Childhood asthma is a syndrome composed of heterogeneous phenotypes; furthermore, intrinsic biologic variation among racial/ethnic populations suggests possible genetic ancestry variation in childhood asthma. The objective of the study is to identify clinically homogeneous asthma subphenotypes in a diverse sample of asthmatic children and to assess subphenotype-specific genetic ancestry in African-American asthmatic children.

Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in adults and older children have shown that abnormal network properties may be useful as discriminative features and have exploited machine learning models for early diagnosis in a variety of neurological conditions. However, analogous studies in neonates are rare and with limited significant findings. In this paper, we propose an artificial neural network (ANN) framework for early prediction of cognitive deficits in very preterm infants based on functional connectome data from resting state fMRI. Specifically, we conducted feature selection via stacked sparse autoencoder and outcome prediction via support vector machine (SVM). The proposed ANN model was unsupervised learned using brain connectome data from 884 subjects in autism brain imaging data exchange database and SVM was cross-validated on 28 very preterm infants (born at 23-31 weeks of gestation and without brain injury; scanned at term-equivalent postmenstrual age). Using 90 regions of interests, we found that the ANN model applied to functional connectome data from very premature infants can predict cognitive outcome at 2 years of corrected age with an accuracy of 70.6% and area under receiver operating characteristic curve of 0.76. We also noted that several frontal lobe and somatosensory regions, significantly contributed to prediction of cognitive deficits 2 years later. Our work can be considered as a proof of concept for utilizing ANN models on functional connectome data to capture the individual variability inherent in the developing brains of preterm infants. The full potential of ANN will be realized and more robust conclusions drawn when applied to much larger neuroimaging datasets, as we plan to do.

Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS) and Childhood Absence Epilepsy (CAE) are the most common childhood epilepsy syndromes and they share a similar age-dependence. However, the two syndromes clearly differ in seizures and EEG patterns. The aim of this study is to investigate whether children of the same age with BECTS, CAE and typically-developing children have significant differences in grey matter volume that may underlie the different profiles of these syndromes.

Lupus nephritis (LN) confers a poor prognosis, mainly from lack of effective laboratory tests to diagnose and to evaluate therapies. We have previously shown that a set of 6 urinary biomarkers (NGAL, KIM-1, MCP-1, adiponectin, hemopexin, and ceruloplasmin) are highly sensitive and specific to identify adult and pediatric patients with active LN using renal biopsy as reference standard. Using these combinatorial urinary biomarkers, the Renal Activity Score for Lupus (RAIL) score was established, with biomarkers measured by enzyme-linked immunosorbent assay (ELISA). To enhance clinical utility of the biomarkers and RAIL, we tested the performance of RAIL with biomarkers measured by ELISA to that of biomarkers measured by the bead multiplex method, hypothesizing that the multiplex bead method would be comparable.

Biomarkers in easily obtained specimens that accurately predict uveitis in children with juvenile idiopathic arthritis (JIA) are needed. Aqueous humor has been studied for biomarkers, but is not routinely available. We evaluated tears from children with chronic anterior uveitis (CAU) for biomarkers reported in aqueous humor. In this pilot study, we used Schirmer strips to collect tears from seven children (nine eyes); three children had JIA- associated uveitis (JIA-U) and four had idiopathic disease (I-CAU). Liquid chromatography-tandem mass spectrometry was used to identify and quantify tear proteins. The Mann-Whitney U test identified differential tear protein expression between children with JIA-U and those with I-CAU.

Verbal information is better retained when it is self-generated rather than when it is received passively. The application of self-generation procedures has been found to improve memory in healthy elderly and in individuals with impaired cognition. Overall, the available studies support the notion that active participation in verbal encoding engages memory mechanisms that supplement those used during passive observation. Thus, the objective of this study was to investigate the age-related changes in the neural mechanisms involved in the encoding of paired-associates using a self-generation method that has been shown to improve memory performance across the lifespan. Subjects were 113 healthy right-handed adults (Edinburgh Handedness Inventory >50; 67 females) ages 18-76, native speakers of English with no history of neurological or psychiatric disorders. Subjects underwent fMRI at 3 T while performing didactic learning ("read") or self-generation learning ("generate") of 30 word pairs per condition. After fMRI, recognition memory for the second word in each pair was evaluated outside of the scanner. On the post-fMRI testing more "generate" words were correctly recognized than "read" words (p < 0.001) with older adults recognizing the "generated" words less accurately (p < 0.05). Independent component analysis of fMRI data identified task-related brain networks. Several components were positively correlated with the task reflecting multiple cognitive processes involved in self-generated encoding; other components correlated negatively with the task, including components of the default-mode network. Overall, memory performance on generated words decreased with age, but the benefit from self-generation remained consistently significant across ages. Independent component analysis of the neuroimaging data revealed an extensive set of components engaged in self-generation learning compared with didactic learning, and identified areas that were associated with age-related changes independent of performance.

Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.

Quantifying change in brain activation patterns associated with post-stroke recovery and reorganization of language function over time requires accurate understanding of inter-scan and inter-subject variability. Here we report inter-scan variability measures for fMRI activation patterns associated with verb generation (VG) and semantic decision/tone decision (SDTD) tasks in 4 healthy controls and 4 aphasic left middle cerebral artery (LMCA) stroke subjects. A series of 10 fMRI scans was completed on a 4T Varian scanner for each task for each subject, except for one stroke subject who completed 5 and 6 scans for SDTD and VG, thus yielding 35 and 36 total stroke subject scans for SDTD and VG, respectively. Group composite and intraclass correlation coefficient (ICC) maps were computed across all subjects and trials for each task. The patterns of reliable activation for the VG and SDTD tasks correspond well to those regions typically activated by these tasks in healthy and aphasic subjects. ICCs for activation were consistently high (R(0.05) approximately 0.8) for individual tasks among both control and aphasic subjects. These voxel-wise measures of reliability highlight regions of low inter-scan variability within language circuitry for control and post-recovery stroke subjects. ICCs computed from the combination of the SDTD/VG data were markedly reduced for both control and aphasic subjects as compared with the ICCs for the individual tasks. These quantitative measures of inter-scan variability support the proposed use of these fMRI paradigms for longitudinal mapping of neural reorganization of language processing following left hemispheric insult.

To measure regional brain microvascular and microstructural changes in childhood-onset SLE (cSLE) using diffusion-weighted imaging (DWI) at multiple b values and investigate relationships of those measures with neurocognitive function and disease activity.

Our objective was to evaluate the independent prognostic value of a novel MRI biomarker-objectively diagnosed diffuse white matter abnormality volume (DWMA; diffuse excessive high signal intensity)-for prediction of motor outcomes in very preterm infants. We prospectively enrolled a geographically-based cohort of very preterm infants without severe brain injury and born before 32 weeks gestational age. Structural brain MRI was obtained at term-equivalent age and DWMA volume was objectively quantified using a published validated algorithm. These results were compared with visually classified DWMA. We used multivariable linear regression to assess the value of DWMA volume, independent of known predictors, to predict motor development as assessed using the Bayley Scales of Infant & Toddler Development, Third Edition at 3 years of age. The mean (SD) gestational age of the cohort was 28.3 (2.4) weeks. In multivariable analyses, controlling for gestational age, sex, and abnormality on structural MRI, DWMA volume was an independent prognostic biomarker of Bayley Motor scores ([Formula: see text]= -12.59 [95% CI -18.70, -6.48] R2 = 0.41). Conversely, visually classified DWMA was not predictive of motor development. In conclusion, objectively quantified DWMA is an independent prognostic biomarker of long-term motor development in very preterm infants and warrants further study.

Early life exposure to air pollution poses a significant risk to brain development from direct exposure to toxicants or via indirect mechanisms involving the circulatory, pulmonary or gastrointestinal systems. In children, exposure to traffic related air pollution has been associated with adverse effects on cognitive, behavioral and psychomotor development. We aimed to determine whether childhood exposure to traffic related air pollution is associated with regional differences in brain volume and cortical thickness among children enrolled in a longitudinal cohort study of traffic related air pollution and child health. We used magnetic resonance imaging to obtain anatomical brain images from a nested subset of 12 year old participants characterized with either high or low levels of traffic related air pollution exposure during their first year of life. We employed voxel-based morphometry to examine group differences in regional brain volume, and with separate analyses, changes in cortical thickness. Smaller regional gray matter volumes were determined in the left pre- and post-central gyri, the cerebellum, and inferior parietal lobe of participants in the high traffic related air pollution exposure group relative to participants with low exposure. Reduced cortical thickness was observed in participants with high exposure relative to those with low exposure, primarily in sensorimotor regions of the brain including the pre- and post-central gyri and the paracentral lobule, but also within the frontal and limbic regions. These results suggest that significant childhood exposure to traffic related air pollution is associated with structural alterations in brain.

