Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images

Dedifferentiation of brain functional gradient captures cognition performance and stroke occurrence: A UK Biobank study

Brain functional dedifferentiation, marked by reduced specificity of brain activity or greater similarity of functional connectivity (FC) among networks, is a hallmark of aging. Traditionally, task functional magnetic resonance imaging studies have explored functional dedifferentiation within specific cognitive domains, while FC-based approaches have focused on regional connectivity patterns. Here, we leverage the principal functional gradient to provide a macro-scale and integrative perspective on functional dedifferentiation in aging, offering a novel framework for understanding its relationship with aging, cognition, and disease. We utilized brain images and clinical data from the UK Biobank, comprising 23,578 participants aged 44-82. Linear regression was employed to assess relationships between the network dedifferentiation along the principal functional gradient and age, and cognitive performance across six domains in a normal aging population. We tested interactions between age, sex, and education to assess their influence on age-related dedifferentiation. Logistic regression was applied to classify stroke in participants with stroke and matched normal aging participants. Our findings revealed a reduced principal functional gradient range with age, indicating reduced FC variability of all brain regions. At the network level, the dedifferentiation between the frontoparietal and other networks was strongly linked to aging and cognitive performance. Males exhibited faster dedifferentiation than females across multiple networks. The somatomotor network was most affected by stroke-related dedifferentiation. Validation via covariate-matched subgroups confirmed the robustness of these findings. This research provides macro-scale insights into age-related brain functional changes, highlighting dedifferentiation along the principal gradient as a network-sensitive indicator of aging and the development of stroke.

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach

The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these trajectories in high-dimensional longitudinal data presents a challenge. To address this, our study proposes a novel approach, Unsupervised Orthogonal Mixed-Effects Trajectory Modeling (UOMETM), that leverages unsupervised learning to generate latent representations of both global and individual trajectories. We design an autoencoder with a latent space where an orthogonal constraint is imposed to separate the space of global trajectories from individual trajectories. We also devise a cross-reconstruction loss to ensure consistency of global trajectories and enhance the orthogonality between representation spaces. To evaluate UOMETM, we conducted simulation experiments on images to verify that every component functions as intended. Furthermore, we evaluated its performance and robustness using longitudinal brain cortical thickness from two Alzheimer's disease (AD) datasets. Comparative analyses with state-of-the-art methods revealed UOMETM's superiority in identifying global and individual longitudinal patterns, achieving a lower reconstruction error, superior orthogonality, and higher accuracy in AD classification and conversion forecasting. Remarkably, we found that the space of global trajectories did not significantly contribute to AD classification compared to the space of individual trajectories, emphasizing their clear separation. Moreover, our model exhibited satisfactory generalization and robustness across different datasets. The study shows the outstanding performance and potential clinical use of UOMETM in the context of longitudinal data analysis.

A global multicohort study to map subcortical brain development and cognition in infancy and early childhood

The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.

Multi-level and joint attention networks on brain functional connectivity for cross-cognitive prediction

Deep learning on resting-state functional MRI (rs-fMRI) has shown great success in predicting a single cognition or mental disease. Nevertheless, cognitive functions or mental diseases may share neural mechanisms that can benefit their prediction/classification. We propose a multi-level and joint attention (ML-Joint-Att) network to learn high-order representations of brain functional connectivities that are specific and shared across multiple tasks. We design the ML-Joint-Att network with edge and node convolutional operators, an adaptive inception module, and three attention modules, including network-wise, region-wise, and region-wise joint attention modules. The adaptive inception learns brain functional connectivity at multiple spatial scales. The network-wise and region-wise attention modules take the multi-scale functional connectivities as input and learn features at the network and regional levels for individual tasks. Moreover, the joint attention module is designed as region-wise joint attention to learn shared brain features that contribute to and compensate for the prediction of multiple tasks. We employed the Adolescent Brain Cognitive Development (ABCD) dataset (n =9092) to evaluate the ML-Joint-Att network for the prediction of cognitive flexibility and inhibition. Our experiments demonstrated the usefulness of the three attention modules and identified brain functional connectivities and regions specific and common between cognitive flexibility and inhibition. In particular, the joint attention module can significantly improve the prediction of both cognitive functions. Moreover, leave-one-site cross-validation showed that the ML-Joint-Att network is robust to independent samples obtained from different sites of the ABCD study. Our network outperformed existing machine learning techniques, including Brain Bias Set (BBS), spatio-temporal graph convolution network (ST-GCN), and BrainNetCNN. We demonstrated the generalization of our method to other applications, such as the prediction of fluid intelligence and crystallized intelligence, which also outperformed the ST-GCN and BrainNetCNN.

Amyloid-β Accumulation in Relation to Functional Connectivity in Aging: a Longitudinal Study

The brain undergoes many changes at pathological and functional levels in healthy aging. This study employed a longitudinal and multimodal imaging dataset from the OASIS-3 study (n=300) and explored possible relationships between amyloid beta (Aβ) accumulation and functional brain organization over time in healthy aging. We used positron emission tomography (PET) with Pittsburgh compound-B (PIB) to quantify the Aβ accumulation in the brain and resting-state functional MRI (rs-fMRI) to measure functional connectivity (FC) among brain regions. Each participant had at least 2 to 3 follow-up visits. A linear mixed-effect model was used to examine longitudinal changes of Aβ accumulation and FC throughout the whole brain. We found that the limbic and frontoparietal networks had a greater annual Aβ accumulation and a slower decline in FC in aging. Additionally, the amount of the Aβ deposition in the amygdala network at baseline slowed down the decline in its FC in aging. Furthermore, the functional connectivity of the limbic, default mode network (DMN), and frontoparietal networks accelerated the Aβ propagation across their functionally highly connected regions. The functional connectivity of the somatomotor and visual networks accelerated the Aβ propagation across the brain regions in the limbic, frontoparietal, and DMN networks. These findings suggested that the slower decline in the functional connectivity of the functional hubs may compensate for their greater Aβ accumulation in aging. The Aβ propagation from one brain region to the other may depend on their functional connectivity strength.

Chinese adult brain atlas with functional and white matter parcellation

Brain atlases play important roles in studying anatomy and function of the brain. As increasing interests in multi-modal magnetic resonance imaging (MRI) approaches, such as combining structural MRI, diffusion weighted imaging (DWI), and resting-state functional MRI (rs-fMRI), there is a need to construct integrated brain atlases based on these three imaging modalities. This study constructed a multi-modal brain atlas for a Chinese aging population (n = 180, age: 22-79 years), which consists of a T1 atlas showing the brain morphology, a high angular resolution diffusion imaging (HARDI) atlas delineating the complex fiber architecture, and a rs-fMRI atlas reflecting brain intrinsic functional organization in one stereotaxic coordinate. We employed large deformation diffeomorphic metric mapping (LDDMM) and unbiased diffeomorphic atlas generation to simultaneously generate the T1 and HARDI atlases. Using spectral clustering, we generated 20 brain functional networks from rs-fMRI data. We demonstrated the use of the atlas to explore the coherent markers among the brain morphology, functional networks, and white matter tracts for aging and gender using joint independent component analysis.

