1
|
Gao Y, Zhao Y, Li M, Lawless RD, Schilling KG, Xu L, Shafer AT, Beason-Held LL, Resnick SM, Rogers BP, Ding Z, Anderson AW, Landman BA, Gore JC. Functional alterations in bipartite network of white and grey matters during aging. Neuroimage 2023; 278:120277. [PMID: 37473978 PMCID: PMC10529380 DOI: 10.1016/j.neuroimage.2023.120277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/23/2023] [Accepted: 07/11/2023] [Indexed: 07/22/2023] Open
Abstract
The effects of normal aging on functional connectivity (FC) within various brain networks of gray matter (GM) have been well-documented. However, the age effects on the networks of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.
Collapse
Affiliation(s)
- Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| |
Collapse
|
2
|
Cooper CP, Shafer AT, Armstrong NM, An Y, Erus G, Davatzikos C, Ferrucci L, Rapp PR, Resnick SM. Associations of baseline and longitudinal change in cerebellum volume with age-related changes in verbal learning and memory. Neuroimage 2023; 272:120048. [PMID: 36958620 DOI: 10.1016/j.neuroimage.2023.120048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 03/25/2023] Open
Abstract
The cerebellum is involved in higher-order cognitive functions, e.g., learning and memory, and is susceptible to age-related atrophy. Yet, the cerebellum's role in age-related cognitive decline remains largely unknown. We investigated cross-sectional and longitudinal associations between cerebellar volume and verbal learning and memory. Linear mixed effects models and partial correlations were used to examine the relationship between changes in cerebellum volumes (total cerebellum, cerebellum white matter [WM], cerebellum hemisphere gray matter [GM], and cerebellum vermis subregions) and changes in verbal learning and memory performance among 549 Baltimore Longitudinal Study of Aging participants (2,292 visits). All models were adjusted by baseline demographic characteristics (age, sex, race, education), and APOE e4 carrier status. In examining associations between change with change, we tested an additional model that included either hippocampal (HC), cuneus, or postcentral gyrus (PoCG) volumes to assess whether cerebellar volumes were uniquely associated with verbal learning and memory. Cross-sectionally, the association of baseline cerebellum GM and WM with baseline verbal learning and memory was age-dependent, with the oldest individuals showing the strongest association between volume and performance. Baseline volume was not significantly associated with change in learning and memory. However, analysis of associations between change in volumes and changes in verbal learning and memory showed that greater declines in verbal memory were associated with greater volume loss in cerebellum white matter, and preserved GM volume in cerebellum vermis lobules VI-VII. The association between decline in verbal memory and decline in cerebellar WM volume remained after adjustment for HC, cuneus, and PoCG volume. Our findings highlight that associations between cerebellum volume and verbal learning and memory are age-dependent and regionally specific.
Collapse
Affiliation(s)
- C'iana P Cooper
- Neurocognitive Aging Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Andrea T Shafer
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Nicole M Armstrong
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland; Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Providence, Rhode Island
| | - Yang An
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Guray Erus
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Peter R Rapp
- Neurocognitive Aging Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Susan M Resnick
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland.
| |
Collapse
|
3
|
Gao Y, Lawless RD, Li M, Zhao Y, Schilling KG, Xu L, Shafer AT, Beason-Held LL, Resnick SM, Rogers BP, Ding Z, Anderson AW, Landman BA, Gore JC. Automatic Preprocessing Pipeline for White Matter Functional Analyses of Large-Scale Databases. Proc SPIE Int Soc Opt Eng 2023; 12464:124640U. [PMID: 37600506 PMCID: PMC10437151 DOI: 10.1117/12.2653132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time-courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.
Collapse
Affiliation(s)
- Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | | |
Collapse
|
4
|
Newlin NR, Cai LY, Yao T, Archer D, Pechman KR, Schilling KG, Jefferson A, Resnick SM, Hohman TJ, Shafer AT, Landman BA. Comparing voxel- and feature-wise harmonization of complex graph measures from multiple sites for structural brain network investigation of aging. Proc SPIE Int Soc Opt Eng 2023; 12464:124642B. [PMID: 37123017 PMCID: PMC10139749 DOI: 10.1117/12.2653947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Complex graph theory measures of brain structural connectomes derived from diffusion weighted images (DWI) provide insight into the network structure of the brain. Further, as the number of available DWI datasets grows, so does the ability to investigate associations in these measures with major biological factors, like age. However, one key hurdle that remains is the presence of scanner effects that can arise from different DWI datasets and confound multisite analyses. Two common approaches to correct these effects are voxel-wise and feature-wise harmonization. However, it is still unclear how to best leverage them for graph-theory analysis of an aging population. Thus, there is a need to better characterize the impact of each harmonization method and their ability to preserve age related features. We investigate this by characterizing four complex graph theory measures (modularity, characteristic path length, global efficiency, and betweenness centrality) in 48 participants aged 55 to 86 from Baltimore Longitudinal Study of Aging (BLSA) and Vanderbilt Memory and Aging Project (VMAP) before and after voxel- and feature-wise harmonization with the Null Space Deep Network (NSDN) and ComBat, respectively. First, we characterize across dataset coefficients of variation (CoV) and find the combination of NSDN and ComBat causes the greatest reduction in CoV followed by ComBat alone then NSDN alone. Second, we reproduce published associations of modularity with age after correcting for other covariates with linear models. We find that harmonization with ComBat or ComBat and NSDN together improves the significance of existing age effects, reduces model residuals, and qualitatively reduces separation between datasets. These results reinforce the efficiency of statistical harmonization on the feature-level with ComBat and suggest that harmonization on the voxel-level is synergistic but may have reduced effect after running through the multiple layers of the connectomics pipeline. Thus, we conclude that feature-wise harmonization improves statistical results, but the addition of biologically informed voxel-based harmonization offers further improvement.
Collapse
Affiliation(s)
- Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
5
|
Archer DB, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason‐Held LL, An Y, Shafer AT, Risacher SL, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Sex differences in white matter microstructure in aging and Alzheimer’s disease: A multi‐site free‐water imaging study. Alzheimers Dement 2022. [DOI: 10.1002/alz.066752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Derek B Archer
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center Nashville TN USA
| | - Niranjana Shashikumar
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Varuna Jasodanand
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Elizabeth E. Moore
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Kimberly R. Pechman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | | | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Shannon L. Risacher
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | | | - Angela L. Jefferson
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
- Indiana University School of Medicine Indianapolis IN USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Timothy J. Hohman
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center Nashville TN USA
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center Nashville TN USA
| | | |
Collapse
|
6
|
Archer DB, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason‐Held LL, An Y, Shafer AT, Risacher SL, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Leveraging longitudinal, multi‐site diffusion MRI data in conjunction with free‐water analysis to characterize patterns of white matter neurodegeneration in aging. Alzheimers Dement 2022. [DOI: 10.1002/alz.068097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Derek B Archer
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center Nashville TN USA
| | - Niranjana Shashikumar
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Varuna Jasodanand
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Elizabeth E. Moore
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Kimberly R. Pechman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | | | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Shannon L. Risacher
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | | | - Angela L. Jefferson
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine Indianapolis IN USA
- Department of Radiology and Imaging Services, Indiana University School of Medicine Indianapolis IN USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Timothy J. Hohman
- Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center Nashville TN USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center Nashville TN USA
| | | |
Collapse
|
7
|
Duggan MR, Peng Z, An Y, Kitner‐Triolo M, Shafer AT, Davatzikos C, Erus G, Karikkineth A, Lewis A, Moghekar A, Walker KA. Herpes Viruses in the Baltimore Longitudinal Study of Aging:Associations with Brain Volumes, Cognitive Performance and Plasma Biomarkers. Alzheimers Dement 2022. [DOI: 10.1002/alz.063612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Michael R Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Zhongsheng Peng
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Melissa Kitner‐Triolo
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| | | | - Guray Erus
- Department of Radiology, University of Pennsylvania Philadelphia PA USA
| | - Ajoy Karikkineth
- Clinical Research Core, National Institute on Aging Baltimore MD USA
| | - Alexandria Lewis
- Department of Neurology, Johns Hopkins University School of Medicine Baltimore MD USA
| | - Abhay Moghekar
- Johns Hopkins University School of Medicine Baltimore MD USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program Baltimore MD USA
| |
Collapse
|
8
|
Hansen CB, Schilling KG, Rheault F, Resnick S, Shafer AT, Beason-Held LL, Landman BA. Contrastive semi-supervised harmonization of single-shell to multi-shell diffusion MRI. Magn Reson Imaging 2022; 93:73-86. [PMID: 35716922 PMCID: PMC9901230 DOI: 10.1016/j.mri.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/06/2022] [Accepted: 06/07/2022] [Indexed: 02/08/2023]
Abstract
Diffusion weighted MRI (DW-MRI) harmonization is necessary for multi-site or multi-acquisition studies. Current statistical methods address the need to harmonize from one site to another, but do not simultaneously consider the use of multiple datasets which are comprised of multiple sites, acquisitions protocols, and age demographics. This work explores deep learning methods which can generalize across these variations through semi-supervised and unsupervised learning while also learning to estimate multi-shell data from single-shell data using the Multi-shell Diffusion MRI Harmonization Challenge (MUSHAC) and Baltimore Longitudinal Study on Aging (BLSA) datasets. We compare disentanglement harmonization models, which seek to encode anatomy and acquisition in separate latent spaces, and a CycleGAN harmonization model, which uses generative adversarial networks (GAN) to perform style transfer between sites, to the baseline preprocessing and to SHORE interpolation. We find that the disentanglement models achieve superior performance in harmonizing all data while at the same transforming the input data to a single target space across several diffusion metrics (fractional anisotropy, mean diffusivity, mean kurtosis, primary eigenvector).
