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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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Yu H, Ying W, Li G, Lin X, Jiang D, Chen G, Chen S, Sun X, Xu Y, Ye J, Zhuo C. Exploring concomitant neuroimaging and genetic alterations in patients with and patients without auditory verbal hallucinations: A pilot study and mini review. J Int Med Res 2021; 48:300060519884856. [PMID: 32696690 PMCID: PMC7376300 DOI: 10.1177/0300060519884856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Objective To explore concomitant neuroimaging and genetic alterations in patients with
schizophrenia with or without auditory verbal hallucinations (AVHs), and to
discuss the use of pattern recognition techniques in the development of an
objective index that may improve diagnostic accuracy and treatment outcomes
for schizophrenia. Methods The pilot study included patients with schizophrenia with AVHs (SCH-AVH
group) and without AVHs (SCH-no AVH group). High throughput sequencing (HTS)
was performed to explore RNA networks. Global functional connectivity
density (gFCD) analysis was performed to assess functional connectivity (FC)
alterations of the default mode network (DMN). Quantitative long noncoding
(lnc) RNA and mRNA expression data were related to peak T values of gFCDs
using Pearson’s correlation coefficient analysis. Results Compared with the SCH-no AVH group (n = 5), patients in the
SCH-AVH group (n = 5) exhibited differences in RNA
expression in RNA networks that were related to AVH severity, and displayed
alterations in FC (reflected by gFCD differences) within the DMN (posterior
cingulate and dorsal-medial prefrontal cortex), and in the right parietal
lobe, left occipital lobe, and left temporal lobe. Peak lncRNA expression
values were significantly related to peak gFCD T values within the DMN. Conclusion Among patients with schizophrenia, there are concomitant FC and genetic
expression alterations associated with AVHs. Data from pattern recognition
studies may inform the development of an objective index aimed at improving
early diagnostic accuracy and treatment planning for patients with
schizophrenia with and without AVHs.
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Affiliation(s)
- Haiping Yu
- Department of Psychiatric-Neuro-Imaging-Genetics Laboratory, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang, China
| | - Wang Ying
- Psychiatric Neuroimaging-Genetic and Comorbidity Laboratory, Tianjin Mental Health Centre, Tianjin Anding Hospital, Tianjin, China
| | - Gang Li
- Department of Psychiatry, Tianshui Third Hospital, Gansu, China
| | - Xiaodong Lin
- Department of Psychiatric-Neuro-Imaging-Genetics Laboratory, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang, China
| | - Deguo Jiang
- Department of Psychiatric-Neuro-Imaging-Genetics Laboratory, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang, China
| | - Guangdong Chen
- Department of Psychiatric-Neuro-Imaging-Genetics Laboratory, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang, China
| | - Suling Chen
- Department of Psychiatric-Neuro-Imaging-Genetics Laboratory, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang, China
| | - Xiuhai Sun
- Department of Neurology, Zoucheng People's Hospital, Jining Medical University Affiliated Zoucheng Hospital, Shandong, China
| | - Yong Xu
- Department of Psychiatry, The First Hospital of Shanxi Medical University, Shanxi, China
| | - Jiaen Ye
- Department of Psychiatric-Neuro-Imaging-Genetics Laboratory, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang, China
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuro-Imaging-Genetics Laboratory, Wenzhou Seventh People's Hospital, Wenzhou, Zhejiang, China.,Psychiatric Neuroimaging-Genetic and Comorbidity Laboratory, Tianjin Mental Health Centre, Tianjin Anding Hospital, Tianjin, China.,Department of Psychiatry, Tianjin Fourth Centre Hospital, Tianjin, China.,Department of Psychiatric-Neuro-Imaging-Genetics Laboratory, School of Mental Health of Jining Medical University, Jining, Shandong, China
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Vandekar SN, Stephens J. Improving the replicability of neuroimaging findings by thresholding effect sizes instead of p-values. Hum Brain Mapp 2021; 42:2393-2398. [PMID: 33660923 PMCID: PMC8090771 DOI: 10.1002/hbm.25374] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/26/2021] [Accepted: 02/04/2021] [Indexed: 12/22/2022] Open
Abstract
The classical approach for testing statistical images using spatial extent inference (SEI) thresholds the statistical image based on the p‐value. This approach has an unfortunate consequence on the replicability of neuroimaging findings because the targeted brain regions are affected by the sample size—larger studies have more power to detect smaller effects. Here, we use simulations based on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) to show that thresholding statistical images by effect sizes has more consistent estimates of activated regions across studies than thresholding by p‐values. Using a constant effect size threshold means that the p‐value threshold naturally scales with the sample size to ensure that the target set is similar across repetitions of the study that use different sample sizes. As a consequence of thresholding by the effect size, the type 1 and type 2 error rates go to zero as the sample size gets larger. We use a newly proposed robust effect size index that is defined for an arbitrary statistical image so that effect size thresholding can be used regardless of the test statistic or model.
