Biology subjects

Hagler, D. J.

Publications and source records attributed to Hagler, D. J..

14 recordsLinked to original sources

bioRxiv2026

Nonlinear associations between body mass index and brain microstructure across adolescence in the ABCD Study

Importance: Adolescent body mass index (BMI) is linked to brain microstructural alterations, but it is not known if this association is uniform across the BMI range. Objective: To characterize the shape of the association between adolescent BMI and brain microstructure across the BMI range. Design: Baseline, and 2-, 4-, and 6-year follow-up brain imaging and BMI from the Adolescent Brain Cognitive DevelopmentSM Study, analyzed between June 2025 and June 2026. Setting: Multicenter, population-based study including 21 sites distributed across the US. Participants: 10,479 adolescents (5,078 [48.5%] female; aged 9-18 years) from this population-based cohort study were included after quality control of complete data. Exposures: Centers for Disease Control BMI extended percentiles and percent from median BMI. Main Outcome and Measure: The primary outcome was the restricted normalized isotropic (RNI) signal fraction derived from diffusion imaging which indexes water confined within spherical cellular structures and is sensitive to microstructural properties including cellularity. Results: A total of 10,479 participants (5,078 [48.5%] female; aged 9-18 years) contributed 22,049 imaging observations across four timepoints. Higher BMI percentile was associated with greater RNI in bilateral reward- and appetite-related subcortical gray matter and white matter tracts, with effects varying spatially within structures, and with generally greater magnitude in males. Across these structures, the association increased steeply above the 80th percentile. This acceleration reflected compression of the percentile scale at high BMI; when BMI was reparameterized as percent from median, the association with RNI was approximately linear. Conclusions and Relevance: Our results suggest that BMI is associated with brain microstructure in a graded manner within white matter tracts and subcortical gray matter related to reward, executive function, and appetitive control. This indicates that the effect of BMI on microstructure is not confined to weight extremes and should be accounted for when modeling brain-body-behavior relationships during adolescence.

neuroscience↗

bioRxiv2025

FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets

While linear mixed-effects (LME) models are common for analyzing longitudinal data, most users rely on random intercepts or simple stationary covariance, due to unavailability of computationally tractable solutions. Here, we extend the Fast and Efficient Mixed-Effects Algorithm (FEMA) and present FEMA-Long, a computationally tractable approach to flexibly modeling longitudinal covariance suitable for high-dimensional data. FEMA-Long can: i) model unstructured covariance, ii) model covariates as smooth functions using splines, iii) discover time-dependent effects of covariates with spline interactions, and iv) use these flexible longitudinal modeling strategies to perform longitudinal genome-wide association studies and discover time-dependent genetic effects, in a computationally scalable manner, suitable for high-dimensional data. Through extensive simulations, we show that estimates from FEMA-Long are accurate, while being up to several thousand times faster and with minimal carbon footprint. To show the utility of FEMA-Long for discovering novel biological signal, using data from the Norwegian Mother, Father and Child Cohort Study (MoBa), we performed a longitudinal genome-wide association study with non-linear SNP-by-time interaction on length, weight, and BMI of 68,273 infants with up to six measurements in the first year of life. We found dynamic patterns of random effects including time-varying heritability and genetic correlations, as well as several genetic variants showing time-dependent effects, highlighting the applicability of FEMA-Long to enable novel discoveries. FEMA-Long is available at: https://github.com/cmig-research-group/cmig_tools. Author summaryMost large-scale datasets have complexities such as repeated measures, related individuals, or other dependencies across samples, preventing the use of standard regression approaches for analysis. In such circumstances, linear mixed-effects modeling is often employed. However, for high-dimensional datasets, fitting these models is quite challenging. Further, most standard uses of linear mixed-effects modeling focus on simpler covariance models, which may not hold. Here, we introduce FEMA-Long, a novel computationally efficient analytical framework for fitting linear mixed-effects models with time-varying random effects, as well as allowing the effect of the covariates to change smoothly over time by using splines. This is particularly relevant when, for example, studying the effect of genetic variants on phenotypes, where the effects could be non-linear over time. The FEMA-Long framework allows time-varying heritability as well as discovery of genetic variants that show time-dependent effects. By performing a genome-wide association study on data from the Norwegian Mother, Father and Child Cohort Study (MoBa) using FEMA-Long, we show the discovery of genetic variants with time-dependent effects on infant length, weight, and BMI during the first year of life. Our results highlight the potential of using FEMA-Long to make novel discoveries that can lead to biological insights on the genetics of complex traits as well as improve the potential of using genetics for personalized prediction.