Collapse
Affiliation(s)
- Colin B Hansen
- Computer Science, Vanderbilt University, Nashville, TN, USA.
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | | | - Bennett A Landman
- Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
9
|
Duggan MR, Peng Z, An Y, Kitner Triolo MH, Shafer AT, Davatzikos C, Erus G, Karikkineth A, Lewis A, Moghekar A, Walker KA. Herpes Viruses in the Baltimore Longitudinal Study of Aging: Associations With Brain Volumes, Cognitive Performance, and Plasma Biomarkers. Neurology 2022; 99:e2014-e2024. [PMID: 35985823 PMCID: PMC9651463 DOI: 10.1212/wnl.0000000000201036] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/15/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Although an infectious etiology of Alzheimer disease (AD) has received renewed attention with a particular focus on herpes viruses, the longitudinal effects of symptomatic herpes virus (sHHV) infection on brain structure and cognition remain poorly understood, as does the effect of sHHV on AD/neurodegeneration biomarkers. METHODS We used a longitudinal, community-based cohort to characterize the association of sHHV diagnoses with changes in 3 T MRI brain volume and cognitive performance. In addition, we related sHHV to cross-sectional differences in plasma biomarkers of AD (β-amyloid [Aβ]42/40), astrogliosis (glial fibrillary acidic protein [GFAP]), and neurodegeneration (neurofilament light [NfL]). Baltimore Longitudinal Study of Aging participants were recruited from the community and assessed with serial brain MRIs and cognitive examinations over an average of 3.4 (SD = 3.2) and 8.6 (SD = 7.7) years, respectively. sHHV classification used International Classification of Diseases, Ninth Revision codes documented at comprehensive health and functional screening evaluations at each study visit. Linear mixed-effects and multivariable linear regression models were used in analyses. RESULTS A total of 1,009 participants were included in the primary MRI analysis, 98% of whom were cognitively normal at baseline MRI (mean age = 65.7 years; 54.8% female). Having a sHHV diagnosis (N = 119) was associated with longitudinal reductions in white matter volume (annual additional rate of change -0.34 cm3/y; p = 0.035), particularly in the temporal lobe. However, there was no association between sHHV and changes in total brain, total gray matter, or AD signature region volumes. Among the 119 participants with sHHV, exposure to antiviral treatment attenuated declines in occipital white matter (p = 0.04). Although the sHHV group had higher cognitive scores at baseline, sHHV diagnosis was associated with accelerated longitudinal declines in attention (annual additional rate of change -0.01 Z-score/year; p = 0.008). In addition, sHHV diagnosis was associated with elevated plasma GFAP, but not related to Aβ42/40 and NfL levels. DISCUSSION These findings suggest an association of sHHV infection with white matter volume loss, attentional decline, and astrogliosis. Although the findings link sHHV to several neurocognitive features, the results do not support an association between sHHV and AD-specific disease processes.
Collapse
Affiliation(s)
- Michael R Duggan
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Zhongsheng Peng
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Yang An
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD.
| | - Melissa H Kitner Triolo
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Andrea T Shafer
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Christos Davatzikos
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Guray Erus
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Ajoy Karikkineth
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alexandria Lewis
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Abhay Moghekar
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Keenan A Walker
- From the Laboratory of Behavioral Neuroscience (M.R.D., Z.P., Y.A., M.H.K.T., A.T.S., K.A.W.), National Institute on Aging, Baltimore, MD; Section of Biomedical Image Analysis (C.D., G.E.), Department of Radiology, University of Pennsylvania, Philadelphia; Clinical Research Core (A.K.), National Institute on Aging; and Department of Neurology (A.L., A.M.), Johns Hopkins University School of Medicine, Baltimore, MD.
| |
Collapse
|
10
|
Shafer AT, Williams OA, Perez E, An Y, Landman BA, Ferrucci L, Resnick SM. Accelerated decline in white matter microstructure in subsequently impaired older adults and its relationship with cognitive decline. Brain Commun 2022; 4:fcac051. [PMID: 35356033 PMCID: PMC8963308 DOI: 10.1093/braincomms/fcac051] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/03/2022] [Accepted: 02/25/2022] [Indexed: 11/12/2022] Open
Abstract
Little is known about a longitudinal decline in white matter microstructure and its associations with cognition in preclinical dementia. Longitudinal diffusion tensor imaging and neuropsychological testing were performed in 50 older adults who subsequently developed mild cognitive impairment or dementia (subsequently impaired) and 200 cognitively normal controls. Rates of white matter microstructural decline were compared between groups using voxel-wise linear mixed-effects models. Associations between change in white matter microstructure and cognition were examined. Subsequently impaired individuals had a faster decline in fractional anisotropy in the right inferior fronto-occipital fasciculus and bilateral splenium of the corpus callosum. A decline in right inferior fronto-occipital fasciculus fractional anisotropy was related to a decline in verbal memory, visuospatial ability, processing speed and mini-mental state examination. A decline in bilateral splenium fractional anisotropy was related to a decline in verbal fluency, processing speed and mini-mental state examination. Accelerated regional white matter microstructural decline is evident during the preclinical phase of mild cognitive impairment/dementia and is related to domain-specific cognitive decline.
