1
|
Gao C, Yang Q, Kim ME, Khairi NM, Cai LY, Newlin NR, Kanakaraj P, Remedios LW, Krishnan AR, Yu X, Yao T, Zhang P, Schilling KG, Moyer D, Archer DB, Resnick SM, Landman BA. Characterizing patterns of DTI variance in aging brains. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.22.23294381. [PMID: 37662348 PMCID: PMC10473788 DOI: 10.1101/2023.08.22.23294381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Purpose We characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions. Methods We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session. Results Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related (p ≪ 0.001) to FA variance in the cuneus and occipital gyrus, but negatively (p ≪ 0.001) in the caudate nucleus. Males show significantly (p ≪ 0.001) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated (p < 0.05) with a decrease in FA variance. Head motion increases during the rescan of DTI (Δμ = 0.045 millimeters per volume). Conclusions The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.
Collapse
Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
| | - Qi Yang
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Michael E Kim
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Nazirah Mohd Khairi
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
| | - Leon Y Cai
- Vanderbilt University, Department of Biomedical Engineering, Nashville, United States
| | - Nancy R Newlin
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | | | - Lucas W Remedios
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Aravind R Krishnan
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
| | - Xin Yu
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Panpan Zhang
- Vanderbilt University Medical Center, Department of Biostatistics, Nashville, United States
| | - Kurt G Schilling
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, USA
- Vanderbilt University, Vanderbilt University Institute of Imaging Science, Nashville, USA
| | - Daniel Moyer
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, USA
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, USA
| | - Susan M Resnick
- National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, United States
| | - Bennett A Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
- Vanderbilt University, Department of Computer Science, Nashville, United States
- Vanderbilt University, Department of Biomedical Engineering, Nashville, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, USA
- Vanderbilt University, Vanderbilt University Institute of Imaging Science, Nashville, USA
| |
Collapse
|
2
|
Hao Y, Xu H, Xia M, Yan C, Zhang Y, Zhou D, Kärkkäinen T, Nickerson LD, Li H, Cong F. Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data. Eur J Neurosci 2023; 58:3466-3487. [PMID: 37649141 PMCID: PMC10659240 DOI: 10.1111/ejn.16120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 09/01/2023]
Abstract
Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.
Collapse
Affiliation(s)
- Yuxing Hao
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
| | - Huashuai Xu
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chenwei Yan
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
| | - Yunge Zhang
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
| | - Dongyue Zhou
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
| | - Tommi Kärkkäinen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Lisa D. Nickerson
- McLean Imaging Center, McLean Hospital, Belmont, Massachusetts, USA
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
| | - Huanjie Li
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
- School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
- Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, Dalian, China
| |
Collapse
|
3
|
Shiohama T, Maikusa N, Kawaguchi M, Natsume J, Hirano Y, Saito K, Takanashi JI, Levman J, Takahashi E, Matsumoto K, Yokota H, Hattori S, Tsujimura K, Sawada D, Uchida T, Takatani T, Fujii K, Naganawa S, Sato N, Hamada H. A Brain Morphometry Study with Across-Site Harmonization Using a ComBat-Generalized Additive Model in Children and Adolescents. Diagnostics (Basel) 2023; 13:2774. [PMID: 37685313 PMCID: PMC10487204 DOI: 10.3390/diagnostics13172774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Regional anatomical structures of the brain are intimately connected to functions corresponding to specific regions and the temporospatial pattern of genetic expression and their functions from the fetal period to old age. Therefore, quantitative brain morphometry has often been employed in neuroscience investigations, while controlling for the scanner effect of the scanner is a critical issue for ensuring accuracy in brain morphometric studies of rare orphan diseases due to the lack of normal reference values available for multicenter studies. This study aimed to provide across-site normal reference values of global and regional brain volumes for each sex and age group in children and adolescents. We collected magnetic resonance imaging (MRI) examinations of 846 neurotypical participants aged 6.0-17.9 years (339 male and 507 female participants) from 5 institutions comprising healthy volunteers or neurotypical patients without neurological disorders, neuropsychological disorders, or epilepsy. Regional-based analysis using the CIVET 2.1.0. pipeline provided regional brain volumes, and the measurements were across-site combined using ComBat-GAM harmonization. The normal reference values of global and regional brain volumes and lateral indices in our study could be helpful for evaluating the characteristics of the brain morphology of each individual in a clinical setting and investigating the brain morphology of ultra-rare diseases.
Collapse
Affiliation(s)
- Tadashi Shiohama
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo 108-8639, Japan
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Masahiro Kawaguchi
- Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan; (M.K.)
| | - Jun Natsume
- Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan; (M.K.)