Predicting diagnosis 4 years prior to Alzheimer's disease incident

This study employed a deep learning longitudinal model, graph convolutional and recurrent neural network (graph-CNN-RNN), on a series of brain structural MRI scans for AD prognosis. It characterized whole-brain morphology via incorporating longitudinal cortical and subcortical morphology and defined a probabilistic risk for the prediction of AD as a function of age prior to clinical diagnosis. The graph-CNN-RNN model was trained on half of the Alzheimer's Disease Neuroimaging Initiative dataset (ADNI, n = 1559) and validated on the other half of the ADNI dataset and the Open Access Series of Imaging Studies-3 (OASIS-3, n = 930). Our findings demonstrated that the graph-CNN-RNN can reliably and robustly diagnose AD at the accuracy rate of 85% and above across all the time points for both datasets. The graph-CNN-RNN predicted the AD conversion from 0 to 4 years before the AD onset at ∼80% of accuracy. The AD probabilistic risk was associated with clinical traits, cognition, and amyloid burden assessed using [18F]-Florbetapir (AV45) positron emission tomography (PET) across all the time points. The graph-CNN-RNN provided the quantitative trajectory of brain morphology from prognosis to overt stages of AD. Such a deep learning tool and the AD probabilistic risk have great potential in clinical applications for AD prognosis.

Revisiting convolutional neural network on graphs with polynomial approximations of Laplace-Beltrami spectral filtering

This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Laplacian with the LB operator. We define spectral filters via the LB operator on a graph and explore the feasibility of Chebyshev, Laguerre, and Hermite polynomials to approximate LB-based spectral filters. We then update the LB operator for pooling in the LB-CNN. We employ the brain image data from Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) to demonstrate the use of the proposed LB-CNN. Based on the cortical thickness of two datasets, we showed that the LB-CNN slightly improves classification accuracy compared to the spectral graph-CNN. The three polynomials had a similar computational cost and showed comparable classification accuracy in the LB-CNN or spectral graph-CNN. The LB-CNN trained via the ADNI dataset can achieve reasonable classification accuracy for the OASIS dataset. Our findings suggest that even though the shapes of the three polynomials are different, deep learning architecture allows us to learn spectral filters such that the classification performance is not dependent on the type of the polynomials or the operators (graph Laplacian and LB operator).

Integrated structural and functional atlases of Asian children from infancy to childhood

The developing brain grows exponentially in the first few years of life. There is a need to have age-appropriate brain atlases that coherently characterize the geometry of the cerebral cortex, white matter tracts, and functional organization. This study employed multi-modal brain images of an Asian cohort and constructed brain structural and functional atlases for 6-month-old infants, 4.5-, 6-, and 7.5-year-old children. We exploited large deformation diffeomorphic metric mapping and probabilistic atlas generation approaches to integrate structural MRI and diffusion weighted images (DWIs) and to create the atlas where white matter tracts well fit into the cortical folding pattern. Based on this structural atlas, we then employed spectral clustering to parcellate the brain into functional networks from resting-state fMRI (rs-fMRI). Our results provided the atlas that characterizes the cortical folding geometry, subcortical regions, deep white matter tracts, as well as functional networks in a stereotaxic coordinate space for the four different age groups. The functional networks consisting of the primary cortex were well established in infancy and remained stable to childhood, while specific higher-order functional networks showed specific patterns of hemispherical, subcortical-cerebellar, and cortical-cortical integration and segregation from infancy to childhood. Our multi-modal fusion analysis demonstrated the use of the integrated structural and functional atlas for understanding coherent patterns of brain anatomical and functional development during childhood. Hence, our atlases can be potentially used to study coherent patterns of brain anatomical and functional development.

Fast mesh data augmentation via Chebyshev polynomial of spectral filtering

Deep neural networks have recently been recognized as one of the powerful learning techniques in computer vision and medical image analysis. Trained deep neural networks need to be generalizable to new data that are not seen before. In practice, there is often insufficient training data available, which can be solved via data augmentation. Nevertheless, there is a lack of augmentation methods to generate data on graphs or surfaces, even though graph convolutional neural network (graph-CNN) has been widely used in deep learning. This study proposed two unbiased augmentation methods, Laplace-Beltrami eigenfunction Data Augmentation (LB-eigDA) and Chebyshev polynomial Data Augmentation (C-pDA), to generate new data on surfaces, whose mean was the same as that of observed data. LB-eigDA augmented data via the resampling of the LB coefficients. In parallel with LB-eigDA, we introduced a fast augmentation approach, C-pDA, that employed a polynomial approximation of LB spectral filters on surfaces. We designed LB spectral bandpass filters by Chebyshev polynomial approximation and resampled signals filtered via these filters in order to generate new data on surfaces. We first validated LB-eigDA and C-pDA via simulated data and demonstrated their use for improving classification accuracy. We then employed brain images of the Alzheimer's Disease Neuroimaging Initiative (ADNI) and extracted cortical thickness that was represented on the cortical surface to illustrate the use of the two augmentation methods. We demonstrated that augmented cortical thickness had a similar pattern to observed data. We also showed that C-pDA was faster than LB-eigDA and can improve the AD classification accuracy of graph-CNN.

Maternal Adverse Childhood Experience and Depression in Relation with Brain Network Development and Behaviors in Children: A Longitudinal Study

Maternal childhood maltreatment and depression increase risks for the psychopathology of the offspring. This study employed a longitudinal dataset of mother-child dyads to investigate the developmental trajectories of brain functional networks and behaviors of children in relation with maternal childhood adverse experience and depression. Maternal childhood trauma was retrospectively assessed via childhood trauma questionnaire, whereas maternal depressive symptoms were prospectively evaluated during pregnancy and after delivery (n = 518). Child brain scans were acquired at age of 4.5, 6, and 7.5 years (n = 163) and behavioral problems were measured at 7.5 years using the Child Behavior Checklist. We found the functional connectivity of the language network with the sensorimotor, frontal, and attentional networks as a function of maternal adverse experience that interacted with sex and age. Girls exposed to mothers with depressive symptoms or childhood abuse showed the increased development of the functional connectivity of the language network with the visual networks, which was associated with social problems. Girls exposed to mothers with depressive symptoms showed the slower growth of the functional connectivity of the language network with the sensorimotor networks. Our findings, in a community sample, suggest the language network organization as neuroendophenotypes for maternal childhood trauma and depression.