Collapse
Affiliation(s)
- Andrea T. Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA,Correspondence to: Andrea T. Shafer 251 Bayview Blvd., Baltimore MD 21224, USA E-mail:
| | - Owen A. Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA,Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Evian Perez
- San Juan Bautista School of Medicine, Caguas, Puerto Rico
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | | | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA,Correspondence may also be addressed to: Susan M. Resnick E-mail:
| |
Collapse
|
11
|
Cai LY, Rheault F, Kerley CI, Aboud KS, Beason-Held LL, Shafer AT, Resnick SM, Jordan LC, Anderson AW, Schilling KG, Landman BA. Joint independent component analysis for hypothesizing spatiotemporal relationships between longitudinal gray and white matter changes in preclinical Alzheimer's disease. Proc SPIE Int Soc Opt Eng 2022; 12032:120321H. [PMID: 36303573 PMCID: PMC9603731 DOI: 10.1117/12.2611562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Characterizing relationships between gray matter (GM) and white matter (WM) in early Alzheimer's disease (AD) would improve understanding of how and when AD impacts the brain. However, modeling these relationships across brain regions and longitudinally remains a challenge. Thus, we propose extending joint independent component analysis (jICA) into spatiotemporal modeling of regional cortical thickness and WM bundle volumes leveraging multimodal MRI. We jointly characterize these GM and WM features in a normal aging (n=316) and an age- and sex-matched preclinical AD cohort (n=81) at each of two imaging sessions spaced three years apart, training on the normal aging population in cross-validation and interrogating the preclinical AD cohort. We find this joint model identifies reproducible, longitudinal changes in GM and WM between the two imaging sessions and that these changes are associated with preclinical AD and are plausible considering the literature. We compare this joint model to two focused models: (1) GM features at the first session and WM at the second and (2) vice versa. The joint model identifies components that correlate poorly with those from the focused models, suggesting the different models resolve different patterns. We find the strength of association with preclinical AD is improved in the GM to WM model, which supports the hypothesis that medial temporal and frontal thinning precedes volume loss in the uncinate fasciculus and inferior anterior-posterior association fibers. These results suggest that jICA effectively generates spatiotemporal hypotheses about GM and WM in preclinical AD, especially when specific intermodality relationships are considered a priori.
Collapse
Affiliation(s)
- Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Francois Rheault
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Cailey I Kerley
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Katherine S Aboud
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health,Baltimore, MD, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health,Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health,Baltimore, MD, USA
| | - Lori C Jordan
- Departments of Pediatrics and Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
12
|
Wang Y, Rheault F, Schilling KG, Beason-Held LL, Shafer AT, Resnick SM, Landman BA. Longitudinal changes of connectomes and graph theory measures in aging. Proc SPIE Int Soc Opt Eng 2022; 12032:120321U. [PMID: 35506128 PMCID: PMC9060568 DOI: 10.1117/12.2611845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Changes in brain structure and connectivity in aging can be probed through diffusion weighted MRI and summarized with structural connectome matrices. Complex network analysis based on graph theory has been applied to provide measures that are correlated with neurobiological variations and can help guide quantitative study of brain function. However, the understanding of how connectomes change longitudinally is limited. In this work, we evaluate modern pipelines to obtain the connectomics data from diffusion weighted MRI scans across different sessions from control subjects (55-99 years old) in the Baltimore Longitudinal Study of Aging and Cambridge Centre for Ageing and Neuroscience. From the connectivity matrices, we compute graph theory measures to understand their brain networks and apply a linear mixed-effects model to study their longitudinal changes with respect to age. With this approach, we computed 14 graph theory measures of 1469 healthy subjects (2476 scans) and found statistically significant correlations between all 14 measures and age. In this analysis: 1) the brain becomes more segregated but less integrated in aging; 2) the overall network cost increases for older subjects; 3) the small-world organizations remain stable; and 4) due to high intra-subject variance, there is not clear trend for longitudinal changes of graph theory measures of individual subjects. Therefore, while useful to investigate brain evolution in aging at the population level, improvements in the connectome reconstruction are needed to decrease single subject variability for individual inference.
Collapse
Affiliation(s)
- Yuzhe Wang
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Francois Rheault
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt G. Schilling
- Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Lori L. Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Andrea T. Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bennett A. Landman
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA,Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
13
|
MacAlpine MF, Bañuelos C, Resnick SM, Bilgel M, Shafer AT. Association between basal forebrain tau and memory in older adults without dementia. Alzheimers Dement 2021. [DOI: 10.1002/alz.057722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Maiya F. MacAlpine
- Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA
| | - Cristina Bañuelos
- Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA
| |
Collapse
|
14
|
Cooper CP, Shafer AT, Armstrong NM, Rossi SL, Young J, Herold C, Gu H, Yang Y, Stein EA, Resnick SM, Rapp PR. Recognition Memory is Associated with Distinct Patterns of Regional Gray Matter Volumes in Young and Aged Monkeys. Cereb Cortex 2021; 32:933-948. [PMID: 34448810 DOI: 10.1093/cercor/bhab257] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 07/02/2021] [Accepted: 07/03/2021] [Indexed: 11/13/2022] Open
Abstract
Cognitive aging varies tremendously across individuals and is often accompanied by regionally specific reductions in gray matter (GM) volume, even in the absence of disease. Rhesus monkeys provide a primate model unconfounded by advanced neurodegenerative disease, and the current study used a recognition memory test (delayed non-matching to sample; DNMS) in conjunction with structural imaging and voxel-based morphometry (VBM) to characterize age-related differences in GM volume and brain-behavior relationships. Consistent with expectations from a long history of neuropsychological research, DNMS performance in young animals prominently correlated with the volume of multiple structures in the medial temporal lobe memory system. Less anticipated correlations were also observed in the cingulate and cerebellum. In aged monkeys, significant volumetric correlations with DNMS performance were largely restricted to the prefrontal cortex and striatum. Importantly, interaction effects in an omnibus analysis directly confirmed that the associations between volume and task performance in the MTL and prefrontal cortex are age-dependent. These results demonstrate that the regional distribution of GM volumes coupled with DNMS performance changes across the lifespan, consistent with the perspective that the aged primate brain retains a substantial capacity for structural reorganization.
Collapse
Affiliation(s)
- C'iana P Cooper
- Neurocognitive Aging Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, United States
| | - Andrea T Shafer
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 02903, United States
| | - Nicole M Armstrong
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI 02903, United States
| | - Sharyn L Rossi
- Neurocognitive Aging Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, United States
| | - Jennifer Young
- Neurocognitive Aging Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, United States
| | - Christa Herold
- Neurocognitive Aging Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, United States
| | - Hong Gu
- Magnetic Resonance Imaging and Spectroscopy Section, Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, United States
| | - Yihong Yang
- Magnetic Resonance Imaging and Spectroscopy Section, Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, United States
| | - Elliot A Stein
- Cognitive and Affective Neuroscience of Addiction Section, Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, MD 21224, United States
| | - Susan M Resnick
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 02903, United States
| | - Peter R Rapp
- Neurocognitive Aging Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, United States
| |
Collapse
|
15
|
Beason-Held LL, Fournier D, Shafer AT, Fabbri E, An Y, Huang CW, Bilgel M, Wong DF, Ferrucci L, Resnick SM. Disease Burden Affects Aging Brain Function. J Gerontol A Biol Sci Med Sci 2021; 77:1810-1818. [PMID: 34329447 PMCID: PMC9757056 DOI: 10.1093/gerona/glab218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Most older adults live with multiple chronic disease conditions, yet the effect of multiple diseases on brain function remains unclear. METHODS We examine the relationship between disease multimorbidity and brain activity using regional cerebral blood flow (rCBF) 15O-water PET scans from 97 cognitively normal participants (mean baseline age 76.5) in the Baltimore Longitudinal Study of Aging (BLSA). Multimorbidity index scores, generated from the presence of 13 health conditions, were correlated with PET data at baseline and in longitudinal change (n=74) over 5.05 (2.74 SD) years. RESULTS At baseline, voxel-based analysis showed that higher multimorbidity scores were associated with lower relative activity in orbitofrontal, superior frontal, temporal pole and parahippocampal regions, and greater activity in lateral temporal, occipital and cerebellar regions. Examination of the individual health conditions comprising the index score showed hypertension and chronic kidney disease individually contributed to the overall multimorbidity pattern of altered activity. Longitudinally, both increases and decreases in activity were seen in relation to increasing multimorbidity over time. These associations were identified in orbitofrontal, lateral temporal, brainstem, and cerebellar areas. CONCLUSION Together, these results show that greater multimorbidity is associated with widespread areas of altered brain activity, supporting a link between health and changes in aging brain function.