- Department of Developmental Disability Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan
| | - Yoshiyuki Hirano
- Research Center for Child Mental Development, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Suita 565-0871, Osaka, Japan
| | - Keito Saito
- Department of Pediatrics and Pediatric Neurology, Tokyo Women’s Medical University Yachiyo Medical Center, 477-96 Owadashinden, Yachiyo-shi 276-8524, Chiba, Japan
| | - Jun-ichi Takanashi
- Department of Pediatrics and Pediatric Neurology, Tokyo Women’s Medical University Yachiyo Medical Center, 477-96 Owadashinden, Yachiyo-shi 276-8524, Chiba, Japan
| | - Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
- Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, 5005 Chapel Square, Antigonish, NS B2G 2W5, Canada
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA
- Nova Scotia Health Authority—Research, Innovation and Discovery Center for Clinical Research, 5790 University Avenue, Halifax, NS B3H 1V7, Canada
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, MA 02129, USA
| | - Koji Matsumoto
- Department of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Hajime Yokota
- Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Shinya Hattori
- Department of Radiology, Chiba University Hospital, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Keita Tsujimura
- Group of Brain Function and Development, Neuroscience Institute of the Graduate School of Science, Nagoya University, Nagoya 466-8550, Aichi, Japan
- Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Nagoya 466-8550, Aichi, Japan
| | - Daisuke Sawada
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Tomoko Uchida
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Tomozumi Takatani
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| | - Katsunori Fujii
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
- Department of Pediatrics, International University of Welfare and Health School of Medicine, Narita 286-8520, Chiba, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466-8550, Aichi, Japan
| | - Noriko Sato
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Hiromichi Hamada
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan
| |
Collapse
|
4
|
Reynolds M, Chaudhary T, Eshaghzadeh Torbati M, Tudorascu DL, Batmanghelich K. ComBat Harmonization: Empirical Bayes versus fully Bayes approaches. Neuroimage Clin 2023; 39:103472. [PMID: 37506457 PMCID: PMC10412957 DOI: 10.1016/j.nicl.2023.103472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023]
Abstract
Studying small effects or subtle neuroanatomical variation requires large-scale sample size data. As a result, combining neuroimaging data from multiple datasets is necessary. Variation in acquisition protocols, magnetic field strength, scanner build, and many other non-biologically related factors can introduce undesirable bias into studies. Hence, harmonization is required to remove the bias-inducing factors from the data. ComBat is one of the most common methods applied to features from structural images. ComBat models the data using a hierarchical Bayesian model and uses the empirical Bayes approach to infer the distribution of the unknown factors. The empirical Bayes harmonization method is computationally efficient and provides valid point estimates. However, it tends to underestimate uncertainty. This paper investigates a new approach, fully Bayesian ComBat, where Monte Carlo sampling is used for statistical inference. When comparing fully Bayesian and empirical Bayesian ComBat, we found Empirical Bayesian ComBat more effectively removed scanner strength information and was much more computationally efficient. Conversely, fully Bayesian ComBat better preserved biological disease and age-related information while performing more accurate harmonization on traveling subjects. The fully Bayesian approach generates a rich posterior distribution, which is useful for generating simulated imaging features for improving classifier performance in a limited data setting. We show the generative capacity of our model for augmenting and improving the detection of patients with Alzheimer's disease. Posterior distributions for harmonized imaging measures can also be used for brain-wide uncertainty comparison and more principled downstream statistical analysis.Code for our new fully Bayesian ComBat extension is available at https://github.com/batmanlab/BayesComBat.
Collapse
Affiliation(s)
- Maxwell Reynolds
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Blvd. Suite 500, Pittsburgh, PA 15206, USA.
| | - Tigmanshu Chaudhary
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Blvd. Suite 500, Pittsburgh, PA 15206, USA.
| | - Mahbaneh Eshaghzadeh Torbati
- Intelligent System Program, University of Pittsburgh School of Computing and Information, 210 South Bouquet Street, Pittsburgh, PA 15260, USA.
| | - Dana L Tudorascu
- Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O'Hara Street, Pittsburgh, PA 15213, USA; Department of Biostatistics, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15213, USA.
| | - Kayhan Batmanghelich
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Blvd. Suite 500, Pittsburgh, PA 15206, USA.
