Browsing by Author "Cong, Shan"

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    Building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans using landmark-free surface registration
    (IEEE, 2016-10) Cong, Shan; Rizkalla, Maher; Salama, Paul; Electrical and Computer Engineering, School of Engineering and Technology
    The hippocampus is widely studied in neuroimaging field as it plays important roles in memory and learning. However, the critical subfield information is often not explored in most hippocampal studies. We previously proposed a method for hippocampal subfield morphometry by integrating FreeSurfer, FSL, and SPHARM tools. But this method had some limitations, including the analysis of T1-weighted MRI scans without detailed subfield information and hippocampal registration without using important subfield information. To bridge these gaps, in this work, we propose a new framework for building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans by integrating state-of-the-art methods for automated segmentation of hippocampal subfields and landmark-free, subfield-aware registration of hippocampal surfaces. Our experimental results have shown the promise of the new framework.
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    Building a Surface Atlas of Hippocampal Subfields from MRI Scans using FreeSurfer, FIRST and SPHARM
    (Institute of Electrical and Electronics Engineers, 2014-08) Cong, Shan; Rizkalla, Maher; Du, Eliza Y.; West, John; Risacher, Shannon; Saykin, Andrew J.; Shen, Li; Alzheimer's Disease Neuroimaging Initiative; Department of Medicine, IU School of Medicine
    The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for brain disorders such as Alzheimer's disease and epilepsy. However, its complex folding anatomy often presents analytical challenges. In particular, the critical hippocampal subfield information is usually ignored by hippocampal registration in detailed morphometric studies. Such an approach is thus inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To bridge this gap, we present our initial effort towards building a computational framework for subfield-guided hippocampal morphometry. This initial effort is focused on surface-based morphometry and aims to build a surface atlas of hippocampal subfields. Using the FreeSurfer software package, we obtain valuable hippocampal subfield information. Using the FIRST software package, we extract reliable hippocampal surface information. Using SPHARM, we develop an approach to create an atlas by mapping interpolated subfield information onto an average surface. The empirical result using ADNI data demonstrates the promise and good reproducibility of the proposed method.
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    Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts
    (Frontiers Media, 2022-02-07) Cong, Shan; Yao, Xiaohui; Xie, Linhui; Yan, Jingwen; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineerin
    Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.
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    Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms
    (Springer, 2018-09) Mussabayeva, Ayagoz; Kroshnin, Alexey; Kurmukov, Anvar; Dodonova, Yulia; Shen, Li; Cong, Shan; Wang, Lei; Gutman, Boris A.; Computer Information and Graphics Technology, School of Engineering and Technology
    We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context. For simplicity, we choose the kernel Fischer Linear Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters in an Expectation-Maximization framework, we define model fidelity via the hinge loss of the decision function. The resulting algorithm optimizes the parameters of the LDDMM norm-inducing differential operator as a solution to a group-wise registration and classification problem. In practice, this may lead to a biology-aware registration, focusing its attention on the predictive task at hand such as identifying the effects of disease. We first tested our algorithm on a synthetic dataset, showing that our parameter selection improves registration quality and classification accuracy. We then tested the algorithm on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our Schizophrenia-Control predictive model showed significant improvement in ROC AUC compared to baseline parameters.