Collapse
Affiliation(s)
| | | | - Andrea T Shafer
- Intramural Research Program, National Institute on Aging, NIH
| | - Elisa Fabbri
- Intramural Research Program, National Institute on Aging, NIH
| | - Yang An
- Intramural Research Program, National Institute on Aging, NIH
| | | | - Murat Bilgel
- Intramural Research Program, National Institute on Aging, NIH
| | - Dean F Wong
- Department of Radiology, Washington University School of Medicine
| | - Luigi Ferrucci
- Intramural Research Program, National Institute on Aging, NIH
| | - Susan M Resnick
- Intramural Research Program, National Institute on Aging, NIH
| |
Collapse
|
16
|
Cai LY, Yang Q, Hansen CB, Nath V, Ramadass K, Johnson GW, Conrad BN, Boyd BD, Begnoche JP, Beason-Held LL, Shafer AT, Resnick SM, Taylor WD, Price GR, Morgan VL, Rogers BP, Schilling KG, Landman BA. PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images. Magn Reson Med 2021; 86:456-470. [PMID: 33533094 PMCID: PMC8387107 DOI: 10.1002/mrm.28678] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document. METHODS The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses. RESULTS Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets. CONCLUSIONS The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.
Collapse
Affiliation(s)
- Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Colin B. Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Graham W. Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Benjamin N. Conrad
- Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA
| | - Brian D. Boyd
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John P. Begnoche
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori L. Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Andrea T. Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Warren D. Taylor
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gavin R. Price
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA
| | - Victoria L. Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Baxter P. Rogers
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G. Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Bennett A. Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
17
|
Hansen CB, Yang Q, Lyu I, Rheault F, Kerley C, Chandio BQ, Fadnavis S, Williams O, Shafer AT, Resnick SM, Zald DH, Cutting LE, Taylor WD, Boyd B, Garyfallidis E, Anderson AW, Descoteaux M, Landman BA, Schilling KG. Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography. Neuroinformatics 2021; 19:447-460. [PMID: 33196967 PMCID: PMC8124084 DOI: 10.1007/s12021-020-09497-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 12/21/2022]
Abstract
Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate "regions" rather than as white matter "bundles" or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different automated state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.
Collapse
Affiliation(s)
- Colin B Hansen
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Qi Yang
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Ilwoo Lyu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Francois Rheault
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Canada
| | - Cailey Kerley
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bramsh Qamar Chandio
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Shreyas Fadnavis
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Owen Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - David H Zald
- Center for Advanced Human Brain Imaging Research, Rutgers University, Piscataway, NJ, USA
| | - Laurie E Cutting
- Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN, USA
| | - Warren D Taylor
- Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN, USA
| | - Brian Boyd
- Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
- Program of Neuroscience, Indiana University, Bloomington, IN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Canada
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| |
Collapse
|
18
|
Moore M, Maclin EL, Iordan AD, Katsumi Y, Larsen RJ, Bagshaw AP, Mayhew S, Shafer AT, Sutton BP, Fabiani M, Gratton G, Dolcos F. Proof-of-concept evidence for trimodal simultaneous investigation of human brain function. Hum Brain Mapp 2021; 42:4102-4121. [PMID: 34160860 PMCID: PMC8357002 DOI: 10.1002/hbm.25541] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/04/2021] [Accepted: 05/13/2021] [Indexed: 12/03/2022] Open
Abstract
The link between spatial (where) and temporal (when) aspects of the neural correlates of most psychological phenomena is not clear. Elucidation of this relation, which is crucial to fully understand human brain function, requires integration across multiple brain imaging modalities and cognitive tasks that reliably modulate the engagement of the brain systems of interest. By overcoming the methodological challenges posed by simultaneous recordings, the present report provides proof‐of‐concept evidence for a novel approach using three complementary imaging modalities: functional magnetic resonance imaging (fMRI), event‐related potentials (ERPs), and event‐related optical signals (EROS). Using the emotional oddball task, a paradigm that taps into both cognitive and affective aspects of processing, we show the feasibility of capturing converging and complementary measures of brain function that are not currently attainable using traditional unimodal or other multimodal approaches. This opens up unprecedented possibilities to clarify spatiotemporal integration of brain function.
Collapse
Affiliation(s)
- Matthew Moore
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Edward L Maclin
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Alexandru D Iordan
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA
| | - Yuta Katsumi
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Psychology, Northeastern University, Boston, Massachusetts, USA
| | - Ryan J Larsen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Andrew P Bagshaw
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Stephen Mayhew
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Andrea T Shafer
- Centre for Neuroscience, University of Alberta, Alta., Canada; now at Laboratory of Behavioral Neuroscience, Brain Imaging and Behavior Section, National Institute on Aging, Baltimore, Maryland, USA
| | - Bradley P Sutton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Monica Fabiani
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Gabriele Gratton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Florin Dolcos
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| |
Collapse
|
19
|
Shafer AT, Beason-Held L, An Y, Williams OA, Huo Y, Landman BA, Caffo BS, Resnick SM. Default mode network connectivity and cognition in the aging brain: the effects of age, sex, and APOE genotype. Neurobiol Aging 2021; 104:10-23. [PMID: 33957555 DOI: 10.1016/j.neurobiolaging.2021.03.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 03/04/2021] [Accepted: 03/24/2021] [Indexed: 01/18/2023]
Abstract
The default mode network (DMN) overlaps with regions showing early Alzheimer's Disease (AD) pathology. Age, sex, and apolipoprotein E ɛ4 are the predominant risk factors for developing AD. How these risk factors interact to influence DMN connectivity and connectivity-cognition relationships before the onset of impairment remains unknown. Here, we examined these issues in 475 cognitively normal adults, targeting total DMN connectivity, its anticorrelated network (acDMN), and the DMN-hippocampal component. There were four main findings. First, in the ɛ3 homozygous group, lower DMN and acDMN connectivity was observed with age. Second, sex and ɛ4 modified the relationship between age and connectivity for the DMN and hippocampus with ɛ4 vs. ɛ3 males showing sustained or higher connectivity with age. Third, in the ɛ3 group, age and sex modified connectivity-cognition relationships with the oldest participants having the most differential patterns due to sex. Fourth, ɛ4 carriers with lower connectivity had poorer cognitive performance. Taken together, our results show the three predominant risk factors for AD interact to influence brain function and function-cognition relationships.
Collapse
Affiliation(s)
- Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD.
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD
| | - Owen A Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD
| | - Yuankai Huo
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN
| | - Bennett A Landman
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD.
| |
Collapse
|
20
|
Yu C, Liu Y, Cai LY, Kerley CI, Xu K, Taylor WD, Kang H, Shafer AT, Beason-Held LL, Resnick SM, Landman BA, Lyu I. Validation of Group-wise Registration for Surface-based Functional MRI Analysis. Proc SPIE Int Soc Opt Eng 2021; 11596:115961X. [PMID: 34531631 PMCID: PMC8442945 DOI: 10.1117/12.2580771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Resting-state functional MRI (rsfMRI) provides important information for studying and mapping the activities and functions of the brain. Conventionally, rsfMRIs are often registered to structural images in the Euclidean space without considering cortical surface geometry. Meanwhile, a surface-based representation offers a relaxed coordinate chart, but this still requires surface registration for group-wise data analysis. In this work, we investigate the performance of two existing surface registration methods in a surface-based rsfMRI analysis framework: FreeSurfer and Hierarchical Spherical Deformation (HSD). To minimize registration bias, we establish shape correspondence using both methods in a group-wise manner that estimates the unbiased average of a given cohort. To evaluate their performance, we focus on neuroanatomical alignment as well as the amount of distortion that can potentially bias surface tessellation for secondary level rsfMRI data analyses. In the pilot analysis, we examine a single timepoint of imaging data from 100 subjects out of an aging cohort. Overall, HSD establishes improved shape correspondence with reduced mean curvature deviation (10.94% less on average per subject, paired t-test: p <10-10) and reduced registration distortion (FreeSurfer: average 41.91% distortion per subject, HSD: 18.63%, paired t-test: p <10-10). Furthermore, HSD introduces less distortion than FreeSurfer in the areas identified in the individual components that were extracted by surface-based independent component analysis (ICA) after spatial smoothing and time series normalization. Consequently, we show that FreeSurfer capture individual components with globally similar but locally different patterns in ICA in visual inspection.