| |
Collapse
|
5
|
Wang G, Song Y, Su J, Fan Z, Xu L, Fang P, Liu C, Long H, Hu C, Zhou L, Huang S, Zhou P, Wang K, Pang N, Shen H, Li S, Hu D, Xiao B, Zeng LL, Long L. Altered cerebellar-motor loop in benign adult familial myoclonic epilepsy type 1: The structural basis of cortical tremor. Epilepsia 2022; 63:3192-3203. [PMID: 36196770 DOI: 10.1111/epi.17430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/01/2022] [Accepted: 10/03/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVE Cortical tremor/myoclonus is the hallmark feature of benign adult familial myoclonic epilepsy (BAFME), the mechanism of which remains elusive. A hypothesis is that a defective control in the preexisting cerebellar-motor loop drives cortical tremor. Meanwhile, the basal ganglia system might also participate in BAFME. This study aimed to discover the structural basis of cortical tremor/myoclonus in BAFME. METHODS Nineteen patients with BAFME type 1 (BAFME1) and 30 matched healthy controls underwent T1-weighted and diffusion tensor imaging scans. FreeSurfer and spatially unbiased infratentorial template (SUIT) toolboxes were utilized to assess the motor cortex and the cerebellum. Probabilistic tractography was generated for two fibers to test the hypothesis: the dentato-thalamo-(M1) (primary motor cortex) and globus pallidus internus (GPi)-thalamic projections. Average fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) of each tract were extracted. RESULTS Cerebellar atrophy and dentate nucleus alteration were observed in the patients. In addition, patients with BAFME1 exhibited reduced AD and FA in the left and right dentato-thalamo-M1 nondecussating fibers, respectively false discovery rate (FDR) correction q < .05. Cerebellar projections showed negative correlations with somatosensory-evoked potential P25-N33 amplitude and were independent of disease duration and medication. BAFME1 patients also had increased FA and decreased MD in the left GPi-thalamic projection. Higher FA and lower RD in the right GPi-thalamic projection were also observed (FDR q < .05). SIGNIFICANCE The present findings support the hypothesis that the cerebello-thalamo-M1 loop might be the structural basis of cortical tremor in BAFME1. The basal ganglia system also participates in BAFME1 and probably serves a regulatory role.
Collapse
Affiliation(s)
- Ge Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Yanmin Song
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Emergency, Xiangya Hospital, Central South University, Changsha, China
| | - Jianpo Su
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Zhipeng Fan
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Lin Xu
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Peng Fang
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.,Department of Military Medical Psychology, Air Force Medical University, Xian, China
| | - Chaorong Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Hongyu Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Chongyu Hu
- Department of Neurology, Hunan People's Hospital, Changsha, China
| | - Luo Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Sha Huang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Pinting Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Kangrun Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Nan Pang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Pediatric, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Shuyu Li
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
| | - Lili Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| |
Collapse
|
6
|
Behler A, Lulé D, Ludolph AC, Kassubek J, Müller HP. Longitudinal monitoring of amyotrophic lateral sclerosis by diffusion tensor imaging: Power calculations for group studies. Front Neurosci 2022; 16:929151. [PMID: 36117627 PMCID: PMC9479493 DOI: 10.3389/fnins.2022.929151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/20/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction Diffusion tensor imaging (DTI) can be used to map disease progression in amyotrophic lateral sclerosis (ALS) and therefore is a promising candidate for a biomarker in ALS. To this end, longitudinal study protocols need to be optimized and validated regarding group sizes and time intervals between visits. The objective of this study was to assess the influences of sample size, the schedule of follow-up measurements, and measurement uncertainties on the statistical power to optimize longitudinal DTI study protocols in ALS. Patients and methods To estimate the measurement uncertainty of a tract-of–interest-based DTI approach, longitudinal test-retest measurements were applied first to a normal data set. Then, DTI data sets of 80 patients with ALS and 50 healthy participants were analyzed in the simulation of longitudinal trajectories, that is, longitudinal fractional anisotropy (FA) values for follow-up sessions were simulated for synthetic patient and control groups with different rates of FA decrease in the corticospinal tract. Monte Carlo simulations of synthetic longitudinal study groups were used to estimate the statistical power and thus the potentially needed sample sizes for a various number of scans at one visit, different time intervals between baseline and follow-up measurements, and measurement uncertainties. Results From the simulation for different longitudinal FA decrease rates, it was found that two scans per session increased the statistical power in the investigated settings unless sample sizes were sufficiently large and time intervals were appropriately long. The positive effect of a second scan per session on the statistical power was particularly pronounced for FA values with high measurement uncertainty, for which the third scan per session increased the statistical power even further. Conclusion With more than one scan per session, the statistical power of longitudinal DTI studies can be increased in patients with ALS. Consequently, sufficient statistical power can be achieved even with limited sample sizes. An improved longitudinal DTI study protocol contributes to the detection of small changes in diffusion metrics and thereby supports DTI as an applicable and reliable non-invasive biomarker in ALS.
