1
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Vandekar SN, Kang K, Woodward ND, Huang A, McHugo M, Garbett S, Stephens J, Shinohara RT, Schwartzman A, Blume J. Evaluation of resampling-based inference for topological features of neuroimages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571377. [PMID: 38168311 PMCID: PMC10760090 DOI: 10.1101/2023.12.12.571377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Many recent studies have demonstrated the inflated type 1 error rate of the original Gaussian random field (GRF) methods for inference of neuroimages and identified resampling (permutation and bootstrapping) methods that have better performance. There has been no evaluation of resampling procedures when using robust (sandwich) statistical images with different topological features (TF) used for neuroimaging inference. Here, we consider estimation of distributions TFs of a statistical image and evaluate resampling procedures that can be used when exchangeability is violated. We compare the methods using realistic simulations and study sex differences in life-span age-related changes in gray matter volume in the Nathan Kline Institute Rockland sample. We find that our proposed wild bootstrap and the commonly used permutation procedure perform well in sample sizes above 50 under realistic simulations with heteroskedasticity. The Rademacher wild bootstrap has fewer assumptions than the permutation and performs similarly in samples of 100 or more, so is valid in a broader range of conditions. We also evaluate the GRF-based pTFCE method and show that it has inflated error rates in samples less than 200. Our R package, pbj , is available on Github and allows the user to reproducibly implement various resampling-based group level neuroimage analyses.
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2
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Zhou J, Sun WW, Zhang J, Li L. Partially Observed Dynamic Tensor Response Regression. J Am Stat Assoc 2021; 118:424-439. [PMID: 37333062 PMCID: PMC10274377 DOI: 10.1080/01621459.2021.1938082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 05/22/2021] [Accepted: 05/25/2021] [Indexed: 10/21/2022]
Abstract
In modern data science, dynamic tensor data prevail in numerous applications. An important task is to characterize the relationship between dynamic tensor datasets and external covariates. However, the tensor data are often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with a partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rankness, sparsity, and fusion structures on the regression coefficient tensor, and consider a loss function projected over the observed entries. We develop an efficient nonconvex alternating updating algorithm, and derive the finite-sample error bound of the actual estimator from each step of our optimization algorithm. Unobserved entries in the tensor response have imposed serious challenges. As a result, our proposal differs considerably in terms of estimation algorithm, regularity conditions, as well as theoretical properties, compared to the existing tensor completion or tensor response regression solutions. We illustrate the efficacy of our proposed method using simulations and two real applications, including a neuroimaging dementia study and a digital advertising study.
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Affiliation(s)
- Jie Zhou
- Department of Management Science, University of Miami Herbert Business School, Miami, FL
| | - Will Wei Sun
- Krannert School of Management, Purdue University, West Lafayette, IN
| | - Jingfei Zhang
- Department of Management Science, University of Miami Herbert Business School, Miami, FL
| | - Lexin Li
- Division of Biostatistics, University of California, Berkeley, Berkeley, CA
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3
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Martí-Juan G, Sanroma-Guell G, Piella G. A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105348. [PMID: 31995745 DOI: 10.1016/j.cmpb.2020.105348] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 01/10/2020] [Accepted: 01/18/2020] [Indexed: 05/02/2023]
Abstract
BACKGROUND AND OBJECTIVES Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging. METHODS We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines. RESULTS After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions. CONCLUSIONS Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.
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Affiliation(s)
- Gerard Martí-Juan
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | | | - Gemma Piella
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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4
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Xia CH, Ma Z, Cui Z, Bzdok D, Thirion B, Bassett DS, Satterthwaite TD, Shinohara RT, Witten DM. Multi-scale network regression for brain-phenotype associations. Hum Brain Mapp 2020; 41:2553-2566. [PMID: 32216125 PMCID: PMC7383128 DOI: 10.1002/hbm.24982] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/31/2020] [Accepted: 02/26/2020] [Indexed: 02/03/2023] Open
Abstract
Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships.
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Affiliation(s)
- Cedric Huchuan Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Zongming Ma
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Danilo Bzdok
- Department of Psychiatry, Psychopathology and Psychosomatics, RWTH Aachen University, Aachen, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.,Université Paris-Saclay, CEA, Inria, Gif-sur-Yvette, France.,Department of Bioengineering, McGill University, Montreal, Canada
| | | | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Physics and Astronomy, School of Arts and Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Russell T Shinohara
- Penn Statistics and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Biomedical Imaging Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniela M Witten
- Department of Statistics, College of Arts and Science, University of Washington, Seattle, Washington, USA.,Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington, USA
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5
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Huang M, Yu Y, Yang W, Feng Q. Incorporating spatial-anatomical similarity into the VGWAS framework for AD biomarker detection. Bioinformatics 2019; 35:5271-5280. [PMID: 31095298 PMCID: PMC6954655 DOI: 10.1093/bioinformatics/btz401] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 04/03/2019] [Accepted: 05/07/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The detection of potential biomarkers of Alzheimer's disease (AD) is crucial for its early prediction, diagnosis and treatment. Voxel-wise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues. RESULTS We introduce a novel method to incorporate spatial correlations into a VGWAS framework for the detection of potential AD biomarkers. To consider the characteristics of AD, we first present a modification of a simple linear iterative clustering method for spatial grouping in an anatomically meaningful manner. Second, we propose a spatial-anatomical similarity matrix to incorporate correlations among voxels. Finally, we detect the potential AD biomarkers from imaging and genetic data by using a fast VGWAS method and test our method on 708 subjects obtained from an Alzheimer's Disease Neuroimaging Initiative dataset. Results show that our method can successfully detect some new risk genes and clusters of AD. The detected imaging and genetic biomarkers are used as predictors to classify AD/normal control subjects, and a high accuracy of AD/normal control classification is achieved. To the best of our knowledge, the association between imaging and genetic data has yet to be systematically investigated while building statistical models for classifying AD subjects to create a link between imaging genetics and AD. Therefore, our method may provide a new way to gain insights into the underlying pathological mechanism of AD. AVAILABILITY AND IMPLEMENTATION https://github.com/Meiyan88/SASM-VGWAS.