Collapse
Affiliation(s)
- Chang Yu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Yue Liu
- College of Information Science and Engineering, Northeastern University, Shenyang, China
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Cailey I Kerley
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Warren D Taylor
- Psychiatry & Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Psychiatry & Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ilwoo Lyu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
21
|
Cai LY, Kerley CI, Yu C, Aboud KS, Beason-Held LL, Shafer AT, Resnick SM, Jordan LC, Anderson AW, Schilling KG, Lyu I, Landman BA. Joint cortical surface and structural connectivity analysis of Alzheimer's Disease. Proc SPIE Int Soc Opt Eng 2021; 11596:1159630. [PMID: 34354323 PMCID: PMC8336655 DOI: 10.1117/12.2580956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Prior neuroimaging studies have demonstrated isolated structural and connectivity changes in the brain due to Alzheimer's Disease (AD). However, how these changes relate to each other is not well understood. We present a preliminary study to begin to fill this gap by leveraging joint independent component analysis (jICA). We explore how jICA performs in an analysis of T1 and diffusion weighted MRI by characterizing the joint changes of complex cortical surface and structural connectivity metrics in AD in subjects from the Baltimore Longitudinal Study of Aging. We calculate 588 region-based cortical metrics and 4,753 fractional anisotropy-based connectivity metrics and project them into a low-dimensional manifold with principal component analysis. We perform jICA on the manifold and subsequently backproject the independent components to the original data space. We demonstrate component stability with 3-fold cross validation and find differential component loadings between 776 cognitively unimpaired control subjects and 23 with AD that generalizes across folds. In addition, we perform the same analysis on the surface and connectivity metrics separately and find that the joint approach identifies both novel and similar components to the separate approaches. To illustrate the joint approach's primary utility, we provide an example hypothesis for how surface and connectivity components may vary together with AD. These preliminary results suggest jointly varying independent cortical surface and structural connectivity components can be consistently extracted from MRI data and provide a data-driven way for generating novel hypotheses about AD that may not be captured by separate analyses.
Collapse
Affiliation(s)
- Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Cailey I Kerley
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Chang Yu
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Katherine S Aboud
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori C Jordan
- Department of Pediatrics, Division of Pediatric Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Ilwoo Lyu
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
22
|
Shafer AT, Benoit JR, Brown MRG, Greenshaw AJ, Van Vliet KJ, Vohra S, Dolcos F, Singhal A. Differences in attentional control and white matter microstructure in adolescents with attentional, affective, and behavioral disorders. Brain Imaging Behav 2021; 14:599-614. [PMID: 31838614 DOI: 10.1007/s11682-019-00211-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Adolescence is a critical time of physiological, cognitive, and social development. It is also a time of increased risk-taking and vulnerability for psychopathology. White matter (WM) changes during adolescence have been better elucidated in the last decade, but how WM is impacted by psychopathology during this time remains unclear. Here, we examined the link between WM microstructure and psychopathology during adolescence. Twenty youth diagnosed with affective, attentional, and behavioral disorders (clinical sample), and 20 age-matched controls were recruited to examine group differences in WM microstructure, attentional control, and the link between them. The main results showed that clinical sample had relatively lower attentional control and fractional anisotropy (FA) in WM throughout the brain: two association tracts were identified, and many differences were found in areas rich in callosal and projection fibers. Moreover, increased FA was positively associated with attention performance in the clinical sample in structures supporting ventral WM pathways, whereas a similar link was identified in controls in dorsal WM association fibers. Overall, these results support a model of general impairment in WM microstructure combined with reliance on altered, perhaps less efficient, pathways for attentional control in youth with affective, attentional, and behavioral disorders.
Collapse
Affiliation(s)
- Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA.
| | - James R Benoit
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Matthew R G Brown
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Andy J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - K Jessica Van Vliet
- Department of Educational Psychology, University of Alberta, Edmonton, AB, Canada
| | - Sunita Vohra
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.,Departments of Pediatrics and Medicine, University of Alberta, Edmonton, AB, Canada
| | - Florin Dolcos
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.,Psychology Department and Neuroscience Program, University of Illinois, Urbana-Champaign, IL, USA.,Beckman Institute for Advanced Science & Technology, University of Illinois, Urbana-Champaign, IL, USA
| | - Anthony Singhal
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada. .,Department of Psychology, University of Alberta, Edmonton, AB, Canada.
| |
Collapse
|
23
|
Schilling KG, Blaber J, Hansen C, Cai L, Rogers B, Anderson AW, Smith S, Kanakaraj P, Rex T, Resnick SM, Shafer AT, Cutting LE, Woodward N, Zald D, Landman BA. Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps. PLoS One 2020; 15:e0236418. [PMID: 32735601 PMCID: PMC7394453 DOI: 10.1371/journal.pone.0236418] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 07/06/2020] [Indexed: 02/04/2023] Open
Abstract
Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.
Collapse
Affiliation(s)
- Kurt G. Schilling
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
| | - Justin Blaber
- Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Colin Hansen
- Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - Leon Cai
- Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Baxter Rogers
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
| | - Adam W. Anderson
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
- Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - Seth Smith
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
- Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - Praitayini Kanakaraj
- Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Tonia Rex
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Andrea T. Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Laurie E. Cutting
- Special Education, Vanderbilt University, Nashville, TN, United States of America
| | - Neil Woodward
- Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - David Zald
- Neuroscience, Vanderbilt University, Nashville, TN, United States of America
| | - Bennett A. Landman
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
- Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America
| |
Collapse
|
24
|
Williams OA, An Y, Armstrong NM, Shafer AT, Helphrey J, Kitner-Triolo M, Ferrucci L, Resnick SM. Apolipoprotein E ε4 allele effects on longitudinal cognitive trajectories are sex and age dependent. Alzheimers Dement 2019; 15:1558-1567. [PMID: 31561966 DOI: 10.1016/j.jalz.2019.07.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 07/08/2019] [Accepted: 07/14/2019] [Indexed: 10/26/2022]
Abstract
INTRODUCTION Questions remain about whether apolipoprotein E (APOE)-ε4 effects on cognitive decline are similar in men and women and how APOE-ε4 and age interact to influence decline in different cognitive domains. METHODS In sex-stratified analyses, baseline age-dependent associations between APOE-ε4 status and longitudinal cognitive trajectories were examined in cognitively normal Caucasian older adults (631 men, 561 women, baseline age range: 50-93, 6733 assessments). RESULTS In men, older baseline age was associated with greater effects of APOE-ε4 on longitudinal decline in memory and executive function, detectible from baseline age of 64 and 68, respectively. In women, older baseline age was associated with greater APOE-ε4 effects on longitudinal decline in attention, detectible at baseline age of 66. No significant APOE-ε4 effects were found for language, visual-spatial ability, or processing speed. DISCUSSION Results highlight the importance of considering sex and age when assessing APOE-ε4-associated vulnerability to cognitive decline.