Collapse
Affiliation(s)
- Anna Behler
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Dorothée Lulé
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Albert C Ludolph
- Department of Neurology, University of Ulm, Ulm, Germany.,Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany.,Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Ulm, Germany
| | | |
Collapse
|
7
|
Longitudinal corpus callosum microstructural decline in early-stage Parkinson’s disease in association with akinetic-rigid symptom severity. NPJ Parkinsons Dis 2022; 8:108. [PMID: 36038586 PMCID: PMC9424284 DOI: 10.1038/s41531-022-00372-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 08/02/2022] [Indexed: 12/26/2022] Open
Abstract
Previous diffusion tensor imaging (DTI) studies of Parkinson’s disease (PD) show reduced microstructural integrity of the corpus callosum (CC) relative to controls, although the characteristics of such callosal degradation remain poorly understood. Here, we utilized a longitudinal approach to identify microstructural decline in the entire volume of the CC and its functional subdivisions over 2 years and related the callosal changes to motor symptoms in early-stage PD. The study sample included 61 PD subjects (N = 61, aged 45–82, 38 M & 23 F, H&Y ≤ 2) from the Parkinson’s Progressive Markers Initiative database (PPMI). Whole-brain voxel-wise results revealed significant fractional anisotropy (FA) and mean diffusivity (MD) changes in the CC, especially in the genu and splenium. Using individually drawn CC regions of interest (ROI), our analysis further revealed that almost all subdivisions of the CC show significant decline in FA to certain extents over the two-year timeframe. Additionally, FA seemed lower in the right hemisphere of the CC at both time-points, and callosal FA decline was associated with FA and MD decline in widespread cortical and subcortical areas. Notably, multiple regression analysis revealed that across-subject akinetic-rigid severity was negatively associated with callosal FA at baseline and 24 months follow-up, and the effect was strongest in the anterior portion of the CC. These results suggest that callosal microstructure alterations in the anterior CC may serve as a viable biomarker for akinetic-rigid symptomology and disease progression, even in early PD.
Collapse
|
8
|
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] [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
|
9
|
Zhang Z, Gewandter JS, Geha P. Brain Imaging Biomarkers for Chronic Pain. Front Neurol 2022; 12:734821. [PMID: 35046881 PMCID: PMC8763372 DOI: 10.3389/fneur.2021.734821] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 12/08/2021] [Indexed: 12/14/2022] Open
Abstract
The prevalence of chronic pain has reached epidemic levels. In addition to personal suffering chronic pain is associated with psychiatric and medical co-morbidities, notably substance misuse, and a huge a societal cost amounting to hundreds of billions of dollars annually in medical cost, lost wages, and productivity. Chronic pain does not have a cure or quantitative diagnostic or prognostic tools. In this manuscript we provide evidence that this situation is about to change. We first start by summarizing our current understanding of the role of the brain in the pathogenesis of chronic pain. We particularly focus on the concept of learning in the emergence of chronic pain, and the implication of the limbic brain circuitry and dopaminergic signaling, which underly emotional learning and decision making, in this process. Next, we summarize data from our labs and from other groups on the latest brain imaging findings in different chronic pain conditions focusing on results with significant potential for translation into clinical applications. The gaps in the study of chronic pain and brain imaging are highlighted in throughout the overview. Finally, we conclude by discussing the costs and benefits of using brain biomarkers of chronic pain and compare to other potential markers.
Collapse
Affiliation(s)
- Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jennifer S Gewandter
- Anesthesiology and Perioperative Medicine, School of Medicine and Dentistry, University of Rochester, Rochester, NY, United States
| | - Paul Geha
- Department of Psychiatry, School of Medicine and Dentistry, University of Rochester, Rochester, NY, United States.,Department of Neurology, School of Medicine and Dentistry, University of Rochester, Rochester, NY, United States.,Del Monte Neuroscience Institute, University of Rochester, Rochester, NY, United States
| |
Collapse
|
10
|
Xia Q, Thompson JA, Koestler DC. Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE). Stat Appl Genet Mol Biol 2021; 20:101-119. [PMID: 34905304 PMCID: PMC9617207 DOI: 10.1515/sagmb-2021-0020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/29/2021] [Indexed: 11/15/2022]
Abstract
Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose Batch effect Reduction of mIcroarray data with Dependent samples usinGEmpirical Bayes (BRIDGE), a three-step parametric empirical Bayes approach that leverages technical replicate samples profiled at multiple timepoints/batches, so-called "bridge samples", to inform batch-effect reduction/attenuation in longitudinal microarray studies. Extensive simulation studies and an analysis of a real biological data set were conducted to benchmark the performance of BRIDGE against both ComBat and longitudinalComBat. Our results demonstrate that while all methods perform well in facilitating accurate estimates of time effects, BRIDGE outperforms both ComBat and longitudinal ComBat in the removal of batch-effects in data sets with bridging samples, and perhaps as a result, was observed to have improved statistical power for detecting genes with a time effect. BRIDGE demonstrated competitive performance in batch effect reduction of confounded longitudinal microarray studies, both in simulated and a real data sets, and may serve as a useful preprocessing method for researchers conducting longitudinal microarray studies that include bridging samples.