Do intrinsic brain functional networks predict working memory from childhood to adulthood?

Working memory (WM) is defined as the ability to maintain a representation online to guide goal‐directed behavior. Its capacity in early childhood predicts academic achievements in late childhood and its deficits are found in various neurodevelopmental disorders. We employed resting‐state fMRI (rs‐fMRI) of 468 participants aged from 4 to 55 years and connectome‐based predictive modeling (CPM) to explore the potential predictive power of intrinsic functional networks to WM in preschoolers, early and late school‐age children, adolescents, and adults. We defined intrinsic functional networks among brain regions identified by activation likelihood estimation (ALE) meta‐analysis on existing WM functional studies (ALE‐based intrinsic functional networks) and intrinsic functional networks generated based on the whole brain (whole‐brain intrinsic functional networks). We employed the CPM on these networks to predict WM in each age group. The CPM using the ALE‐based and whole‐brain intrinsic functional networks predicted WM of individual adults, while the prediction power of the ALE‐based intrinsic functional networks was superior to that of the whole‐brain intrinsic functional networks. Nevertheless, the CPM using the whole‐brain but not the ALE‐based intrinsic functional networks predicted WM in adolescents. And, the CPM using neither the ALE‐based nor whole‐brain networks predicted WM in any of the children groups. Our findings showed the trend of the prediction power of the intrinsic functional networks to cognition in individuals from early childhood to adulthood.

Multimodal cross-registration and quantification of metric distortions in marmoset whole brain histology using diffeomorphic mappings

Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified in-vivo to ex-vivo distortions in brain geometry from tissue processing. Further, existing approaches focus on registering unimodal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research and the importance of addressing individual brain architecture variations, new algorithms are necessary to cross-register multimodal data sets including MRIs and multiple histological series. Here we present a computational approach for same-subject multimodal MRI-guided reconstruction of a series of consecutive histological sections, jointly with diffeomorphic mapping to a reference atlas. We quantify the scale change during different stages of brain histological processing using the Jacobian determinant of the diffeomorphic transformations involved. By mapping the final image stacks to the ex-vivo post-fixation MRI, we show that (a) tape-transfer assisted histological sections can be reassembled accurately into 3D volumes with a local scale change of 2.0 ± 0.4% per axis dimension; in contrast, (b) tissue perfusion/fixation as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs shows a larger median absolute scale change of 6.9 ± 2.1% per axis dimension. This is the first systematic quantification of local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species. These local scale changes will be important for computing local properties to create reference brain maps.

Behavioral Heterogeneity in Relation with Brain Functional Networks in Young Children

This study aimed to identify distinct behavioral profiles in a population-based sample of 654 4-year-old children and characterize their relationships with brain functional networks using resting-state functional magnetic resonance imaging data. Young children showed 7 behavioral profiles, including a super healthy behavioral profile with the lowest scores across all Child Behavior CheckList (CBCL) subscales (G1) and other 6 behavioral profiles, respectively with pronounced withdrawal (G2), somatic complaints (G3), anxiety and withdrawal (G4), somatic complaints and withdrawal (G5), the mixture of emotion, withdrawal, and aggression (G6), and attention (G7) problems. Compared with children in G1, children with withdrawal shared abnormal functional connectivities among the sensorimotor networks. Children in emotionally relevant problems shared the common pattern among the attentional and frontal networks. Nevertheless, children in sole withdrawal problems showed a unique pattern of connectivity alterations among the sensorimotor, cerebellar, and salience networks. Children with somatic complaints showed abnormal functional connectivities between the attentional and subcortical networks, and between the language and posterior default mode networks. This study provides novel evidence on the existence of behavioral heterogeneity in early childhood and its associations with specific functional networks that are clinically relevant phenotypes for mental illness and are apparent from early childhood.

Sex-Dependent Associations among Maternal Depressive Symptoms, Child Reward Network, and Behaviors in Early Childhood

Maternal depression is associated with disrupted neurodevelopment in offspring. This study examined relationships among postnatal maternal depressive symptoms, the functional reward network and behavioral problems in 4.5-year-old boys (57) and girls (65). We employed canonical correlation analysis to evaluate whether the resting-state functional connectivity within a reward network, identified through an activation likelihood estimation (ALE) meta-analysis of fMRI studies, was associated with postnatal maternal depressive symptoms and child behaviors. The functional reward network consisted of three subnetworks, that is, the mesolimbic, mesocortical, and amygdala–hippocampus reward subnetworks. Postnatal maternal depressive symptoms were associated with the functional connectivity of the mesocortical subnetwork with the mesolimbic and amygdala–hippocampus complex subnetworks in girls and with the functional connectivity within the mesocortical subnetwork in boys. The functional connectivity of the amygdala–hippocampus subnetwork with the mesocortical and mesolimbic subnetworks was associated with both internalizing and externalizing problems in girls, while in boys, the functional connectivity of the mesocortical subnetwork with the amygdala–hippocampus complex and the mesolimbic subnetworks was associated with the internalizing and externalizing problems, respectively. Our findings suggest that the functional reward network might be a promising neural phenotype for effects of maternal depression and potential intervention to nurture child behavioral development.

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations

Combining machine learning with neuroimaging data has a great potential for early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it remains unclear how well the classifiers built on one population can predict MCI/AD diagnosis of other populations. This study aimed to employ a spectral graph convolutional neural network (graph-CNN), that incorporated cortical thickness and geometry, to identify MCI and AD based on 3089 T1-weighted MRI data of the ADNI-2 cohort, and to evaluate its feasibility to predict AD in the ADNI-1 cohort (n = 3602) and an Asian cohort (n = 347). For the ADNI-2 cohort, the graph-CNN showed classification accuracy of controls (CN) vs. AD at 85.8% and early MCI (EMCI) vs. AD at 79.2%, followed by CN vs. late MCI (LMCI) (69.3%), LMCI vs. AD (65.2%), EMCI vs. LMCI (60.9%), and CN vs. EMCI (51.8%). We demonstrated the robustness of the graph-CNN among the existing deep learning approaches, such as Euclidean-domain-based multilayer network and 1D CNN on cortical thickness, and 2D and 3D CNNs on T1-weighted MR images of the ADNI-2 cohort. The graph-CNN also achieved the prediction on the conversion of EMCI to AD at 75% and that of LMCI to AD at 92%. The find-tuned graph-CNN further provided a promising CN vs. AD classification accuracy of 89.4% on the ADNI-1 cohort and >90% on the Asian cohort. Our study demonstrated the feasibility to transfer AD/MCI classifiers learned from one population to the other. Notably, incorporating cortical geometry in CNN has the potential to improve classification performance.