genetics↗

bioRxiv2025

Unintended bias in the pursuit of collinearity solutions in fMRI analysis

In task functional magnetic resonance imaging (fMRI), collinearity between task regressors in time series models may impact power. When collinearity is identified after data collection, researchers often modify the model in an effort to reduce collinearity. However, some model adjustments are suboptimal and may introduce bias into parameter estimates. Although relevant to many task-fMRI studies, we highlight these issues using the Monetary Incentive Delay (MID) task data from the Adolescent Brain Cognitive Development (ABCD(R)) study. We introduce a procedure to more directly quantify the impact of collinearity on task-relevant measures: a contrast-based variance inflation factor (cVIF). We also show that collinearity reduction strategies--such as omitting regressors for specific task components, using impulse regressors for extended activations, and ignoring response time variability--can bias contrast estimates. Finally, we present a "Saturated" model that includes all task components, including response times, aiming to reduce these biases while maintaining comparable levels of collinearity, as assessed by cVIF.

neuroscience↗

bioRxiv2023

Reports of the death of brain-behavior associations have been greatly exaggerated

Magnetic resonance imaging (MRI) is a popular and useful non-invasive method to map patterns of brain structure and function to complex human traits. Recently published observations in multiple large scale studies cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional MRI, which seems to account for little behavioral variability. We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM (ABCD(R)) Study to inform the replication sample size required with both univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and [~]100 subjects for structural MRI. Even with 100 random re-samplings of 50 subjects in the discovery sample, prediction can be adequately powered with 98 subjects in the replication sample for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many investigators research programs and grants.

neuroscience↗

bioRxiv2022

Fast, Efficient Multimodal Image Normalisation Tool (FEMINisT) for the Adolescent Brain and Cognitive Development study: the FEMINisT ABCD Atlas

The Adolescent Brain and Cognitive Development (ABCD) study aims to measure the trajectories of brain, cognitive, and emotional development. Cognitive and behavioural development during late childhood and adolescence have been associated with a myriad of microstructural and morphological alterations across the brain, as measured by magnetic resonance imaging (MRI). These associations may be strongly localised or spatially diffuse, therefore, it would be advantageous to analyse multimodal MRI data in concert, and across the whole brain. The ABCD study presents the unique challenge of integrating multimodal data from tens of thousands of scans at multiple timepoints, within a reasonable computation time. To address the need for a multimodal registration and atlas for the ABCD dataset, we present the synthesis of an ABCD atlas using the Multimodal Image Normalisation Tool (MINT). The MINT ABCD atlas was generated from baseline and two-year follow up imaging data using an iterative approach to synthesise a cohort-specific atlas from linear and nonlinear deformations of eleven channels of diffusion and structural MRI data. We evaluated the performance of MINT against two widely used methods and show that MINT achieves comparable alignment to current state-of-the-art multimodal registration, at a fraction of the computation time. To validate the use of the ABCD MINT atlas in whole brain, voxelwise analysis, we replicate and expand on previously published region-of-interest analysis between diffusion MRI-derived measures and body mass index (BMI). We also report novel association between BMI and brain morphology derived from the registration deformations. We present the ABCD MINT atlas as a publicly available resource to facilitate whole brain voxelwise analyses for the ABCD study.

neuroscience↗

bioRxiv2021

Spatially heterogeneous microstructural development within subcortical regions from 9-13 years

