Development of sensorimotor-visual connectome gradient at birth predicts neurocognitive outcomes at 2 years of age.

Functional connectome gradients represent fundamental organizing principles of the brain. Here, we report the development of the connectome gradients in preterm and term babies aged 31-42 postmenstrual weeks using task-free functional MRI and its association with postnatal cognitive growth. We show that the principal sensorimotor-to-visual gradient is present during the late preterm period and continuously evolves toward a term-like pattern. The global measurements of this gradient, characterized by explanation ratio, gradient range, and gradient variation, increased with age (p < 0.05, corrected). Focal gradient development mainly occurs in the sensorimotor, lateral, and medial parietal regions, and visual regions (p < 0.05, corrected). The connectome gradient at birth predicts cognitive and language outcomes at 2-year follow-up (p < 0.005). These results are replicated using an independent dataset from the Developing Human Connectome Project. Our findings highlight early emergent rules of the brain connectome gradient and their implications for later cognitive growth.

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence.

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

Functional connectome through the human life span.

The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.

Baseline cerebral structural morphology predict freezing of gait in early drug-naïve Parkinson's disease.

Freezing of gait (FOG) greatly impacts the daily life of patients with Parkinson's disease (PD). However, predictors of FOG in early PD are limited. Moreover, recent neuroimaging evidence of cerebral morphological alterations in PD is heterogeneous. We aimed to develop a model that could predict the occurrence of FOG using machine learning, collaborating with clinical, laboratory, and cerebral structural imaging information of early drug-naïve PD and investigate alterations in cerebral morphology in early PD. Data from 73 healthy controls (HCs) and 158 early drug-naïve PD patients at baseline were obtained from the Parkinson's Progression Markers Initiative cohort. The CIVET pipeline was used to generate structural morphological features with T1-weighted imaging (T1WI). Five machine learning algorithms were calculated to assess the predictive performance of future FOG in early PD during a 5-year follow-up period. We found that models trained with structural morphological features showed fair to good performance (accuracy range, 0.67-0.73). Performance improved when clinical and laboratory data was added (accuracy range, 0.71-0.78). For machine learning algorithms, elastic net-support vector machine models (accuracy range, 0.69-0.78) performed the best. The main features used to predict FOG based on elastic net-support vector machine models were the structural morphological features that were mainly distributed in the left cerebrum. Moreover, the bilateral olfactory cortex (OLF) showed a significantly higher surface area in PD patients than in HCs. Overall, we found that T1WI morphometric markers helped predict future FOG occurrence in patients with early drug-naïve PD at the individual level. The OLF exhibits predominantly cortical expansion in early PD.

Structural insight into the individual variability architecture of the functional brain connectome.

Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

Individual identification and individual variability analysis based on cortical folding features in developing infant singletons and twins.

Studying the early dynamic development of cortical folding with remarkable individual variability is critical for understanding normal early brain development and related neurodevelopmental disorders. This study focuses on the fingerprinting capability and the individual variability of cortical folding during early brain development. Specifically, we aim to explore (a) whether the developing neonatal cortical folding is unique enough to be considered as a "fingerprint" that can reliably identify an individual within a cohort of infants; (b) which cortical regions manifest more individual variability and thus contribute more for infant identification; (c) whether the infant twins can be distinguished by cortical folding. Hence, for the first time, we conduct infant individual identification and individual variability analysis involving twins based on the developing cortical folding features (mean curvature, average convexity, and sulcal depth) in 472 neonates with 1,141 longitudinal MRI scans. Experimental results show that the infant individual identification achieves 100% accuracy when using the neonatal cortical folding features to predict the identities of 1- and 2-year-olds. Besides, we observe high identification capability in the high-order association cortices (i.e., prefrontal, lateral temporal, and inferior parietal regions) and two unimodal cortices (i.e., precentral gyrus and lateral occipital cortex), which largely overlap with the regions encoding remarkable individual variability in cortical folding during the first 2 years. For twins study, we show that even for monozygotic twins with identical genes and similar developmental environments, their cortical folding features are unique enough for accurate individual identification; and in some high-order association cortices, the differences between monozygotic twin pairs are significantly lower than those between dizygotic twins. This study thus provides important insights into individual identification and individual variability based on cortical folding during infancy.

CORTICAL FOLDINGPRINTS FOR INFANT IDENTIFICATION.