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Affiliation(s)
- Simon N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jeremy Stephens
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Breznik E, Malmberg F, Kullberg J, Ahlström H, Strand R. Multiple comparison correction methods for whole-body magnetic resonance imaging. J Med Imaging (Bellingham) 2020; 7:014005. [PMID: 32206683 PMCID: PMC7047011 DOI: 10.1117/1.jmi.7.1.014005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 02/10/2020] [Indexed: 12/02/2022] Open
Abstract
Purpose: Voxel-level hypothesis testing on images suffers from test multiplicity. Numerous correction methods exist, mainly applied and evaluated on neuroimaging and synthetic datasets. However, newly developed approaches like Imiomics, using different data and less common analysis types, also require multiplicity correction for more reliable inference. To handle the multiple comparisons in Imiomics, we aim to evaluate correction methods on whole-body MRI and correlation analyses, and to develop techniques specifically suited for the given analyses. Approach: We evaluate the most common familywise error rate (FWER) limiting procedures on whole-body correlation analyses via standard (synthetic no-activation) nominal error rate estimation as well as smaller prior-knowledge based stringency analysis. Their performance is compared to our anatomy-based method extensions. Results: Results show that nonparametric methods behave better for the given analyses. The proposed prior-knowledge based evaluation shows that the devised extensions including anatomical priors can achieve the same power while keeping the FWER closer to the desired rate. Conclusions: Permutation-based approaches perform adequately and can be used within Imiomics. They can be improved by including information on image structure. We expect such method extensions to become even more relevant with new applications and larger datasets.
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Affiliation(s)
- Eva Breznik
- Uppsala University, Centre for Image Analysis, Division of Visual Information and Interaction, Department of Information Technology, Uppsala, Sweden
| | - Filip Malmberg
- Uppsala University, Centre for Image Analysis, Division of Visual Information and Interaction, Department of Information Technology, Uppsala, Sweden.,Uppsala University, Section of Radiology, Department of Surgical Sciences, Uppsala, Sweden
| | - Joel Kullberg
- Uppsala University, Section of Radiology, Department of Surgical Sciences, Uppsala, Sweden.,Antaros Medical, Mölndal, Sweden
| | - Håkan Ahlström
- Uppsala University, Section of Radiology, Department of Surgical Sciences, Uppsala, Sweden.,Antaros Medical, Mölndal, Sweden
| | - Robin Strand
- Uppsala University, Centre for Image Analysis, Division of Visual Information and Interaction, Department of Information Technology, Uppsala, Sweden.,Uppsala University, Section of Radiology, Department of Surgical Sciences, Uppsala, Sweden
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Dworkin JD, Linn KA, Solomon AJ, Satterthwaite TD, Raznahan A, Bakshi R, Shinohara RT. A local group differences test for subject-level multivariate density neuroimaging outcomes. Biostatistics 2019; 22:646-661. [PMID: 31875881 DOI: 10.1093/biostatistics/kxz058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 11/24/2019] [Accepted: 11/29/2019] [Indexed: 11/14/2022] Open
Abstract
A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes can be quantified using summary statistics of voxel intensities. However, the manifestation of a disease process in imaging data is often unknown, or appears as a complex and nonlinear relationship between the voxel intensities on various modalities. When the relevant pattern is unknown, summary statistics are often unable to capture differences between disease groups, and their use may encourage post hoc searches for the optimal summary measure. In this study, we introduce the multi-modal density testing (MMDT) framework for the naive discovery of group differences in voxel intensity profiles. MMDT operationalizes multi-modal magnetic resonance imaging (MRI) data as multivariate subject-level densities of voxel intensities and utilizes kernel density estimation to develop a local two-sample test for individual points within the density space. Through simulations, we show that this method controls type I error and recovers relevant differences when applied to a specified point. Additionally, we demonstrate the ability to maintain power while controlling the family-wise error rate and false discovery rate when applying the test over a grid of points within the density space. Finally, we apply this method to a study of subjects with either multiple sclerosis (MS) or conditions that tend to mimic MS on MRI, and find significant differences between the two groups in their voxel intensity profiles within the thalamus.