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Affiliation(s)
- Meiyan Huang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Yuwei Yu
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Wei Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Qianjin Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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Howell BR, Ahn M, Shi Y, Godfrey JR, Hu X, Zhu H, Styner M, Sanchez MM. Disentangling the effects of early caregiving experience and heritable factors on brain white matter development in rhesus monkeys. Neuroimage 2019; 197:625-642. [PMID: 30978495 PMCID: PMC7179761 DOI: 10.1016/j.neuroimage.2019.04.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 03/30/2019] [Accepted: 04/03/2019] [Indexed: 10/27/2022] Open
Abstract
Early social experiences, particularly maternal care, shape behavioral and physiological development in primates. Thus, it is not surprising that adverse caregiving, such as child maltreatment leads to a vast array of poor developmental outcomes, including increased risk for psychopathology across the lifespan. Studies of the underlying neurobiology of this risk have identified structural and functional alterations in cortico-limbic brain circuits that seem particularly sensitive to these early adverse experiences and are associated with anxiety and affective disorders. However, it is not understood how these neurobiological alterations unfold during development as it is very difficult to study these early phases in humans, where the effects of maltreatment experience cannot be disentangled from heritable traits. The current study examined the specific effects of experience ("nurture") versus heritable factors ("nature") on the development of brain white matter (WM) tracts with putative roles in socioemotional behavior in primates from birth through the juvenile period. For this we used a randomized crossfostering experimental design in a naturalistic rhesus monkey model of infant maltreatment, where infant monkeys were randomly assigned at birth to either a mother with a history of maltreating her infants, or a competent mother. Using a longitudinal diffusion tensor imaging (DTI) atlas-based tract-profile approach we identified widespread, but also specific, maturational changes on major brain tracts, as well as alterations in a measure of WM integrity (fractional anisotropy, FA) in the middle longitudinal fasciculus (MdLF) and the inferior longitudinal fasciculus (ILF), of maltreated animals, suggesting decreased structural integrity in these tracts due to early adverse experience. Exploratory voxelwise analyses confirmed the tract-based approach, finding additional effects of early adversity, biological mother, social dominance rank, and sex in other WM tracts. These results suggest tract-specific effects of postnatal maternal care experience versus heritable or biological factors on primate WM microstructural development. Further studies are needed to determine the specific behavioral outcomes and biological mechanisms associated with these alterations in WM integrity.
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Affiliation(s)
- Brittany R Howell
- Department of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA, USA; Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
| | - Mihye Ahn
- Department of Mathematics and Statistics, University of Nevada, Reno, NV, USA; Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Yundi Shi
- Department. of Psychiatry and Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Jodi R Godfrey
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
| | - Xiaoping Hu
- Biomedical Imaging Technology Center, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Hongtu Zhu
- Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Martin Styner
- Department. of Psychiatry and Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Mar M Sanchez
- Department of Psychiatry & Behavioral Sciences, Emory University, Atlanta, GA, USA; Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
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7
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Zimmerman B, Finnegan M, Paul S, Schmidt S, Tai Y, Roth K, Chen Y, Husain FT. Functional Brain Changes During Mindfulness-Based Cognitive Therapy Associated With Tinnitus Severity. Front Neurosci 2019; 13:747. [PMID: 31396035 PMCID: PMC6667657 DOI: 10.3389/fnins.2019.00747] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 07/05/2019] [Indexed: 12/24/2022] Open
Abstract
Mindfulness-based therapies have been introduced as a treatment option to reduce the psychological severity of tinnitus, a currently incurable chronic condition. This pilot study of twelve subjects with chronic tinnitus investigates the relationship between measures of both task-based and resting state functional magnetic resonance imaging (fMRI) and measures of tinnitus severity, assessed with the Tinnitus Functional Index (TFI). MRI was measured at three time points: before, after, and at follow-up of an 8-week long mindfulness-based cognitive therapy intervention. During the task-based fMRI with affective sounds, no significant changes were observed between sessions, nor was the activation to emotionally salient compared to neutral stimuli significantly predictive of TFI. Significant results were found using resting state fMRI. There were significant decreases in functional connectivity among the default mode network, cingulo-opercular network, and amygdala across the intervention, but no differences were seen in connectivity with seeds in the dorsal attention network (DAN) or fronto-parietal network and the rest of the brain. Further, only resting state connectivity between the brain and the amygdala, DAN, and fronto-parietal network significantly predicted TFI. These results point to a mostly differentiated landscape of functional brain measures related to tinnitus severity on one hand and mindfulness-based therapy on the other. However, overlapping results of decreased amygdala connectivity with parietal areas and the negative correlation between amygdala-parietal connectivity and TFI is suggestive of a brain imaging marker of successful treatment.
