Long-term stability of the cortical volumetric profile and the functional human connectome throughout childhood and adolescence

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

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

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Short- and long-term effects of chronic toluene exposure on spatial memory in adolescent and adult male Wistar rats

Addiction to toluene-containing volatile inhalants is of significant medical and social concern, particularly among youth. These concerns are underscored by the fact that the majority of adult abusers of toluene started as teenagers. Surprisingly, however, the lasting effects of chronic toluene exposure, especially in various age groups, have not been well investigated. Recently, we reported that adolescent and adult male Wistar rats show differential responses to chronic toluene exposure in recognition memory tasks. Since different cognitive functions may be differentially affected by drugs of abuse, we used the same model to evaluate the short- and long-term effects of chronic toluene on spatial learning and memory using Morris water maze. Daily exposure to toluene (2000 ppm) for 40 days (5 min/day) resulted in age-dependent behavioral changes. For example, only adolescent animals showed a decrease in time and distance travelled to find the hidden platform 24 h after the last toluene exposure. In contrast, only adult rats exhibited a decrease in acquisition time and distance travelled at 90 days' post toluene exposure. Our data provide further support for the contention that age-dependent responses should be taken into consideration in interventional attempts to overcome specific detrimental consequences of chronic toluene exposure.

Brain Morphology Predictors of Alcohol, Tobacco, and Cannabis Use in Adolescence: A Systematic Review

In the last decade, extensive research has emerged on the predictive value of brain morphology for substance use initiation and related problems during adolescence. This systematic review provides an overview of longitudinal studies on pre-existing brain variations and later initiation of alcohol, tobacco, and cannabis use (N = 18). Adolescent structural neuroimaging studies that started before substance use initiation suggest that a smaller anterior cingulate cortex (ACC) volume, thicker or smaller superior frontal gyrus, and larger nucleus accumbens (NAcc) volume are associated with future alcohol use. Also, both smaller and larger orbitofrontal cortex (OFC) volumes were associated with future cannabis and combined alcohol/cannabis use. Smaller amygdala volumes were related to future daily tobacco smoking. These findings could point to specific vulnerabilities for adolescent substance use, as these brain areas are involved in cognitive control (ACC), reward (NAcc), motivation (OFC), and emotional memory (amygdala). However, the reported findings were inconsistent in directionality and laterality, and the largest study on alcohol use predictors reported null findings. Therefore, large population-based longitudinal studies should investigate the robustness and mechanisms of these associations. We suggested future research directions regarding sample selection, timing of baseline and follow-up measurements, and a harmonization approach of study methods.

Functional connectomes become more longitudinally self-stable, but not more distinct from others, across early childhood

Functional connectomes, as measured with functional magnetic resonance imaging (fMRI), are highly individualized, and evidence suggests this individualization may increase across childhood. A connectome can become more individualized either by increasing self-stability or decreasing between-subject-similarity. Here we used a longitudinal early childhood dataset to investigate age associations with connectome self-stability, between-subject-similarity, and developmental individualization, defined as an individual's self-stability across a 12-month interval relative to their between-subject-similarity. fMRI data were collected during an 18-minute passive viewing scan from 73 typically developing children aged 4-7 years, at baseline and 12-month follow-up. We found that young children had highly individualized connectomes, with sufficient self-stability across 12-months for 98% identification accuracy. Linear models showed a significant relationship between age and developmental individualization across the whole brain and in most networks. This association appeared to be largely driven by an increase in self-stability with age, with only weak evidence for relationships between age and similarity across participants. Together our findings suggest that children's connectomes become more individualized across early childhood, and that this effect is driven by increasing self-stability rather than decreasing between-subject-similarity.

A longitudinal study of functional connectome uniqueness and its association with psychological distress in adolescence

Each human brain has a unique functional synchronisation pattern (functional connectome) analogous to a fingerprint that underpins brain functions and related behaviours. Here we examine functional connectome (whole-brain and 13 networks) maturation by measuring its uniqueness in adolescents who underwent brain scans longitudinally from 12 years of age every four months. The uniqueness of a functional connectome is defined as its ratio of self-similarity (from the same subject at a different time point) to the maximal similarity-to-others (from a given subject and any others at a different time point). We found that the unique whole brain connectome exists in 12 years old adolescents, with 92% individuals having a whole brain uniqueness value greater than one. The cingulo-opercular network (CON; a long-acting 'brain control network' configuring information processing) demonstrated marginal uniqueness in early adolescence with 56% of individuals showing uniqueness greater than one (i.e., more similar to her/his own CON four months later than those from any other subjects) and this increased longitudinally. Notably, the low uniqueness of the CON correlates (β = -18.6, FDR-Q < < 0.001) with K10 levels at the subsequent time point. This association suggests that the individualisation of CON network is related to psychological distress levels. Our findings highlight the potential of longitudinal neuroimaging to capture mental health problems in young people who are undergoing profound neuroplasticity and environment sensitivity period.