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    Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
    (Springer Nature, 2017-09) Petrov, Dmitry; Gutman, Boris A.; Yu, Shih-Hua (Julie); van Erp, Theo G.M.; Turner, Jessica A.; Schmaal, Lianne; Veltman, Dick; Wang, Lei; Alpert, Kathryn; Isaev, Dmitry; Zavaliangos-Petropulu, Artemis; Ching, Christopher R.K.; Calhoun, Vince; Glahn, David; Satterthwaite, Theodore D.; Andreasen, Ole Andreas; Borgwardt, Stefan; Howells, Fleur; Groenewold, Nynke; Voineskos, Aristotle; Radua, Joaquim; Potkin, Steven G.; Crespo-Facorro, Benedicto; Tordesillas-Gutirrez, Diana; Shen, Li; Lebedeva, Irina; Spalletta, Gianfranco; Donohoe, Gary; Kochunov, Peter; Rosa, Pedro G.P.; James, Anthony; Dannlowski, Udo; Baune, Berhard T.; Aleman, Andre; Gotlib, Ian H.; Walter, Henrik; Walter, Martin; Soares, Jair C.; Ehrlich, Stefan; Gur, Ruben C.; Doan, N. Trung; Agartz, Ingrid; Westlye, Lars T.; Harrisberger, Fabienne; Richer-Rossler, Anita; Uhlmann, Anne; Stein, Dan J.; Dickie, Erin W.; Pomarol-Clotet, Edith; Fuentes-Claramonte, Paola; Canales-Rodriguez, Erick Jorge; Salvador, Raymond; Huang, Alexander J.; Roiz-Santianez, Roberto; Cong, Shan; Tomyshev, Alexander; Piras, Fabrizio; Vecchio, Daniela; Banaj, Nerisa; Ciullo, Valentina; Hong, Elliot; Busatto, Geraldo; Zanetti, Marcus V.; Serpa, Mauricio H.; Cervenka, Simon; Kelly, Sinead; Grotegerd, Dominik; Sacchet, Matthew D.; Veer, Illya M.; Li, Meng; Wu, Mon-Ju; Irungu, Benson; Walton, Esther; Thompson, Paul M.; Medicine, School of Medicine
    As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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    Morphometric analysis of hippocampal subfields : segmentation, quantification and surface modeling
    (2014) Cong, Shan; Rizkalla, Maher E.; Shen, Li (Radiologist); Du, Yingzi, 1975-
    Object segmentation, quantification, and shape modeling are important areas inmedical image processing. By combining these techniques, researchers can find valuableways to extract and represent details on user-desired structures, which can functionas the base for subsequent analyses such as feature classification, regression, and prediction. This thesis presents a new framework for building a three-dimensional (3D) hippocampal atlas model with subfield information mapped onto its surface, with which hippocampal surface registration can be done, and the comparison and analysis can be facilitated and easily visualized. This framework combines three powerful tools for automatic subcortical segmentation and 3D surface modeling. Freesurfer and Functional magnetic resonance imaging of the brain's Integrated Registration and Segmentation Tool (FIRST) are employed for hippocampal segmentation and quantification, while SPherical HARMonics (SPHARM) is employed for parametric surface modeling. This pipeline is shown to be effective in creating a hippocampal surface atlas using the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity and phase 2 (ADNI GO/2) dataset. Intra-class Correlation Coefficients (ICCs) are calculated for evaluating the reliability of the extracted hippocampal subfields. The complex folding anatomy of the hippocampus offers many analytical challenges, especially when informative hippocampal subfields are usually ignored in detailed morphometric studies. Thus, current research results are inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To address this challenge, one contribution of this study is to model the hippocampal surface using a parametric spherical harmonic model, which is a Fourier descriptor for general a 3D surface. The second contribution of this study is to extend hippocampal studies by incorporating valuable hippocampal subfield information. Based on the subfield distributions, a surface atlas is created for both left and right hippocampi. The third contribution is achieved by calculating Fourier coefficients in the parametric space. Based on the coefficient values and user-desired degrees, a pair of averaged hippocampal surface atlas models can be reconstructed. These contributions lay a solid foundation to facilitate a more accurate, subfield-guided morphometric analysis of the hippocampus and have the potential to reveal subtle hippocampal structural damage associated.
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    A New Statistical Image Analysis Approach and Its Application to Hippocampal Morphometry
    (Springer, 2016) Inlow, Mark; Cong, Shan; Risacher, Shannon L.; West, John; Rizkalla, Maher; Salama, Paul; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of Medicine
    In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer’s disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods.