Very preterm infants (born at less than 32 weeks gestational age) are at high risk for serious motor impairments, including cerebral palsy (CP). The brain network changes that antecede the early development of CP in infants are not well characterized, and a better understanding may suggest new strategies for risk-stratification at term, which could lead to earlier access to therapies. Graph theoretical methods applied to diffusion MRI-derived brain connectomes may help quantify the organization and information transfer capacity of the preterm brain with greater nuance than overt structural or regional microstructural changes. Our aim was to shed light on the pathophysiology of early CP development, before the occurrence of early intervention therapies and other environmental confounders, to help identify the best early biomarkers of CP risk in VPT infants. In a cohort of 395 very preterm infants, we extracted cortical morphometrics and brain volumes from structural MRI and also applied graph theoretical methods to diffusion MRI connectomes, both acquired at term-equivalent age. Metrics from graph network analysis, especially global efficiency, strength values of the major sensorimotor tracts, and local efficiency of the motor nodes and novel non-motor regions were strongly inversely related to early CP diagnosis. These measures remained significantly associated with CP after correction for common risk factors of motor development, suggesting that metrics of brain network efficiency at term may be sensitive biomarkers for early CP detection. We demonstrate for the first time that in VPT infants, early CP diagnosis is anteceded by decreased brain network segregation in numerous nodes, including motor regions commonly-associated with CP and also novel regions that may partially explain the high rate of cognitive impairments concomitant with CP diagnosis. These advanced MRI biomarkers may help identify the highest risk infants by term-equivalent age, facilitating earlier interventions that are informed by early pathophysiological changes.

Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.

Necrotizing enterocolitis (NEC) is a devastating intestinal disease that afflicts 10% of extremely preterm infants. The contribution of early intestinal colonization to NEC onset is not understood, and predictive biomarkers to guide prevention are lacking. We analyzed banked stool and urine samples collected prior to disease onset from infants <29 weeks gestational age, including 11 infants who developed NEC and 21 matched controls who survived free of NEC. Stool bacterial communities were profiled by 16S rRNA gene sequencing. Urinary metabolomic profiles were assessed by NMR.

Historical approaches to protect the brain from outside the skull (eg, helmets and mouthpieces) have been ineffective in reducing internal injury to the brain that arises from energy absorption during sports-related collisions. We aimed to evaluate the effects of a neck collar, which applies gentle bilateral jugular vein compression, resulting in cerebral venous engorgement to reduce head impact energy absorption during collision. Specifically, we investigated the effect of collar wearing during head impact exposure on brain microstructure integrity following a competitive high school American football season.

Diffusion tensor imaging (DTI) is a non-invasive MRI technique that has been used to quantify CNS abnormalities in various pathologic conditions. This study was designed to quantify the anisotropic diffusion properties in the brain of neonatal rats with hydrocephalus (HCP) and to investigate association between DTI measurements and cytopathology.

Poor phonological processing has typically been considered the main cause of dyslexia. However, visuo-attentional processing abnormalities have been described as well. The goal of the present study was to determine the involvement of visual attention during fluent reading in children with dyslexia and typical readers. Here, 75 children (8-12 years old; 36 typical readers, 39 children with dyslexia) completed cognitive and reading assessments. Neuroimaging data were acquired while children performed a fluent reading task with (a) a condition where the text remained on the screen (Still) versus (b) a condition in which the letters were being deleted (Deleted). Cognitive assessment data analysis revealed that visual attention, executive functions, and phonological awareness significantly contributed to reading comprehension in both groups. A seed-to-voxel functional connectivity analysis was performed on the fluency functional magnetic resonance imaging task. Typical readers showed greater functional connectivity between the dorsal attention network and the left angular gyrus while performing the Still and Deleted reading tasks versus children with dyslexia. Higher connectivity values were associated with higher reading comprehension. The control group showed increased functional connectivity between the ventral attention network and the fronto-parietal network during the Deleted text condition (compared with the Still condition). Children with dyslexia did not display this pattern. The results suggest that the synchronized activity of executive, visual attention, and reading-related networks is a pattern of functional integration which children with dyslexia fail to achieve. The present evidence points toward a critical role of visual attention in dyslexia.

