Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months

Journal of Neuroscience, Год журнала: 2021, Номер 42(3), С. 377 - 389

Опубликована: Ноя. 17, 2021

The functional connectome fingerprint is a cluster of individualized brain connectivity patterns that are capable distinguishing one individual from others. Although its existence has been demonstrated in adolescents and adults, whether such exist during infancy barely investigated despite importance identifying the origin intrinsic potentially mirror distinct behavioral phenotypes. To fill this knowledge gap, capitalizing on longitudinal high-resolution structural resting-state MRI dataset with 104 human infants (53 females) 806 scans (age, 16–876 d) infant-specific parcellation maps, we observe may since keeps stable over months early development. Specifically, achieve an ∼78% identification rate by using ∼5% selected connections, compared best 60% without connection selection. frontoparietal networks recognized as most contributive adult fingerprinting retain their superiority being widely acknowledged rapidly developing systems childhood. stability further validated adjacent age groups. Moreover, show infant can reach similar accuracy predicting learning composite scores whole-brain connectome, again resembling observations adults highlighting relevance to cognitive performance. For first time, these results suggest each unique marker SIGNIFICANCE STATEMENT Functional featuring rapid development remains almost uninvestigated even though it essential for understanding individual-level pattern organization relationship With infant-tailored selection validation strategy, strive provide delineation examining existence, stability, We 2 years. identified key connections also verified be highly predictive score prediction, which reveals association between

Язык: Английский

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Network controllability of structural connectomes in the neonatal brain

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Сен. 19, 2023

Abstract White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with moves between distinct mental states based on white connectivity. However, it remains unclear how networks support functions at birth, a time of rapid changes in Here, we investigate development controllability during perinatal period and effect preterm birth 521 neonates. We provide evidence that elements are exhibited infant’s as early third trimester develop rapidly across period. Preterm disrupts altered energy required to drive state transitions different levels. In addition, is associated ability 18 months. Our results suggest develops but could environmental impacts like birth.

Язык: Английский

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Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation

NeuroImage, Год журнала: 2024, Номер 295, С. 120660 - 120660

Опубликована: Май 28, 2024

The topological organization of the macroscopic cortical networks are important for development complex brain functions. However, how morphometric develops during third trimester and whether it demonstrates sexual individual differences at this particular stage remain unclear. Here, we constructed similarity network (MSN) based on morphological microstructural features derived from multimodal MRI two independent cohorts (cross-sectional longitudinal) scanned 30-44 postmenstrual weeks (PMW). Sex difference inter-individual variations MSN were also examined these cohorts. cross-sectional analysis revealed that both integration segregation changed in a nonlinear biphasic trajectory, which was supported by results obtained longitudinal analysis. community structure showed remarkable consistency between bilateral hemispheres maintained stability across PMWs. Connectivity within primary cortex strengthened faster than high-order communities. Compared to females, male neonates significant reduction participation coefficient prefrontal parietal cortices, while their overall architecture remained comparable. Furthermore, using as features, achieved over 65% accuracy identifying an term-equivalent age images acquired after birth, vice versa. These findings provide comprehensive insights into throughout perinatal cortex, enhancing our understanding establishment neuroanatomical early life.

Язык: Английский

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Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction

Neuropsychopharmacology, Год журнала: 2024, Номер 50(1), С. 114 - 123

Опубликована: Авг. 15, 2024

Язык: Английский

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Toward a more informative representation of the fetal–neonatal brain connectome using variational autoencoder

eLife, Год журнала: 2023, Номер 12

Опубликована: Май 15, 2023

Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation fetal–neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike brain, fetal newborn develops extraordinarily rapidly, far outpacing any other development period across life span. Consequently, conventional computational models may not adequately capture these accelerated complex neurodevelopmental during this critical along prenatal-neonatal continuum. obtain a nuanced understanding development, including nonlinear growth, for first time, we developed quantitative, systems-wide representations activity large sample (>500) fetuses, preterm, full-term neonates using an unsupervised deep generative model called variational autoencoder (VAE), shown be superior representing resting-state healthy adults. Here, demonstrated that features, is, latent variables, derived with VAE pretrained rsfMRI human adults, carried important individual neural signatures, leading improved representation maturational patterns more accurate stable age prediction neonate cohort compared models. Using decoder, also revealed distinct networks spanning sensory default mode networks. VAE, are able reliably quantify complex, connectivity. This will lay foundation detailed mapping aberrant signatures their origins life.