Collapse
Affiliation(s)
- Qing Xia
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160
| | - Jeffrey A. Thompson
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160
| | - Devin C. Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160
| |
Collapse
|
11
|
Merenstein JL, Corrada MM, Kawas CH, Bennett IJ. Age affects white matter microstructure and episodic memory across the older adult lifespan. Neurobiol Aging 2021; 106:282-291. [PMID: 34332220 DOI: 10.1016/j.neurobiolaging.2021.06.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 02/07/2023]
Abstract
Diffusion imaging studies have observed age-related degradation of white matter that contributes to cognitive deficits separately in younger-old (ages 65-89) and oldest-old (ages 90+) adults. But it remains unclear whether these age effects are magnified in advanced age groups, which may reflect disease-related pathology. Here, we tested whether age-related differences in white matter microstructure followed linear or nonlinear patterns across the entire older adult lifespan (65-98 years), these patterns were influenced by oldest-old adults at increased risk of dementia (cognitive impairment no dementia, CIND), and they explained age effects on episodic memory. Results revealed nonlinear microstructure declines across fiber classes (medial temporal, callosal, association, projection and/or thalamic) that were largest for medial temporal fibers. These patterns remained after excluding oldest-old participants with CIND, indicating that aging of white matter microstructure cannot solely be explained by pathology associated with early cognitive impairment. Moreover, finding that the effect of age on episodic memory was mediated by medial temporal fiber microstructure suggests it is essential for facilitating memory-related neural signals across the older adult lifespan.
Collapse
Affiliation(s)
| | - María M Corrada
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA; Department of Neurology, University of California, Irvine, CA, USA; Department of Epidemiology, University of California, Irvine, CA, USA
| | - Claudia H Kawas
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA; Department of Neurology, University of California, Irvine, CA, USA; Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Ilana J Bennett
- Department of Psychology, University of California, Riverside, CA, USA
| |
Collapse
|
12
|
Armstrong NM, Williams OA, Landman BA, Deal JA, Lin FR, Resnick SM. Association of Poorer Hearing With Longitudinal Change in Cerebral White Matter Microstructure. JAMA Otolaryngol Head Neck Surg 2021; 146:1035-1042. [PMID: 32880621 DOI: 10.1001/jamaoto.2020.2497] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Importance There is a dearth of studies that examine the association between poorer hearing and change in cerebral white matter (WM) microstructure. Objective To examine the association of poorer hearing with baseline and change in WM microstructure among older adults. Design, Setting, and Participants This was a prospective cohort study that evaluated speech-in-noise, pure-tone audiometry, and WM microstructure, as measured by mean diffusivity (MD) and fractional anisotropy (FA), both of which were evaluated by diffusion tensor imaging (DTI) in 17 WM regions. Data were collected between October 2012 and December 2018 and analyzed between March 2019 and August 2019 with a mean follow-up time of 1.7 years. The study evaluated responses to the Baltimore Longitudinal Study of Aging among 356 cognitively normal adults who had at least 1 hearing assessment and DTI session. Excluded were those with baseline cognitive impairment, stroke, head injuries, Parkinson disease, and/or bipolar disorder. Exposures Peripheral auditory function was measured by pure-tone average in the better-hearing ear. Central auditory function was measured by signal-to-noise ratio score from a speech-in-noise task and adjusted by pure-tone average. Main Outcomes and Measures Linear mixed-effects models with random intercepts and slopes were used to examine the association of poorer peripheral and central auditory function with baseline and longitudinal DTI metrics in 17 WM regions, adjusting for baseline characteristics (age, sex, race, hypertension, elevated total cholesterol, and obesity). Results Of 356 cognitively normal adults included in the study, the mean (SD) age was 73.5 (8.8) years, and 204 (57.3%) were women. There were no baseline associations between hearing and DTI measures. Longitudinally, poorer peripheral hearing was associated with increases in MD in the inferior fronto-occipital fasciculus (β = 0.025; 95% CI, 0.008-0.042) and the body (β = 0.050; 95% CI, 0.015-0.085) of the corpus callosum, but there were no associations of peripheral hearing with FA changes in these tracts. Poorer central auditory function was associated with longitudinal MD increases (β = 0.031; 95% CI, 0.010-0.052) and FA declines (β = -1.624; 95% CI, -2.511 to -0.738) in the uncinate fasciculus. Conclusions and Relevance Findings of this cohort study suggest that poorer hearing is related to change in integrity of specific WM regions involved with auditory processing.
