Partial entropy decomposition reveals higher-order information structures in human brain activity

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(30)

Published: July 19, 2023

The standard approach to modeling the human brain as a complex system is with network, where basic unit of interaction pairwise link between two regions. While powerful, this limited by inability assess higher-order interactions involving three or more elements directly. In work, we explore method for capturing dependencies in multivariate data: partial entropy decomposition (PED). Our decomposes joint whole into set nonnegative atoms that describe redundant, unique, and synergistic compose system's structure. PED gives insight mathematics functional connectivity its limitation. When applied resting-state fMRI data, find robust evidence synergies are largely invisible analyses. can also be localized time, allowing frame-by-frame analysis how distributions redundancies change over course recording. We different ensembles regions transiently from being redundancy-dominated synergy-dominated temporal pattern structured time. These results provide strong there exists large space unexplored structures data have been missed focus on bivariate network models. This structure dynamic time likely will illuminate interesting links behavior. Beyond brain-specific application, provides very general understanding variety systems.

Language: Английский

---

Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Feb. 8, 2024

Abstract Brain dynamic functional connectivity characterises transient connections between brain regions. Features of dynamics have been linked to emotion and cognition in adult individuals, atypical patterns associated with neurodevelopmental conditions such as autism. Although reliable networks consistently identified neonates, little is known about the early development connectivity. In this study we characterise magnetic resonance imaging (fMRI) first few weeks postnatal life term-born ( n = 324) preterm-born 66) individuals. We show that a landscape already established by time birth human brain, characterised six states neonatal changing through period. The pattern infants, social, sensory, repetitive behaviours measured Quantitative Checklist for Autism Toddlers (Q-CHAT) scores at 18 months age.

Language: Английский

---

Individualized event structure drives individual differences in whole-brain functional connectivity

NeuroImage, Journal Year: 2022, Volume and Issue: 252, P. 118993 - 118993

Published: Feb. 19, 2022

Resting-state functional connectivity is typically modeled as the correlation structure of whole-brain regional activity. It studied widely, both to gain insight into brain's intrinsic organization but also develop markers sensitive changes in an individual's cognitive, clinical, and developmental state. Despite this, origins drivers connectivity, especially at level densely sampled individuals, remain elusive. Here, we leverage novel methodology decompose its precise framewise contributions. Using two dense sampling datasets, investigate individualized focusing specifically on role brain network "events" - short-lived peaked patterns high-amplitude cofluctuations. a statistical test identify events empirical recordings. We show that cofluctuation expressed during are repeated across multiple scans same individual represent idiosyncratic variants template group level. Lastly, propose simple model based event cofluctuations, demonstrating group-averaged cofluctuations suboptimal for explaining participant-specific connectivity. Our work complements recent studies implicating brief instants primary static, extends those studies, individualized, positing dynamic basis

Language: Английский

---

High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle

Network Neuroscience, Journal Year: 2023, Volume and Issue: 7(3), P. 1181 - 1205

Published: Jan. 1, 2023

Abstract Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and connectivity. However, how hormonal impact fast changes network organization over short timescales remains unknown. Here, we leverage a recently proposed framework for modeling co-fluctuations activity of pairs regions at framewise timescale. In previous showed time points corresponding to high-amplitude disproportionately contributed time-averaged functional connectivity pattern these co-fluctuation patterns could be clustered into low-dimensional set recurring “states.” assessed relationship states quotidian variation concentrations. Specifically, were interested whether frequency with which occurred was related concentration. We addressed this question using dense-sampling dataset (N = 1 brain). dataset, single individual sampled course two states: natural menstrual cycle while subject underwent selective progesterone suppression via oral contraceptives. During each cycle, 30 daily resting-state fMRI scans blood draws. Our analysis imaging data revealed repeating states. found state scan sessions significantly correlated follicle-stimulating luteinizing also constructed representative networks session only “event frames”—those when an event determined occurred. weights specific subsets connections robustly concentration not hormones, but estradiol.

Language: Английский

---

Optimized attention-enhanced U-Net for autism detection and region localization in MRI

Psychiatry Research Neuroimaging, Journal Year: 2025, Volume and Issue: 349, P. 111970 - 111970

Published: March 14, 2025

Language: Английский

---

Living on the edge: network neuroscience beyond nodes

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 27(11), P. 1068 - 1084

Published: Sept. 15, 2023

Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This come at expense anatomical functional connections that link these to one another. A new perspective namely emphasizes 'edges' may prove fruitful in addressing outstanding questions network neuroscience. We highlight recently proposed 'edge-centric' method review its current applications, merits, limitations. also seek establish conceptual mathematical links between this previously approaches science neuroimaging literature. conclude by presenting several avenues for future work extend refine existing edge-centric analysis.

Language: Английский

---

BOLD cofluctuation ‘events’ are predicted from static functional connectivity

NeuroImage, Journal Year: 2022, Volume and Issue: 260, P. 119476 - 119476

Published: July 14, 2022

Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low points. This suggested that events might be a discrete, temporally sparse signal drives connectivity (FC) over the timeseries. However, different, not yet explored possibility is differences between are driven by sampling variability on constant, static, noisy signal. Using combination real and simulated data, we examined relationship structure asked if this was unique, or it could arise from alone. First, show discrete - there gradually increasing cofluctuation; ∼50% samples very strong structure. Second, using simulations predicted static FC. Finally, randomly selected can capture about as well events, largely because their temporal spacing. Together, these results suggest that, while exhibit particularly representations FC, little evidence unique timepoints drive FC Instead, parsimonious explanation for data but noisy,

Language: Английский

---

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

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: Jan. 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.

Language: Английский

---

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

Human Brain Mapping, Journal Year: 2024, Volume and Issue: 45(5)

Published: March 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.

Language: Английский

---

Transdiagnostic network alterations and associated neurotransmitter signatures across major psychiatric disorders in adolescents: Evidence from edge-centric analysis of time-varying functional brain networks

Journal of Affective Disorders, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

---