Biology subjects

Tustison, N. J.

Publications and source records attributed to Tustison, N. J..

13 recordsLinked to original sources

bioRxiv2026

Deep Computational Anatomy via Latent-Aligned Multiview Normalizing Flows

In modeling complex probability distributions, normalizing flows provide exact-likelihood, bijective mappings between empirical data and tractable latent spaces. Building on this foundation, latent-aligned multiview normalizing (LAMNr) flows leverage these salient properties to learn shared latent subspaces across heterogeneous, multimodal datasets while simultaneously topologically unfolding the sampled data manifold into a continuous vector space. Formal latent-alignment constraints are used to model shared structural features separate from view-specific variations, coordinating latent projections into a shared geometric subspace. By applying this transformation in the context of biological imaging, the framework establishes a potential basis for a deep learning interpretation of foundational computational anatomy concepts, such as the population template, latent distances, and geodesic pairwise image interpolation. Additionally, the proposed framework enables closed-form conditional modeling for exact cross-view imputation and other latent space manipulations. Evaluations and illustrations on both imaging-derived phenotypes (IDPs) and multimodal MRI demonstrate the proposed framework and potential applications. To further motivate our work, we provide a robust and comprehensive, 2D and 3D open-source implementation in PyTorch, natively integrated with the ANTsX ecosystem (i.e., ANTsTorch) for efficient training and subsequent data transformation, manipulation, and analysis.

bioinformatics↗

bioRxiv2025

Anatomy-aware, label-informed approach improves image registration for challenging datasets

Image registration-based volumetric morphometrics have emerged as a valuable method for identifying subtle morphological differences in neuroimaging and other biomedical images. However, accurate registration out-of-the-box remains challenging when overt morphological phenotypes--such as those observed in developmental and comparative studies--are present in a dataset. A new label-informed image registration function developed in the ANTsX ecosystem provides an easy to use, generalizable solution for anatomy-aware registration of a wide diversity of morphological variation. In this approach, segmentations (i.e., labels) provide a priori regional correspondences that guide the registration. These labels can be generated by any method-manually, using semi-automated tools, or through deep learning-based approaches-and allow morphological experts to define regions of correspondence based on biological concepts of homology (e.g., tissue origin, gene expression patterns). Here we demonstrate the utility of this label-informed image registration approach for improving the registration knockout mouse embryos which fail to register to a wildtype (normative) template image by traditional registration methods. E15.5 Gli2-/- mouse embryos show a severe scoliosis and radical topological rearrangement of the internal organs. Compared to traditional, intensity-only registration, the new label-informed image registration improved the correspondence of knockout subjects to the canonical template image, which resulted in increased power and sensitivity of downstream statistical analyses. All in all, label-informed image registration provides a flexible and customizable method to allow image registration in datasets for which registration-based morphometrics were previously unfeasible, unlocking new potential applications of registration-based morphometrics in developmental, comparative, and evolutionary studies.

developmental biology↗

bioRxiv2024

Parallel neuroinflammatory pathways to cerebrovascular injury and amyloid-beta in Alzheimer's disease