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Affiliation(s)
- Jordan D Dworkin
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Kristin A Linn
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Andrew J Solomon
- Department of Neurological Sciences, Larner College of Medicine at The University of Vermont, 149 Beaumont Avenue, Burlington, VT 05405, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA
| | - Rohit Bakshi
- Departments of Neurology and Radiology, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
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Vandekar SN, Satterthwaite TD, Xia CH, Adebimpe A, Ruparel K, Gur RC, Gur RE, Shinohara RT. Robust spatial extent inference with a semiparametric bootstrap joint inference procedure. Biometrics 2019; 75:1145-1155. [PMID: 31282994 DOI: 10.1111/biom.13114] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2018] [Accepted: 06/24/2019] [Indexed: 11/28/2022]
Abstract
Spatial extent inference (SEI) is widely used across neuroimaging modalities to adjust for multiple comparisons when studying brain-phenotype associations that inform our understanding of disease. Recent studies have shown that Gaussian random field (GRF)-based tools can have inflated family-wise error rates (FWERs). This has led to substantial controversy as to which processing choices are necessary to control the FWER using GRF-based SEI. The failure of GRF-based methods is due to unrealistic assumptions about the spatial covariance function of the imaging data. A permutation procedure is the most robust SEI tool because it estimates the spatial covariance function from the imaging data. However, the permutation procedure can fail because its assumption of exchangeability is violated in many imaging modalities. Here, we propose the (semi-) parametric bootstrap joint (PBJ; sPBJ) testing procedures that are designed for SEI of multilevel imaging data. The sPBJ procedure uses a robust estimate of the spatial covariance function, which yields consistent estimates of standard errors, even if the covariance model is misspecified. We use the methods to study the association between performance and executive functioning in a working memory functional magnetic resonance imaging study. The sPBJ has similar or greater power to the PBJ and permutation procedures while maintaining the nominal type 1 error rate in reasonable sample sizes. We provide an R package to perform inference using the PBJ and sPBJ procedures.
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Affiliation(s)
- Simon N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
| | - Theodore D Satterthwaite
- Department of Psychiatry, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cedric H Xia
- Department of Psychiatry, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Azeez Adebimpe
- Department of Psychiatry, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Kosha Ruparel
- Department of Psychiatry, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Department of Psychiatry, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Department of Psychiatry, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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7
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Zhang F, Gou J. Control of false positive rates in clusterwise fMRI inferences. J Appl Stat 2019. [DOI: 10.1080/02664763.2019.1573883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Fengqing Zhang
- Department of Psychology, Drexel University, Philadelphia, PA, USA
| | - Jiangtao Gou
- Department of Biostatistics and Bioinformatics, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA, USA
- Department of Mathematics & Statistics, Villanova University, Villanova, PA, USA
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