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Affiliation(s)
- Benjamin Zimmerman
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Megan Finnegan
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, United States.,Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Subhadeep Paul
- Department of Statistics, The Ohio State University, Columbus, OH, United States
| | - Sara Schmidt
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, United States.,Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Yihsin Tai
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Kelly Roth
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Yuguo Chen
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Fatima T Husain
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, United States.,Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, United States
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8
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Husain FT, Zimmerman B, Tai Y, Finnegan MK, Kay E, Khan F, Menard C, Gobin RL. Assessing mindfulness-based cognitive therapy intervention for tinnitus using behavioural measures and structural MRI: a pilot study. Int J Audiol 2019; 58:889-901. [PMID: 31223049 DOI: 10.1080/14992027.2019.1629655] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Objective: We used a minimally-modified version of Mindfulness-Based Cognitive Therapy (MBCT) to treat symptoms of distress associated with tinnitus.Design: Audiological screening (establishing a baseline) was conducted prior to treatment and at three time-points: pre-intervention, post-intervention and follow-up, 8 weeks after completion of training. MRI tests were also conducted at these three time-points.Study sample: Twenty-one participants were enrolled in the study, of whom 15 completed training and audiological testing and eight completed the MRI portion of the study.Results: Scores on tinnitus-related questionnaires showed a significant decline either from pre- to post-intervention or from pre-intervention to follow-up, despite no significant change during baseline. Voxel-based morphometric analysis of the structural MRI scans revealed clusters in bilateral superior frontal gyrus that exhibited significant increases in grey matter volume over the period of intervention and follow-up. Further, grey matter changes in occipital and cingulate regions correlated with declines in tinnitus handicap.Conclusions: This pilot study supports MBCT as an adequate approach for treating distressing tinnitus and suggests that neuroanatomical changes may reflect reductions in tinnitus-related severity. Although our small sample size precludes drawing strong conclusions, there is potential for assessing neuroanatomical changes due to mindfulness-based interventions in tinnitus.
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Affiliation(s)
- Fatima T Husain
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Benjamin Zimmerman
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yihsin Tai
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Megan K Finnegan
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Emily Kay
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Faaiza Khan
- Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Christopher Menard
- Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Robyn L Gobin
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL, USA
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9
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Zhang X, Li L, Zhou H, Zhou Y, Shen D. TENSOR GENERALIZED ESTIMATING EQUATIONS FOR LONGITUDINAL IMAGING ANALYSIS. Stat Sin 2019; 29:1977-2005. [PMID: 32523321 DOI: 10.5705/ss.202017.0153] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Longitudinal neuroimaging studies are becoming increasingly prevalent, where brain images are collected on multiple subjects at multiple time points. Analyses of such data are scientifically important, but also challenging. Brain images are in the form of multidimensional arrays, or tensors, which are characterized by both ultrahigh dimensionality and a complex structure. Longitudinally repeated images and induced temporal correlations add a further layer of complexity. Despite some recent efforts, there exist very few solutions for longitudinal imaging analyses. In response to the increasing need to analyze longitudinal imaging data, we propose several tensor generalized estimating equations (GEEs). The proposed GEE approach accounts for intra-subject correlation, and an imposed low-rank structure on the coefficient tensor effectively reduces the dimensionality. We also propose a scalable estimation algorithm, establish the asymptotic properties of the solution to the tensor GEEs, and investigate sparsity regularization for the purpose of region selection. We demonstrate the proposed method using simulations and by analyzing a real data set from the Alzheimer's Disease Neuroimaging Initiative.
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Affiliation(s)
- Xiang Zhang
- Department of Statistics, North Carolina State University, Raleigh, NC 27697, USA
| | - Lexin Li
- Division of Biostatistcs, University of California, Berkeley, CA 94720, USA
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, CA 90095, USA
| | - Yeqing Zhou
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China
| | - Dinggang Shen
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA
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10
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Knickmeyer RC, Xia K, Lu Z, Ahn M, Jha SC, Zou F, Zhu H, Styner M, Gilmore JH. Impact of Demographic and Obstetric Factors on Infant Brain Volumes: A Population Neuroscience Study. Cereb Cortex 2018; 27:5616-5625. [PMID: 27797836 DOI: 10.1093/cercor/bhw331] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Indexed: 11/14/2022] Open
Abstract
Individual differences in neuroanatomy are associated with intellectual ability and psychiatric risk. Factors responsible for this variability remain poorly understood. We tested whether 17 major demographic and obstetric variables were associated with individual differences in brain volumes in 756 neonates assessed with MRI. Gestational age at MRI, sex, gestational age at birth, and birthweight were the most significant predictors, explaining 31% to 59% of variance. Unexpectedly, earlier born babies had larger brains than later born babies after adjusting for other predictors. Our results suggest earlier born children experience accelerated brain growth, either as a consequence of the richer sensory environment they experience outside the womb or in response to other factors associated with delivery. In the full sample, maternal and paternal education, maternal ethnicity, maternal smoking, and maternal psychiatric history showed marginal associations with brain volumes, whereas maternal age, paternal age, paternal ethnicity, paternal psychiatric history, and income did not. Effects of parental education and maternal ethnicity are partially mediated by differences in birthweight. Remaining effects may reflect differences in genetic variation or cultural capital. In particular late initiation of prenatal care could negatively impact brain development. Findings could inform public health policy aimed at optimizing child development.