ImportanceWhile the hallmark pathologies of amyloid-beta (A{beta}) and tau in Alzheimers disease (AD) are well documented and even part of the definition, upstream neuroinflammation is thought to play an important role but remains poorly understood. ObjectivesWe tested whether two distinct neuroinflammatory markers are associated with cerebrovascular injury and A{beta}, and whether these markers are associated with plasma phosphorylated tau (pTau) concentration, medial temporal lobe (MTL) cortical and hippocampal atrophy, and memory deficits. We examined neuroinflammatory markers plasma YKL-40 and GFAP, due to previous conflicting evidence relating YKL-40 and GFAP to AD pathogenic markers. DesignCross-sectional data from a community observational study (Biomarker Exploration in Aging, Cognition, and Neurodegeneration - BEACoN) were included. SettingAll participants were enrolled in a single site, at University of California, Irvine. Participants126 participants were included if they had at least one of the following measures available: neuropsychological data, MRI, A{beta}-PET, or plasma. ExposuresPlasma YKL-40 and plasma glial fibrillary acidic protein (GFAP) levels. Main outcomes and measuresWhite matter hyperintensity (WMH) volume, 18F-florbetapir (FBP) PET mean SUVR, plasma phosphorylated tau (pTau-217) concentration, MTL cortical thickness, hippocampal volume, and memory function assessed by Rey Auditory Verbal Learning Test. Using path analysis, we tested whether higher plasma YKL-40 and GFAP are associated with WMH and A{beta}, and whether these converge to downstream markers of tauopathy, MTL neurodegeneration, and memory deficits. ResultsIn older adults without dementia (N=126, age=70.60+6.29, 62% women), we found that higher plasma YKL-40 concentration was associated with greater WMH volume, while higher plasma GFAP concentration was related to increased FBP SUVR. Further, higher plasma GFAP, WMH and FBP SUVR were independently associated with increased pTau-217. In turn, plasma pTau-217 was associated with reduced MTL cortical thickness and hippocampal volume. Subsequently, only reduced hippocampal volume was related to lower memory function. Conclusions and RelevanceNeuroinflammatory markers contribute to parallel pathways of cerebrovascular injury and A{beta}, which converge to tau-associated neurodegeneration and memory deficits in older adults. These observations underscore the need for a more comprehensive approach to developing an AD framework and treatment strategies. KEY POINTSO_ST_ABSQuestionC_ST_ABSHow does neuroinflammation impact downstream features of cerebrovascular injury and amyloid-beta (A{beta}) in Alzheimers disease? FindingsIn this study of 126 older adults without dementia, we found evidence for two distinct neuroinflammatory pathways that lead to neurodegeneration and memory deficits. One path involves plasma YKL-40 and its impact on cerebrovascular injury, as measured by white matter hyperintensities (WMH) on MRI scans. The other involves plasma glial fibrillary acidic protein (GFAP) and its impact on A{beta} deposition measured via 18F-florbetapir (FBP) PET. Both pathways converged on tauopathy, measured by plasma pTau-217, which was associated with lower medial temporal lobe (MTL) cortical thickness and hippocampal volume, and consequently, memory deficits. MeaningInflammation acts on Alzheimers disease mechanisms via multiple distinct and parallel pathways which converge downstream onto neurodegeneration. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/616579v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@2d3c39org.highwire.dtl.DTLVardef@1ccc453org.highwire.dtl.DTLVardef@6d40bdorg.highwire.dtl.DTLVardef@b543cb_HPS_FORMAT_FIGEXP M_FIG C_FIG Credit: BioRender was used to help create this graphical abstract.

neuroscience↗

bioRxiv2024

The ANTsX Ecosystem for Mapping the Mouse Brain

Precision mapping techniques coupled with high resolution image acquisition of the mouse brain permit the study of the spatial organization of gene expression and their mutual interaction for a comprehensive view of salient structural/functional relationships. Such research is facilitated by standardized anatomical coordinate systems, such as the well-known Allen Common Coordinate Framework (AllenCCFv3), and the ability to spatially map to such standardized spaces. The Advanced Normalization Tools Ecosystem is a comprehensive open-source software toolkit for generalized quantitative imaging with applicability to multiple organ systems, modalities, and animal species. Herein, we illustrate the utility of ANTsX for generating precision spatial mappings of the mouse brain and potential subsequent quantitation. We describe ANTsX-based workflows for mapping domain-specific image data to AllenCCFv3 accounting for common artefacts and other confounds. Novel contributions include ANTsX functionality for velocity flow-based mapping spanning the spatiotemporal domain of a longitudinal trajectory which we apply to the Developmental Common Coordinate Framework. Additionally, we present an automated structural morphological pipeline for determining volumetric and cortical thickness measurements analogous to the well-utilized ANTsX pipeline for human neuroanatomical structural morphology which illustrates a general open-source framework for tailored brain parcellations.