Collapse
Affiliation(s)
- Nicole M Armstrong
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland.,Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - Owen A Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | | | - Jennifer A Deal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.,Department of Otolaryngology-Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Frank R Lin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.,Department of Otolaryngology-Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| |
Collapse
|
13
|
Vanier C, Pandey T, Parikh S, Rodriguez A, Knoblauch T, Peralta J, Hertzler A, Ma L, Nam R, Musallam S, Taylor H, Vickery T, Zhang Y, Ranzenberger L, Nguyen A, Kapostasy M, Asturias A, Fazzini E, Snyder T. Interval-censored survival analysis of mild traumatic brain injury with outcome based neuroimaging clinical applications. JOURNAL OF CONCUSSION 2020. [DOI: 10.1177/2059700220947194] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Objective The purpose of this study was to assess the relationship between MRI findings and clinical presentation and outcomes in patients following mild traumatic brain injury (mTBI). We hypothesize that imaging findings other than hemorrhages and contusions may be used to predict symptom presentation and longevity following mTBI. Methods Patients (n = 250) diagnosed with mTBI and in litigation for brain injury underwent 3T magnetic resonance imaging (MRI). A retrospective chart review was performed to assess symptom presentation and improvement/resolution. To account for variable times of clinical presentation, nonuniform follow-up, and uncertainty in the dates of symptom resolution, a right censored, interval censored statistical analysis was performed. Incidence and resolution of headache, balance, cognitive deficit, fatigue, anxiety, depression, and emotional lability were compared among patients. Image findings analyzed included white matter hyperintensities (WMH), Diffusion Tensor Imaging (DTI) fractional anisotropy (FA) values, MR perfusion, auditory functional MRI (fMRI) activation, hippocampal atrophy (HA) and hippocampal asymmetry as defined by NeuroQuant ® volumetric software. Results Patients who reported LOC were significantly more likely to present with balance problems (p < 0.001), cognitive deficits (p = 0.010), fatigue (p = 0.025), depression (p = 0.002), and emotional lability (p = 0.002). Patients with LOC also demonstrated significantly slower recovery of cognitive function than those who did not lose consciousness (p = 0.044). Patients over the age of 40 had significantly higher odds of presenting with balance problems (p = 0.006). Additionally, these older patients were slower to recover cognitive function (p = 0.001) and less likely to experience improvement of headaches (p = 0.007). Abnormal MRI did not correlate significantly with symptom presentation, but was a strong indicator of symptom progression, with slower recovery of balance (p = 0.009) and cognitive deficits (p < 0.001). Conclusion This analysis demonstrates the utility of clinical data analysis using interval-censored survival statistical technique in head trauma patients. Strong statistical associations between neuroimaging findings and aggregate clinical outcomes were identified in patients with mTBI.
Collapse
Affiliation(s)
- Cheryl Vanier
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Trisha Pandey
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Shaunaq Parikh
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
- IMGEN LLC., Las Vegas, NV, USA
- Department of Family Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA, USA
| | | | | | - John Peralta
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Amanda Hertzler
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Leon Ma
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Ruslan Nam
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Sami Musallam
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Hallie Taylor
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Taylor Vickery
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Yolanda Zhang
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Logan Ranzenberger
- Department of Radiology, Michigan State University, East Lansing, MI, USA
- Department of Radiology, McClaren Health Care, Flint, MI, USA
| | - Andrew Nguyen
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Mike Kapostasy
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
- IMGEN LLC., Las Vegas, NV, USA
| | - Alex Asturias
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Enrico Fazzini
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
| | - Travis Snyder
- Department of Research, Touro University Nevada, Las Vegas, NV, USA
- IMGEN LLC., Las Vegas, NV, USA
- SimonMed Imaging, Las Vegas, NV, USA
| |
Collapse
|
14
|
Tong Q, Gong T, He H, Wang Z, Yu W, Zhang J, Zhai L, Cui H, Meng X, Tax CWM, Zhong J. A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols. Magn Reson Imaging 2020; 73:31-44. [PMID: 32822818 DOI: 10.1016/j.mri.2020.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/13/2020] [Accepted: 08/14/2020] [Indexed: 01/02/2023]
Abstract
Multicenter magnetic resonance imaging is gaining more popularity in large-sample projects. Since both varying hardware and software across different centers cause unavoidable data heterogeneity across centers, its impact on reliability in study outcomes has also drawn much attention recently. One fundamental issue arises in how to derive model parameters reliably from image data of varying quality. This issue is even more challenging for advanced diffusion methods such as diffusion kurtosis imaging (DKI). Recently, deep learning-based methods have been demonstrated with their potential for robust and efficient computation of diffusion-derived measures. Inspired by these approaches, the current study specifically designed a framework based on a three-dimensional hierarchical convolutional neural network, to jointly reconstruct and harmonize DKI measures from multicenter acquisition to reformulate these to a state-of-the-art hardware using data from traveling subjects. The results from the harmonized data acquired with different protocols show that: 1) the inter-scanner variation of DKI measures within white matter was reduced by 51.5% in mean kurtosis, 65.9% in axial kurtosis, 53.7% in radial kurtosis, and 61.5% in kurtosis fractional anisotropy, respectively; 2) data reliability of each single scanner was enhanced and brought to the level of the reference scanner; and 3) the harmonization network was able to reconstruct reliable DKI values from high data variability. Overall the results demonstrate the feasibility of the proposed deep learning-based method for DKI harmonization and help to simplify the protocol setup procedure for multicenter scanners with different hardware and software configurations.
