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"Maloney, Thomas"
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Reduced gray matter volume and cortical thickness associated with traffic-related air pollution in a longitudinally studied pediatric cohort
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.
Journal Article
ABCD_Harmonizer: An Open-source Tool for Mapping and Controlling for Scanner Induced Variance in the Adolescent Brain Cognitive Development Study
Data from multisite magnetic resonance imaging (MRI) studies contain variance attributable to the scanner that can reduce statistical power and potentially bias results if not appropriately managed. The Adolescent Cognitive Brain Development (ABCD) study is an ongoing, longitudinal neuroimaging study acquiring data from over 11,000 children starting at 9–10 years of age. These scans are acquired on 29 different scanners of 5 different model types manufactured by 3 different vendors. Publicly available data from the ABCD study include structural MRI (sMRI) measures such as cortical thickness and diffusion MRI (dMRI) measures such as fractional anisotropy. In this work, we 1) quantify the variance attributable to scanner effects in the sMRI and dMRI datasets, 2) demonstrate the effectiveness of the data harmonization approach called ComBat to address scanner effects, and 3) present a simple, open-source tool for investigators to harmonize image features from the ABCD study. Scanner-induced variance was present in every image feature and varied in magnitude by feature type and brain location. For almost all features, scanner variance exceeded variability attributable to age and sex. ComBat harmonization was shown to effectively remove scanner induced variance from all image features while preserving the biological variability in the data. Moreover, we show that for studies examining relatively small subsamples of the ABCD dataset, the use of ComBat harmonized data provides more accurate estimates of effect sizes compared to controlling for scanner effects using ordinary least squares regression.
Journal Article
Examining reaction time variability on the stop-signal task in the ABCD study
Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs.
This study utilized trial-level data from the stop signal task from 8916 children (9-10 years old) to examine Gaussian, ex-Gaussian, and DM indicators of RTV. In addition to describing RTV patterns, we examined interrelations among these indicators, temporal patterns, and sex differences.
There was no one-to-one correspondence between DM and ex-Gaussian parameters. Nonetheless, drift rate was most strongly associated with standard deviation of RT and tau, while nondecisional processes were most strongly associated with RT, mu, and sigma. Performance worsened across time with changes driven primarily by decreasing drift rate. Boys were faster and less variable than girls, likely attributable to girls' wide boundary separation.
Intercorrelations among model parameters are similar in children as has been observed in adults. Computational approaches play a crucial role in understanding performance changes over time and can also clarify mechanisms of group differences. For example, standard RT models may incorrectly suggest slowed processing speed in girls that is actually attributable to other factors.
Journal Article
Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework
Enlarged perivascular spaces (EPVS), specifically in stroke patients, has been shown to strongly correlate with other measures of small vessel disease and cognitive impairment at 1 year follow-up. Typical grading of EPVS is often challenging and time consuming and is usually based on a subjective visual rating scale. The purpose of the current study was to develop an interpretable, 3D neural network for grading enlarged perivascular spaces (EPVS) severity at the level of the basal ganglia using clinical-grade imaging in a heterogenous acute stroke cohort, in the context of total cerebral small vessel disease (CSVD) burden. T2-weighted images from a retrospective cohort of 262 acute stroke patients, collected in 2015 from 5 regional medical centers, were used for analyses. Patients were given a label of 0 for none-to-mild EPVS (< 10) and 1 for moderate-to-severe EPVS (≥ 10). A three-dimensional residual network of 152 layers (3D-ResNet-152) was created to predict EPVS severity and 3D gradient class activation mapping (3DGradCAM) was used for visual interpretation of results. Our model achieved an accuracy 0.897 and area-under-the-curve of 0.879 on a hold-out test set of 15% of the total cohort (n = 39). 3DGradCAM showed areas of focus that were in physiologically valid locations, including other prevalent areas for EPVS. These maps also suggested that distribution of class activation values is indicative of the confidence in the model’s decision. Potential clinical implications of our results include: (1) support for feasibility of automated of EPVS scoring using clinical-grade neuroimaging data, potentially alleviating rater subjectivity and improving confidence of visual rating scales, and (2) demonstration that explainable models are critical for clinical translation.
Journal Article
Socio-economic status and fertility decline: Insights from historical transitions in Europe and North America
The timings of historical fertility transitions in different regions are well understood by demographers, but much less is known regarding their specific features and causes. In the study reported in this paper, we used longitudinal micro-level data for five local populations in Europe and North America to analyse the relationship between socio-economic status and fertility during the fertility transition. Using comparable analytical models and class schemes for each population, we examined the changing socio-economic differences in marital fertility and related these to common theories on fertility behaviour. Our results do not provide support for the hypothesis of universally high fertility among the upper classes in pre-transitional society, but do support the idea that the upper classes acted as forerunners by reducing their fertility before other groups. Farmers and unskilled workers were the latest to start limiting their fertility. Apart from these similarities, patterns of class differences in fertility varied significantly between populations.