neuroscience↗

bioRxiv2023

Developmental Mouse Brain Common Coordinate Framework

3D standard reference brains serve as key resources to understand the spatial organization of the brain and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of standard 3D reference atlases for developing mouse brains has hindered advancement of our understanding of brain development. Here, we present a multimodal 3D developmental common coordinate framework (DevCCF) spanning mouse embryonic day (E) 11.5, E13.5, E15.5, E18.5, and postnatal day (P) 4, P14, and P56 with anatomical segmentations defined by a developmental ontology. At each age, the DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging and co-registered high-resolution templates from light sheet fluorescence microscopy. Expert-curated 3D anatomical segmentations at each age adhere to an updated prosomeric model and can be explored via an interactive 3D web-visualizer. As a use case, we employed the DevCCF to unveil the emergence of GABAergic neurons in embryonic brains. Moreover, we integrated the Allen CCFv3 into the P56 template with stereotaxic coordinates and mapped spatial transcriptome cell-type data with the developmental ontology. In summary, the DevCCF is an openly accessible resource that can be used for large-scale data integration to gain a comprehensive understanding of brain development.

neuroscience↗

bioRxiv2023

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

neuroscience↗

bioRxiv2023

Multivariate Residualization in Medical Imaging Analysis

Nuisance variables in medical imaging research are common, complicating association and prediction studies based on image data. Medical image data are typically high dimensional, often consisting of many highly correlated features. As a result, computationally efficient and robust methods to address nuisance variables are difficult to implement. By-region univariate residualization is commonly used to remove the influence of nuisance variables, as are various extensions. However, these methods neglect multivariate properties and may fail to fully remove influence related to the joint distribution of these regions. Some methods, such as functional regression and others, do consider multivariate properties when controlling for nuisance variables. However, the utility of these methods is limited for data with many image regions due to computational and model complexity. We develop a multivariate residualization method to estimate the association between the image and nuisance variable using a machine learning algorithm and then compute the orthogonal projection of each subjects image data onto this space. We illustrate this methods performance in a set of simulation studies and apply it to data from the Alzheimers Disease Neuroimaging Initiative (ADNI).

bioinformatics↗

bioRxiv2022

Posterior white matter hyperintensities are associated with reduced medial temporal lobe subregional integrity and long-term memory in older adults

White matter hyperintensities are a marker of small vessel cerebrovascular disease that are strongly related to cognition in older adults. Similarly, medial temporal lobe atrophy is well-documented in aging and Alzheimers disease and is associated with memory decline. Here, we assessed the relationship between lobar white matter hyperintensities, medial temporal lobe subregional volumes, and hippocampal memory in older adults. We collected MRI scans in a sample of 139 older adults without dementia (88 females, mean age (SD) = 76.95 (10.61)). Participants were administered the Rey Auditory Verbal Learning Test (RAVLT). Regression analyses tested for associations among medial temporal lobe subregional volumes, regional white matter hyperintensities and memory, while adjusting for age, sex, and education and correcting for multiple comparisons. Increased occipital white matter hyperintensities were related to worse RAVLT delayed recall performance, and to reduced CA1, dentate gyrus, perirhinal cortex (Brodmann area 36), and parahippocampal cortex volumes. These medial temporal lobe subregional volumes were related to delayed recall performance. The association of occipital white matter hyperintensities with delayed recall performance was fully mediated statistically only by perirhinal cortex volume. These results suggest that white matter hyperintensities may be associated with memory decline through their impact on medial temporal lobe atrophy. These findings provide new insights into the role of vascular pathologies in memory loss in older adults and suggest that future studies should further examine the neural mechanisms of these relationships in longitudinal samples.

neuroscience↗

bioRxiv2020

Differential effects of emotional valence on mnemonic performance with greater hippocampal maturity