Collapse
Affiliation(s)
- Owen A Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Nicole M Armstrong
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Jessica Helphrey
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Melissa Kitner-Triolo
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA.
| |
Collapse
|
25
|
Ziontz J, Bilgel M, Shafer AT, Moghekar A, Elkins W, Helphrey J, Gomez G, June D, McDonald MA, Dannals RF, Azad BB, Ferrucci L, Wong DF, Resnick SM. Tau pathology in cognitively normal older adults. Alzheimers Dement (Amst) 2019; 11:637-645. [PMID: 31517026 PMCID: PMC6732758 DOI: 10.1016/j.dadm.2019.07.007] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Introduction Tau pathology, a hallmark of Alzheimer's disease, is observed in the brains of virtually all individuals over 70 years. Methods Using 18F-AV-1451 (18F-flortaucipir) positron emission tomography, we evaluated tau pathology in 54 cognitively normal participants (mean age: 77.5 years, SD: 8.9) from the Baltimore Longitudinal Study of Aging. We assessed associations between positron emission tomography signal and age, sex, race, and amyloid positivity. We investigated relationships between regional signal and retrospective rates of change in regional volumes and cognitive function adjusting for age, sex, and amyloid status. Results Greater age, male sex, black race, and amyloid positivity were associated with higher 18F-AV-1451 retention in distinct brain regions. Retention in the entorhinal cortex was associated with lower entorhinal volume (β = −1.124, SE = 0.485, P = .025) and a steeper decline in memory performance (β = −0.086, SE = 0.039, P = .029). Discussion Assessment of medial temporal tau pathology will provide insights into early structural brain changes associated with later cognitive impairment and Alzheimer's disease. Age and amyloid-associated tau positron emission tomography (PET) differences in frontal, temporal, and occipital areas. Entorhinal tau PET associated with lower volume in the same region. Medial temporal tau PET related to memory decline in older cognitively normals.
Collapse
Affiliation(s)
- Jacob Ziontz
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Murat Bilgel
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Andrea T Shafer
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Abhay Moghekar
- Department of Neurology, JHU School of Medicine, Baltimore, MD, USA
| | - Wendy Elkins
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Jessica Helphrey
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Gabriela Gomez
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Danielle June
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Michael A McDonald
- Department of Radiology and Radiological Science, Johns Hopkins University School (JHU) of Medicine, Baltimore, MD, USA
| | - Robert F Dannals
- Department of Radiology and Radiological Science, Johns Hopkins University School (JHU) of Medicine, Baltimore, MD, USA
| | - Babak Behnam Azad
- Department of Radiology and Radiological Science, Johns Hopkins University School (JHU) of Medicine, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Dean F Wong
- Department of Neurology, JHU School of Medicine, Baltimore, MD, USA.,Department of Radiology and Radiological Science, Johns Hopkins University School (JHU) of Medicine, Baltimore, MD, USA.,Department of Psychiatry and Behavioral Sciences, JHU School of Medicine, Baltimore, MD, USA.,Department of Neuroscience, JHU School of Medicine, Baltimore, MD, USA
| | - Susan M Resnick
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| |
Collapse
|
26
|
Schilling KG, Blaber J, Huo Y, Newton A, Hansen C, Nath V, Shafer AT, Williams O, Resnick SM, Rogers B, Anderson AW, Landman BA. Synthesized b0 for diffusion distortion correction (Synb0-DisCo). Magn Reson Imaging 2019; 64:62-70. [PMID: 31075422 DOI: 10.1016/j.mri.2019.05.008] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 04/02/2019] [Accepted: 05/04/2019] [Indexed: 02/07/2023]
Abstract
Diffusion magnetic resonance images typically suffer from spatial distortions due to susceptibility induced off-resonance fields, which may affect the geometric fidelity of the reconstructed volume and cause mismatches with anatomical images. State-of-the art susceptibility correction (for example, FSL's TOPUP algorithm) typically requires data acquired twice with reverse phase encoding directions, referred to as blip-up blip-down acquisitions, in order to estimate an undistorted volume. Unfortunately, not all imaging protocols include a blip-up blip-down acquisition, and cannot take advantage of the state-of-the art susceptibility and motion correction capabilities. In this study, we aim to enable TOPUP-like processing with historical and/or limited diffusion imaging data that include only a structural image and single blip diffusion image. We utilize deep learning to synthesize an undistorted non-diffusion weighted image from the structural image, and use the non-distorted synthetic image as an anatomical target for distortion correction. We evaluate the efficacy of this approach (named Synb0-DisCo) and show that our distortion correction process results in better matching of the geometry of undistorted anatomical images, reduces variation in diffusion modeling, and is practically equivalent to having both blip-up and blip-down non-diffusion weighted images.
Collapse
Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America.
| | - Justin Blaber
- Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - Yuankai Huo
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Allen Newton
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Colin Hansen
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Vishwesh Nath
- Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Owen Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Baxter Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States of America; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, United States of America; Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States of America
| |
Collapse
|
27
|
Abstract
The development of the brain, particularly the protracted maturation of the prefrontal cortex (PFC), supports the development of episodic memory. Yet how different regions of the PFC functionally mature to support age-related increases in memory performance remains unclear. We investigated the PFC contribution to subsequent memory (SM) of encoded visual scenes in children, adolescents, and young adults (n = 83). We identified distinct patterns of PFC activations supporting SM: regions in the lateral PFC showed positive SM effects, whereas regions in the superior and medial PFC showed negative SM effects. Both positive and negative SM effects increased with age. The magnitude of negative SM effects in the superior PFC partially mediated the age-related increase in memory. Functional connectivity between lateral PFC and regions in the medial temporal lobe (MTL) increased with age during successful memory formation. In contrast, functional connectivity between the superior PFC and regions in the MTL decreased with age, suggesting an age-related increase in the anti-correlation between these regions. These findings highlight the differential involvement of regions within the PFC supporting memory formation.
Collapse
Affiliation(s)
- Lingfei Tang
- Department of Psychology, Wayne State University, Detroit, MI, USA
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
- Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI, USA
| | - Andrea T Shafer
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Noa Ofen
- Department of Psychology, Wayne State University, Detroit, MI, USA
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
- Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI, USA
| |
Collapse
|
28
|
Shafer AT, Beason-Held LL, Ziontz J, Landman B, Huo Y, Resnick SM. P2‐392: CROSS‐SECTIONAL EFFECTS OF AGE, SEX, AND APOE ON FUNCTIONAL CONNECTIVITY OF DEFAULT MODE NETWORK REGIONS IN THE BALTIMORE LONGITUDINAL STUDY OF AGING. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.1083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
| | | | | | | | | | - Susan M. Resnick
- National Institute on Aging/National Institutes of Health (NIA/NIH)BaltimoreMDUSA
| |
Collapse
|
29
|
Beason-Held LL, Shafer AT, June D, Ziontz J, Resnick SM. IC‐P‐117: FMRI AND THE AGING BRAIN: COMMON AREAS OF SIGNAL DISRUPTION IN AN OLDER POPULATION. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.2183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
| | | | | | | | - Susan M. Resnick
- National Institute on Aging/National Institutes of HealthBaltimoreMDUSA
| |
Collapse
|
30
|
Abstract
Taboo stimuli are highly arousing, but it has been suggested that they also have inherent taboo-specific properties such as tabooness, offensiveness, or shock value. Prior studies have shown that taboo words have slower response times in lexical decision and higher recall probabilities in free recall; however, taboo words often differ from other words on more than just arousal and taboo properties. Here, we replicated both of these findings and conducted detailed item analyses to determine which word properties drive these behavioural effects. We found that lexical-decision performance was best explained by measures of lexical accessibility (e.g., word frequency) and tabooness, rather than arousal, valence, or offensiveness. However, free-recall performance was primarily driven by emotional word properties, and tabooness was the most important emotional word property for model fit. Our results suggest that the processing of taboo words is influenced by distinct sets of factors and by an intrinsic taboo-specific property.