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-subcortical circuits. Understanding microstructural changes in subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered and restricted compartments. This yields greater specificity than conventional methods of characterizing intracellular diffusion. Using RSI, we modelled voxelwise restricted isotropic, N0, and anisotropic, ND, diffusion across the brain and measured cross-sectional and longitudinal age associations in a large sample (n=8,039) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-13 years. Older participants had higher N0 and ND across subcortical regions. The largest associations for N0 were within the basal ganglia and for ND within the ventral diencephalon. Importantly, age associations varied with respect to the internal cytoarchitecture within subcortical structures, for example age associations differed across thalamic nuclei. This suggests that developmental effects may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this subcortical microstructural developmental for behavioral outcomes.

neuroscience↗

bioRxiv2021

Microstructural development across white matter from 9-13 years

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n=8,086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.

neuroscience↗

bioRxiv2020

Associations between Early Midlife Lifestyle Behaviors, Young Adult Cognitive Reserve and Advanced Predicted Brain Age in Late Midlife

ImportanceBoth cognitive reserve and modifiable lifestyle behaviors are associated with dementia risk. The effect of early lifestyle behaviors and cognitive reserve on late midlife brain aging could inform early identification and risk reduction of future dementia. ObjectiveDetermine associations of young adult cognitive reserve, early midlife lifestyle behaviors, and the reserve-by-lifestyle interaction on late midlife brain age. Examine the relationship between mild cognitive impairment (MCI) and brain age. DesignParticipants were from the nationally representative Vietnam Era Twin Study of Aging (VETSA). Cognitive reserve was assessed at median age 20 years (IQR=1.38) with the Armed Forces Qualification Test (AFQT). Lifestyle behaviors (smoking, alcohol consumption, and social engagement) were assessed at median age 41 (IQR=5.00). Structural brain imaging conducted at median age 69 (IQR=4.74) was used to construct predicted brain age difference scores (PBAD=chronological age minus predicted brain age) and MCI was ascertained. SettingIn-person cognitive testing (ages 20 and 69); mailed survey (age 41); structural MRI, MCI diagnosis at University of California, San Diego (age 69). Participants431 community-dwelling men. ExposuresAFQT; self-reported health and lifestyle behaviors. Main outcomes and measuresPBAD scores; MCI. ResultsIn fully adjusted mixed linear models, age 20 cognitive reserve and the age 41 lifestyle composite were associated with age 69 PBAD [t (104)=2.62, p=0.01, 95%CI 0.874, 6.285; t (104)=3.37, p=0.001, 95%CI 0.583, 2.249 respectively] as was the reserve-by-lifestyle interaction [t (104) = -2.29, p=0.02, 95%CI -2.330, -0.167]. Unfavorable lifestyle predicted more advanced brain age, but only for those with lower young adult cognitive reserve. The MCI group had more advanced brain age (F (2,130) = 3.13; p=0.05). Conclusions and relevanceFavorable lifestyle behaviors promoted resistance to accelerated brain aging 3 decades later for those with lower young adult cognitive reserve. High reserve appeared to be protective regardless of lifestyle. The association with MCI suggests that advanced PBAD scores reflect poorer brain integrity, although it is unclear if PBAD is related to Alzheimers dementia specifically. Lower cognitive reserve increases risk for dementia, but early lifestyle modification may promote healthier brain aging and dementia risk reduction, particularly in those with lower reserve. Study TypeCohort Study Key PointsO_ST_ABSQuestionC_ST_ABSDo modifiable lifestyle behaviors in early midlife predict later accelerated brain aging and is that association moderated by cognitive reserve? FindingsA lifestyle composite of smoking, alcohol consumption and social engagement at age 41 was associated with estimated brain age in late midlife. There was a significant moderation effect whereby more unfavorable lifestyle behaviors predicted more advanced brain aging, but only in those with low-to-moderate cognitive reserve. MeaningFavorable lifestyle behaviors appear to be protective for brain integrity especially among those with lower cognitive reserve. Early midlife efforts at prevention could be prioritized among those with lower cognitive reserve.

neuroscience↗

bioRxiv2020

Multivariate genome-wide association study identifies 1735 unique genetic loci associated with cortical morphology.

