Structural and functional connectome relationships in early childhood.

There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual's functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models.

Early Childhood Development of Node Centrality in the White Matter Connectome and Its Relationship to IQ at Age 6 Years.

BACKGROUND: The white matter (WM) connectome is important for cognitive development and intelligence and is altered in neuropsychiatric illnesses. Little is known about how the WM connectome develops or its relationship to IQ in early childhood. METHODS: The development of node centrality in the WM connectome was studied in a longitudinal cohort of 226 (123 female) children from the University of North Carolina Early Brain Development Study. Structural and diffusion-weighted images were acquired after birth and at 1, 2, 4, and 6 years, and IQ was assessed at 6 years. Eigenvector centrality, betweenness centrality, and the global graph metrics of global efficiency, small worldness, and modularity were determined at each age. RESULTS: The greatest developmental change in eigenvector centrality and betweenness centrality occurred during the first year of life, with relative stability between ages 1 and 6 years. Most of the high-centrality hubs at age 6 were also high-centrality hubs at 1 year, and many were already high-centrality hubs at birth. There were generally small but significant changes in global efficiency and modularity from birth to 6 years, while small worldness increased between 2 and 4 years. Individual node centrality was not significantly correlated with IQ at 6 years. CONCLUSIONS: Node centrality in the WM connectome is established very early in childhood and is relatively stable from age 1 to 6 years. Many high-centrality hubs are established before birth, and most are present by age 1.

Maternal adverse childhood experiences and infant subcortical brain volume.

Background: A large body of research supports the deleterious effects of adverse childhood experiences (ACEs) on disease susceptibility and health for both the exposed individual and the next generation. It is likely that there is an intergenerational transmission of risk from mother to child; however, the mechanisms through which such risk is conferred remain unknown. The current study evaluated the association between maternal ACEs, neonatal brain development of the amygdala and hippocampus, and later infant negative emotionality at six months of age. Methods: The sample included 85 mother-infant dyads (44 female infants) from a longitudinal study. Maternal ACEs were assessed with the Adverse Childhood Experiences Questionnaire (ACE-Q) and neonatal hippocampal and amygdala volume was assessed using structural magnetic resonance imaging (MRI). Infant negative emotionality was assessed at 6 months using the Infant Behavior Questionnaire (IBQ). Results: Multivariate analyses demonstrated that maternal ACEs were associated with bilateral amygdala volume (F(2,78) = 3.697,p = .029). Specifically, higher maternal ACEs were associated with smaller left (ß = -0.220, t(79) = -2.661, p = .009, R2 = 0.494, and right (ß = -0.167, t(79) = -2.043, p = .044, R2 = 0.501) amygdala volume. No significant association between maternal ACEs and bilateral hippocampal volume (F(2,78) = 0.215,p = .0807) was found. Follow-up regression analyses demonstrated that both high maternal ACEs and smaller left amygdala volume were associated with higher infant negative emotionality at six months of age (ß = .232, p = .040, R2 = 0.094, and ß = -0.337, p = .022, R2 = 0.16, respectively) although statistically significant mediation of this effect was not observed (Indirect effect = 0.0187, 95% CI [-0.0016-0.0557]). Conclusions: Maternal ACEs are associated with both newborn amygdala volume and subsequent infant negative emotionality. These findings linking maternal adverse childhood experiences and infant brain development and temperament provide evidence to support the intergenerational transmission of adversity from mother to child.

Longitudinal and prospective assessment of prenatal maternal sleep quality and associations with newborn hippocampal and amygdala volume.

BACKGROUND: The rapid maturation of the fetal brain renders the fetus susceptible to prenatal environmental signals. Prenatal maternal sleep quality is known to have important health implications for newborns including risk for preterm birth, however, the effect on the fetal brain is poorly understood. METHOD: Participants included 94 pregnant participants and their newborns (53% female). Pregnant participants (Mage = 30; SDage= 5.29) reported on sleep quality three times throughout pregnancy. Newborn hippocampal and amygdala volumes were assessed using structural magnetic resonance imaging. Multilevel modeling was used to test the associations between trajectories of prenatal maternal sleep quality and newborn hippocampal and amygdala volume. RESULTS: The overall trajectory of prenatal maternal sleep quality was associated with hippocampal volume (left: b = 0.00003, p = 0.013; right: b = 0.00003, p = .008). Follow up analyses assessing timing of exposure indicate that poor sleep quality early in pregnancy was associated with larger hippocampal volume bilaterally (e.g., late gestation left: b = 0.002, p = 0.24; right: b = 0.004, p = .11). Prenatal sleep quality was not associated with amygdala volume. CONCLUSION: These findings highlight the implications of poor prenatal maternal sleep quality and its role in contributing to newborn hippocampal development.

CONTINUITY: CONnectivity Tool with INtegration of sUbcortical regions, regIstration and visualization of TractographY.