Journal Article
Latino Residential Isolation and the Risk of Obesity in Utah: The Role of Neighborhood Socioeconomic, Built-Environmental, and Subcultural Context
The prevalence rate of obesity in the United States has been persistently high in recent decades, and disparities in obesity risks are routinely observed. Both individual and contextual factors should be considered when addressing health disparities. This study examines how Latino-white spatial segregation is associated with the risk of obesity for Latinos and whites, whether neighborhood socioeconomic resources, the built environment, and subcultural orientation serve as the underlying mechanisms, and whether neighborhood context helps explain obesity disparities across ethnic and immigrant groups. The study was based on an extensive database containing self-reported BMI measures obtained from driver license records in Utah merged with census data and several GIS-based data. Multilevel analyses were performed to examine the research questions. For both men and women, Latino residential isolation is significantly and positively linked to the risk of obesity; after controlling for immigrant concentration, this effect gets amplified. Moreover, for men and women, the segregation effect is partly attributable to neighborhood SES and the built environment; and only for women is it partly attributable to obesity prevalence in the neighborhood. Place matters for individual risk of obesity for both men and women and there are multifarious pathways linking residence to obesity. Among the demographic, socioeconomic, physical, and cultural aspects of neighborhood context examined in this study, perhaps the most modifiable environment features that could prevent weight gain and its associated problems would be the built environmental factors such as greenness, park access, and mixed land use.
Journal Article
Transforming Ocean Conservation: Applying the Genetic Rescue Toolkit
Although oceans provide critical ecosystem services and support the most abundant populations on earth, the extent of damage impacting oceans and the diversity of strategies to protect them is disconcertingly, and disproportionately, understudied. While conventional modes of conservation have made strides in mitigating impacts of human activities on ocean ecosystems, those strategies alone cannot completely stem the tide of mounting threats. Biotechnology and genomic research should be harnessed and developed within conservation frameworks to foster the persistence of viable ocean ecosystems. This document distills the results of a targeted survey, the Ocean Genomics Horizon Scan, which assessed opportunities to bring novel genetic rescue tools to marine conservation. From this Horizon Scan, we have identified how novel approaches from synthetic biology and genomics can alleviate major marine threats. While ethical frameworks for biotechnological interventions are necessary for effective and responsible practice, here we primarily assessed technological and social factors directly affecting technical development and deployment of biotechnology interventions for marine conservation. Genetic insight can greatly enhance established conservation methods, but the severity of many threats may demand genomic intervention. While intervention is controversial, for many marine areas the cost of inaction is too high to allow controversy to be a barrier to conserving viable ecosystems. Here, we offer a set of recommendations for engagement and program development to deploy genetic rescue safely and responsibly.
Journal Article
Divergent neurodegeneration associations with choroid plexus volume and degree of calcification in cognitively normal APOE ε4 carriers and non-carriers
Choroid plexus (CP), best known for producing CSF, also regulate inflammation and clear metabolic waste to maintain brain homeostasis. CP dysfunction is implicated in Alzheimer’s Disease (AD), with MRI studies showing CP enlargement in AD. The basis for CP enlargement is unknown. We hypothesized that calcium deposition within CP, which increases with aging and in certain neurodegenerative conditions, might underlie pathologic CP enlargement and be linked to neurodegeneration. In 166 cognitively normal participants, we used multimodal imaging to examine CP structure (MRI-measured overall volume, CT-measured calcium volume), PET-measured Aβ, age, and APOE genotype as predictors of neurodegeneration, indexed as hippocampal volume. CP enlargement was associated with reduced hippocampal volume, particularly in APOE4 carriers. CP calcium was not independently associated with hippocampal volume. However, a significant interaction revealed APOE4 genotype-specific associations between CP calcium and neurodegeneration, with APOE4 carriers showing greater hippocampal volumes in association with greater CP calcium—opposite to our hypothesis. Results suggest that a factor other than calcium drives pathologic CP enlargement associated with neurodegeneration, with this factor especially important in APOE4 carriers. Candidate factors include lipids and inflammatory cells, which are known to accumulate in CP and be regulated by APOE. Our findings highlight CP as a critical locus for studying AD pathogenesis and the mechanisms by which APOE4 promotes AD.
Journal Article
DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults
BackgroundAlthough MR elastography allows for quantitative evaluation of liver stiffness to assess chronic liver diseases, it has associated drawbacks related to additional scanning time, patient discomfort, and added costs.ObjectiveTo develop a machine learning model that can categorically classify the severity of liver stiffness using both anatomical T2-weighted MRI and clinical data for children and young adults with known or suspected pediatric chronic liver diseases.Materials and methodsWe included 273 subjects with known or suspected chronic liver disease. We extracted data including axial T2-weighted fast spin-echo fat-suppressed images, clinical data (e.g., demographic/anthropomorphic data, particular medical diagnoses, laboratory values) and MR elastography liver stiffness measurements. We propose DeepLiverNet (a deep transfer learning model) to classify patients into one of two groups: no/mild liver stiffening (<3 kPa) or moderate/severe liver stiffening (≥3 kPa). We conducted internal cross-validation using 178 subjects, and external validation using an independent cohort of 95 subjects. We assessed diagnostic performance using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AuROC).ResultsIn the internal cross-validation experiment, the combination of clinical and imaging data produced the best performance (AuROC=0.86) compared to clinical (AuROC=0.83) or imaging (AuROC=0.80) data alone. Using both clinical and imaging data, the DeepLiverNet correctly classified patients with accuracy of 88.0%, sensitivity of 74.3% and specificity of 94.6%. In our external validation experiment, this same deep learning model achieved an accuracy of 80.0%, sensitivity of 61.1%, specificity of 91.5% and AuROC of 0.79.ConclusionA deep learning model that incorporates clinical data and anatomical T2-weighted MR images might provide a means of risk-stratifying liver stiffness and directing the use of MR elastography.
Journal Article