Changes in Centrality Frequency of the Default Mode Network in Individuals With Subjective Cognitive Decline

Despite subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), being widely studied in recent years, studies on centrality frequency in individuals with SCD are lacking. This study aimed to investigate the differences in centrality frequency between individuals with SCD and normal controls (NCs). Forty individuals with SCD and 53 well-matched NCs underwent a resting-state functional magnetic resonance imaging scan. We assessed individual dynamic functional connectivity using sliding window correlations. In each time window, brain regions with a high degree centrality were defined as hubs. Across the entire time window, the proportion of time that the hub appeared was characterized as centrality frequency. The centrality frequency correlated with cognitive performance differently in individuals with SCD and NCs. Our results revealed that in individuals with SCD, compared with NCs, correlations between centrality frequency of the anterior cortical regions and cognitive performance decreased (79.2% for NCs and 43.5% for individuals with SCD). In contrast, correlations between centrality frequency of the posterior cortical regions and cognitive performance increased in SCD individuals compared with NCs (20.8% for NCs and 56.5% for individuals with SCD). Moreover, the changes mainly focused on the anterior (93.3% for NCs and 45.5% for individuals with SCD) and posterior (6.7% for NCs and 54.5% for individuals with SCD) regions associated with the default mode network (DMN). In addition, we used absolute thresholds (correlation efficient r = 0.2, 0.25) and proportional thresholds (sparsity = 0.2, 0.25) to verify the results. Dynamic results are relative stable at absolute thresholds while static results are relative stable at proportional thresholds. Converging findings provide a new framework for the detection of the changes occurring in individuals with SCD via centrality frequency of the DMN.

Maternal sensitivity predicts anterior hippocampal functional networks in early childhood

Maternal care influences child hippocampal development. The hippocampus is functionally organized along an anterior-posterior axis. Little is known with regards to the extent maternal care shapes offspring anterior and posterior hippocampal (aHPC, pHPC) functional networks. This study examined maternal behavior, especially maternal sensitivity, at 6 months postpartum in relation to aHPC and pHPC functional networks of children at age 4 and 6 years. Maternal sensitivity was assessed at 6 months via the "Maternal Behavior Q Sort (MBQS) mini for video". Subsequently, 61 and 76 children underwent resting-state functional magnetic resonance imaging (rs-fMRI), respectively, at 4 and 6 years of age. We found that maternal sensitivity assessed at 6 months postpartum was associated with the right aHPC functional networks in children at both 4 and 6 years of age. At age 4 years, maternal sensitivity was associated positively with the right aHPC's functional connectivity with the sensorimotor network and negatively with the aHPC's functional connectivity with the top-down cognitive control network. At 6 years of age, maternal sensitivity was linked positively with the right aHPC's functional connectivity with the visual-processing network. Our findings suggested that maternal sensitivity in infancy has a long-term impact on the anterior hippocampal functional network in preschool children, implicating a potential role of maternal care in shaping child brain development in early life.

Functional and structural networks of lateral and medial orbitofrontal cortex as potential neural pathways for depression in childhood

BACKGROUND

Converging evidence suggests that the lateral and medial orbitofrontal cortices (lOFC and mOFC) may contribute distinct neural mechanisms in depression. This study investigated the relations of their functional and structural organizations with postnatal maternal depressive symptoms in young children.

METHODS

Resting-state functional magnetic resonance imaging and structural magnetic resonance imaging were acquired in children at age 4 (n = 199) and 6 years (n = 234). Child's withdrawal behavior problems were assessed using Child's Behavior Checklist.

RESULTS

In 4-year-old girls, postnatal maternal depressive symptoms were positively associated with the lOFC functional connectivity with the visual network but negatively with the cognitive control network. The lOFC functional connectivity with the visual network and cerebellum, which was influenced by postnatal maternal depressive symptoms, was also associated with child's withdrawal behavior problems in 6-year-old girls. Moreover, postnatal maternal depressive symptoms were also negatively associated with the mOFC functional connectivity with the cognitive control and motor networks in 4-year-old girls. Furthermore, postnatal maternal depressive symptoms influenced the structural connectivity of left mOFC with the right middle frontal cortex and left inferior temporal cortex in 4-year-old girls. Unlike girls, boys showed that postnatal maternal depressive symptoms selectively impacted the mOFC functional connectivity with the memory system at age 6 years.

CONCLUSION

Our study provided novel evidence on the distinct neural mechanisms of the lOFC and mOFC structural and functional organizations for intergenerational transmission of maternal depression to the offspring. Boys and girls may potentially employ different neural mechanisms to adapt to maternal environment at different timings of early life.

ESTIMATING DIFFEOMORPHIC MAPPINGS BETWEEN TEMPLATES AND NOISY DATA: VARIANCE BOUNDS ON THE ESTIMATED CANONICAL VOLUME FORM

Anatomy is undergoing a renaissance driven by the availability of large digital data sets generated by light microscopy. A central computational task is to map individual data volumes to standardized templates. This is accomplished by regularized estimation of a diffeomorphic transformation between the coordinate systems of the individual data and the template, building the transformation incrementally by integrating a smooth flow field. The canonical volume form of this transformation is used to quantify local growth, atrophy, or cell density. While multiple implementations exist for this estimation, less attention has been paid to the variance of the estimated diffeomorphism for noisy data. Notably, there is an infinite dimensional unobservable space defined by those diffeomorphisms which leave the template invariant. These form the stabilizer subgroup of the diffeomorphic group acting on the template. The corresponding flat directions in the energy landscape are expected to lead to increased estimation variance. Here we show that a least-action principle used to generate geodesics in the space of diffeomor-phisms connecting the subject brain to the template removes the stabilizer. This provides reduced-variance estimates of the volume form. Using simulations we demonstrate that the asymmetric large deformation diffeomorphic mapping methods (LDDMM), which explicitly incorporate the asymmetry between idealized template images and noisy empirical images, provide lower variance estimators than their symmetrized counterparts (cf. ANTs). We derive Cramer-Rao bounds for the variances in the limit of small deformations. Analytical results are shown for the Jacobian in terms of perturbations of the vector fields and divergence of the vector field.

