fALFF right SFG bilateral supplementary motor area (SMA). A significantly MFG, orbital part interior observed compared controls. Additionally, values were positively correlated serum thyroid-related hormones antibodies. Conclusion findings rs-fMRI demonstrate that particular regions’ activity aberrant individuals especially area. This finding may help better understanding pathophysiology

Language: Английский

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Representing Functional Connectivity with Structural Detour: A New Perspective to Decipher Structure-Function Coupling Mechanism

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 367 - 377

Published: Jan. 1, 2024

Language: Английский

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Functional gradient dysfunction in drug-naïve first-episode schizophrenia and its correlation with specific transcriptional patterns and treatment predictions

Psychological Medicine, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 13

Published: Nov. 18, 2024

First-episode schizophrenia (FES) is a progressive psychiatric disorder influenced by genetics, environmental factors, and brain function. The functional gradient deficits of drug-naïve FES its relationship to gene expression profiles treatment outcomes are unknown.

Language: Английский

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Analogous cortical reorganization accompanies entry into states of reduced consciousness during anesthesia and sleep

Cerebral Cortex, Journal Year: 2023, Volume and Issue: 33(17), P. 9850 - 9866

Published: June 22, 2023

Abstract Theories of consciousness suggest that brain mechanisms underlying transitions into and out unconsciousness are conserved no matter the context or precipitating conditions. We compared signatures these using intracranial electroencephalography in neurosurgical patients during propofol anesthesia overnight sleep found strikingly similar reorganization human cortical networks. computed “effective dimensionality” normalized resting state functional connectivity matrix to quantify network complexity. Effective dimensionality decreased stages reduced (anesthesia unresponsiveness, N2 N3 sleep). These changes were not region-specific, suggesting global reorganization. When data embedded a low-dimensional space which proximity represents similarity, we observed greater distances between regions consciousness, individual recording sites became closer their nearest neighbors. corresponded differentiation integration correlated with decreases effective dimensionality. This constitutes neural signature states is common sleep. results establish framework for understanding correlates practical evaluation loss recovery consciousness.

Language: Английский

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Localizing the Sources of Diffusion Mediating Structure-Function Mapping Using Graph Diffusion Wavelets

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 13, 2024

ABSTRACT The intricate link between brain functional connectivity (FC) and structural (SC) is explored through models performing diffusion on SC to derive FC, using varied methodologies from single multiple graph kernels. However, existing studies have not correlated scales with specific regions of interest (RoIs), limiting the applicability diffusion. We propose a novel approach heat wavelets learn appropriate scale for each RoI accurately estimate SC-FC mapping. Using open HCP dataset, we achieve an average Pearson’s correlation value 0.833, surpassing state-of-the-art methods prediction FC. It important note that proposed architecture entirely linear, computationally efficient, notably demonstrates power-law distribution scales. Our results show bilateral frontal pole, by virtue it having large scale, forms community structure. finding in line current literature role pole resting-state networks. Overall, underscore potential wavelet framework understanding how structure leads connectivity. AUTHOR SUMMARY In network paradigm structure-to-function mapping, noticed limitations such as manually decided absence RoI-level analysis. addressed this problem independently developing multiscale multiresolution property. Each region associated defines extent spatial communication. wavelets, are able predict connectome (SoTA) results. observe follow degree distribution, which indicative scale-free process brain. dominant member various networks, our model associate higher region. method only excels downstream task but also provides insights into structure-function relation.

Language: Английский

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