Large Deformation Multiresolution Diffeomorphic Metric Mapping for Multiresolution Cortical Surfaces: A Coarse-to-Fine Approach

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.

White matter functional gradients and their formation in adolescence

It is well known that functional magnetic resonance imaging (fMRI) is a widely used tool for studying brain activity. Recent research has shown that fluctuations in fMRI data can reflect functionally meaningful patterns of brain activity within the white matter. We leveraged resting-state fMRI from an adolescent population to characterize large-scale white matter functional gradients and their formation during adolescence. The white matter showed gray-matter-like unimodal-to-transmodal and sensorimotor-to-visual gradients with specific cognitive associations and a unique superficial-to-deep gradient with nonspecific cognitive associations. We propose two mechanisms for their formation in adolescence. One is a "function-molded" mechanism that may mediate the maturation of the transmodal white matter via the transmodal gray matter. The other is a "structure-root" mechanism that may support the mutual mediation roles of the unimodal and transmodal white matter maturation during adolescence. Thus, the spatial layout of the white matter functional gradients is in concert with the gray matter functional organization. The formation of the white matter functional gradients may be driven by brain anatomical wiring and functional needs.

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.

Interindividual variability in functional connectivity discovers differential development of cognition and transdiagnostic dimensions of psychopathology in youth

Cognitive and psychological development during adolescence is different from one another, which is rooted in individual differences in maturational changes in the adolescent brain. This study employed multi-modal MRI data and characterized interindividual variability in functional connectivity (IVFC) and its associations with cognition and psychopathology using the Philadelphia Neurodevelopmental Cohort (PNC) of 755 youth. We employed resting state functional MRI (rs-fMRI) and diffusion weighted images (DWIs) to estimate brain structural and functional networks. We computed the IVFC of individuals and examined its relation with structural and functional organizations. We further employed sparse partial least squares (sparse-PLS) and meta-analysis to examine the developmental associations of the IVFC with cognition and transdiagnostic dimensions of psychopathology in early, middle, and late adolescence. Our results revealed that the IVFC spatial topography reflects the brain functional integration and structure-function decoupling. Age effects on the IVFC of association networks were mediated by the FC among the triple networks, including frontoparietal, salience, and default mode networks (DMN), while those of primary and cerebellar networks were mediated by the cerebello-cortical FC. The IVFC of the triple and cerebellar networks explained the variance of executive functions and externalizing behaviors in early adolescence and then the variance of emotion and internalizing and psychosis in middle and late adolescence. We further evaluated this finding via meta-analysis on task-based studies on cognition and psychopathology. These findings implicate the emerging importance of the IVFC of the triple and cerebellar networks in cognitive, emotional, and psychopathological development during adolescence.

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.

Measurement(s)	water content • water diffusion • blood oxygenation level-dependent signal
Technology Type(s)	Magnetic Resonance Imaging • Diffusion Tensor Imaging • Resting State Functional Connectivity Magnetic Resonance Imaging

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.

Common functional brain networks between attention deficit and disruptive behaviors in youth

Attention deficits (AD) and disruptive behavior (DB) are highly comorbid youth externalizing behaviors. This study aimed to study reliable functional brain networks shared by AD and DB in youth aged from 8 to 21 years from the Philadelphia Neurodevelopmental Cohort (PNC). The PNC study assessed AD and DB behaviors via Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADS). This study employed sparse canonical correlation analysis (SCCA) to examine the correlation of AD and DB behaviors with resting-state functional connectivity maps of the brain regions identified via activation likelihood estimation (ALE) meta-analyses on attention deficit/hyperactivity disorder (ADHD) and DB disorder (DBD). Our meta-analyses identified that the middle cingulate cortex, pre-supplementary motor area (pre-SMA), and striatum had a great consensus in existing ADHD studies and the amygdala and inferior parietal lobule were consistently found in existing DBD studies. Our SCCA analysis revealed that the AD and DB behavioral items relevant to inattention and delinquency were correlated with the functional connectivity of the pre-SMA with the ventral attentional and frontoparietal networks (FPN), and the striatum with the default mode (DMN) and dorsal attentional networks. The AD and DB behavioral items relevant to inattention and irritability were associated with the functional connectivity between the amygdala and the DMN and FPN. Our findings suggest that the functional organization of the ADHD- and DBD-related brain regions provides insights on the shared neural basis in AD and DB.

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.

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.

Incorporation of Structural Tensor and Driving Force Into Log-Demons for Large-Deformation Image Registration

Large-deformation image registration is important in theory and application in computer vision, but is a difficult task for non-rigid registration methods. In this paper, we propose a structural Tensor and Driving force-based Log-Demons algorithm for it, named TDLog-Demons for short. The structural tensor of an image is proposed to obtain a highly accurate deformation field. The driving force is proposed to solve the registration issue of large-deformation that often causes Log-Demons to trap into local minima. It is defined as a point correspondence obtained via multisupport-region-order-based gradient histogram descriptor matching on image's boundary points. It is integrated into an exponentially decreasing form with the velocity field of Log-Demons to move the points accurately and to speed up a registration process. Consequently, the driving force-based Log-Demons can well deal with large-deformation image registration. Extensive experiments demonstrate that the TDLog-Demons not only captures large deformations at a high accuracy but also yields a smooth deformation.

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.

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Graph CNN incorporates cortical thickness and geometry.

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Graph CNN is more robust than other image-based CNN.

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Graph CNN well identified MCI and AD based on 3089 ADNI-2 MRI data.

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Graph CNN built on ADNI-2 was transferable to the ADNI-1 and Asian cohorts.

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.

Long-term Influences of Prenatal Maternal Depressive Symptoms on the Amygdala-Prefrontal Circuitry of the Offspring From Birth to Early Childhood

BACKGROUND

Prenatal maternal depression may have long-term impacts on amygdala-cortical development. This study explored associations of prenatal maternal depressive symptoms on the amygdala-cortical structural covariance of the offspring from birth to early childhood, derived from a longitudinal birth cohort.

METHODS

Structural magnetic resonance imaging was performed to obtain the amygdala volume and cortical thickness at each time point. Prenatal maternal depressive symptoms were measured using the Edinburgh Postnatal Depression Scale at 26 weeks of pregnancy. Regression analysis was used to examine the effects of the Edinburgh Postnatal Depression Scale on a structural coupling between the amygdala volume and cortical thickness at birth (n = 167) and 4.5 years of age (n = 199).

RESULTS

Girls whose mothers had high prenatal maternal depressive symptoms showed a positive coupling between the amygdala volume and insula thickness at birth (β = .617, p = .001) but showed a negative coupling between the amygdala volume and inferior frontal thickness at 4.5 years of age (β = -.369, p = .008). No findings were revealed in boys at any time point.

CONCLUSIONS

The development of the amygdala-prefrontal circuitry is vulnerable to environmental factors related to depression. Such a vulnerability might be sex dependent.

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.

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects.

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.

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.

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.