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Affiliation(s)
- Rebecca C Knickmeyer
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - Zhaohua Lu
- Department of Human Development and Family Studies, Quantitative Developmental Systems Methodology, Pennsylvania State University, University Park, PA 16802, USA
| | - Mihye Ahn
- Department of Mathematics and Statistics, University of Nevada, Reno, NV 89557-0084, USA
| | - Shaili C Jha
- Curriculum in Neurobiology,University of North Carolina, Chapel Hill, NC 27599-7320, USA
| | - Fei Zou
- Department of Biostatistics,University of North Carolina, Chapel Hill, NC 27599-7420, USA
| | - Hongtu Zhu
- Department of Biostatistics,University of North Carolina, Chapel Hill, NC 27599-7420, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
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11
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Fiford CM, Ridgway GR, Cash DM, Modat M, Nicholas J, Manning EN, Malone IB, Biessels GJ, Ourselin S, Carmichael OT, Cardoso MJ, Barnes J. Patterns of progressive atrophy vary with age in Alzheimer's disease patients. Neurobiol Aging 2018; 63:22-32. [PMID: 29220823 PMCID: PMC5805840 DOI: 10.1016/j.neurobiolaging.2017.11.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 10/14/2017] [Accepted: 11/06/2017] [Indexed: 01/18/2023]
Abstract
Age is not only the greatest risk factor for Alzheimer's disease (AD) but also a key modifier of disease presentation and progression. Here, we investigate how longitudinal atrophy patterns vary with age in mild cognitive impairment (MCI) and AD. Data comprised serial longitudinal 1.5-T magnetic resonance imaging scans from 153 AD, 339 MCI, and 191 control subjects. Voxel-wise maps of longitudinal volume change were obtained and aligned across subjects. Local volume change was then modeled in terms of diagnostic group and an interaction between group and age, adjusted for total intracranial volume, white-matter hyperintensity volume, and apolipoprotein E genotype. Results were significant at p < 0.05 with family-wise error correction for multiple comparisons. An age-by-group interaction revealed that younger AD patients had significantly faster atrophy rates in the bilateral precuneus, parietal, and superior temporal lobes. These results suggest younger AD patients have predominantly posterior progressive atrophy, unexplained by white-matter hyperintensity, apolipoprotein E, or total intracranial volume. Clinical trials may benefit from adapting outcome measures for patient groups with lower average ages, to capture progressive atrophy in posterior cortices.
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Affiliation(s)
- Cassidy M Fiford
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.
| | - Gerard R Ridgway
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | | | - Emily N Manning
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Ian B Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Sebastien Ourselin
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | | | - M Jorge Cardoso
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
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12
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Affiliation(s)
- Lexin Li
- Division of Biostatistics, University of California at Berkeley, Berkley, CA
| | - Xin Zhang
- Department of Statistics, Florida State University, Tallahassee, FL
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13
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Knyazev GG, Savostyanov AN, Bocharov AV, Slobodskaya HR, Bairova NB, Tamozhnikov SS, Stepanova VV. Effortful control and resting state networks: A longitudinal EEG study. Neuroscience 2017; 346:365-381. [DOI: 10.1016/j.neuroscience.2017.01.031] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Revised: 01/14/2017] [Accepted: 01/17/2017] [Indexed: 10/20/2022]
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14
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Khalili-Mahani N, Rombouts SARB, van Osch MJP, Duff EP, Carbonell F, Nickerson LD, Becerra L, Dahan A, Evans AC, Soucy JP, Wise R, Zijdenbos AP, van Gerven JM. Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry. Hum Brain Mapp 2017; 38:2276-2325. [PMID: 28145075 DOI: 10.1002/hbm.23516] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 11/21/2016] [Accepted: 01/04/2017] [Indexed: 12/11/2022] Open
Abstract
A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,PERFORM Centre, Concordia University, Montreal, Canada
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.,Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | | | - Eugene P Duff
- Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.,Oxford Centre for Functional MRI of the Brain, Oxford University, Oxford, United Kingdom
| | | | - Lisa D Nickerson
- McLean Hospital, Belmont, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Lino Becerra
- Center for Pain and the Brain, Harvard Medical School & Boston Children's Hospital, Boston, Massachusetts
| | - Albert Dahan
- Department of Anesthesiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Jean-Paul Soucy
- PERFORM Centre, Concordia University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Richard Wise
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Alex P Zijdenbos
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada.,Biospective Inc, Montreal, Quebec, Canada
| | - Joop M van Gerven
- Centre for Human Drug Research, Leiden University Medical Centre, Leiden, The Netherlands
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15
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Lu ZH, Khondker Z, Ibrahim JG, Wang Y, Zhu H. Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies. Neuroimage 2017; 149:305-322. [PMID: 28143775 DOI: 10.1016/j.neuroimage.2017.01.052] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/27/2016] [Accepted: 01/22/2017] [Indexed: 12/29/2022] Open
Abstract
To perform a joint analysis of multivariate neuroimaging phenotypes and candidate genetic markers obtained from longitudinal studies, we develop a Bayesian longitudinal low-rank regression (L2R2) model. The L2R2 model integrates three key methodologies: a low-rank matrix for approximating the high-dimensional regression coefficient matrices corresponding to the genetic main effects and their interactions with time, penalized splines for characterizing the overall time effect, and a sparse factor analysis model coupled with random effects for capturing within-subject spatio-temporal correlations of longitudinal phenotypes. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. Simulations show that the L2R2 model outperforms several other competing methods. We apply the L2R2 model to investigate the effect of single nucleotide polymorphisms (SNPs) on the top 10 and top 40 previously reported Alzheimer disease-associated genes. We also identify associations between the interactions of these SNPs with patient age and the tissue volumes of 93 regions of interest from patients' brain images obtained from the Alzheimer's Disease Neuroimaging Initiative.