Cortical folding of the adult brain is highly convoluted and encodes inter-subject variable characteristics. Recent studies suggest that it is useful for individual identification in adults. However, little is known about whether the infant cortical folding, which undergoes dynamic postnatal development, can be used for individual identification. To fill this gap, we propose to explore cortical folding patterns for infant subject identification. This study thus aims to address two important questions in neuroscience: 1) whether the infant cortical folding is unique for individual identification; and 2) considering the region-specific inter-subject variability, which cortical regions are more distinct and reliable for infant identification. To this end, we propose a novel discriminative descriptor of regional cortical folding based on multi-scale analysis of curvature maps via spherical wavelets, called FoldingPrint. Experiments are carried out on a large longitudinal dataset with 1,141 MRI scans from 472 infants. Despite the dramatic development in the first two years, successful identification of 1-year-olds and 2-year-olds using their neonatal cortical folding (with accuracy > 98%) indicates the effectiveness of the proposed method. Moreover, we reveal that regions with high identification accuracy and large inter-subject variability mainly distribute in high-order association cortices.

Spherical U-Net on Cortical Surfaces: Methods and Applications.

Convolutional Neural Networks (CNNs) have been providing the state-of-the-art performance for learning-related problems involving 2D/3D images in Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have a spherical topology in a manifold space, e.g., brain cortical or subcortical surfaces represented by triangular meshes, with large inter-subject and intra-subject variations in vertex number and local connectivity. Hence, there is no consistent neighborhood definition and thus no straightforward convolution/transposed convolution operations for cortical/subcortical surface data. In this paper, by leveraging the regular and consistent geometric structure of the resampled cortical surface mapped onto the spherical space, we propose a novel convolution filter analogous to the standard convolution on the image grid. Accordingly, we develop corresponding operations for convolution, pooling, and transposed convolution for spherical surface data and thus construct spherical CNNs. Specifically, we propose the Spherical U-Net architecture by replacing all operations in the standard U-Net with their spherical operation counterparts. We then apply the Spherical U-Net to two challenging and neuroscientifically important tasks in infant brains: cortical surface parcellation and cortical attribute map development prediction. Both applications demonstrate the competitive performance in the accuracy, computational efficiency, and effectiveness of our proposed Spherical U-Net, in comparison with the state-of-the-art methods.

Exploring folding patterns of infant cerebral cortex based on multi-view curvature features: Methods and applications.

The highly convoluted cortical folding of the human brain is intriguingly complex and variable across individuals. Exploring the underlying representative patterns of cortical folding is of great importance for many neuroimaging studies. At term birth, all major cortical folds are established and are minimally affected by the complicated postnatal environments; hence, neonates are the ideal candidates for exploring early postnatal cortical folding patterns, which yet remain largely unexplored. In this paper, we propose a novel method for exploring the representative regional folding patterns of infant brains. Specifically, first, multi-view curvature features are constructed to comprehensively characterize the complex characteristics of cortical folding. Second, for each view of curvature features, a similarity matrix is computed to measure the similarity of cortical folding in a specific region between any pair of subjects. Next, a similarity network fusion method is adopted to nonlinearly and adaptively fuse all the similarity matrices into a single one for retaining both shared and complementary similarity information of the multiple characteristics of cortical folding. Finally, based on the fused similarity matrix and a hierarchical affinity propagation clustering approach, all subjects are automatically grouped into several clusters to obtain the representative folding patterns. To show the applications, we have applied the proposed method to a large-scale dataset with 595 normal neonates and discovered representative folding patterns in several cortical regions, i.e., the superior temporal gyrus (STG), inferior frontal gyrus (IFG), precuneus, and cingulate cortex. Meanwhile, we have revealed sex difference in STG, IFG, and cingulate cortex, as well as hemispheric asymmetries in STG and cingulate cortex in terms of cortical folding patterns. Moreover, we have also validated the proposed method on a public adult dataset, i.e., the Human Connectome Project (HCP), and revealed that certain major cortical folding patterns of adults are largely established at term birth.

CONSTRUCTION OF SPATIOTEMPORAL INFANT CORTICAL SURFACE ATLAS OF RHESUS MACAQUE.