Fronto-parietal numerical networks in relation with early numeracy in young children

Early numeracy provides the foundation of acquiring mathematical skills that is essential for future academic success. This study examined numerical functional networks in relation to counting and number relational skills in preschoolers at 4 and 6 years of age. The counting and number relational skills were assessed using school readiness test (SRT). Resting-state fMRI (rs-fMRI) was acquired in 123 4-year-olds and 146 6-year-olds. Among them, 61 were scanned twice over the course of 2 years. Meta-analysis on existing task-based numeracy fMRI studies identified the left parietal-dominant network for both counting and number relational skills and the right parietal-dominant network only for number relational skills in adults. We showed that the fronto-parietal numerical networks, observed in adults, already exist in 4-year and 6-year-olds. The counting skills were associated with the bilateral fronto-parietal network in 4-year-olds and with the right parietal-dominant network in 6-year-olds. Moreover, the number relational skills were related to the bilateral fronto-parietal and right parietal-dominant networks in 4-year-olds and had a trend of the significant relationship with the right parietal-dominant network in 6-year-olds. Our findings suggested that neural fine-tuning of the fronto-parietal numerical networks may subserve the maturation of numeracy in early childhood.

Multiscale Frame-Based Kernels for Large Deformation Diffeomorphic Metric Mapping

We present a set of multiscale frame-based kernels that can be used to construct diffeomorphic transformation in the large deformation diffeomorphic metric mapping (LDDMM) framework. We construct multiscale kernels via compact wavelet frames that are equipped with the hierarchical multiresolution analysis. We show that these kernels under certain conditions can form reproducing kernel Hilbert spaces of smooth velocity fields and hence can be used to generate multiscale diffeomorphic transformation for LDDMM. As a proof of concept, we incorporate these kernels in the LDDMM framework. We show the improvement of whole brain mapping accuracy using the LDDMM with frame-based kernels in comparison to that obtained using the LDDMM with Gaussian kernels. Moreover, we evaluate whole brain mapping accuracy of the LDDMM with frame-based kernels against that obtained from the 14 brain mapping methods given by Klein et al.. Our results suggest that the LDDMM with frame-based kernels has the potential to outperform the 14 brain mapping methods for whole brain mapping.

Double Diffeomorphism: Combining Morphometry and Structural Connectivity Analysis

The brain is composed of several neural circuits which may be seen as anatomical complexes composed of grey matter structures interconnected by white matter tracts. Grey and white matter components may be modeled as 3-D surfaces and curves, respectively. Neurodevelopmental disorders involve morphological and organizational alterations which cannot be jointly captured by usual shape analysis techniques based on single diffeomorphisms. We propose a new deformation scheme, called double diffeomorphism, which is a combination of two diffeomorphisms. The first one captures changes in structural connectivity, whereas the second one recovers the global morphological variations of both grey and white matter structures. This deformation model is integrated into a Bayesian framework for atlas construction. We evaluate it on a data-set of 3-D structures representing the neural circuits of patients with Gilles de la Tourette syndrome (GTS). We show that this approach makes it possible to localise, quantify, and easily visualise the pathological anomalies altering the morphology and organization of the neural circuits. Furthermore, results also indicate that the proposed deformation model better discriminates between controls and GTS patients than a single diffeomorphism.

Cerebellar development and its mediation role in cognitive planning in childhood

Recent evidence suggests that the cerebellum contributes not only to the planning and execution of movement but also to the high-order cognitive planning. Childhood is a critical period for development of the cerebellum and cognitive planning. This study aimed (a) to examine the development of cerebellar morphology and microstructure and (b) to examine the cerebellar mediation roles in the relationship between age and cognitive planning in 6- to 10-year-old children (n = 126). We used an anatomical parcellation to quantify cerebellar regional gray matter (GM) and white matter (WM) volumes, and WM microstructure, including fractional anisotropy (FA) and mean diffusivity (MD). We assessed planning ability using the Stockings of Cambridge (SOC) task in all children. We revealed (a) a measure-specific anterior-to-posterior gradient of the cerebellar development in childhood, that is, smaller GM volumes and greater WM FA of the anterior segment of the cerebellum but larger GM volumes and lower WM FA in the posterior segment of the cerebellum in older children; (b) an age-related improvement of the SOC performance at the most demanding level of five-move problems; and (c) a mediation role of the lateral cerebellar WM volumes in age-related improvement in the SOC performance in childhood. These results highlight the differential development of the cerebellum during childhood and provide evidence that brain adaptation to the acquisition of planning ability during childhood could partially be achieved through the engagement of the lateral cerebellum.

TRACE: A Topological Graph Representation for Automatic Sulcal Curve Extraction

A proper geometric representation of the cortical regions is a fundamental task for cortical shape analysis and landmark extraction. However, a significant challenge has arisen due to the highly variable, convoluted cortical folding patterns. In this paper, we propose a novel topological graph representation for automatic sulcal curve extraction (TRACE). In practice, the reconstructed surface suffers from noise influences introduced during image acquisition/surface reconstruction. In the presence of noise on the surface, TRACE determines stable sulcal fundic regions by employing the line simplification method that prevents the sulcal folding pattern from being significantly smoothed out. The sulcal curves are then traced over the connected graph in the determined regions by the Dijkstra's shortest path algorithm. For validation, we used the state-of-the-art surface reconstruction pipelines on a reproducibility data set. The experimental results showed higher reproducibility and robustness to noise in TRACE than the existing method (Li et al. 2010) with over 20% relative improvement in error for both surface reconstruction pipelines. In addition, the extracted sulcal curves by TRACE were well-aligned with manually delineated primary sulcal curves. We also provided a choice of parameters to control quality of the extracted sulcal curves and showed the influences of the parameter selection on the resulting curves.

Asynchronous Development of Cerebellar, Cerebello-Cortical, and Cortico-Cortical Functional Networks in Infancy, Childhood, and Adulthood

Evidence from clinical studies shows that early cerebellar injury can cause abnormal development of the cerebral cortex in children. Characterization of normative development of the cerebellar and cerebello-cortical organization in early life is of great clinical importance. Here, we analyzed cerebellar, cerebello-cortical, and cortico-cortical functional networks using resting-state functional magnetic resonance imaging data of healthy infants (6 months, n = 21), children (4-10 years, n = 68), and adults (23-38 years, n = 25). We employed independent component analysis and identified 7 cerebellar functional networks in infants and 12 in children and adults. We revealed that the cerebellum was functionally connected with the sensorimotor cortex in infants but with the sensorimotor, executive control, and default mode systems of the cortex in children and adults. The functional connectivity strength in the cerebello-cortical functional networks of sensorimotor, executive control, and default mode systems was the strongest in middle childhood, but was weaker in adulthood. In contrast, the functional coherence of the cortico-cortical networks was stronger in adulthood. These findings suggest early synchronization of the cerebello-cortical networks in infancy, particularly in the early developing primary sensorimotor system. Conversely, age-related differences of cerebellar, cerebello-cortical, and cortico-cortical functional networks in childhood and adulthood suggest potential asynchrony of the cerebellar and cortical functional maturation.