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Affiliation(s)
- Zhao-Hua Lu
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Zakaria Khondker
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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16
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Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease. Sci Rep 2017; 7:39880. [PMID: 28079104 PMCID: PMC5227696 DOI: 10.1038/srep39880] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 11/29/2016] [Indexed: 01/18/2023] Open
Abstract
Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important information on the longitudinal changes, which can be used to classify MCI subjects as either MCI conversion (MCIc) or MCI non-conversion (MCInc) individuals. Moreover, a hierarchical framework was introduced to the classifier to manage high feature dimensionality issues and incorporate spatial information for improving the prediction accuracy. The proposed method was evaluated using 131 patients with MCI (70 MCIc and 61 MCInc) based on MRI scans taken at different time points. Results showed that the proposed method achieved 79.4% accuracy for the classification of MCIc versus MCInc, thereby demonstrating very promising performance for AD prediction.
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17
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Jha SC, Meltzer-Brody S, Steiner RJ, Cornea E, Woolson S, Ahn M, Verde AR, Hamer RM, Zhu H, Styner M, Gilmore JH, Knickmeyer RC. Antenatal depression, treatment with selective serotonin reuptake inhibitors, and neonatal brain structure: A propensity-matched cohort study. Psychiatry Res 2016; 253:43-53. [PMID: 27254086 PMCID: PMC4930375 DOI: 10.1016/j.pscychresns.2016.05.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Revised: 05/08/2016] [Accepted: 05/22/2016] [Indexed: 11/17/2022]
Abstract
The aim of this propensity-matched cohort study was to evaluate the impact of prenatal SSRI exposure and a history of maternal depression on neonatal brain volumes and white matter microstructure. SSRI-exposed neonates (n=27) were matched to children of mothers with no history of depression or SSRI use (n=54). Additionally, neonates of mothers with a history of depression, but no prenatal SSRI exposure (n=41), were matched to children of mothers with no history of depression or SSRI use (n=82). Structural magnetic resonance imaging and diffusion weighted imaging scans were acquired with a 3T Siemens Allegra scanner. Global tissue volumes were characterized using an automatic, atlas-moderated expectation maximization segmentation tool. Local differences in gray matter volumes were examined using deformation-based morphometry. Quantitative tractography was performed using an adaptation of the UNC-Utah NA-MIC DTI framework. SSRI-exposed neonates exhibited widespread changes in white matter microstructure compared to matched controls. Children exposed to a history of maternal depression but no SSRIs showed no significant differences in brain development compared to matched controls. No significant differences were found in global or regional tissue volumes. Additional research is needed to clarify whether SSRIs directly alter white matter development or whether this relationship is mediated by depressive symptoms during pregnancy.
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Affiliation(s)
- Shaili C Jha
- Curriculum in Neurobiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Samantha Meltzer-Brody
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rachel J Steiner
- Psychological Sciences, Vanderbilt University, Nasheville, TN 37240, USA
| | - Emil Cornea
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - Mihye Ahn
- Department of Mathematics and Statistics, University of Nevada, Reno, NV 89557, USA
| | - Audrey R Verde
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Robert M Hamer
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - John H Gilmore
- Curriculum in Neurobiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rebecca C Knickmeyer
- Curriculum in Neurobiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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18
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STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data. Neuroimage 2016; 134:550-562. [PMID: 27103140 DOI: 10.1016/j.neuroimage.2016.04.023] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Revised: 03/31/2016] [Accepted: 04/10/2016] [Indexed: 01/17/2023] Open
Abstract
Longitudinal neuroimaging data plays an important role in mapping the neural developmental profile of major neuropsychiatric and neurodegenerative disorders and normal brain. The development of such developmental maps is critical for the prevention, diagnosis, and treatment of many brain-related diseases. The aim of this paper is to develop a spatio-temporal Gaussian process (STGP) framework to accurately delineate the developmental trajectories of brain structure and function, while achieving better prediction by explicitly incorporating the spatial and temporal features of longitudinal neuroimaging data. Our STGP integrates a functional principal component model (FPCA) and a partition parametric space-time covariance model to capture the medium-to-large and small-to-medium spatio-temporal dependence structures, respectively. We develop a three-stage efficient estimation procedure as well as a predictive method based on a kriging technique. Two key novelties of STGP are that it can efficiently use a small number of parameters to capture complex non-stationary and non-separable spatio-temporal dependence structures and that it can accurately predict spatio-temporal changes. We illustrate STGP using simulated data sets and two real data analyses including longitudinal positron emission tomography data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and longitudinal lateral ventricle surface data from a longitudinal study of early brain development.
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19
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McFarquhar M, McKie S, Emsley R, Suckling J, Elliott R, Williams S. Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data. Neuroimage 2016; 132:373-389. [PMID: 26921716 PMCID: PMC4862963 DOI: 10.1016/j.neuroimage.2016.02.053] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 02/15/2016] [Accepted: 02/17/2016] [Indexed: 11/24/2022] Open
Abstract
Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest.
