Author name cluster

James C. Gee

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

32 papers

2 author rows

YNICL Journal 2026 Journal Article

Contusions bias cortical thickness estimates after traumatic brain injury: A TRACK-TBI study

Daniel Brennan
Andrea L.C. Schneider
Russell Taki Shinohara
Ramon Diaz-Arrastia
Philip A. Cook
James C. Gee
James J. Gugger

BACKGROUND: Traumatic brain injury (TBI) is characterized by both focal and diffuse pathology. Automated cortical thickness estimation is widely used to quantify structural brain changes following TBI; however, the impact of focal pathology such as contusions on cortical thickness estimates in TBI remains unknown. METHODS: We evaluated lesion-induced bias in cortical thickness under three lesion-handling strategies in 86 TRACK-TBI participants with MRI at 2 weeks and 6 months post-injury. Cortical thickness was estimated using the ANTsNetCT longitudinal pipeline with the default pipeline (No Masking), masking lesion voxels from summarization (Atlas Masking), and masking lesion voxels from cortical thickness estimation (Full Masking). Cross-sectional and longitudinal cortical thickness in unilaterally lesioned regions were compared with their contralesional homologues using linear mixed-effects models. The effectiveness of each lesion handling strategy was then evaluated using nonparametric bootstrap analyses to test whether bias was systematically present across all regions. RESULTS: At 2 weeks post-injury, six cortical regions demonstrated significant lesion-associated bias. Collectively across all regions, bias was observed in the No Masking and Atlas-Masking approaches. This bias was significantly attenuated in the Fully Masked approach. Longitudinally, the unmasked data also showed significant lesion-related differences in cortical thickness change across multiple temporal and frontal regions, with persistent effects in the Atlas- and Full Masking approaches. CONCLUSIONS: Contusions appear to introduce cross-sectional and longitudinal bias in cortical thickness estimates, inflating cross-sectional values and potentially exaggerating atrophy longitudinally. Excluding lesion voxels from tissue probability maps attenuates cross-sectional bias, providing a baseline that improves accuracy and interpretation of neuroimaging biomarkers in TBI.

ICML Conference 2025 Conference Paper

Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization

Michael S. Yao
James C. Gee
Osbert Bastani

The goal of offline model-based optimization (MBO) is to propose new designs that maximize a reward function given only an offline dataset. However, an important desiderata is to also propose a diverse set of final candidates that capture many optimal and near-optimal design configurations. We propose D iversit y I n A dversarial M odel-based O ptimization ( DynAMO ) as a novel method to introduce design diversity as an explicit objective into any MBO problem. Our key insight is to formulate diversity as a distribution matching problem where the distribution of generated designs captures the inherent diversity contained within the offline dataset. Extensive experiments spanning multiple scientific domains show that DynAMO can be used with common optimization methods to significantly improve the diversity of proposed designs while still discovering high-quality candidates.

YNICL Journal 2025 Journal Article

Executive dysfunction relates to salience network desegregation in behavioural variant frontotemporal dementia

Melanie A. Matyi
Hamsanandini Radhakrishnan
Christopher A. Olm
Jeffrey S. Phillips
Philip A. Cook
Emma Rhodes
James C. Gee
David J. Irwin

BACKGROUND: The organization of the brain into distinct networks increases (i.e., differentiation) during development and decreases (i.e., de-differentiation) during healthy aging, changes that are associated with improvements and worsening of cognition, respectively. Given that behavioral variant frontotemporal degeneration (bvFTD) is a neurodegenerative disease associated with executive dysfunction and selective vulnerability of the salience network, we tested the hypotheses that bvFTD structural networks are de-differentiated compared to cognitively normal controls (CNC) and that network de-differentiation relates to worse executive function. METHODS: In a sample of 90 patients with bvFTD and 71 age-matched CNC with diffusion MRI data we generated probabilistic tractography maps and calculated system segregation, a metric that compares within-network to between-network connectivity, to reflect the extent to which brain networks were differentiated. Patients with bvFTD also completed tests of executive function (digit span backwards, phonemic fluency, category fluency) and a control task (lexical retrieval). We assessed group differences in system segregation, reflecting network differentiation, and, within bvFTD, associations between system segregation and neuropsychological test performance. RESULTS: = 0.021) but not lexical retrieval. CONCLUSIONS: Results demonstrate associations between executive dysfunction and salience network de-differentiation in patients with bvFTD. Our findings indicate that brain network de-differentiation, reflecting reduced neural capacity for specialized processing, may contribute to the emergence of executive dysfunction in bvFTD.