In this work, we present CONTINUITY, a novel, open-source interactive computation and visualization tool for brain connectome data. The connectome processing pipeline performs surface based processing as the main mode of operation. The automated processing includes structural-to-diffusion image co-registration, surface reconstruction for subcortical structures, as well as fiber tractography. The tool supports 3 different probabilistic methods of tractography offered by the tractography frameworks in FSL, MRtrix and DIPY. All methods employ brain and subcortical surfaces as seeds to initialize the tractography algorithms. CONTINUITY implements a friendly Graphical User Interface (GUI) to make the workflow accessible for nontechnical users. Additionally, it offers the possibility to visualize the results of the brain connectome in several interactive plot types such as a hierarchical edge bundling circle plot and over 2D/3D brain templates. This visualization tool can also be applied to connectome matrices computed with other tools and pipelines.

Joint hub identification for brain networks by multivariate graph inference.

Recent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging. Current popular hub identification methods often work in a univariate manner, i.e., selecting the hub nodes one after another based on either heuristic of the connectivity profile at each node or predefined settings of network modules. Since the topological information of the entire network (such as network modules) is not fully utilized, current methods have limited power to identify hubs that link multiple modules (connector hubs) and are biased toward identifying hubs having many connections within the same module (provincial hubs). To address this challenge, we propose a novel multivariate hub identification method. Our method identifies connector hubs as those that partition the network into disconnected components when they are removed from the network. Furthermore, we extend our hub identification method to find the population-based hub nodes from a group of network data. We have compared our hub identification method with existing methods on both simulated and human brain network data. Our proposed method achieves more accurate and replicable discovery of hub nodes and exhibits enhanced statistical power in identifying network alterations related to neurological disorders such as Alzheimer's disease and obsessive-compulsive disorder.

Exposure to prenatal maternal distress and infant white matter neurodevelopment.

The prenatal period represents a critical time for brain growth and development. These rapid neurological advances render the fetus susceptible to various influences with life-long implications for mental health. Maternal distress signals are a dominant early life influence, contributing to birth outcomes and risk for offspring psychopathology. This prospective longitudinal study evaluated the association between prenatal maternal distress and infant white matter microstructure. Participants included a racially and socioeconomically diverse sample of 85 mother-infant dyads. Prenatal distress was assessed at 17 and 29 weeks' gestational age (GA). Infant structural data were collected via diffusion tensor imaging at 42-45 weeks' postconceptional age. Findings demonstrated that higher prenatal maternal distress at 29 weeks' GA was associated with increased fractional anisotropy (b = .283, t(64) = 2.319, p = .024) and with increased axial diffusivity (b = .254, t(64) = 2.067, p = .043) within the right anterior cingulate white matter tract. No other significant associations were found with prenatal distress exposure and tract fractional anisotropy or axial diffusivity at 29 weeks' GA, nor earlier in gestation.

Automated profiling of growth cone heterogeneity defines relations between morphology and motility.

Growth cones are complex, motile structures at the tip of an outgrowing neurite. They often exhibit a high density of filopodia (thin actin bundles), which complicates the unbiased quantification of their morphologies by software. Contemporary image processing methods require extensive tuning of segmentation parameters, require significant manual curation, and are often not sufficiently adaptable to capture morphology changes associated with switches in regulatory signals. To overcome these limitations, we developed Growth Cone Analyzer (GCA). GCA is designed to quantify growth cone morphodynamics from time-lapse sequences imaged both in vitro and in vivo, but is sufficiently generic that it may be applied to nonneuronal cellular structures. We demonstrate the adaptability of GCA through the analysis of growth cone morphological variation and its relation to motility in both an unperturbed system and in the context of modified Rho GTPase signaling. We find that perturbations inducing similar changes in neurite length exhibit underappreciated phenotypic nuance at the scale of the growth cone.

Direct Microtubule-Binding by Myosin-10 Orients Centrosomes toward Retraction Fibers and Subcortical Actin Clouds.

Positioning of centrosomes is vital for cell division and development. In metazoan cells, spindle positioning is controlled by a dynamic pool of subcortical actin that organizes in response to the position of retraction fibers. These actin "clouds" are proposed to generate pulling forces on centrosomes and mediate spindle orientation. However, the motors that pull astral microtubules toward these actin structures are not known. Here, we report that the unconventional myosin, Myo10, couples actin-dependent forces from retraction fibers and subcortical actin clouds to centrosomes. Myo10-mediated centrosome positioning requires its direct microtubule binding. Computational image analysis of large microtubule populations reveals a direct effect of Myo10 on microtubule dynamics and microtubule-cortex interactions. Myo10's role in centrosome positioning is distinct from, but overlaps with, that of dynein. Thus, Myo10 plays a key role in integrating the actin and microtubule cytoskeletons to position centrosomes and mitotic spindles.

