Brain Development and Aging

The first 1000 days, from conception to the first 3 postnatal years1, are critical periods during which the human brain undergoes a remarkable process of growth and reorganization2. One of the most important aspects of this process is to unravel the developmental rules of segregation and integration of functional connectomes. Understanding how these processes mature during this critical period is a crucial step in elucidating the mechanisms underlying typical and atypical development. Here, we investigated the continuous, longitudinal developmental process of functional segregation and integration during the first 1000 days and the potential genetic contributions underlying connectome growth. 

Cortical maturation from childhood to adolescence plays a crucial role in neurodevelopment, shaping cognition, emotions, and behaviors. Convergent evidence suggests that neurodevelopment proceeds in a hierarchical manner, with heterogeneous structural and functional maturation patterns. However, the relationship between the established static functional patterns and the brain's intrinsic spatiotemporal dynamics remains underexplored. To address this gap, we employ Complex Principal Component Analysis (CPCA), a technique capable of reducing the complexity of high-dimensional spatiotemporal data on multiple development datasets. In this study, we aim to understand how spatiotemporal patterns develop with age, both locally and globally, focusing on three distinct propagation pathways from childhood to adolescence. 

Asymmetry is a key organizing principle of the brain, and has previously been shown to support healthy cognition [1], with alterations in asymmetry observed across neuropsychiatric disorders [2,3]. While asymmetry emerges during fetal development [4], its lifelong dynamics and variability remain unknown [4]. The recent development of normative reference charts for the human brain have enabled individuals to be benchmarked against population-level norms across the lifespan [5]. This study significantly extends this previous work by including regional (left/right) cortical and subcortical brain areas from over 100,000 participants, and comprehensively mapping left-right asymmetry trajectories across the human lifespan and between clinical cohorts. 

Microstructural magnetic resonance imaging (MRI) can be used to measure brain tissue properties in vivo and has been used extensively in scientific research to derive biomarkers sensitive to disease pathology and progression, like normal-appearing white matter alterations in multiple sclerosis (MS) [1] and small vessel disease (SVD) [2] and iron accumulation in the subcortical grey matter in Parkinson's disease (PD) [3]. However, these biomarkers have been seldom implemented in clinical practice. We posit that this limited clinical translation is due, in part, to the difficult interpretability of raw microstructural maps to detect subtle pathological tissue alterations distinct from normal variations. For example, white matter deterioration and subcortical grey matter iron accumulation are both normal age-related phenomena [4,5]. Here, we constructed voxel-wise normative models of microstructural MRI and produced subject-wise z-scored maps of microstructural abnormality relative to these age- and sex-specific averages. These maps bring important context regarding normal brain tissue integrity and permit intuitive visual assessment of brain tissue abnormalities at first glance. 

In brain age prediction (BAP) studies, machine learning, especially deep learning, is commonly used to estimate 'brain age' (BA). The brain age gap (BAG) is measured as the difference between predicted brain age and chronological age (CA) and offers a quantitative measure for assessing normal versus abnormal aging.
Various modalities of brain MRI data, including T1, T2-FLAIR, functional, and diffusion MRI, provide distinct features of brain structure and function that change with aging. T1 MRI is optimal for evaluating morphological changes in the gray matter (GM) and white matter (WM), while the visibility of lesions, such as white matter hyperintensities (WMH) or ischemic stroke lesions, is more pronounced on T2-FLAIR [8]. Recently, studies have employed T2-FLAIR to predict brain age trajectories from the whole WM volume [7] or WMH volumes [2]. However, they do not examine the relationship between the spatial distribution of WMH and aging. Given the distinctive association of cardiovascular diseases with WMHs in deep WM and periventricular WMHs [4], we aimed to examine brain age as predicted by the spatial distribution of WMH. We modeled medial surfaces generated at various depths from the WM-GM boundary to the ventricles and projected T2-FLAIR intensity values onto these medial surfaces. These values at different depth surfaces were then inputted into graph convolutional networks (GCN) to predict brain age. We hypothesize that the BAGs derived from T2-FLAIR signals sampled at various depth levels within WM represent WM-specific brain aging and will be associated with cardiovascular risks.