Язык: Английский

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Structural and functional connectome relationships in early childhood

Developmental Cognitive Neuroscience, Год журнала: 2023, Номер 64, С. 101314 - 101314

Опубликована: Окт. 14, 2023

There is strong evidence that the functional connectome highly related to white matter in older children and adults, though little known about structure-function relationships early childhood. We investigated development of cortical coupling longitudinally scanned at 1, 2, 4, 6 years age (N = 360) a comparison sample adults 89). also applied novel graph convolutional neural network-based deep learning model with new loss function better capture inter-subject heterogeneity predict an individual's connectivity from corresponding structural connectivity. found regional patterns childhood were consistent adult patterns. In addition, our improved prediction individual its counterpart compared existing models.

Язык: Английский

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Separating Group- and Individual-level Brain Signatures in the Newborn Functional Connectome: A Deep Learning Approach

NeuroImage, Год журнала: 2024, Номер 299, С. 120806 - 120806

Опубликована: Авг. 23, 2024

Recent studies indicate that differences in cognition among individuals may be partially attributed to unique brain wiring patterns. While functional connectivity (FC)-based fingerprinting has demonstrated high accuracy identifying adults, early on neonates suggest individualized FC signatures are absent. We posit individual uniqueness is present neonatal data and conventional linear models fail capture the rapid developmental trajectories characteristic of newborn brains. To explore this hypothesis, we employed a deep generative model, known as variational autoencoder (VAE), leveraging two extensive public datasets: one comprising resting-state MRI (rs-fMRI) scans from 100 adults other 464 neonates. VAE trained rs-fMRI both newborns produced superior age prediction performance (with r between predicted- actual ∼ 0.7) identification (∼45 %) compared solely adult or data. The model also showed significantly higher than (=10∼30 %). Importantly, differentiated connections reflecting age-related changes those indicative uniqueness, distinction not possible with models. Moreover, derived 20 latent variables, each corresponding distinct patterns cortical network (CFNs). These CFNs varied their representation maturation signatures; notably, certain failed neurodevelopmental traits, fact, exhibited signatures. associated neurodevelopment predominantly encompassed unimodal regions such visual sensorimotor areas, whereas linked spanned multimodal transmodal regions. VAE's capacity extract features beyond capabilities positions it valuable tool for delineating cognitive traits inherent exploring imaging phenotypes.

Язык: Английский

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Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases

PLoS Computational Biology, Год журнала: 2025, Номер 21(1), С. e1012743 - e1012743

Опубликована: Янв. 21, 2025

Neurodegenerative diseases are a group of disorders characterized by progressive degeneration or death neurons. The complexity clinical symptoms and irreversibility disease progression significantly affects individual lives, leading to premature mortality. prevalence neurodegenerative keeps increasing, yet the specific pathogenic mechanisms remain incompletely understood effective treatment strategies lacking. In recent years, convergent experimental evidence supports “prion-like transmission” assumption that abnormal proteins induce misfolding normal proteins, these misfolded propagate throughout neural networks cause neuronal death. To elucidate this dynamic process in vivo from computational perspective, researchers have proposed three connectome-based biophysical models simulate spread pathological proteins: Network Diffusion Model, Epidemic Spreading agent-based Susceptible-Infectious-Removed model. These demonstrated promising predictive capabilities. This review focuses on explanations their fundamental principles applications. Then, we compare strengths weaknesses models. Building upon foundation, introduce new directions for model optimization propose unified framework evaluation We expect could lower entry barrier field, accelerate optimization, thereby advance translation

Язык: Английский

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Measuring the effects of motion corruption in fetal fMRI

Human Brain Mapping, Год журнала: 2025, Номер 46(2)

Опубликована: Янв. 23, 2025

Abstract Irregular and unpredictable fetal movement is the most common cause of artifacts in utero functional magnetic resonance imaging (fMRI), affecting analysis limiting our understanding early brain development. The accurate detection corrupted connectivity (FC) resulting from motion or preprocessing, instead neural activity, a prerequisite for reliable valid FC Approaches to address this problem adult data are limited utility fMRI. In study, we evaluate novel technique robust computational assessment artifacts, quantitative comparison regression models artifact removal analysis. It exploits association between dynamic non‐stationarity movement, detect residual noise. To validate detail, used parametric generative model events fMRI blood oxygenation level‐dependent (BOLD) signal. We conducted systematic evaluation 11 commonly sample 70 fetuses with gestational age 19–39 weeks. Results demonstrate that proposed method has better accuracy identifying compared methods designed adults. technique, suggests censoring, global signal anatomical component‐based effective compensating motion. benchmarking realistic BOLD enables investigators conducting effectively quantify impact alternative strategies mitigating impact. code publicly available at: https://github.com/cirmuw/fetalfMRIproc .

Язык: Английский

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The overgrowth of structure-function coupling in premature brain during infancy

Developmental Cognitive Neuroscience, Год журнала: 2025, Номер 72, С. 101535 - 101535

Опубликована: Фев. 21, 2025

Язык: Английский

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