Multiparametric analysis of CLASP-interacting protein functions during interphase microtubule dynamics.

The microtubule (MT) plus-end tracking protein (+TIP) CLASP mediates dynamic cellular behaviors and interacts with numerous cytoplasmic proteins. While the influence of some CLASP interactors on MT behavior is known, a comprehensive survey of the proteins in the CLASP interactome as MT regulators is missing. Ultimately, we are interested in understanding how CLASP collaborates with functionally linked proteins to regulate MT dynamics. Here, we utilize multiparametric analysis of time-lapse MT +TIP imaging data acquired in Drosophila melanogaster S2R+ cells to assess the effects on individual microtubule dynamics for RNA interference-mediated depletion of 48 gene products previously identified to be in vivo genetic CLASP interactors. While our analysis corroborates previously described functions of several known CLASP interactors, its multiparametric resolution reveals more detailed functional profiles (fingerprints) that allow us to precisely classify the roles that CLASP-interacting genes play in MT regulation. Using these data, we identify subnetworks of proteins with novel yet overlapping MT-regulatory roles and also uncover subtle distinctions between the functions of proteins previously thought to act via similar mechanisms.

plusTipTracker: Quantitative image analysis software for the measurement of microtubule dynamics.

Here we introduce plusTipTracker, a Matlab-based open source software package that combines automated tracking, data analysis, and visualization tools for movies of fluorescently-labeled microtubule (MT) plus end binding proteins (+TIPs). Although +TIPs mark only phases of MT growth, the plusTipTracker software allows inference of additional MT dynamics, including phases of pause and shrinkage, by linking collinear, sequential growth tracks. The algorithm underlying the reconstruction of full MT trajectories relies on the spatially and temporally global tracking framework described in Jaqaman et al. (2008). Post-processing of track populations yields a wealth of quantitative phenotypic information about MT network architecture that can be explored using several visualization modalities and bioinformatics tools included in plusTipTracker. Graphical user interfaces enable novice Matlab users to track thousands of MTs in minutes. In this paper, we describe the algorithms used by plusTipTracker and show how the package can be used to study regional differences in the relative proportion of MT subpopulations within a single cell. The strategy of grouping +TIP growth tracks for the analysis of MT dynamics has been introduced before (Matov et al., 2010). The numerical methods and analytical functionality incorporated in plusTipTracker substantially advance this previous work in terms of flexibility and robustness. To illustrate the enhanced performance of the new software we thus compare computer-assembled +TIP-marked trajectories to manually-traced MT trajectories from the same movie used in Matov et al. (2010).

Anatomy of a simple acyl intermediate in enzyme catalysis: combined biophysical and modeling studies on ornithine acetyl transferase.

Acyl-enzyme complexes are intermediates in reactions catalyzed by many hydrolases and related enzymes which employ nucleophilic catalysis. However, most of the reported structural data on acyl-enzyme complexes has been acquired under noncatalytic conditions. Recent IR analyses have indicated that some acyl-enzyme complexes may be more flexible than most crystallographic analyses have implied. OAT2 is a member of the N-terminal nucleophile (Ntn) hydrolase enzyme superfamily and catalyzes the reversible transfer of an acetyl group between the alpha-amino groups of ornithine and glutamate in a mechanism proposed to involve an acyl-enzyme complex. We have carried out biophysical analyses on ornithine acetyl transferase (OAT2), both in solution and in the crystalline state. Mass spectrometric studies identified Thr-181 as the residue acetylated during OAT2 catalysis; (13)C NMR analyses implied the presence of an acyl-enzyme complex in solution. Crystallization of OAT2 in the presence of N-alpha-acetyl-L-glutamate led to a structure in which Thr-181 was acetylated; the carbonyl oxygen of the acyl-enzyme complex was located in an oxyanion hole and positioned to hydrogen bond with the backbone amide NH of Gly-112 and the alcohol of Thr-111. While the crystallographic analyses revealed only one structure, IR spectroscopy demonstrated the presence of two distinct acyl-enzyme complex structures with carbonyl stretching frequencies at 1691 and 1701 cm(-1). Modeling studies implied two possible acyl-enzyme complex structures, one of which correlates with that observed in the crystal structure and with the 1691 cm(-1) IR absorption. The second acyl-enzyme complex structure, which has only a single oxyanion hole hydrogen bond, is proposed to give rise to the 1701 cm(-1) IR absorption. The two acyl-enzyme complex structures can interconvert by movement of the Thr-111 side-chain alcohol hydrogen away from the oxyanion hole to hydrogen bond with the backbone carbonyl of the acylated residue, Thr-181. Overall, the results reveal that acyl-enzyme complex structures may be more dynamic than previously thought and support the use of a comprehensive biophysical and modeling approach in studying such intermediates.