Biology subjects

Pecheva, D.

Publications and source records attributed to Pecheva, D..

9 recordsLinked to original sources

bioRxiv2026

A 3D Brain Geometry Toolkit for Multisite Neuroimaging Analysis

Compared to traditional gross volumetrics, surface-based models provide greater spatial precision for understanding brain alterations related to developmental, neurological, and psychiatric disorders. Large-scale brain initiatives are combining data from around the world to discover and improve illness-related brain markers. Here, we present a toolkit for 3D brain geometry analysis aimed at addressing key challenges facing large-scale neuroimaging studies. Our framework incorporates scalable methods for multisite data integration, site-specific confound correction, accelerated statistical modeling, interpretable machine learning, and interactive results visualization. The toolkit was tested on data from 21 independently collected study samples participating in the ENIGMA Bipolar Disorder Working Group (N = 3,373). Compared to traditional volume features, we show how subcortical shape measures can be combined across study sites to capture spatially complex differences between diagnostic groups and associations with common treatments. Statistical modeling was accelerated using the Fast and Efficient Mixed-Effects Algorithm (FEMA) and achieved a 16-fold reduction in computation time compared to traditional approaches. Machine learning models showed shape features may provide greater predictive performance over traditional volumes for both diagnostic and treatment prediction tasks, with interpretable weight maps providing insights into the local features driving model performance.

neuroscience↗

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

Diffusion MRI Processing in the HEALthy Brain and Child Development Study: Innovations and Applications

The landmark ongoing HEALthy Brain and Cognitive Development (HBCD) study will longitudinally chart brain development in a large sample (projected n=7,200) of infants through age 10 years with multimodal neuroimaging that includes an advanced diffusion MRI (dMRI) acquisition. Here, we detail advances in dMRI image processing developed for HBCD, incorporated into the widely used QSIPrep pipeline. Major changes to preprocessing include improvements in infant brain extraction, distortion correction, and normalization to infant-specific templates. Additionally, we describe a new software package - QSIRecon - that yields rich derived data including diverse maps of tissue microstructure as well as person-specific white matter bundles. Using dMRI data from a subset of the HBCD 1.0 release where age information was available (n=529 sessions across two time points), we observe critical improvements in data quality with preprocessing and see expected developmental patterns. Moving forward, the publicly-available data from HBCD will rapidly grow to become the largest study of brain development in infancy and early childhood using dMRI. QSIPrep and QSIRecon are openly available and can be applied to other infant and pediatric dMRI datasets.

neuroscience↗

bioRxiv2025

A Scalable Toolkit for Modeling 3D Surface-based Brain Geometry

3D surface-based computational mapping is more sensitive to localized brain alterations in neurological, developmental and psychiatric conditions than traditional gross volumetric analysis, providing fine-scale 3D maps of a wide range of surface-based features. Here we introduce a scalable toolkit for large-scale computational surface analysis, with efficient algorithms for multisite data integration, statistical harmonization, accelerated multivariate statistics, and visualization. We showcase the utility of the toolkit by mapping subcortical shape variations and factors that affect them across 21 international samples from the ENIGMA Bipolar Disorder Working Group (N=3,373).

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↗

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

FEMA: Fast and efficient mixed-effects algorithm for population-scale whole brain imaging data

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME, however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

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↗