Computational anatomy and diffeomorphometry: A dynamical systems model of neuroanatomy in the soft condensed matter continuum

The nonlinear systems models of computational anatomy that have emerged over the past several decades are a synthesis of three significant areas of computational science and biological modeling. First is the algebraic model of biological shape as a Riemannian orbit, a set of objects under diffeomorphic action. Second is the embedding of anatomical shapes into the soft condensed matter physics continuum via the extension of the Euler equations to geodesic, smooth flows with inverses, encoding divergence for the compressibility of atrophy and expansion of growth. Third, is making human shape and form a metrizable space via geodesic connections of coordinate systems. These three themes place our formalism into the modern data science world of personalized medicine supporting inference of high-dimensional anatomical phenotypes for studying neurodegeneration and neurodevelopment. The dynamical systems model of growth and atrophy that emerges is one which is organized in terms of forces, accelerations, velocities, and displacements, with the associated Hamiltonian momentum and the diffeomorphic flow acting as the state, and the smooth vector field the control. The forces that enter the model derive from external measurements through which the dynamical system must flow, and the internal potential energies of structures making up the soft condensed matter. We examine numerous examples on growth and atrophy. This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Imaging Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

Trade-off of cerebello-cortical and cortico-cortical functional networks for planning in 6-year-old children

Childhood is a critical period for the development of cognitive planning. There is a lack of knowledge on its neural mechanisms in children. This study aimed to examine cerebello-cortical and cortico-cortical functional connectivity in association with planning skills in 6-year-olds (n = 76). We identified the cerebello-cortical and cortico-cortical functional networks related to cognitive planning using activation likelihood estimation (ALE) meta-analysis on existing functional imaging studies on spatial planning, and data-driven independent component analysis (ICA) of children's resting-state functional MRI (rs-fMRI). We investigated associations of cerebello-cortical and cortico-cortical functional connectivity with planning ability in 6-year-olds, as assessed using the Stockings of Cambridge task. Long-range functional connectivity of two cerebellar networks (lobules VI and lateral VIIa) with the prefrontal and premotor cortex were greater in children with poorer planning ability. In contrast, cortico-cortical association networks were not associated with the performance of planning in children. These results highlighted the key contribution of the lateral cerebello-frontal functional connectivity, but not cortico-cortical association functional connectivity, for planning ability in 6-year-olds. Our results suggested that brain adaptation to the acquisition of planning ability during childhood is partially achieved through the engagement of the cerebello-cortical functional connectivity.

Effects of Antenatal Maternal Depressive Symptoms and Socio-Economic Status on Neonatal Brain Development are Modulated by Genetic Risk

This study included 168 and 85 mother–infant dyads from Asian and United States of America cohorts to examine whether a genomic profile risk score for major depressive disorder (GPRS_MDD) moderates the association between antenatal maternal depressive symptoms (or socio-economic status, SES) and fetal neurodevelopment, and to identify candidate biological processes underlying such association. Both cohorts showed a significant interaction between antenatal maternal depressive symptoms and infant GPRS_MDD on the right amygdala volume. The Asian cohort also showed such interaction on the right hippocampal volume and shape, thickness of the orbitofrontal and ventromedial prefrontal cortex. Likewise, a significant interaction between SES and infant GPRS_MDD was on the right amygdala and hippocampal volumes and shapes. After controlling for each other, the interaction effect of antenatal maternal depressive symptoms and GPRS_MDD was mainly shown on the right amygdala, while the interaction effect of SES and GPRS_MDD was mainly shown on the right hippocampus. Bioinformatic analyses suggested neurotransmitter/neurotrophic signaling, SNAp REceptor complex, and glutamate receptor activity as common biological processes underlying the influence of antenatal maternal depressive symptoms on fetal cortico-limbic development. These findings suggest gene–environment interdependence in the fetal development of brain regions implicated in cognitive–emotional function. Candidate biological mechanisms involve a range of brain region-specific signaling pathways that converge on common processes of synaptic development.

Perinatal maternal depressive symptoms alter amygdala functional connectivity in girls

Perinatal maternal depressive symptoms influence brain development of offspring. Such effects are particularly notable in the amygdala, a key structure involved in emotional processes. This study investigated whether the functional organization of the amygdala varies as a function of pre- and postnatal maternal depressive symptoms. The amygdala functional network was assessed using resting-state functional magnetic resonance imaging (rs-fMRI) in 128 children at age of 4.4 to 4.8 years. Maternal depressive symptoms were obtained at 26 weeks of gestation, 3 months, 1, 2, 3, and 4.5 years after delivery. Linear regression was used to examine associations between maternal depressive symptoms and the amygdala functional network. Prenatal maternal depressive symptoms were significantly associated with the functional connectivity between the amygdala and the cortico-striatal circuitry, especially the orbitofrontal cortex (OFC), insula, subgenual anterior cingulate (ACC), temporal pole, and striatum. Interestingly, greater pre- than post-natal depressive symptoms were associated with lower functional connectivity of the left amygdala with the bilateral subgenual ACC and left caudate and with lower functional connectivity of the right amygdala with the left OFC, insula, and temporal pole. These findings were only observed in girls but not in boys. Early exposure to maternal depressive symptoms influenced the functional organization of the cortico-striato-amygdala circuitry, which is intrinsic to emotional perception and regulation in girls. This suggests its roles in the transgenerational transmission of vulnerability for socio-emotional problems and depression. Moreover, this study underscored the importance of gender-dependent developmental pathways in defining the neural circuitry that underlies the risk for depression.

Parametric Surface Diffeomorphometry for Low Dimensional Embeddings of Dense Segmentations and Imagery

In the field of Computational Anatomy, biological form (including our focus, neuroanatomy) is studied quantitatively through the action of the diffeomorphism group on example anatomies - a technique called diffeomorphometry. Here we design an algorithm within this framework to pass from dense objects common in neuromaging studies (binary segmentations, structural images) to a sparse representation defined on the surface boundaries of anatomical structures, and embedded into the low dimensional coordinates of a parametric model. Our main new contribution is to introduce an expanded group action to simultaneously deform surfaces through direct mapping of points, as well as images through functional composition with the inverse. This allows us to index the diffeomorphisms with respect to two-dimensional surface geometries like subcortical gray matter structures, but explicitly map onto cost functions determined by noisy 3-dimensional measurements. We consider models generated from empirical covariance of training data, as well as bandlimited (Laplace-Beltrami eigenfunction) models when no such data is available. We show applications to noisy or anomalous segmentations, and other typical problems in neuroimaging studies. We reproduce statistical results detecting changes in Alzheimer's disease, despite dimensionality reduction. Lastly we apply our algorithm to the common problem of segmenting subcortical structures from T1 MR images.

