Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains

Communications Biology, Год журнала: 2024, Номер 7(1)

Опубликована: Янв. 24, 2024

Abstract Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no applied this data collected from model organisms. Here, we analyze structural and functional imaging lightly anesthetized mice through lens. We find evidence of “bursty” events - brief periods high-amplitude connectivity. Further, show that on a per-frame basis best explain static FC can be divided into series hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas largely adhere the boundaries algorithmically detected brain systems. then investigate connectivity undergirding patterns. induce modular bipartitions inter-areal axonal projections. Finally, replicate these same findings dataset. In summary, report recapitulates organism many phenomena observed previously analyses data. However, unlike subjects, murine nervous system is amenable invasive experimental perturbations. Thus, sets stage for future investigation causal origins co-fluctuations. Moreover, cross-species consistency reported enhances likelihood translation.

Язык: Английский

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Higher‐order functional connectivity analysis of resting‐state functional magnetic resonance imaging data using multivariate cumulants

Human Brain Mapping, Год журнала: 2024, Номер 45(5)

Опубликована: Март 23, 2024

Abstract Blood‐level oxygenation‐dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study connectivity in human brain. Most research date has focused on between pairs of brain regions. However, attention recently turned towards involving more than two regions, that is, higher‐order connectivity. It not yet clear how can best be quantified. The measures are currently use cannot distinguish pairwise (i.e., second‐order) and We show genuine quantified by using multivariate cumulants. explore cumulants for quantifying performance block bootstrapping statistical inference. In particular, we formulate a generative model fMRI signals exhibiting it assess bias, standard errors, detection probabilities. Application resting‐state data from Human Connectome Project demonstrates spontaneous organized into networks distinct second‐order networks. clinical cohort patients with multiple sclerosis further used classify disease groups explain behavioral variability. Hence, present novel framework reliably estimate which constructing hyperedges, finally, readily applied populations neuropsychiatric or cognitive neuroscientific experiments.

Язык: Английский

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Multi-policy models of interregional communication in the human connectome

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Май 9, 2022

Network models of communication, e.g. shortest paths, diffusion, navigation, have become useful tools for studying structure-function relationships in the brain. These generate estimates communication efficiency between all pairs brain regions, which can then be linked to correlation structure recorded activity, i.e. functional connectivity (FC). At present, however, a number limitations, including difficulty adjudicating and absence generic framework modeling multiple interacting policies at regional level. Here, we present that allows us incorporate region-specific fit them empirical FC. Briefly, show many policies, paths greedy modeled as biased random walks, enabling these incorporated into same multi-policy model alongside unbiased processes, diffusion. We outperform existing measures while yielding neurobiologically interpretable preferences. Further, explain majority variance time-varying patterns Collectively, our represents an advance network-based establishes strong link Our findings open up new avenues future inquiries flexible anatomically-constrained communication.

Язык: Английский

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Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity

Communications Biology, Год журнала: 2023, Номер 6(1)

Опубликована: Июль 10, 2023

Abstract Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within duration a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed computation an edge time series (ETS) and their derivatives. Evidence suggests that is driven by few points high-amplitude co-fluctuation (HACF) ETS, which may also contribute disproportionately interindividual differences. However, it remains unclear what degree different actually brain-behaviour associations. Here, we systematically evaluate this question assessing predictive utility estimates at levels using machine learning (ML) approaches. We demonstrate lower intermediate provide overall highest subject specificity as well capacity individual-level phenotypes.

Язык: Английский

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A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Сен. 21, 2023

Abstract Large-scale brain networks reveal structural connections as well functional synchronization between distinct regions of the brain. The latter, referred to connectivity (FC), can be derived from neuroimaging techniques such magnetic resonance imaging (fMRI). FC studies have shown that are severely disrupted by stroke. However, since data usually large and high-dimensional, extracting clinically useful information this vast amount is still a great challenge, our understanding consequences stroke remains limited. Here, we propose dimensionality reduction approach simplify analysis complex neural data. By using autoencoders, find low-dimensional representation encoding fMRI which preserves typical anomalies known present in patients. employing latent representations emerging enhanced patients’ diagnostics severity classification. Furthermore, showed how increased accuracy recovery prediction.

Язык: Английский

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