Collapse
Affiliation(s)
- Qiqi Tong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China; Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China.
| | - Ting Gong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Zheng Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China.
| | - Wenwen Yu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China.
| | - Jianjun Zhang
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Lihao Zhai
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Hongsheng Cui
- Department of Radiology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China
| | - Xin Meng
- Department of Radiology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China
| | - Chantal W M Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom.
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China; Department of Imaging Sciences, University of Rochester, Rochester, NY, USA.
| |
Collapse
|
15
|
Beer JC, Tustison NJ, Cook PA, Davatzikos C, Sheline YI, Shinohara RT, Linn KA. Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data. Neuroimage 2020; 220:117129. [PMID: 32640273 PMCID: PMC7605103 DOI: 10.1016/j.neuroimage.2020.117129] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 06/24/2020] [Accepted: 06/29/2020] [Indexed: 12/19/2022] Open
Abstract
While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.
Collapse
Affiliation(s)
- Joanne C Beer
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States.
| | - Nicholas J Tustison
- Department of Radiology and Medical Imaging, University of Virginia, United States
| | - Philip A Cook
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
| | - Christos Davatzikos
- Center for Biomedical Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, United States
| | - Kristin A Linn
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, United States.
| | | |
Collapse
|
16
|
Denoising scanner effects from multimodal MRI data using linked independent component analysis. Neuroimage 2020; 208:116388. [DOI: 10.1016/j.neuroimage.2019.116388] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 11/14/2019] [Accepted: 11/20/2019] [Indexed: 01/24/2023] Open
|
17
|
Luque Laguna PA, Combes AJE, Streffer J, Einstein S, Timmers M, Williams SCR, Dell'Acqua F. Reproducibility, reliability and variability of FA and MD in the older healthy population: A test-retest multiparametric analysis. NEUROIMAGE-CLINICAL 2020; 26:102168. [PMID: 32035272 PMCID: PMC7011084 DOI: 10.1016/j.nicl.2020.102168] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Revised: 01/10/2020] [Accepted: 01/10/2020] [Indexed: 12/13/2022]
Abstract
In older healthy subjects, FA and MD show overall good test-retest reliability & reproducibility. MD is sistematically more reproducible than FA across the entire brain anatomy. FA is more reliable than MD in subcortical white matter regions. In high reliability & low reproducibility regions, variability between subjects is high and statistical power is low. In low reliability & high reproducibility regions, variability between subjects is low and statistical power is high.
Collapse
Affiliation(s)
- Pedro A Luque Laguna
- Department 5 of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; Natbrainlab, Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK; Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.
| | - Anna J E Combes
- Department 5 of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Johannes Streffer
- UCB Biopharma SPRL, Chemin du Foriest B-1420 Braine-l'Alleud, Belgium; Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steven Einstein
- Janssen Research and Development LLC, Titusville, NJ, US; UCB Biopharma SPRL, Chemin du Foriest B-1420 Braine-l'Alleud, Belgium
| | - Maarten Timmers
- Janssen Research and Development, a division of Janssen Pharmaceutica NV, Beerse, Belgium; Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steve C R Williams
- Department 5 of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Flavio Dell'Acqua
- Natbrainlab, Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK; Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.
| |
Collapse
|
18
|
Tax CM, Grussu F, Kaden E, Ning L, Rudrapatna U, John Evans C, St-Jean S, Leemans A, Koppers S, Merhof D, Ghosh A, Tanno R, Alexander DC, Zappalà S, Charron C, Kusmia S, Linden DE, Jones DK, Veraart J. Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms. Neuroimage 2019; 195:285-299. [PMID: 30716459 PMCID: PMC6556555 DOI: 10.1016/j.neuroimage.2019.01.077] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 01/16/2019] [Accepted: 01/30/2019] [Indexed: 01/01/2023] Open
Abstract
Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain 'truly quantitative measures' and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with 'standard' and 'state-of-the-art' protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques.