NeurIPS Conference 2024 Conference Paper

A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis

Yue Yang
Mona Gandhi
Yufei Wang
Yifan Wu
Michael S. Yao
Chris Callison-Burch
James C. Gee
Mark Yatskar

While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled from different hospitals or data confounded by demographic variables such as sex, race, etc, in the context of chest X-rays and skin lesion images. A key finding we show empirically is that existing visual backbones lack an appropriate prior from the architecture for reliable generalization in these settings. Taking inspiration from medical training, we propose giving deep networks a prior grounded in explicit medical knowledge communicated in natural language. To this end, we introduce Knowledge-enhanced Bottlenecks (KnoBo), a class of concept bottleneck models that incorporates knowledge priors that constrain it to reason with clinically relevant factors found in medical textbooks or PubMed. KnoBo uses retrieval-augmented language models to design an appropriate concept space paired with an automatic training procedure for recognizing the concept. We evaluate different resources of knowledge and recognition architectures on a broad range of domain shifts across 20 datasets. In our comprehensive evaluation with two imaging modalities, KnoBo outperforms fine-tuned models on confounded datasets by 32. 4% on average. Finally, evaluations reveal that PubMed is a promising resource for making medical models less sensitive to domain shift, outperforming other resources on both diversity of information and final prediction performance.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Deep Learning in Medical Image Registration: Magic or Mirage?

Rohit Jena
Deeksha Sethi
Pratik Chaudhari
James C. Gee

Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature. In this paper, we make an explicit correspondence between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical registration methods. This strong correlation hints to the fact that architectural designs in learning-based methods is unlikely to affect this correlation, and therefore, the performance of learning-based methods. This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. However, learning-based methods with weak supervision can perform high-fidelity intensity and label registration, which is not possible with classical methods. Next, we show that this high-fidelity feature learning does not translate to invariance to domain shift, and learning-based methods are sensitive to such changes in the data distribution. We reassess and recalibrate performance expectations from classical and DLIR methods under access to label supervision, training time, and its generalization capabilities under minor domain shifts.

PDF Details DOI

NeurIPS Conference 2024 Conference Paper

Generative Adversarial Model-Based Optimization via Source Critic Regularization

Michael S. Yao
Yimeng Zeng
Hamsa Bastani
Jacob Gardner
James C. Gee
Osbert Bastani

Offline model-based optimization seeks to optimize against a learned surrogate model without querying the true oracle objective function during optimization. Such tasks are commonly encountered in protein design, robotics, and clinical medicine where evaluating the oracle function is prohibitively expensive. However, inaccurate surrogate model predictions are frequently encountered along offline optimization trajectories. To address this limitation, we propose generative adversarial model-based optimization using adaptive source critic regularization (aSCR) —a task- and optimizer- agnostic framework for constraining the optimization trajectory to regions of the design space where the surrogate function is reliable. We propose a computationally tractable algorithm to dynamically adjust the strength of this constraint, and show how leveraging aSCR with standard Bayesian optimization outperforms existing methods on a suite of offline generative design tasks. Our code is available at https: //github. com/michael-s-yao/gabo.

PDF Details DOI

YNICL Journal 2023 Journal Article

Event-based modeling of T1-weighted MRI is related to pathology in frontotemporal lobar degeneration due to tau and TDP

Christopher A. Olm
Sarah E. Burke
Claire Peterson
Edward B. Lee
John Q. Trojanowski
Lauren Massimo
David J. Irwin
Murray Grossman

BACKGROUND: In previous studies of patients with frontotemporal lobar degeneration due to tau (FTLD-tau) and FTLD due to TDP (FTLD-TDP), cortical volumes derived from T1-weighted MRI have been used to identify a sequence of volume loss according to arbitrary volumetric criteria. Event-based modeling (EBM) is a probabilistic, generative machine learning model that determines the characteristic sequence of changes, or "events", occurring during disease progression. EBM also estimates an individual patient's disease "stage" by identifying which events have already occurred. In the present study, we use an EBM analysis to derive stages of regional anatomic atrophy in FTLD-tau and FTLD-TDP, and validated these stages against pathologic burden. METHODS: Sporadic autopsy-confirmed patients with FTLD-tau (N = 42) and FTLD-TDP (N = 21), and 167 healthy controls with available T1-weighted images were identified. A subset of patients had quantitative digital histopathology of cortex performed at autopsy (FTLD-tau = 30, FTLD-TDP = 17). MRI images were processed, producing regional measures of cortical volumes. K-means clustering was used to find cortical regions with similar amounts of GM volume changes (n = 5 clusters). EBM was used to determine the characteristic sequence of cortical atrophy of identified clusters in autopsy-confirmed FTLD-tau and FTLD-TDP, and estimate each patient's disease stage by cortical volume biomarkers. Linear regressions related pathologic burden to EBM-estimated disease stages. RESULTS: EBM for cortical volume biomarkers generated statistically robust characteristic sequences of cortical atrophy in each group of patients. Cortical volume-based EBM-estimated disease stage was associated with pathologic burden in FTLD-tau (R2 = 0.16, p = 0.017) and FTLD-TDP (R2 = 0.51, p = 0.0008). CONCLUSIONS: We provide evidence that EBM can identify sequences of pathologically-confirmed cortical atrophy in sporadic FTLD-tau and FTLD-TDP.