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Affiliation(s)
- Martyn McFarquhar
- Neuroscience & Psychiatry Unit, Stopford Building, The University of Manchester, Oxford Road, Manchester M13 9PL, UK.
| | - Shane McKie
- Neuroscience & Psychiatry Unit, Stopford Building, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Richard Emsley
- Centre for Biostatistics, Jean McFarlane Building, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - John Suckling
- Brain Mapping Unit, Herchel Smith Building for Brain and Mind Sciences, University of Cambridge, Robinson Way, Cambridge CB2 0SZ, UK
| | - Rebecca Elliott
- Neuroscience & Psychiatry Unit, Stopford Building, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Stephen Williams
- Imaging Sciences, Stopford Building, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
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20
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Ziegler G, Penny WD, Ridgway GR, Ourselin S, Friston KJ. Estimating anatomical trajectories with Bayesian mixed-effects modeling. Neuroimage 2015; 121:51-68. [PMID: 26190405 PMCID: PMC4607727 DOI: 10.1016/j.neuroimage.2015.06.094] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 03/04/2015] [Accepted: 06/30/2015] [Indexed: 01/29/2023] Open
Abstract
We introduce a mass-univariate framework for the analysis of whole-brain structural trajectories using longitudinal Voxel-Based Morphometry data and Bayesian inference. Our approach to developmental and aging longitudinal studies characterizes heterogeneous structural growth/decline between and within groups. In particular, we propose a probabilistic generative model that parameterizes individual and ensemble average changes in brain structure using linear mixed-effects models of age and subject-specific covariates. Model inversion uses Expectation Maximization (EM), while voxelwise (empirical) priors on the size of individual differences are estimated from the data. Bayesian inference on individual and group trajectories is realized using Posterior Probability Maps (PPM). In addition to parameter inference, the framework affords comparisons of models with varying combinations of model order for fixed and random effects using model evidence. We validate the model in simulations and real MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We further demonstrate how subject specific characteristics contribute to individual differences in longitudinal volume changes in healthy subjects, Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD).
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Affiliation(s)
- G Ziegler
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; Dementia Research Centre, Institute of Neurology, University College London, UK.
| | - W D Penny
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
| | - G R Ridgway
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK; FMRIB, Nuffield Dept. of Clinical Neurosciences, University of Oxford, UK
| | - S Ourselin
- Dementia Research Centre, Institute of Neurology, University College London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, UK
| | - K J Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK
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21
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Sabuncu MR. A Sparse Bayesian Learning Algorithm for Longitudinal Image Data. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2015; 9351:411-418. [PMID: 26636136 PMCID: PMC4664208 DOI: 10.1007/978-3-319-24574-4_49] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/25/2024]
Abstract
Longitudinal imaging studies, where serial (multiple) scans are collected on each individual, are becoming increasingly widespread. The field of machine learning has in general neglected the longitudinal design, since many algorithms are built on the assumption that each datapoint is an independent sample. Thus, the application of general purpose machine learning tools to longitudinal image data can be sub-optimal. Here, we present a novel machine learning algorithm designed to handle longitudinal image datasets. Our approach builds on a sparse Bayesian image-based prediction algorithm. Our empirical results demonstrate that the proposed method can offer a significant boost in prediction performance with longitudinal clinical data.
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Affiliation(s)
- Mert R Sabuncu
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
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22
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Huang M, Nichols T, Huang C, Yang Y, Lu Z, Feng Q, Knickmeyer RC, Zhu H. FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data. Neuroimage 2015; 118:613-27. [PMID: 26025292 PMCID: PMC4554832 DOI: 10.1016/j.neuroimage.2015.05.043] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2015] [Revised: 04/09/2015] [Accepted: 05/16/2015] [Indexed: 01/17/2023] Open
Abstract
More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing genome-wide (NC>12 million known variants) associations with signals at millions of locations (NV~10(6)) in the brain from thousands of subjects (n~10(3)). The aim of this paper is to develop a Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to efficiently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (GSIS) procedure, and a detection procedure based on wild bootstrap methods. Specifically, for standard linear association, the computational complexity is O (nNVNC) for voxelwise genome wide association analysis (VGWAS) method compared with O ((NC+NV)n(2)) for FVGWAS. Simulation studies show that FVGWAS is an efficient method of searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. Finally, we have successfully applied FVGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 193,275voxels in RAVENS maps, and 501,584 SNPs, and the total processing time was 203,645s for a single CPU. Our FVGWAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing.
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Affiliation(s)
- Meiyan Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Thomas Nichols
- Department of Statistics, University of Warwick, Coventry, UK
| | - Chao Huang
- Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yu Yang
- Department of Statistics and Operation Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhaohua Lu
- Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Qianjing Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Rebecca C Knickmeyer
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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23
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Lin JA, Zhu H, Mihye A, Sun W, Ibrahim JG. Functional-mixed effects models for candidate genetic mapping in imaging genetic studies. Genet Epidemiol 2014; 38:680-91. [PMID: 25270690 PMCID: PMC4236266 DOI: 10.1002/gepi.21854] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2014] [Revised: 07/29/2014] [Accepted: 08/13/2014] [Indexed: 01/09/2023]
Abstract
The aim of this paper is to develop a functional-mixed effects modeling (FMEM) framework for the joint analysis of high-dimensional imaging data in a large number of locations (called voxels) of a three-dimensional volume with a set of genetic markers and clinical covariates. Our FMEM is extremely useful for efficiently carrying out the candidate gene approaches in imaging genetic studies. FMEM consists of two novel components including a mixed effects model for modeling nonlinear genetic effects on imaging phenotypes by introducing the genetic random effects at each voxel and a jumping surface model for modeling the variance components of the genetic random effects and fixed effects as piecewise smooth functions of the voxels. Moreover, FMEM naturally accommodates the correlation structure of the genetic markers at each voxel, while the jumping surface model explicitly incorporates the intrinsically spatial smoothness of the imaging data. We propose a novel two-stage adaptive smoothing procedure to spatially estimate the piecewise smooth functions, particularly the irregular functional genetic variance components, while preserving their edges among different piecewise-smooth regions. We develop weighted likelihood ratio tests and derive their exact approximations to test the effect of the genetic markers across voxels. Simulation studies show that FMEM significantly outperforms voxel-wise approaches in terms of higher sensitivity and specificity to identify regions of interest for carrying out candidate genetic mapping in imaging genetic studies. Finally, FMEM is used to identify brain regions affected by three candidate genes including CR1, CD2AP, and PICALM, thereby hoping to shed light on the pathological interactions between these candidate genes and brain structure and function.