Neuroimaging research in surgically treated pediatric hydrocephalus patients remains challenging due to the artifact caused by programmable shunt. Our previous study has demonstrated significant alterations in the whole brain white matter structural connectivity based on diffusion tensor imaging (DTI) and graph theoretical analysis in children with hydrocephalus prior to surgery or in surgically treated children without programmable shunts. This study seeks to investigate the impact of brain injury on the topological features in the left hemisphere, contratelateral to the shunt placement, which will avoid the influence of shunt artifacts and makes further group comparisons feasible for children with programmable shunt valves. Three groups of children (34 in the control group, 12 in the 3-month post-surgery group, and 24 in the 12-month post-surgery group, age between 1 and 18 years) were included in the study. The structural connectivity data processing and analysis were performed based on DTI and graph theoretical analysis. Specific procedures were revised to include only left brain imaging data in normalization, parcellation, and fiber counting from DTI tractography. Our results showed that, when compared to controls, children with hydrocephalus in both the 3-month and 12-month post-surgery groups had significantly lower normalized clustering coefficient, lower small-worldness, and higher global efficiency (all p < 0.05, corrected). At a regional level, both patient groups showed significant alteration in one or more regional connectivity measures in a series of brain regions in the left hemisphere (8 and 10 regions in the 3-month post-surgery and the 12-month post-surgery group, respectively, all p < 0.05, corrected). No significant correlation was found between any of the global or regional measures and the contemporaneous neuropsychological outcomes [the General Adaptive Composite (GAC) from the Adaptive Behavior Assessment System, Second Edition (ABAS-II)]. However, one global network measure (global efficiency) and two regional network measures in the insula (local efficiency and between centrality) tested at 3-month post-surgery were found to correlate with GAC score tested at 12-month post-surgery with statistical significance (all p < 0.05, corrected). Our data showed that the structural connectivity analysis based on DTI and graph theory was sensitive in detecting both global and regional network abnormality when the analysis was conducted in the left hemisphere only. This approach provides a new avenue enabling the application of advanced neuroimaging analysis methods in quantifying brain damage in children with hydrocephalus surgically treated with programmable shunts.

Spatial normalization and segmentation of infant brain MRI data based on adult or pediatric reference data may not be appropriate due to the developmental differences between the infant input data and the reference data. In this study we have constructed infant templates and a priori brain tissue probability maps based on the MR brain image data from 76 infants ranging in age from 9 to 15 months. We employed two processing strategies to construct the infant template and a priori data: one processed with and one without using a priori data in the segmentation step. Using the templates we constructed, comparisons between the adult templates and the new infant templates are presented. Tissue distribution differences are apparent between the infant and adult template, particularly in the gray matter (GM) maps. The infant a priori information classifies brain tissue as GM with higher probability than adult data, at the cost of white matter (WM), which presents with lower probability when compared to adult data. The differences are more pronounced in the frontal regions and in the cingulate gyrus. Similar differences are also observed when the infant data is compared to a pediatric (age 5 to 18) template. The two-pass segmentation approach taken here for infant T1W brain images has provided high quality tissue probability maps for GM, WM, and CSF, in infant brain images. These templates may be used as prior probability distributions for segmentation and normalization; a key to improving the accuracy of these procedures in special populations.

Very preterm (VPT) infants are at high-risk for neurodevelopmental impairments, however there are few validated biomarkers at term-equivalent age that accurately measure abnormal brain development and predict future impairments. Our objectives were to quantify and contrast cortical features between full-term and VPT infants at term and to associate two key antecedent risk factors, bronchopulmonary dysplasia (BPD) and retinopathy of prematurity (ROP), with cortical maturational changes in VPT infants. We prospectively enrolled a population-based cohort of 110 VPT infants (gestational age ≤31 weeks) and 51 healthy full-term infants (gestational age 38-42 weeks). Structural brain MRI was performed at term. 94 VPT infants and 46 full-term infants with high-quality T2-weighted MRI were analyzed. As compared to full-term infants, VPT infants exhibited significant global cortical maturational abnormalities, including reduced surface area (-5.9%) and gyrification (-6.7%) and increased curvature (5.9%). In multivariable regression controlled for important covariates, BPD was significantly negatively correlated with lobar and global cortical surface area and ROP was significantly negatively correlated with lobar and global sulcal depth in VPT infants. Our cohort of VPT infants exhibited widespread cortical maturation abnormalities by term-equivalent age that were in part anteceded by two of the most potent neonatal diseases, BPD and ROP.