YNICL Journal 2021 Journal Article

Automated multiclass tissue segmentation of clinical brain MRIs with lesions

David A. Weiss
Rachit Saluja
Long Xie
James C. Gee
Leo P Sugrue
Abhijeet Pradhan
R. Nick Bryan
Andreas M. Rauschecker

Delineation and quantification of normal and abnormal brain tissues on Magnetic Resonance Images is fundamental to the diagnosis and longitudinal assessment of neurological diseases. Here we sought to develop a convolutional neural network for automated multiclass tissue segmentation of brain MRIs that was robust at typical clinical resolutions and in the presence of a variety of lesions. We trained a 3D U-Net for full brain multiclass tissue segmentation from a prior atlas-based segmentation method on an internal dataset that consisted of 558 clinical T1-weighted brain MRIs (453/52/53; training/validation/test) of patients with one of 50 different diagnostic entities (n = 362) or with a normal brain MRI (n = 196). We then used transfer learning to refine our model on an external dataset that consisted of 7 patients with hand-labeled tissue types. We evaluated the tissue-wise and intra-lesion performance with different loss functions and spatial prior information in the validation set and applied the best performing model to the internal and external test sets. The network achieved an average overall Dice score of 0.87 and volume similarity of 0.97 in the internal test set. Further, the network achieved a median intra-lesion tissue segmentation accuracy of 0.85 inside lesions within white matter and 0.61 inside lesions within gray matter. After transfer learning, the network achieved an average overall Dice score of 0.77 and volume similarity of 0.96 in the external dataset compared to human raters. The network had equivalent or better performance than the original atlas-based method on which it was trained across all metrics and produced segmentations in a hundredth of the time. We anticipate that this pipeline will be a useful tool for clinical decision support and quantitative analysis of clinical brain MRIs in the presence of lesions.

YNICL Journal 2018 Journal Article

Longitudinal structural gray matter and white matter MRI changes in presymptomatic progranulin mutation carriers

Christopher A. Olm
Corey T. McMillan
David J. Irwin
Vivianna M. Van Deerlin
Philip A. Cook
James C. Gee
Murray Grossman

Introduction: mutation carriers (pGRN+) compared to young controls (yCTL). Methods: = 11, mean age = 53.6) were identified. They completed a MRI session with T1-weighted imaging to assess GM density (GMD) and diffusion-weighted imaging (DWI) to assess fractional anisotropy (FA). Participants completed a follow-up session with T1 and DWI imaging (pGRN+ mean interval 2.20 years; yCTL mean interval 3.27 years). Annualized changes of GMD and FA were also compared. Results: Relative to yCTL, pGRN+ individuals displayed reduced GMD at baseline in bilateral orbitofrontal, insular, and anterior temporal cortices. pGRN+ also showed greater annualized GMD changes than yCTL at follow-up in right orbitofrontal and left occipital cortices. We also observed reduced FA at baseline in bilateral superior longitudinal fasciculus, left corticospinal tract, and frontal corpus callosum in pGRN+ relative to yCTL, and pGRN+ displayed greater annualized longitudinal FA change in right superior longitudinal fasciculus and frontal corpus callosum. Conclusions: Longitudinal MRI provides evidence of progressive GM and WM changes in pGRN+ participants relative to yCTL. Structural MRI illustrates the natural history of presymptomatic GRN carriers, and may provide an endpoint during disease-modifying treatment trials for pGRN+ individuals at risk for FTD.

YNICL Journal 2016 Journal Article

Computational analysis in epilepsy neuroimaging: A survey of features and methods

Lohith G. Kini
James C. Gee
Brian Litt

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients. Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10 years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy.

YNIMG Journal 2015 Journal Article

Decomposing cerebral blood flow MRI into functional and structural components: A non-local approach based on prediction

Benjamin M. Kandel
Danny J.J. Wang
John A. Detre
James C. Gee
Brian B. Avants

We present RIPMMARC (Rotation Invariant Patch-based Multi-Modality Analysis aRChitecture), a flexible and widely applicable method for extracting information unique to a given modality from a multi-modal data set. We use RIPMMARC to improve the interpretation of arterial spin labeling (ASL) perfusion images by removing the component of perfusion that is predicted by the underlying anatomy. Using patch-based, rotation invariant descriptors derived from the anatomical image, we learn a predictive relationship between local neuroanatomical structure and the corresponding perfusion image. This relation allows us to produce an image of perfusion that would be predicted given only the underlying anatomy and a residual image that represents perfusion information that cannot be predicted by anatomical features. Our learned structural features are significantly better at predicting brain perfusion than tissue probability maps, which are the input to standard partial volume correction techniques. Studies in test–retest data show that both the anatomically predicted and residual perfusion signals are highly replicable for a given subject. In a pediatric population, both the raw perfusion and structurally predicted images are tightly linked to age throughout adolescence throughout the brain. Interestingly, the residual perfusion also shows a strong correlation with age in selected regions including the hippocampi (corr=0. 38, p-value <10−6), precuneus (corr=−0. 44, p <10−5), and combined default mode network regions (corr=−0. 45, p <10−8) that is independent of global anatomy-perfusion trends. This finding suggests that there is a regionally heterogeneous pattern of functional specialization that is distinct from that of cortical structural development.

YNIMG Journal 2014 Journal Article

Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI

Daniel H. Adler
John Pluta
Salmon Kadivar
Caryne Craige
James C. Gee
Brian B. Avants
Paul A. Yushkevich

Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200μm spacing and 5μm slice thickness; stained using the Kluver–Barrera method) onto a postmortem 9. 4Tesla MRI scan of the intact, whole hippocampal formation acquired with 160μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1cm-thick tissue sub-blocks acquired with 200μm isotropic resolution. These 1cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.