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Affiliation(s)
- Ja-An Lin
- Departments of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Departments of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ahn Mihye
- Departments of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Wei Sun
- Departments of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Departments of Biostatistics Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Joseph G Ibrahim
- Departments of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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24
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Guillaume B, Hua X, Thompson PM, Waldorp L, Nichols TE. Fast and accurate modelling of longitudinal and repeated measures neuroimaging data. Neuroimage 2014; 94:287-302. [PMID: 24650594 PMCID: PMC4073654 DOI: 10.1016/j.neuroimage.2014.03.029] [Citation(s) in RCA: 131] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Revised: 02/25/2014] [Accepted: 03/10/2014] [Indexed: 02/01/2023] Open
Abstract
Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e.g., assumption of Compound Symmetry--the state of all equal variances and equal correlations--or spatially homogeneous longitudinal correlations). While some new methods have been proposed to more accurately account for such data, these methods are based on iterative algorithms that are slow and failure-prone. In this article, we propose the use of the Sandwich Estimator method which first estimates the parameters of interest with a simple Ordinary Least Square model and second estimates variances/covariances with the "so-called" Sandwich Estimator (SwE) which accounts for the within-subject correlation existing in longitudinal data. Here, we introduce the SwE method in its classic form, and we review and propose several adjustments to improve its behaviour, specifically in small samples. We use intensive Monte Carlo simulations to compare all considered adjustments and isolate the best combination for neuroimaging data. We also compare the SwE method to other popular methods and demonstrate its strengths and weaknesses. Finally, we analyse a highly unbalanced longitudinal dataset from the Alzheimer's Disease Neuroimaging Initiative and demonstrate the flexibility of the SwE method to fit within- and between-subject effects in a single model. Software implementing this SwE method has been made freely available at http://warwick.ac.uk/tenichols/SwE.
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Affiliation(s)
- Bryan Guillaume
- Cyclotron Research Centre, University of Liège, 4000 Liège, Belgium; Department of Statistics, University of Warwick, Coventry, UK; Global Imaging Unit, GlaxoSmithKline, Stevenage, UK
| | - Xue Hua
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology & Psychiatry, UCLA School of Medicine, Los Angeles, CA 90095, USA
| | - Paul M Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology & Psychiatry, UCLA School of Medicine, Los Angeles, CA 90095, USA
| | - Lourens Waldorp
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Thomas E Nichols
- Department of Statistics, University of Warwick, Coventry, UK; Warwick Manufacturing Group, University of Warwick, Coventry, UK; Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
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25
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Lv J, Guo L, Zhu D, Zhang T, Hu X, Han J, Liu T. Group-wise FMRI activation detection on DICCCOL landmarks. Neuroinformatics 2014; 12:513-34. [PMID: 24777386 DOI: 10.1007/s12021-014-9226-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Group-wise activation detection in task-based fMRI has been widely used because of its robustness to noises and its capacity to deal with variability of individual brains. However, current group-wise fMRI activation detection methods typically rely on the co-registration of individual brains' fMRI images, which has difficulty in dealing with the remarkable anatomic variation of different brains. As a consequence, the resulted misalignments could significantly degrade the required inter-subject correspondences, thus substantially reducing the sensitivity and specificity of group-wise fMRI activation detection. To deal with these challenges, this paper presents a novel approach to detecting group-wise fMRI activation on our recently developed and validated Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL). The basic idea here is that the first-level general linear model (GLM) analysis is first performed on the fMRI signal of each corresponding DICCCOL landmark in individual brain's own space, and then the estimated effect sizes of the same landmark from a group of subjects are statistically assessed with the mixed-effect model at the group level. Finally, the consistently activated DICCCOL landmarks are determined and declared in a group-wise fashion in response to external block-based stimuli. Our experimental results have demonstrated that the proposed approach can detect meaningful activations.
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Affiliation(s)
- Jinglei Lv
- School of Automation, Northwestern Polytechnical University, Xi'an, China
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26
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Hyun JW, Li Y, Gilmore JH, Lu Z, Styner M, Zhu H. SGPP: spatial Gaussian predictive process models for neuroimaging data. Neuroimage 2014; 89:70-80. [PMID: 24269800 PMCID: PMC4134945 DOI: 10.1016/j.neuroimage.2013.11.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2013] [Revised: 10/22/2013] [Accepted: 11/11/2013] [Indexed: 11/29/2022] Open
Abstract
The aim of this paper is to develop a spatial Gaussian predictive process (SGPP) framework for accurately predicting neuroimaging data by using a set of covariates of interest, such as age and diagnostic status, and an existing neuroimaging data set. To achieve a better prediction, we not only delineate spatial association between neuroimaging data and covariates, but also explicitly model spatial dependence in neuroimaging data. The SGPP model uses a functional principal component model to capture medium-to-long-range (or global) spatial dependence, while SGPP uses a multivariate simultaneous autoregressive model to capture short-range (or local) spatial dependence as well as cross-correlations of different imaging modalities. We propose a three-stage estimation procedure to simultaneously estimate varying regression coefficients across voxels and the global and local spatial dependence structures. Furthermore, we develop a predictive method to use the spatial correlations as well as the cross-correlations by employing a cokriging technique, which can be useful for the imputation of missing imaging data. Simulation studies and real data analysis are used to evaluate the prediction accuracy of SGPP and show that SGPP significantly outperforms several competing methods, such as voxel-wise linear model, in prediction. Although we focus on the morphometric variation of lateral ventricle surfaces in a clinical study of neurodevelopment, it is expected that SGPP is applicable to other imaging modalities and features.