A posterior-to-anterior shift of brain functional dynamics in aging

Convergent evidence from task-based functional magnetic resonance imaging (fMRI) studies suggests a posterior-to-anterior shift as an adaptive compensatory scaffolding mechanism for aging. This study aimed to investigate whether brain functional dynamics at rest follow the same scaffolding mechanism for aging using a large Chinese sample aged from 22 to 79 years (n = 277). We defined a probability of brain regions being hubs over a period of time to characterize functional hub dynamic, and defined variability of the functional connectivity to characterize dynamic functional connectivity using resting-state fMRI. Our results revealed that both functional hub dynamics and dynamic functional connectivity posited an age-related posterior-to-anterior shift. Specifically, the posterior brain region showed attenuated dynamics, while the anterior brain regions showed augmented dynamics in aging. Interestingly, our analysis further indicated that the age-related episodic memory decline was associated with the age-related decrease in the brain functional dynamics of the posterior regions. Hence, these findings provided a new dimension to view the scaffolding mechanism for aging based on the brain functional dynamics.

Neonatal neural networks predict children behavioral profiles later in life

This study aimed to examine heterogeneity of neonatal brain network and its prediction to child behaviors at 24 and 48 months of age. Diffusion tensor imaging (DTI) tractography was employed to construct brain anatomical network for 120 neonates. Clustering coefficients of individual structures were computed and used to classify neonates with similar brain anatomical networks into one group. Internalizing and externalizing behavioral problems were assessed using maternal reports of the Child Behavior Checklist (CBCL) at 24 and 48 months of age. The profile of CBCL externalizing and internalizing behaviors was then examined in the groups identified based on the neonatal brain network. Finally, support vector machine and canonical correlation analysis were used to identify brain structures whose clustering coefficients together significantly contribute the variation of the behaviors at 24 and 48 months of age. Four meaningful groups were revealed based on the brain anatomical networks at birth. Moreover, the clustering coefficients of the brain regions that most contributed to this grouping of neonates were significantly associated with childhood internalizing and externalizing behaviors assessed at 24 and 48 months of age. Specially, the clustering coefficient of the right amygdala was associated with both internalizing and externalizing behaviors at 24 months of age, while the clustering coefficients of the right inferior frontal cortex and insula were associated with externalizing behaviors at 48 months of age. Our findings suggested that neural organization established during fetal development could to some extent predict individual differences in behavioral-emotional problems in early childhood. Hum Brain Mapp 38:1362-1373, 2017. © 2016 Wiley Periodicals, Inc.

Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering

The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children.

Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson

The Computational Anatomy project is the morphome-scale study of shape and form, which we model as an orbit under diffeomorphic group action. Metric comparison calculates the geodesic length of the diffeomorphic flow connecting one form to another. Geodesic connection provides a positioning system for coordinatizing the forms and positioning their associated functional information. This article reviews progress since the Euler-Lagrange characterization of the geodesics a decade ago. Geodesic positioning is posed as a series of problems in Hamiltonian control, which emphasize the key reduction from the Eulerian momentum with dimension of the flow of the group, to the parametric coordinates appropriate to the dimension of the submanifolds being positioned. The Hamiltonian viewpoint provides important extensions of the core setting to new, object-informed positioning systems. Several submanifold mapping problems are discussed as they apply to metamorphosis, multiple shape spaces, and longitudinal time series studies of growth and atrophy via shape splines.

Multiresolution Diffeomorphic Mapping for Cortical Surfaces

Due to the convoluted folding pattern of the cerebral cortex, accurate alignment of cortical surfaces remains challenging. In this paper, we present a multiresolution diffeomorphic surface mapping algorithm under the framework of large deformation diffeomorphic metric mapping (LDDMM). Our algorithm takes advantage of multiresolution analysis (MRA) for surfaces and constructs cortical surfaces at multiresolution. This family of multiresolution surfaces are used as natural sparse priors of the cortical anatomy and provide the anchor points where the parametrization of deformation vector fields is supported. This naturally constructs tangent bundles of diffeomorphisms at different resolution levels and hence generates multiresolution diffeomorphic transformation. We show that our construction of multiresolution LDDMM surface mapping can potentially reduce computational cost and improves the mapping accuracy of cortical surfaces.

Robust estimation of group-wise cortical correspondence with an application to macaque and human neuroimaging studies

We present a novel group-wise registration method for cortical correspondence for local cortical thickness analysis in human and non-human primate neuroimaging studies. The proposed method is based on our earlier template based registration that estimates a continuous, smooth deformation field via sulcal curve-constrained registration employing spherical harmonic decomposition of the deformation field. This pairwise registration though results in a well-known template selection bias, which we aim to overcome here via a group-wise approach. We propose the use of an unbiased ensemble entropy minimization following the use of the pairwise registration as an initialization. An individual deformation field is then iteratively updated onto the unbiased average. For the optimization, we use metrics specific for cortical correspondence though all of these are straightforwardly extendable to the generic setting: The first focused on optimizing the correspondence of automatically extracted sulcal landmarks and the second on that of sulcal depth property maps. We further propose a robust entropy metric and a hierarchical optimization by employing spherical harmonic basis orthogonality. We also provide the detailed methodological description of both our earlier work and the proposed method with a set of experiments on a population of human and non-human primate subjects. In the experiment, we have shown that our method achieves superior results on consistency through quantitative and visual comparisons as compared to the existing methods.

Eye size and shape in newborn children and their relation to axial length and refraction at 3 years

PURPOSE

To determine if eye size and shape at birth are associated with eye size and refractive error 3 years later.

METHODS

A subset of 173 full-term newborn infants from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort underwent magnetic resonance imaging (MRI) to measure the dimensions of the internal eye. Eye shape was assessed by an oblateness index, calculated as 1 - (axial length/width) or 1 - (axial length/height). Cycloplegic autorefraction (Canon Autorefractor RK-F1) and optical biometry (IOLMaster) were performed 3 years later.

RESULTS

Both eyes of 173 children were analysed. Eyes with longer axial length at birth had smaller increases in axial length at 3 years (p < 0.001). Eyes with larger baseline volumes and surface areas had smaller increases in axial length at 3 years (p < 0.001 for both). Eyes which were more oblate at birth had greater increases in axial length at 3 years (p < 0.001). Using width to calculate oblateness, prolate eyes had smaller increases in axial length at 3 years compared to oblate eyes (p < 0.001), and, using height, prolate and spherical eyes had smaller increases in axial length at 3 years compared to oblate eyes (p < 0.001 for both). There were no associations between eye size and shape at birth and refraction, corneal curvature or myopia at 3 years.