YNIMG Journal 2014 Journal Article

Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements

Nicholas J. Tustison
Philip A. Cook
Arno Klein
Gang Song
Sandhitsu R. Das
Jeffrey T. Duda
Benjamin M. Kandel
Niels van Strien

Many studies of the human brain have explored the relationship between cortical thickness and cognition, phenotype, or disease. Due to the subjectivity and time requirements in manual measurement of cortical thickness, scientists have relied on robust software tools for automation which facilitate the testing and refinement of neuroscientific hypotheses. The most widely used tool for cortical thickness studies is the publicly available, surface-based FreeSurfer package. Critical to the adoption of such tools is a demonstration of their reproducibility, validity, and the documentation of specific implementations that are robust across large, diverse imaging datasets. To this end, we have developed the automated, volume-based Advanced Normalization Tools (ANTs) cortical thickness pipeline comprising well-vetted components such as SyGN (multivariate template construction), SyN (image registration), N4 (bias correction), Atropos (n-tissue segmentation), and DiReCT (cortical thickness estimation). In this work, we have conducted the largest evaluation of automated cortical thickness measures in publicly available data, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets (IXI, MMRR, NKI, and OASIS), with parcellation based on the recently proposed Desikan–Killiany–Tourville (DKT) cortical labeling protocol. We found good scan–rescan repeatability with both FreeSurfer and ANTs measures. Given that such assessments of precision do not necessarily reflect accuracy or an ability to make statistical inferences, we further tested the neurobiological validity of these approaches by evaluating thickness-based prediction of age and gender. ANTs is shown to have a higher predictive performance than FreeSurfer for both of these measures. In promotion of open science, we make all of our scripts, data, and results publicly available which complements the use of open image data sets and the open source availability of the proposed ANTs cortical thickness pipeline.

YNIMG Journal 2014 Journal Article

Relating brain anatomy and cognitive ability using a multivariate multimodal framework

Philip A. Cook
Corey T. McMillan
Brian B. Avants
Jonathan E. Peelle
James C. Gee
Murray Grossman

Linking structural neuroimaging data from multiple modalities to cognitive performance is an important challenge for cognitive neuroscience. In this study we examined the relationship between verbal fluency performance and neuroanatomy in 54 patients with frontotemporal degeneration (FTD) and 15 age-matched controls, all of whom had T1- and diffusion-weighted imaging. Our goal was to incorporate measures of both gray matter (voxel-based cortical thickness) and white matter (fractional anisotropy) into a single statistical model that relates to behavioral performance. We first used eigenanatomy to define data-driven regions of interest (DD-ROIs) for both gray matter and white matter. Eigenanatomy is a multivariate dimensionality reduction approach that identifies spatially smooth, unsigned principal components that explain the maximal amount of variance across subjects. We then used a statistical model selection procedure to see which of these DD-ROIs best modeled performance on verbal fluency tasks hypothesized to rely on distinct components of a large-scale neural network that support language: category fluency requires a semantic-guided search and is hypothesized to rely primarily on temporal cortices that support lexical-semantic representations; letter-guided fluency requires a strategic mental search and is hypothesized to require executive resources to support a more demanding search process, which depends on prefrontal cortex in addition to temporal network components that support lexical representations. We observed that both types of verbal fluency performance are best described by a network that includes a combination of gray matter and white matter. For category fluency, the identified regions included bilateral temporal cortex and a white matter region including left inferior longitudinal fasciculus and frontal–occipital fasciculus. For letter fluency, a left temporal lobe region was also selected, and also regions of frontal cortex. These results are consistent with our hypothesized neuroanatomical models of language processing and its breakdown in FTD. We conclude that clustering the data with eigenanatomy before performing linear regression is a promising tool for multimodal data analysis.

YNIMG Journal 2014 Journal Article

Subject-specific functional parcellation via Prior Based Eigenanatomy

Paramveer S. Dhillon
David A. Wolk
Sandhitsu R. Das
Lyle H. Ungar
James C. Gee
Brian B. Avants

We present a new framework for prior-constrained sparse decomposition of matrices derived from the neuroimaging data and apply this method to functional network analysis of a clinically relevant population. Matrix decomposition methods are powerful dimensionality reduction tools that have found widespread use in neuroimaging. However, the unconstrained nature of these totally data-driven techniques makes it difficult to interpret the results in a domain where network-specific hypotheses may exist. We propose a novel approach, Prior Based Eigenanatomy (p-Eigen), which seeks to identify a data-driven matrix decomposition but at the same time constrains the individual components by spatial anatomical priors (probabilistic ROIs). We formulate our novel solution in terms of prior-constrained ℓ 1 penalized (sparse) principal component analysis. p-Eigen starts with a common functional parcellation for all the subjects and refines it with subject-specific information. This enables modeling of the inter-subject variability in the functional parcel boundaries and allows us to construct subject-specific networks with reduced sensitivity to ROI placement. We show that while still maintaining correspondence across subjects, p-Eigen extracts biologically-relevant and patient-specific functional parcels that facilitate hypothesis-driven network analysis. We construct default mode network (DMN) connectivity graphs using p-Eigen refined ROIs and use them in a classification paradigm. Our results show that the functional connectivity graphs derived from p-Eigen significantly aid classification of mild cognitive impairment (MCI) as well as the prediction of scores in a Delayed Recall memory task when compared to graph metrics derived from 1) standard registration-based seed ROI definitions, 2) totally data-driven ROIs, 3) a model based on standard demographics plus hippocampal volume as covariates, and 4) Ward Clustering based data-driven ROIs. In summary, p-Eigen incarnates a new class of prior-constrained dimensionality reduction tools that may improve our understanding of the relationship between MCI and functional connectivity.