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we exploit the distributed nature of genetic effects across the brain and apply the Multivariate Omnibus Statistical Test (MOSTest) to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology. This reflects an approximate 10-fold increase compared to the commonly applied univariate GWAS methods. Power analysis indicates that applying MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. Functional follow up including gene-based analyses implicate 10% of all protein-coding genes and point towards pathways involved in neurogenesis and cell differentiation.

genetics↗

bioRxiv2020

Atypical genomic patterning of the cerebral cortex in autism with poor early language outcome

Cortical regional identities develop through anterior-posterior (A-P) and dorsal-ventral (D-V) prenatal genomic patterning gradients. Here we find that A-P and D-V genomic patterning of cortical surface area (SA) and thickness (CT) is intact in typically developing and autistic toddlers with good language outcome, but is absent in autistic toddlers with poor early language outcome. Genes driving this effect are prominent in midgestational A-P and D-V gene expression gradients and prenatal cell types driving SA and CT variation (e.g., progenitor cells versus excitatory neurons). These genes are also important for vocal learning, human-specific evolution, and prenatal co-expression networks enriched for high-penetrance autism risk genes. Autism with poor early language outcome may be linked to atypical genomic cortical patterning starting in prenatal periods and which impacts later development of regional functional specialization and circuit formation. One Sentence SummaryGenomic patterning of the cortex is atypical in autistic toddlers with poor early language outcome.

neuroscience↗

bioRxiv2020

Biophysical modeling of the neural origin of EEG and MEG signals

Electroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals. Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details. In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/181875v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@47f029org.highwire.dtl.DTLVardef@e8244dorg.highwire.dtl.DTLVardef@a765c1org.highwire.dtl.DTLVardef@96550_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LICurrent dipoles are computed from biophysically detailed simulated neuron and network activity C_LIO_LIExtracted current dipoles allow for accurate computation of EEG and MEG signals in simplified and detailed head models C_LIO_LICurrent-diplole approximation generally not suitable for accurate calculations of ECoG signals C_LIO_LIMethod provides biophysics-based link between detailed neural activity and systems-level signals C_LI

neuroscience↗

bioRxiv2019

Behavioral and neural signatures of working memory in childhood

Working memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during development, however, are not well understood. Here we establish associations between working memory, cognitive abilities, and functional MRI activation in data from over 4,000 9-10-year-olds enrolled in the Adolescent Brain Cognitive Development study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotional n-back task demonstrate that frontoparietal activity in response to an explicit memory challenge indexes working memory ability. Furthermore, this relationship is domain-specific, such that fMRI activation related to emotion processing during the emotional n-back task, inhibitory control during a stop-signal task, and reward processing during a monetary incentive delay task does not track memory abilities. Together these results inform our understanding of the emergence of individual differences in working memory and lay the groundwork for characterizing the ways in which they change across adolescence.

neuroscience↗

bioRxiv2018

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The ABCD Study is a collaborative effort, including a Coordinating Center, 21 data acquisition sites across the United States, and a Data Analysis and Informatics Center (DAIC). The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data will provide a resource of unprecedented scale and depth for studying typical and atypical development. Here, we describe the baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes. Neuroimaging processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI.\n\nHighlightsO_LIAn overview of the MRI processing pipeline for the ABCD Study\nC_LIO_LIA discussion on the challenges of large, multisite population studies\nC_LIO_LIA methodological reference for users of publicly shared data from the ABCD Study\nC_LI

neuroscience↗

bioRxiv2017

Brain structure mediates the association between height and cognitive ability

Height and general cognitive ability (GCA) are positively associated, but the underlying mechanisms of this relationship are unclear. We used a sample of 515 middle-aged male twins with structural magnetic resonance imaging data to study if the association between height and cognitive ability is mediated by cortical size. We used genetically, ontogenetically and phylogenetically distinct cortical metrics of cortical surface area (SA) and cortical thickness (CT). Our results indicate that the well-replicated height-GCA association is accounted for by individual differences in total cortical SA (highly heritable metric related to global brain size), and not mean CT, and that the genetic association between SA and GCA underlies the phenotypic height-GCA relationship.

neuroscience↗