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    Regional imaging genetic enrichment analysis
    (Oxford University Press, 2020-04-15) Yao, Xiaohui; Cong, Shan; Yan, Jingwen; Risacher, Shannon L.; Saykin, Andrew J.; Moore, Jason H.; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Motivation: Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful interpretation and modest dimensionality compared with voxelwise approaches. Typical ROI-level measures used in these studies are summary statistics from voxelwise measures in the region, without making full use of individual voxel signals. Results: In this article, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxelwise enrichment analysis, which embraces the collective effect of weak voxel-level signals and integrates brain anatomical annotation information. Our proposed method achieves three goals at the same time: (i) increase the statistical power by substantially reducing the burden of multiple comparison correction; (ii) employ brain annotation information to enable biologically meaningful interpretation and (iii) make full use of fine-grained voxelwise signals. We demonstrate our method on an imaging genetic analysis using data from the Alzheimer's Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxelwise FDG-positron emission tomography measures between 116 ROIs and 565 373 single-nucleotide polymorphisms. Compared with traditional ROI-wise and voxelwise approaches, our method identified 2946 novel imaging genetic associations in addition to 33 ones overlapping with the two benchmark methods. In particular, two newly reported variants were further supported by transcriptome evidences from region-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain. Availability and implementation: The R code and sample data are freely available at https://github.com/lshen/RIGEA.
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    Surface-Based Morphometric Analysis of Hippocampal Subfields in Mild Cognitive Impairment and Alzheimer's Disease
    (IEEE Xplore, 2015-08) Cong, Shan; Rizkalla, Maher; Salama, Paul; West, John; Risacher, Shannon; Saykin, Andrew; Shen, Li; Department of Electrical and Computer Engineering, School of Engineering and Technology
    The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for Alzheimer's disease (AD). Its complex folding anatomy often presents analytical challenges. In particular, the critical subfield information is typically not addressed by the existing hippocampal shape studies. To bridge this gap, we present a computational framework for surface-based morphometric analysis of hippocampal subfields. The major strengths of this framework are as follows: (a) it performs detailed hippocampal shape analysis, (b) it embraces, rather than ignores, the important hippocampal subfield information, and (c) it analyzes regular magnetic resonance imaging scans and is applicable to large scale studies. We demonstrate its effectiveness by applying it to the identification of regional hippocampal subfield atrophy patterns associated with mild cognitive impairment and AD.
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    Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans
    (Springer, 2018-01-05) Cong, Shan; Risacher, Shannon L.; West, John D.; Wu, Yu-Chien; Apostolova, Liana G.; Tallman, Eileen; Rizkalla, Maher; Salama, Paul; Saykin, Andrew J.; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    The hippocampus has been widely studied using neuroimaging, as it plays an important role in memory and learning. However, hippocampal subfield information is difficult to capture by standard magnetic resonance imaging (MRI) techniques. To facilitate morphometric study of hippocampal subfields, ADNI introduced a high resolution (0.4 mm in plane) T2-weighted turbo spin-echo sequence that requires 8 min. With acceleration, the protocol can be acquired in 4 min. We performed a comparative study of hippocampal subfield volumes using standard and accelerated protocols on a Siemens Prisma 3T MRI in an independent sample of older adults that included 10 cognitively normal controls, 9 individuals with subjective cognitive decline, 10 with mild cognitive impairment, and 6 with a clinical diagnosis of Alzheimer’s disease (AD). The Automatic Segmentation of Hippocampal Subfields (ASHS) software was used to segment 9 primary labeled regions including hippocampal subfields and neighboring cortical regions. Intraclass correlation coefficients were computed for reliability tests between 4 and 8 min scans within and across the four groups. Pairwise group analyses were performed, covaried for age, sex and total intracranial volume, to determine whether the patterns of group differences were similar using 4 vs. 8 min scans. The 4 and 8 min protocols, analyzed by ASHS segmentation, yielded similar volumetric estimates for hippocampal subfields as well as comparable patterns of differences between study groups. The accelerated protocol can provide reliable imaging data for investigation of hippocampal subfields in AD-related MRI studies and the decreased scan time may result in less vulnerability to motion.