YNIMG Journal 2012 Journal Article

A diffusion tensor brain template for Rhesus Macaques

Nagesh Adluru
Hui Zhang
Andrew S. Fox
Steven E. Shelton
Chad M. Ennis
Anne M. Bartosic
Jonathan A. Oler
Do P.M. Tromp

Diffusion tensor imaging (DTI) is a powerful and noninvasive imaging method for characterizing tissue microstructure and white matter organization in the brain. While it has been applied extensively in research studies of the human brain, DTI studies of non-human primates have been performed only recently. The growing application of DTI in rhesus monkey studies would significantly benefit from a standardized framework to compare findings across different studies. A very common strategy for image analysis is to spatially normalize (co-register) the individual scans to a representative template space. This paper presents the development of a DTI brain template, UWRMAC-DTI271, for adolescent Rhesus Macaque (Macaca mulatta) monkeys. The template was generated from 271 rhesus monkeys, collected as part of a unique brain imaging genetics study. It is the largest number of animals ever used to generate a computational brain template, which enables the generation of a template that has high image quality and accounts for variability in the species. The quality of the template is further ensured with the use of DTI-TK, a well-tested and high-performance DTI spatial normalization method in human studies. We demonstrated its efficacy in monkey studies for the first time by comparing it to other commonly used scalar-methods for DTI normalization. It is anticipated that this template will play an important role in facilitating cross-site voxelwise DTI analyses in Rhesus Macaques. Such analyses are crucial in investigating the role of white matter structure in brain function, development, and other psychopathological disorders for which there are well-validated non-human primate models.

YNIMG Journal 2011 Journal Article

A reproducible evaluation of ANTs similarity metric performance in brain image registration

Brian B. Avants
Nicholas J. Tustison
Gang Song
Philip A. Cook
Arno Klein
James C. Gee

The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2. 0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0. 958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0. 669±0. 022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling.

YNIMG Journal 2010 Journal Article

Cerebral cortical folding analysis with multivariate modeling and testing: Studies on gender differences and neonatal development

Suyash P. Awate
Paul A. Yushkevich
Zhuang Song
Daniel J. Licht
James C. Gee

This paper presents a novel statistical framework for human cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (ROI). The ROI-based approach avoids problems faced by spatial normalization-based approaches stemming from the deficiency of homologous features between typical human cerebral cortices. Unlike typical ROI-based methods that summarize folding by a single number, the proposed descriptor unifies multiple characteristics of surface geometry in a high-dimensional space (hundreds/thousands of dimensions). In this way, the proposed framework couples the reliability of ROI-based analysis with the richness of the novel cortical folding pattern descriptor. This paper presents new mathematical insights into the relationship of cortical complexity with intra-cranial volume (ICV). It shows that conventional complexity descriptors implicitly handle ICV differences in different ways, thereby lending different meanings to “complexity”. The paper proposes a new application of a nonparametric permutation-based approach for rigorous statistical hypothesis testing with multivariate cortical descriptors. The paper presents two cross-sectional studies applying the proposed framework to study folding differences between genders and in neonates with complex congenital heart disease. Both studies lead to novel interesting results.

YNIMG Journal 2010 Journal Article

Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis

Brian B. Avants
Philip A. Cook
Lyle Ungar
James C. Gee
Murray Grossman

We use a new, unsupervised multivariate imaging and analysis strategy to identify related patterns of reduced white matter integrity, measured with the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI), and decreases in cortical thickness, measured by high resolution T1-weighted imaging, in Alzheimer's disease (AD) and frontotemporal dementia (FTD). This process is based on a novel computational model derived from sparse canonical correlation analysis (SCCA) that allows us to automatically identify mutually predictive, distributed neuroanatomical regions from different imaging modalities. We apply the SCCA model to a dataset that includes 23 control subjects that are demographically matched to 49 subjects with autopsy or CSF-biomarker-diagnosed AD (n =24) and FTD (n =25) with both DTI and T1-weighted structural imaging. SCCA shows that the FTD-related frontal and temporal degeneration pattern is correlated across modalities with permutation corrected p <0. 0005. In AD, we find significant association between cortical thinning and reduction in white matter integrity within a distributed parietal and temporal network (p <0. 0005). Furthermore, we show that—within SCCA identified regions—significant differences exist between FTD and AD cortical-connective degeneration patterns. We validate these distinct, multimodal imaging patterns by showing unique relationships with cognitive measures in AD and FTD. We conclude that SCCA is a potentially valuable approach in image analysis that can be applied productively to distinguishing between neurodegenerative conditions.