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Affiliation(s)
- Jung Won Hyun
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Yimei Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhaohua Lu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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Bernal-Rusiel JL, Reuter M, Greve DN, Fischl B, Sabuncu MR. Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data. Neuroimage 2013; 81:358-370. [PMID: 23702413 PMCID: PMC3816382 DOI: 10.1016/j.neuroimage.2013.05.049] [Citation(s) in RCA: 98] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2013] [Revised: 04/30/2013] [Accepted: 05/11/2013] [Indexed: 02/02/2023] Open
Abstract
We present an extension of the Linear Mixed Effects (LME) modeling approach to be applied to the mass-univariate analysis of longitudinal neuroimaging (LNI) data. The proposed method, called spatiotemporal LME or ST-LME, builds on the flexible LME framework and exploits the spatial structure in image data. We instantiated ST-LME for the analysis of cortical surface measurements (e.g. thickness) computed by FreeSurfer, a widely-used brain Magnetic Resonance Image (MRI) analysis software package. We validate the proposed ST-LME method and provide a quantitative and objective empirical comparison with two popular alternative methods, using two brain MRI datasets obtained from the Alzheimer's disease neuroimaging initiative (ADNI) and Open Access Series of Imaging Studies (OASIS). Our experiments revealed that ST-LME offers a dramatic gain in statistical power and repeatability of findings, while providing good control of the false positive rate.
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Affiliation(s)
- Jorge L Bernal-Rusiel
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA
| | - Martin Reuter
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mert R Sabuncu
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
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28
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Raizada RDS, Lee YS. Smoothness without smoothing: why Gaussian naive Bayes is not naive for multi-subject searchlight studies. PLoS One 2013; 8:e69566. [PMID: 23922740 PMCID: PMC3724912 DOI: 10.1371/journal.pone.0069566] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 06/14/2013] [Indexed: 11/20/2022] Open
Abstract
Spatial smoothness is helpful when averaging fMRI signals across multiple subjects, as it allows different subjects' corresponding brain areas to be pooled together even if they are slightly misaligned. However, smoothing is usually not applied when performing multivoxel pattern-based analyses (MVPA), as it runs the risk of blurring away the information that fine-grained spatial patterns contain. It would therefore be desirable, if possible, to carry out pattern-based analyses which take unsmoothed data as their input but which produce smooth images as output. We show here that the Gaussian Naive Bayes (GNB) classifier does precisely this, when it is used in “searchlight” pattern-based analyses. We explain why this occurs, and illustrate the effect in real fMRI data. Moreover, we show that analyses using GNBs produce results at the multi-subject level which are statistically robust, neurally plausible, and which replicate across two independent data sets. By contrast, SVM classifiers applied to the same data do not generate a replication, even if the SVM-derived searchlight maps have smoothing applied to them. An additional advantage of GNB classifiers for searchlight analyses is that they are orders of magnitude faster to compute than more complex alternatives such as SVMs. Collectively, these results suggest that Gaussian Naive Bayes classifiers may be a highly non-naive choice for multi-subject pattern-based fMRI studies.
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Affiliation(s)
- Rajeev D. S. Raizada
- Dept. of Psychology, Cornell University, Ithaca, New York, United States of America
| | - Yune-Sang Lee
- Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
- Center for Cognitive Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
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29
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Knickmeyer RC, Wang J, Zhu H, Geng X, Woolson S, Hamer RM, Konneker T, Styner M, Gilmore JH. Impact of sex and gonadal steroids on neonatal brain structure. Cereb Cortex 2013; 24:2721-31. [PMID: 23689636 DOI: 10.1093/cercor/bht125] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
There are numerous reports of sexual dimorphism in brain structure in children and adults, but data on sex differences in infancy are extremely limited. Our primary goal was to identify sex differences in neonatal brain structure. Our secondary goal was to explore whether brain structure was related to androgen exposure or sensitivity. Two hundred and ninety-three neonates (149 males) received high-resolution structural magnetic resonance imaging scans. Sensitivity to androgen was measured using the number of cytosine, adenine, guanine (CAG) triplets in the androgen receptor gene and the ratio of the second to fourth digit, provided a proxy measure of prenatal androgen exposure. There was a significant sex difference in intracranial volume of 5.87%, which was not related to CAG triplets or digit ratios. Tensor-based morphometry identified extensive areas of local sexual dimorphism. Males had larger volumes in medial temporal cortex and rolandic operculum, and females had larger volumes in dorsolateral prefrontal, motor, and visual cortices. Androgen exposure and sensitivity had minor sex-specific effects on local gray matter volume, but did not appear to be the primary determinant of sexual dimorphism at this age. Comparing our study with the existing literature suggests that sex differences in cortical structure vary in a complex and highly dynamic way across the human lifespan.
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Affiliation(s)
| | - Jiaping Wang
- Department of Mathematics, University of North Texas at Denton, Denton, TX, USA
| | | | - Xiujuan Geng
- School of Humanities, The University of Hong Kong, Hong Kong, Hong Kong and
| | | | | | - Thomas Konneker
- Department of Biomolecular Engineering, University of California at Santa Cruz, CA, USA
| | - Martin Styner
- Department of Psychiatry, Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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