As a widely used animal model in MR imaging studies, rhesus macaque helps to better understand both normal and abnormal neural development in the human brain. However, the available adult macaque brain atlases are not well suitable for study of brain development at the early postnatal stage, since this stage undergoes dramatic changes in brain appearances and structures. Building age matched atlases for this critical period is thus highly desirable yet still lacking. In this paper, we construct the first spatiotemporal (4D) cortical surface atlases for rhesus macaques from 2 weeks to 24 months, using 138 longitudinal MRI scans from 32 healthy rhesus monkeys. Specifically, we first perform intra-subject cortical surface registration to obtain within-subject mean cortical surfaces. Then, we perform inter-subject registration of within-subject mean surfaces to obtain unbiased and longitudinally-consistent 4D cortical surface atlases. Based on our 4D rhesus monkey atlases, we further chart the first developmental-trajectories-based parcellation maps using the local surface area and spectral clustering algorithm. Our 4D macaque surface atlases and parcellation maps will greatly facilitate early brain development studies of macaques.

LONGITUDINAL MULTI-SCALE MAPPING OF INFANT CORTICAL FOLDING USING SPHERICAL WAVELETS.

The dynamic development of brain cognition and motor functions during infancy are highly associated with the rapid changes of the convoluted cortical folding. However, little is known about how the cortical folding, which can be characterized on different scales, develops in the first two postnatal years. In this paper, we propose a curvature-based multi-scale method using spherical wavelets to map the complicated longitudinal changes of cortical folding during infancy. Specifically, we first decompose the cortical curvature map, which encodes the cortical folding information, into multiple spatial-frequency scales, and then measure the scale-specific wavelet power at 6 different scales as quantitative indices of cortical folding degree. We apply this method on 219 longitudinal MR images from 73 healthy infants at 0, 1, and 2 years of age. We reveal that the changing patterns of cortical folding are both scale-specific and region-specific. Particularly, at coarser spatial-frequency levels, the majority of the primary folds flatten out, while at finer spatial-frequency levels, the majority of the minor folds become more convoluted. This study provides valuable insights into the longitudinal changes of infant cortical folding.

Exploring Gyral Patterns of Infant Cortical Folding based on Multi-view Curvature Information.

The human cortical folding is intriguingly complex in its variability and regularity across individuals. Exploring the principal patterns of cortical folding is of great importance for neuroimaging research. The term-born neonates with minimum exposure to the complicated environments are the ideal candidates to mine the postnatal origins of principal cortical folding patterns. In this work, we propose a novel framework to study the gyral patterns of neonatal cortical folding. Specifically, first, we leverage multi-view curvature-derived features to comprehensively characterize the complex and multi-scale nature of cortical folding. Second, for each feature, we build a dissimilarity matrix for measuring the difference of cortical folding between any pair of subjects. Then, we convert these dissimilarity matrices as similarity matrices, and nonlinearly fuse them into a single matrix via a similarity network fusion method. Finally, we apply a hierarchical affinity propagation clustering approach to group subjects into several clusters based on the fused similarity matrix. The proposed framework is generic and can be applied to any cortical region, or even the whole cortical surface. Experiments are carried out on a large dataset with 600+ term-born neonates to mine the principal folding patterns of three representative gyral regions.

A hypo-status in drug-dependent brain revealed by multi-modal MRI.

Drug addiction is a chronic brain disorder with no proven effective cure. Assessing both structural and functional brain alterations by using multi-modal, rather than purely unimodal imaging techniques, may provide a more comprehensive understanding of the brain mechanisms underlying addiction, which in turn may facilitate future treatment strategies. However, this type of research remains scarce in the literature. We acquired multi-modal magnetic resonance imaging from 20 cocaine-addicted individuals and 19 age-matched controls. Compared with controls, cocaine addicts showed a multi-modal hypo-status with (1) decreased brain tissue volume in the medial and lateral orbitofrontal cortex (OFC); (2) hypo-perfusion in the prefrontal cortex, anterior cingulate cortex, insula, right temporal cortex and dorsolateral prefrontal cortex and (3) reduced irregularity of resting state activity in the OFC and limbic areas, as well as the cingulate, visual and parietal cortices. In the cocaine-addicted brain, larger tissue volume in the medial OFC, anterior cingulate cortex and ventral striatum and smaller insular tissue volume were associated with higher cocaine dependence levels. Decreased perfusion in the amygdala and insula was also correlated with higher cocaine dependence levels. Tissue volume, perfusion, and brain entropy in the insula and prefrontal cortex, all showed a trend of negative correlation with drug craving scores. The three modalities showed voxel-wise correlation in various brain regions, and combining them improved patient versus control brain classification accuracy. These results, for the first time, demonstrate a comprehensive cocaine-dependence and craving-related hypo-status regarding the tissue volume, perfusion and resting brain irregularity in the cocaine-addicted brain.