Collapse
Affiliation(s)
- Christopher R. Madan
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Department of Psychology, Boston College, Chestnut Hill, MA, USA
| | - Andrea T. Shafer
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Michelle Chan
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | - Anthony Singhal
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| |
Collapse
|
31
|
Wisse LE, Daugherty AM, Olsen RK, Berron D, Carr VA, Stark CE, Amaral RS, Amunts K, Augustinack JC, Bender AR, Bernstein JD, Boccardi M, Bocchetta M, Burggren A, Chakravarty MM, Chupin M, Ekstrom A, de Flores R, Insausti R, Kanel P, Kedo O, Kennedy KM, Kerchner GA, LaRocque KF, Liu X, Maass A, Malykhin N, Mueller SG, Ofen N, Palombo DJ, Parekh MB, Pluta JB, Pruessner JC, Raz N, Rodrigue KM, Schoemaker D, Shafer AT, Steve TA, Suthana N, Wang L, Winterburn JL, Yassa MA, Yushkevich PA, la Joie R. A harmonized segmentation protocol for hippocampal and parahippocampal subregions: Why do we need one and what are the key goals? Hippocampus 2017; 27:3-11. [PMID: 27862600 PMCID: PMC5167633 DOI: 10.1002/hipo.22671] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 10/06/2016] [Accepted: 10/17/2016] [Indexed: 01/08/2023]
Abstract
The advent of high-resolution magnetic resonance imaging (MRI) has enabled in vivo research in a variety of populations and diseases on the structure and function of hippocampal subfields and subdivisions of the parahippocampal gyrus. Because of the many extant and highly discrepant segmentation protocols, comparing results across studies is difficult. To overcome this barrier, the Hippocampal Subfields Group was formed as an international collaboration with the aim of developing a harmonized protocol for manual segmentation of hippocampal and parahippocampal subregions on high-resolution MRI. In this commentary we discuss the goals for this protocol and the associated key challenges involved in its development. These include differences among existing anatomical reference materials, striking the right balance between reliability of measurements and anatomical validity, and the development of a versatile protocol that can be adopted for the study of populations varying in age and health. The commentary outlines these key challenges, as well as the proposed solution of each, with concrete examples from our working plan. Finally, with two examples, we illustrate how the harmonized protocol, once completed, is expected to impact the field by producing measurements that are quantitatively comparable across labs and by facilitating the synthesis of findings across different studies. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Laura E.M. Wisse
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Ana M. Daugherty
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Champaign, USA
| | - Rosanna K. Olsen
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - David Berron
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg, Germany
| | - Valerie A. Carr
- Department of Psychology, Stanford University, Palo Alto, USA
- Department of Psychology, San Jose State University, San Jose, USA
| | - Craig E.L. Stark
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, USA
| | - Robert S.C. Amaral
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Katrin Amunts
- Institute of Neuroscience and Medicine, INM-1, Research Center Jülich, Jülich, Germany
- JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
- C. and O. Vogt Institute for Brain Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jean C. Augustinack
- AA Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, USA
| | - Andrew R. Bender
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Jeffrey D. Bernstein
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, USA
| | - Marina Boccardi
- LANVIE Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
| | - Alison Burggren
- Department of Psychiatry and Biobehavioural Sciences, University of California Los Angeles, Los Angeles, USA
| | - M. Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Marie Chupin
- INSERM, CNRS, UMR-S975, Institut du Cerveau et de la Moelle Epinière (ICM), Paris, France
| | - Arne Ekstrom
- Center for Neuroscience, University of California Davis, Davis, USA
- Department of Psychology, University of California Davis, Davis, USA
| | - Robin de Flores
- INSERM U1077, Université de Caen Normandie, UMR-S1077, Ecole Pratique des Hautes Etudes, Centre Hospitalier Universitaire de Caen, Caen, France
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory and C.R.I.B., School of Medicine, University of Castilla-La Mancha, Albacete, Spain
| | - Prabesh Kanel
- Department of Computer Science, Florida State University, Tallahassee, USA
| | - Olga Kedo
- Institute of Neuroscience and Medicine, INM-1, Research Center Jülich, Jülich, Germany
| | - Kristen M. Kennedy
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, USA
| | - Geoffrey A. Kerchner
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, USA
| | | | - Xiuwen Liu
- Department of Computer Science, Florida State University, Tallahassee, USA
| | - Anne Maass
- School of Public Health and Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, USA
| | - Nicolai Malykhin
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
- The Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
- Department of Psychiatry, University of Alberta, Edmonton, Canada
| | - Susanne G. Mueller
- Department of Radiology, University of California, San Francisco, USA
- Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, San Francisco, USA
| | - Noa Ofen
- Psychology Department, Wayne State University, Detroit, USA
- Institute of Gerontology, Wayne State University, Detroit, USA
| | | | - Mansi B. Parekh
- Department of Radiology, Stanford University, Palo Alto, USA
| | - John B. Pluta
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jens C. Pruessner
- McGill Centre for Studies in Aging, Faculty of Medicine, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Naftali Raz
- Psychology Department, Wayne State University, Detroit, USA
- Institute of Gerontology, Wayne State University, Detroit, USA
| | - Karen M. Rodrigue
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, USA
| | - Dorothee Schoemaker
- McGill Centre for Studies in Aging, Faculty of Medicine, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Andrea T. Shafer
- Psychology Department, Wayne State University, Detroit, USA
- Institute of Gerontology, Wayne State University, Detroit, USA
| | - Trevor A. Steve
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Nanthia Suthana
- Department of Psychiatry and Biobehavioural Sciences, University of California Los Angeles, Los Angeles, USA
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, USA
| | - Lei Wang
- Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Julie L. Winterburn
- Cerebral Imaging Centre, Douglas Mental Health University Institute, McGill University, Montreal, Canada
- Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Michael A. Yassa
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, USA
- Department of Neurology, University of California Irvine, Irvine, USA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Renaud la Joie
- INSERM U1077, Université de Caen Normandie, UMR-S1077, Ecole Pratique des Hautes Etudes, Centre Hospitalier Universitaire de Caen, Caen, France
| | | |
Collapse
|
32
|
Hrybouski S, Aghamohammadi-Sereshki A, Madan CR, Shafer AT, Baron CA, Seres P, Beaulieu C, Olsen F, Malykhin NV. Amygdala subnuclei response and connectivity during emotional processing. Neuroimage 2016; 133:98-110. [PMID: 26926791 DOI: 10.1016/j.neuroimage.2016.02.056] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 02/16/2016] [Accepted: 02/18/2016] [Indexed: 02/08/2023] Open
Abstract
The involvement of the human amygdala in emotion-related processing has been studied using functional magnetic resonance imaging (fMRI) for many years. However, despite the amygdala being comprised of several subnuclei, most studies investigated the role of the entire amygdala in processing of emotions. Here we combined a novel anatomical tracing protocol with event-related high-resolution fMRI acquisition to study the responsiveness of the amygdala subnuclei to negative emotional stimuli and to examine intra-amygdala functional connectivity. The greatest sensitivity to the negative emotional stimuli was observed in the centromedial amygdala, where the hemodynamic response amplitude elicited by the negative emotional stimuli was greater and peaked later than for neutral stimuli. Connectivity patterns converge with extant findings in animals, such that the centromedial amygdala was more connected with the nuclei of the basal amygdala than with the lateral amygdala. Current findings provide evidence of functional specialization within the human amygdala.