YNIMG Journal 2010 Journal Article

Early parental care is important for hippocampal maturation: Evidence from brain morphology in humans

Hengyi Rao
Laura Betancourt
Joan M. Giannetta
Nancy L. Brodsky
Marc Korczykowski
Brian B. Avants
James C. Gee
Jiongjiong Wang

The effects of early life experience on later brain structure and function have been studied extensively in animals, yet the relationship between childhood experience and normal brain development in humans remains largely unknown. Using a unique longitudinal data set including ecologically valid in-home measures of early experience during childhood (at age 4 and 8 years) and high-resolution structural brain imaging during adolescence (mean age 14 years), we examined the effects on later brain morphology of two dimensions of early experience: parental nurturance and environmental stimulation. Parental nurturance at age 4 predicts the volume of the left hippocampus in adolescence, with better nurturance associated with smaller hippocampal volume. In contrast, environmental stimulation did not correlate with hippocampal volume. Moreover, the association between hippocampal volume and parental nurturance disappears at age 8, supporting the existence of a sensitive developmental period for brain maturation. These findings indicate that variation in normal childhood experience is associated with differences in brain morphology, and hippocampal volume is specifically associated with early parental nurturance. Our results provide neuroimaging evidence supporting the important role of warm parental care during early childhood for brain maturation.

YNIMG Journal 2010 Journal Article

Evaluation of volume-based and surface-based brain image registration methods

Arno Klein
Satrajit S. Ghosh
Brian Avants
B.T.T. Yeo
Bruce Fischl
Babak Ardekani
James C. Gee
J.J. Mann

Establishing correspondences across brains for the purposes of comparison and group analysis is almost universally done by registering images to one another either directly or via a template. However, there are many registration algorithms to choose from. A recent evaluation of fully automated nonlinear deformation methods applied to brain image registration was restricted to volume-based methods. The present study is the first that directly compares some of the most accurate of these volume registration methods with surface registration methods, as well as the first study to compare registrations of whole-head and brain-only (de-skulled) images. We used permutation tests to compare the overlap or Hausdorff distance performance for more than 16, 000 registrations between 80 manually labeled brain images. We compared every combination of volume-based and surface-based labels, registration, and evaluation. Our primary findings are the following: 1. de-skulling aids volume registration methods; 2. custom-made optimal average templates improve registration over direct pairwise registration; and 3. resampling volume labels on surfaces or converting surface labels to volumes introduces distortions that preclude a fair comparison between the highest ranking volume and surface registration methods using present resampling methods. From the results of this study, we recommend constructing a custom template from a limited sample drawn from the same or a similar representative population, using the same algorithm used for registering brains to the template.

YNIMG Journal 2010 Journal Article

The optimal template effect in hippocampus studies of diseased populations

Brian B. Avants
Paul Yushkevich
John Pluta
David Minkoff
Marc Korczykowski
John Detre
James C. Gee

We evaluate the impact of template choice on template-based segmentation of the hippocampus in epilepsy. Four dataset-specific strategies are quantitatively contrasted: the “closest to average” individual template, the average shape version of the closest to average template, a best appearance template and the best appearance and shape template proposed here and implemented in the open source toolkit Advanced Normalization Tools (ANTS). The cross-correlation similarity metric drives the correspondence model and is used consistently to determine the optimal appearance. Minimum shape distance in the diffeomorphic space determines optimal shape. Our evaluation results show that, with respect to gold-standard manual labeling of hippocampi in epilepsy, optimal shape and appearance template construction outperforms the other strategies for gaining data-derived templates. Our results also show the improvement is most significant on the diseased side and insignificant on the healthy side. Thus, the importance of the template increases when used to study pathology and may be less critical for normal control studies. Furthermore, explicit geometric optimization of the shape component of the unbiased template positively impacts the study of diseased hippocampi.

AIIM Journal 2009 Journal Article

Morphometric analysis of brain images with reduced number of statistical tests: A study on the gender-related differentiation of the corpus callosum

Despina Kontos
Vasileios Megalooikonomou
James C. Gee

Objective We evaluate the feasibility of applying dynamic recursive partitioning (DRP), an image analysis technique, to perform morphometric analysis. We apply DRP to detect and characterize discriminative morphometric characteristics between anatomical brain structures from different groups of subjects. Our method reduces the number of statistical tests, commonly required by pixel-wise statistics, alleviating the effect of the multiple comparison problem. Methods and Materials The main idea of DRP is to partition the two-dimensional (2D) image adaptively into progressively smaller subregions until statistically significant discriminative regions are detected. The partitioning process is guided by statistical tests applied on groups of pixels. By performing statistical tests on groups of pixels rather than on individual pixels, the number of statistical tests is effectively reduced. This reduction of statistical tests restricts the effect of the multiple comparison problem (i. e. , type-I error). We demonstrate an application of DRP for detecting gender-related morphometric differentiation of the corpus callosum. DRP was applied to template deformation fields computed from registered magnetic resonance images of the corpus callosum to detect regions of significant expansion or contraction between female and male subjects. Results DRP was able to detect regions comparable to those of pixel-wise analysis, while reducing the number of required statistical tests up to almost 50%. The detected regions were in agreement with findings previously reported in the literature. Statistically significant discriminative morphological variability was detected in the posterior corpus callosum region, the isthmus and the anterior corpus callosum. In addition, by operating on groups of pixels, DRP appears to be less prone to detecting spatially diffused and isolated outlier pixels as significant. Conclusion DRP can be a viable approach for detecting discriminative morphometric characteristics among groups of subjects, having the potential to alleviate the multiple comparisons’ effect by significantly reducing the number of required statistical tests.