CONCLUSIONS

Eyes that are larger and have prolate or spherical shapes at birth exhibit smaller increases in axial length over the first 3 years of life. Eye size and shape at birth influence subsequent eye growth but not refractive error development.

Adaptation of brain functional and structural networks in aging

The human brain, especially the prefrontal cortex (PFC), is functionally and anatomically reorganized in order to adapt to neuronal challenges in aging. This study employed structural MRI, resting-state fMRI (rs-fMRI), and high angular resolution diffusion imaging (HARDI), and examined the functional and structural reorganization of the PFC in aging using a Chinese sample of 173 subjects aged from 21 years and above. We found age-related increases in the structural connectivity between the PFC and posterior brain regions. Such findings were partially mediated by age-related increases in the structural connectivity of the occipital lobe within the posterior brain. Based on our findings, it is thought that the PFC reorganization in aging could be partly due to the adaptation to age-related changes in the structural reorganization of the posterior brain. This thus supports the idea derived from task-based fMRI that the PFC reorganization in aging may be adapted to the need of compensation for resolving less distinctive stimulus information from the posterior brain regions. In addition, we found that the structural connectivity of the PFC with the temporal lobe was fully mediated by the temporal cortical thickness, suggesting that the brain morphology plays an important role in the functional and structural reorganization with aging.

Gestational age and neonatal brain microstructure in term born infants: a birth cohort study

OBJECTIVE

Understanding healthy brain development in utero is crucial in order to detect abnormal developmental trajectories due to developmental disorders. However, in most studies neuroimaging was done after a significant postnatal period, and in those studies that performed neuroimaging on fetuses, the quality of data has been affected due to complications of scanning during pregnancy. To understand healthy brain development between 37-41 weeks of gestational age, our study assessed the in utero growth of the brain in healthy term born babies with DTI scanning soon after birth.

METHODS

A cohort of 93 infants recruited from maternity hospitals in Singapore underwent diffusion tensor imaging between 5 to 17 days after birth. We did a cross-sectional examination of white matter microstructure of the brain among healthy term infants as a function of gestational age via voxel-based analysis on fractional anisotropy.

RESULTS

Greater gestational age at birth in term infants was associated with larger fractional anisotropy values in early developing brain regions, when corrected for age at scan. Specifically, it was associated with a cluster located at the corpus callosum (corrected p<0.001), as well as another cluster spanning areas of the anterior corona radiata, anterior limb of internal capsule, and external capsule (corrected p<0.001).

CONCLUSIONS

Our findings show variation in brain maturation associated with gestational age amongst 'term' infants, with increased brain maturation when born with a relatively higher gestational age in comparison to those infants born with a relatively younger gestational age. Future studies should explore if these differences in brain maturation between 37 and 41 weeks of gestational age will persist over time due to development outside the womb.

Diffeomorphic metric mapping and probabilistic atlas generation of hybrid diffusion imaging based on BFOR signal basis

We first propose a large deformation diffeomorphic metric mapping algorithm to align multiple b-value diffusion weighted imaging (mDWI) data, specifically acquired via hybrid diffusion imaging (HYDI). We denote this algorithm as LDDMM-HYDI. We then propose a Bayesian probabilistic model for estimating the white matter atlas from HYDIs. We adopt the work given in Hosseinbor et al. (2013) and represent the q-space diffusion signal with the Bessel Fourier orientation reconstruction (BFOR) signal basis. The BFOR framework provides the representation of mDWI in the q-space and the analytic form of the emsemble average propagator (EAP) reconstruction, as well as reduces memory requirement. In addition, since the BFOR signal basis is orthonormal, the L(2) norm that quantifies the differences in the q-space signals of any two mDWI datasets can be easily computed as the sum of the squared differences in the BFOR expansion coefficients. In this work, we show that the reorientation of the q-space signal due to spatial transformation can be easily defined on the BFOR signal basis. We incorporate the BFOR signal basis into the LDDMM framework and derive the gradient descent algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally, we extend the previous Bayesian atlas estimation framework for scalar-valued images to HYDIs and derive the expectation-maximization algorithm for solving the HYDI atlas estimation problem. Using real HYDI datasets, we show that the Bayesian model generates the white matter atlas with anatomical details. Moreover, we show that it is important to consider the variation of mDWI reorientation due to a small change in diffeomorphic transformation in the LDDMM-HYDI optimization and to incorporate the full information of HYDI for aligning mDWI. Finally, we show that the LDDMM-HYDI outperforms the LDDMM algorithm with diffusion tensors generated from each shell of HYDI.

Individualized diffeomorphic mapping of brains with large cortical infarcts

Whole brain mapping of stroke patients with large cortical infarcts is not trivial due to the complexity of infarcts' anatomical location and appearance in magnetic resonance image. In this study, we proposed an individualized diffeomorphic mapping framework for solving this problem. This framework is based on our recent work of large deformation diffeomorphic metric mapping (LDDMM) in Du et al. (2011) and incorporates anatomical features, such as sulcal/gyral curves, cortical surfaces, brain intensity image, and masks of infarcted regions, in order to align a normal brain to the brain of stroke patients. We applied this framework to synthetic data and data of stroke patients and validated the mapping accuracy in terms of the alignment of gyral/sulcal curves, sulcal regions, and brain segmentation. Our results revealed that this framework provided comparable mapping results for stroke patients and healthy controls, suggesting the importance of incorporating individualized anatomical features in whole brain mapping of brains with large cortical infarcts.

Group-wise cortical correspondence via sulcal curve-constrained entropy minimization

We present a novel cortical correspondence method employing group-wise registration in a spherical parametrization space for the use in local cortical thickness analysis in human and non-human primate neuroimaging studies. The proposed method is unbiased registration that estimates a continuous smooth deformation field into an unbiased average space via sulcal curve-constrained entropy minimization using spherical harmonic decomposition of the spherical deformation field. We initialize a correspondence by our pair-wise method that establishes a surface correspondence with a prior template. Since this pair-wise correspondence is biased to the choice of a template, we further improve the correspondence by employing unbiased ensemble entropy minimization across all surfaces, which yields a deformation field onto the iteratively updated unbiased average. The specific entropy metric incorporates two terms: the first focused on optimizing the correspondence of automatically extracted sulcal landmarks and the second on that of sulcal depth maps. We also propose an encoding scheme for spherical deformation via spherical harmonics as well as a novel method to choose an optimal spherical polar coordinate system for the most efficient deformation field estimation. The experimental results show evidence that the proposed method improves the correspondence quality in non-human primate and human subjects as compared to the pair-wise method.

Deformable templates guided discriminative models for robust 3D brain MRI segmentation

Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

Surface fluid registration of conformal representation: application to detect disease burden and genetic influence on hippocampus

In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E[element of]4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.