Collapse
Affiliation(s)
- Stanislau Hrybouski
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | | | - Christopher R Madan
- Department of Psychology, University of Alberta, Edmonton, AB T6G 2E9, Canada; Department of Psychology, Boston College, Chestnut Hill, MA 02467, USA
| | - Andrea T Shafer
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada; Institute of Gerontology, Wayne State University, Detroit, MI 48202, USA
| | - Corey A Baron
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 2V2, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 2V2, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 2V2, Canada
| | - Fraser Olsen
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada; Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 2V2, Canada
| | - Nikolai V Malykhin
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada; Department of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 2V2, Canada; Department of Psychiatry, University of Alberta, Edmonton, AB T6G 2B7, Canada.
| |
Collapse
|
33
|
Shafer AT, Dolcos F. Dissociating retrieval success from incidental encoding activity during emotional memory retrieval, in the medial temporal lobe. Front Behav Neurosci 2014; 8:177. [PMID: 24917798 PMCID: PMC4042186 DOI: 10.3389/fnbeh.2014.00177] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Accepted: 04/25/2014] [Indexed: 11/30/2022] Open
Abstract
The memory-enhancing effect of emotion has been linked to the engagement of emotion- and memory-related medial temporal lobe (MTL) regions (amygdala-AMY; hippocampus-HC; parahippocampus-PHC), during both encoding and retrieval. However, recognition tasks used to investigate the neural correlates of retrieval make it difficult to distinguish MTL engagement linked to retrieval success (RS) from that linked to incidental encoding success (ES) during retrieval. This issue has been investigated for retrieval of non-emotional memories, but not for emotional memory retrieval. To address this, we used event-related functional MRI in conjunction with an emotional distraction and two episodic memory tasks (one testing memory for distracter items and the other testing memory for new/lure items presented in the first memory task). This paradigm allowed for dissociation of MTL activity specifically linked to RS from that linked to both RS and incidental ES during retrieval. There were two novel findings regarding the neural correlates of emotional memory retrieval. First, greater emotional RS was identified bilaterally in AMY, HC, and PHC. However, AMY activity was most impacted when accounting for ES activity, as only RS activity in left AMY was dissociated from ES activity during retrieval, whereas portions of HC and PHC showing greater emotional RS were largely uninvolved in ES. Second, an earlier and more anteriorly spread response (left AMY and bilateral HC, PHC) was linked to greater emotional RS activity, whereas a later and more posteriorly localized response (right posterior PHC) was linked to greater neutral RS activity. These findings shed light on MTL mechanisms subserving the memory-enhancing effect of emotion at retrieval.
Collapse
Affiliation(s)
- Andrea T Shafer
- Centre for Neuroscience, University of Alberta Edmonton, AB, Canada
| | - Florin Dolcos
- Centre for Neuroscience, University of Alberta Edmonton, AB, Canada ; Social, Cognitive, Personality, and Emotional Neuroscience Laboratory, Psychology Department, Neuroscience Program, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign IL, USA
| |
Collapse
|
34
|
Singhal A, Shafer AT, Russell M, Gibson B, Wang L, Vohra S, Dolcos F. Electrophysiological correlates of fearful and sad distraction on target processing in adolescents with attention deficit-hyperactivity symptoms and affective disorders. Front Integr Neurosci 2012; 6:119. [PMID: 23267319 PMCID: PMC3525949 DOI: 10.3389/fnint.2012.00119] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2012] [Accepted: 11/30/2012] [Indexed: 11/13/2022] Open
Abstract
In this study we used event-related brain potentials (ERP) as neural markers of cognitive operations to examine emotion and attentional processing in a population of high-risk adolescents with mental health problems that included attention deficit and hyperactivity disorder (ADHD), anxiety, and depression. We included a healthy control group for comparison purposes, and employed a modified version of the emotional oddball paradigm, consisting of frequent distracters (scrambled pictures), infrequent distracters (sad, fearful, and neutral pictures), and infrequent targets (circles). Participants were instructed to make a right hand button press to targets and a left hand button press to all other stimuli. EEG/ERP recordings were taken using a high-density 256-channel recording system. Behavioral data showed that for both clinical and non-clinical adolescents, reaction time (RT) was slowest in response to the fearful images. Electrophysiological data differentiated emotion and target processing between clinical and non-clinical adolescents. In the clinical group we observed a larger P100 and late positive potential (LPP) in response to fearful compared to sad or neutral pictures. There were no differences in these ERPs in the healthy sample. Emotional modulation of target processing was also identified in the clinical sample, where we observed an increase in P300 amplitude, and a larger sustained LPP in response to targets that followed emotional pictures (fear and sad) compared to targets that followed neutral pictures or other targets. There were no differences in these target ERPs for the healthy participants. Taken together, we suggest that these data provide important and novel evidence of affective and attention dysfunction in this clinical population of adolescents, and offer an example of the disruptive effects of emotional reactivity on basic cognition.
Collapse
Affiliation(s)
- Anthony Singhal
- Department of Psychology, University of Alberta Edmonton, AB, Canada ; Centre for Neuroscience, University of Alberta Edmonton, AB, Canada
| | | | | | | | | | | | | |
Collapse
|
35
|
Shafer AT, Dolcos F. Neural correlates of opposing effects of emotional distraction on perception and episodic memory: an event-related FMRI investigation. Front Integr Neurosci 2012; 6:70. [PMID: 23049502 PMCID: PMC3446246 DOI: 10.3389/fnint.2012.00070] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2012] [Accepted: 08/21/2012] [Indexed: 11/25/2022] Open
Abstract
A main question in emotion and memory literature concerns the relationship between the immediate impact of emotional distraction on perception and the long-term impact of emotion on memory. While previous research shows both automatic and resource-mediated mechanisms to be involved in initial emotion processing and memory, it remains unclear what the exact relationship between the immediate and long-term effects is, and how this relationship may change as a function of manipulations at perception favoring the engagement of either more automatic or mediated mechanisms. Using event-related functional magnetic resonance imaging, we varied the degree of resource availability for processing task-irrelevant emotional information, to determine how the initial (impairing) impact of emotional distraction related to the long-term (enhancing) impact of emotion on memory. Results showed that a direct relationship between emotional distraction and memory was dependent on automatic mechanisms, as this was found only under conditions of limited resource availability and engagement of amygdala (AMY)-hippocampal (HC) mechanisms to both impairing and enhancing effects. A hemispheric disassociation was also identified in AMY-HC, where while both sides were associated with emotional distraction and left AMY and anterior HC were linked to emotional memory, functional asymmetry was only identified in the posterior HC, with only the left side contributing to emotional memory. Finally, areas dissociating between the two opposing effects included the medial frontal, precentral, superior temporal, and middle occipital gyri (linked to emotional distraction), and the superior parietal cortex (linked to emotional memory). These findings demonstrate the relationship between emotional distraction and memory is context dependent and that specific brain regions may be more or less susceptible to the direction of emotional modulation (increased or decreased), depending on the task manipulation, and processes investigated.
Collapse
Affiliation(s)
- Andrea T Shafer
- Centre for Neuroscience, University of Alberta Edmonton, AB, Canada
| | | |
Collapse
|
36
|
Shafer AT, Matveychuk D, Penney T, O'Hare AJ, Stokes J, Dolcos F. Processing of emotional distraction is both automatic and modulated by attention: evidence from an event-related fMRI investigation. J Cogn Neurosci 2012; 24:1233-52. [PMID: 22332805 DOI: 10.1162/jocn_a_00206] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Traditionally, emotional stimuli have been thought to be automatically processed via a bottom-up automatic "capture of attention" mechanism. Recently, this view has been challenged by evidence that emotion processing depends on the availability of attentional resources. Although these two views are not mutually exclusive, direct evidence reconciling them is lacking. One limitation of previous investigations supporting the traditional or competing views is that they have not systematically investigated the impact of emotional charge of task-irrelevant distraction in conjunction with manipulations of attentional demands. Using event-related fMRI, we investigated the nature of emotion-cognition interactions in a perceptual discrimination task with emotional distraction by manipulating both the emotional charge of the distracting information and the demands of the main task. Our findings show that emotion processing is both automatic and modulated by attention, but emotion and attention were only found to interact when finer assessments of emotional charge (comparison of most vs. least emotional conditions) were considered along with an effective manipulation of processing load (high vs. low). The study also identified brain regions reflecting the detrimental impact of emotional distraction on performance as well as regions involved in coping with such distraction. Activity in the dorsomedial pFC and ventrolateral pFC was linked to a detrimental impact of emotional distraction, whereas the dorsal ACC and lateral occipital cortex were involved in helping with emotional distraction. These findings demonstrate that task-irrelevant emotion processing is subjective to both the emotional content of distraction and the level of attentional demand.
Collapse
|
37
|
Shafer AT. The psychiatric nurse as psychotherapist. Australas Nurses J 1979; 8:1, 3. [PMID: 116644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|