YNIMG Journal 2009 Journal Article

Registration based cortical thickness measurement

Sandhitsu R. Das
Brian B. Avants
Murray Grossman
James C. Gee

Cortical thickness is an important biomarker for image-based studies of the brain. A diffeomorphic registration based cortical thickness (DiReCT) measure is introduced where a continuous one-to-one correspondence between the gray matter–white matter interface and the estimated gray matter–cerebrospinal fluid interface is given by a diffeomorphic mapping in the image space. Thickness is then defined in terms of a distance measure between the interfaces of this sheet like structure. This technique also provides a natural way to compute continuous estimates of thickness within buried sulci by preventing opposing gray matter banks from intersecting. In addition, the proposed method incorporates neuroanatomical constraints on thickness values as part of the mapping process. Evaluation of this method is presented on synthetic images. As an application to brain images, a longitudinal study of thickness change in frontotemporal dementia (FTD) spectrum disorder is reported.

YNIMG Journal 2008 Journal Article

Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study

Junghoon Kim
Brian Avants
Sunil Patel
John Whyte
Branch H. Coslett
John Pluta
John A. Detre
James C. Gee

Traumatic brain injury (TBI) is one of the most common causes of long-term disability. Despite the importance of identifying neuropathology in individuals with chronic TBI, methodological challenges posed at the stage of inter-subject image registration have hampered previous voxel-based MRI studies from providing a clear pattern of structural atrophy after TBI. We used a novel symmetric diffeomorphic image normalization method to conduct a tensor-based morphometry (TBM) study of TBI. The key advantage of this method is that it simultaneously estimates an optimal template brain and topology preserving deformations between this template and individual subject brains. Detailed patterns of atrophies are then revealed by statistically contrasting control and subject deformations to the template space. Participants were 29 survivors of TBI and 20 control subjects who were matched in terms of age, gender, education, and ethnicity. Localized volume losses were found most prominently in white matter regions and the subcortical nuclei including the thalamus, the midbrain, the corpus callosum, the mid- and posterior cingulate cortices, and the caudate. Significant voxel-wise volume loss clusters were also detected in the cerebellum and the frontal/temporal neocortices. Volume enlargements were identified largely in ventricular regions. A similar pattern of results was observed in a subgroup analysis where we restricted our analysis to the 17 TBI participants who had no macroscopic focal lesions (total lesion volume >1. 5 cm3). The current study confirms, extends, and partly challenges previous structural MRI studies in chronic TBI. By demonstrating that a large deformation image registration technique can be successfully combined with TBM to identify TBI-induced diffuse structural changes with greater precision, our approach is expected to increase the sensitivity of future studies examining brain–behavior relationships in the TBI population.

YNIMG Journal 2008 Journal Article

Structure-specific statistical mapping of white matter tracts

Paul A. Yushkevich
Hui Zhang
Tony J. Simon
James C. Gee

We present a new model-based framework for the statistical analysis of diffusion imaging data associated with specific white matter tracts. The framework takes advantage of the fact that several of the major white matter tracts are thin sheet-like structures that can be effectively modeled by medial representations. The approach involves segmenting major tracts and fitting them with deformable geometric medial models. The medial representation makes it possible to average and combine tensor-based features along directions locally perpendicular to the tracts, thus reducing data dimensionality and accounting for errors in normalization. The framework enables the analysis of individual white matter structures, and provides a range of possibilities for computing statistics and visualizing differences between cohorts. The framework is demonstrated in a study of white matter differences in pediatric chromosome 22q11. 2 deletion syndrome.

YNIMG Journal 2007 Journal Article

Hippocampus-specific fMRI group activation analysis using the continuous medial representation

Paul A. Yushkevich
John A. Detre
Dawn Mechanic-Hamilton
María A. Fernández-Seara
Kathy Z. Tang
Angela Hoang
Marc Korczykowski
Hui Zhang

We present a new shape-based approach for regional group activation analysis in fMRI studies. The method restricts anatomical normalization, spatial smoothing and random effects statistical analysis to the space inside and around a structure of interest. Normalization involves finding intersubject correspondences between manually outlined masks, and it leverages the continuous medial representation, which makes it possible to extend surface-based shape correspondences to the space inside and outside of structures. Our approach is an alternative to whole-brain normalization in cases where the latter may fail due to anatomical variability or pathology. It also provides an opportunity to analyze the shape and thickness of structures concurrently with functional activation. We apply the technique to the hippocampus and evaluate it using data from a visual scene encoding fMRI study, where activation in the hippocampus is expected. We produce detailed statistical maps of hippocampal activation, as well as maps comparing activation inside and outside of the hippocampus. We find that random effects statistics computed by the new approach are more significant than those produced using the Statistical Parametric Mapping framework (Friston, K. J. , Holmes, A. P. , Worsley, K. J. , Poline, J. -P. , Firth, C. D. , Frackowiak, R. S. J. 1994, Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, 2(4): 189–210) at low levels of smoothing, suggesting that greater specificity can be achieved by the new method without a severe tradeoff in sensitivity.