The hippocampal formation (HF) facilitates the creation of declarative memories, with subfields providing unique contributions to the discriminability and generalizability of events. The HF itself and its connections with other structures exhibit a protracted development. Maturational differences across subfields facilitate a shift towards memory specificity, with peri-puberty sitting at the inflection point. Peri-puberty also happens to be a sensitive period in the development of anxiety disorders. Taken together, we believe HF development is critical to negative overgeneralization, a common feature of anxiety disorders. To investigate the role of the HF in behavioral discrimination and generalization we examined the relation between behavior and cross-sectional indices of HF maturity derived from subfield volume. Participants aged 9-14 years, recruited from clinical and community sources, performed a recognition task with emotionally valent (positive, negative) and neutral images. T1-weighted and diffusion-weighted structural scans were collected. Partial least squares correlations were used to derive a singular metric of maturity for both HF volume and structural connectivity. We found our volumetric HF maturity index was positively associated with discrimination for neutral images and generalization for negative images. Hippocampal-medial prefrontal cortex structural connectivity maturity metric evidenced a similar trend with behavior as the HF volumetric approach. These findings are important because they reflect a novel developmentally related balance between discrimination and generalization behavior supported by the hippocampus and its connections with other regions. Maturational shifts in this balance may contribute to negative overgeneralization, a common feature of anxiety disorders that escalates during the same developmental window. Significance StatementThe hippocampal formation (HF) facilitates declarative memory specificity and is composed of subfields whose development during adolescence overlaps with the onset of anxiety disorders. Aberrations in mechanisms governing memory specificity may contribute to negative overgeneralization in anxious youth. Participants completed an emotional memory discrimination task while in the scanner. Using a multivariate maturity metric based on subfield volume we found individuals with more "mature" HF were better at differentiating similar neutral images and more likely to generalize similar negative images. These findings are important because they capture a novel developmental mechanism related to the balance between discrimination and generalization. Shifts in this balance, may contribute to negative overgeneralization, a common feature of anxiety disorders.

neuroscience↗

bioRxiv2020

Negative Overgeneralization is Associated with Pattern Completion in Peripubertal Youth

BACKGROUNDThis study examines neural mechanisms of negative overgeneralization in peri-puberty to identify potential contributors to escalating anxiety during this sensitive period. Theories suggest that weak pattern separation (a neurocomputational process by which overlapping representations are made distinct, indexed by DG/CA3 hippocampal subfields) is a major contributor to negative overgeneralization. We alternatively propose that neuromaturation related to generalization and anxiety-related pathology in peri-puberty predicts contributions from strong pattern completion (a partial match of cues reinstates stored representations, indexed by CA1) and related modulatory mechanisms (amygdala, medial prefrontal cortices [mPFC]). METHODSYouth (N=34, 9-14 years) recruited from community and clinic settings participated in an emotional mnemonic similarity task while undergoing MRI. At Study, participants indicated the valence of images; at Test, participants made an old/new recognition memory judgment. Critical lure stimuli, that were similar but not the same as images from Study, were presented at Test, and errors ("false alarms") to negative relative to neutral stimuli reflected negative overgeneralization. Univariate, multivariate, and functional connectivity analyses were performed to evaluate mechanisms of negative overgeneralization. RESULTSNegative overgeneralization was related to greater and more similar patterns of activation in CA1 and both dorsal and ventral mPFC for negative relative to neutral stimuli. At Study, amygdala increased functional coupling with CA1 and dorsal mPFC during negative items that were later generalized. CONCLUSIONSNegative overgeneralization is rooted in amygdala and mPFC modulation at encoding and pattern completion at retrieval. These mechanisms could prove to reflect etiological roots of anxiety that precede symptom escalation across adolescence.

neuroscience↗

bioRxiv2019

A Method for Harmonizing Longitudinal Multi-scanner Imaging Data

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimers Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

neuroscience↗

bioRxiv2019

Removal of Scanner Effects in Covariance Improves Multivariate Pattern Analysis in Neuroimaging Data

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple scanners. These effects have been shown to bias comparison between scanners, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing scanner-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of multivariate pattern analysis (MVPA) has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing scanner effects in mean and variance may not be sufficient for MVPA. This stems from the fact that such methods fail to address how correlations between measurements can vary across scanners. Data from the Alzheimers Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across scanners and that popular harmonization techniques do not address this issue. We also propose a novel methodology that harmonizes covariance of multivariate image measurements across scanners and demonstrate its improved performance in data harmonization.

neuroscience↗

bioRxiv2017

The ANTs Longitudinal Cortical Thickness Pipeline

Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. In this work, we introduce the open-source Advanced Normalization Tools (ANTs) registration-based cortical thickness longitudinal processing pipeline and its application to the first phase of the Alzheimers Disease Neuroimaging Initiative (ADNI-1) comprising over 600 subjects with multiple time points from baseline to 36 months. We demonstrate in these data that the single-subject template construction and same orientation processing results in a simultaneous minimization of residual variability and maximization of between-subject variability immediately estimable from a longitudinal mixed-effects modeling strategy. It is known from the statistical literature that optimizing these dual criteria leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.

neuroscience↗