YNIMG Journal 2007 Journal Article

Multivariate examination of brain abnormality using both structural and functional MRI

Yong Fan
Hengyi Rao
Hallam Hurt
Joan Giannetta
Marc Korczykowski
David Shera
Brian B. Avants
James C. Gee

A multivariate classification approach has been presented to examine the brain abnormalities, i. e. , due to prenatal cocaine exposure, using both structural and functional brain images. First, a regional statistical feature extraction scheme was adopted to capture discriminative features from voxel-wise morphometric and functional representations of brain images, in order to reduce the dimensionality of the features used for classification, as well as to achieve the robustness to registration error and inter-subject variations. Then, this feature extraction method was used in conjunction with a hybrid feature selection method and a nonlinear support vector machine for the classification of brain abnormalities. This brain classification approach has been applied to detecting the brain abnormality associated with prenatal cocaine exposure in adolescents. A promising classification performance was achieved on a data set of 49 subjects (24 normal and 25 prenatally cocaine-exposed teenagers), with a leave-one-out cross-validation. Experimental results demonstrated the efficacy of our method, as well as the importance of incorporating both structural and functional images for brain classification. Moreover, spatial patterns of group difference derived from the constructed classifier were mostly consistent with the results of the conventional statistical analysis method. Therefore, the proposed approach provided not only a multivariate classification method for detecting brain abnormalities, but also an alternative way for group analysis of multimodality images.

YNIMG Journal 2006 Journal Article

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Paul A. Yushkevich
Joseph Piven
Heather Cody Hazlett
Rachel Gimpel Smith
Sean Ho
James C. Gee
Guido Gerig

Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.

YNIMG Journal 2004 Journal Article

Geodesic estimation for large deformation anatomical shape averaging and interpolation

Brian Avants
James C. Gee

The goal of this research is to promote variational methods for anatomical averaging that operate within the space of the underlying image registration problem. This approach is effective when using the large deformation viscous framework, where linear averaging is not valid, or in the elastic case. The theory behind this novel atlas building algorithm is similar to the traditional pairwise registration problem, but with single image forces replaced by average forces. These group forces drive an average transport ordinary differential equation allowing one to estimate the geodesic that moves an image toward the mean shape configuration. This model gives large deformation atlases that are optimal with respect to the shape manifold as defined by the data and the image registration assumptions. We use the techniques in the large deformation context here, but they also pertain to small deformation atlas construction. Furthermore, a natural, inherently inverse consistent image registration is gained for free, as is a tool for constant arc length geodesic shape interpolation. The geodesic atlas creation algorithm is quantitatively compared to the Euclidean anatomical average to elucidate the need for optimized atlases. The procedures generate improved average representations of highly variable anatomy from distinct populations.

AIIM Journal 2004 Journal Article

Structural shape characterization via exploratory factor analysis

Alexei M.C. Machado
James C. Gee
Mario F.M. Campos

This article presents an exploratory factor analytic approach to morphometry in which a high-dimensional set of shape-related variables is examined with the purpose of finding clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insight to knowledge discovery and morphometric investigations. Methods: The information about regional shape is extracted by registering a reference image to a set of test images. Based on the displacement fields obtained form image registration, the amount of pointwise volume enlargement or reduction is computed and statistically analyzed with the purpose of extracting a reduced set of common factors. Experiments: The effectiveness and robustness of the method is demonstrated in a study of gender-related differences of the human corpus callosum anatomy, based on a sample of 84 right-handed normal controls. Results: The method is able to automatically partition the structure into regions of interest, in which the most relevant shape differences can be observed. The confidence of results is evaluated by analyzing the statistical fit of the model and compared to previous experimental works.

YNIMG Journal 2002 Journal Article

Sexual Dimorphism in the Corpus Callosum: A Characterization of Local Size Variations and a Classification Driven Approach to Morphometry

David J. Pettey
James C. Gee

We present two complementary quantitative approaches to the problem of characterizing morphometric variations between two distinct populations. The case presented focuses solely on local size variations, but the general method can easily be applied to other scalar morphometric quantities. The first method uses a statistical parametric map (SPM) to ascertain a P value, which indicates whether any statistically significant differences exist between the populations. The second method focuses on finding the best single measurement which can be used for classifying the two populations. For our case study midsagittal cross sections of the corpora callosa from a population of normal males and females are nonrigidly registered (spatially normalized) to an atlas. The resulting deformations are then used to ascertain (i) whether there are any statistically significant differences between the populations and (ii) whether these differences allow one to perform classification. We make use of the Jacobian of the deformation field and normalize it to account for overall volume changes allowing us to focus on differences which are more related to morphometry than scale. From the (SPM) approach to the problem we find evidence of statistically significant differences in the morphology between the populations. Using a linear discriminant function we find that these differences do not appear to be useful for classification. Thus, this dataset provides an example of how statistically significant effects may not be of much diagnostic value. They may be of interest to the research community, but of little value to the clinician.