Collapse
Affiliation(s)
- Chantal Mw Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Francesco Grussu
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom; Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Enrico Kaden
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Lipeng Ning
- Harvard Medical School, Boston, MA, United States
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - C John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Samuel St-Jean
- Image Sciences Institute, Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Alexander Leemans
- Image Sciences Institute, Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Simon Koppers
- Department of Radiology, University of Pennsylvania and the Children's Hospital of Philadelphia, Philadelphia, PA, United States; Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Dorit Merhof
- Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Aurobrata Ghosh
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Ryutaro Tanno
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Machine Intelligence and Perception Group, Microsoft Research Cambridge, Cambridge, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Stefano Zappalà
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Cyril Charron
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Slawomir Kusmia
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - David Ej Linden
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; Mary McKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
| | - Jelle Veraart
- New York University, New York, NY, United States; imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| |
Collapse
|
19
|
Panman JL, To YY, van der Ende EL, Poos JM, Jiskoot LC, Meeter LHH, Dopper EGP, Bouts MJRJ, van Osch MJP, Rombouts SARB, van Swieten JC, van der Grond J, Papma JM, Hafkemeijer A. Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison. Front Neurosci 2019; 13:729. [PMID: 31379483 PMCID: PMC6648353 DOI: 10.3389/fnins.2019.00729] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 06/28/2019] [Indexed: 12/14/2022] Open
Abstract
Neuroimaging MRI data in scientific research is increasingly pooled, but the reliability of such studies may be hampered by the use of different hardware elements. This might introduce bias, for example when cross-sectional studies pool data acquired with different head coils, or when longitudinal clinical studies change head coils halfway. In the present study, we aimed to estimate this possible bias introduced by using different head coils to create awareness and to avoid misinterpretation of results. We acquired, with both an 8 channel and 32 channel head coil, T1-weighted, diffusion tensor imaging and resting state fMRI images at 3T MRI (Philips Achieva) with stable acquisition parameters in a large group of cognitively healthy participants (n = 77). Standard analysis methods, i.e., voxel-based morphometry, tract-based spatial statistics and resting state functional network analyses, were used in a within-subject design to compare 8 and 32 channel head coil data. Signal-to-noise ratios (SNR) for both head coils showed similar ranges, although the 32 channel SNR profile was more homogeneous. Our data demonstrates specific patterns of gray and white matter volume differences between head coils (relative volume change of 6 to 9%), related to altered image contrast and therefore, altered tissue segmentation. White matter connectivity (fractional anisotropy and diffusivity measures) showed hemispherical dependent differences between head coils (relative connectivity change of 4 to 6%), and functional connectivity in resting state networks was higher using the 32 channel head coil in posterior cortical areas (relative change up to 27.5%). This study shows that, even when acquisition protocols are harmonized, the results of standardized analysis models can be severely affected by the use of different head coils. Researchers should be aware of this when combining multiple neuroimaging MRI datasets, to prevent coil-related bias and avoid misinterpretation of their findings.
Collapse
Affiliation(s)
- Jessica L Panman
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Yang Yang To
- Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Emma L van der Ende
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Jackie M Poos
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Lize C Jiskoot
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Lieke H H Meeter
- Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Elise G P Dopper
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Mark J R J Bouts
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Department of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
| | - Matthias J P van Osch
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
| | - Serge A R B Rombouts
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Department of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | | | - Janne M Papma
- Department of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Anne Hafkemeijer
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Department of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
| |
Collapse
|
20
|
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: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [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
|
21
|
Williams OA, An Y, Beason-Held L, Huo Y, Ferrucci L, Landman BA, Resnick SM. Vascular burden and APOE ε4 are associated with white matter microstructural decline in cognitively normal older adults. Neuroimage 2019; 188:572-583. [PMID: 30557663 PMCID: PMC6601608 DOI: 10.1016/j.neuroimage.2018.12.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 11/20/2018] [Accepted: 12/04/2018] [Indexed: 11/27/2022] Open
Abstract
White matter microstructure can be measured with diffusion tensor imaging (DTI). While increasing age is a predictor of white matter (WM) microstructure changes, roles of other possible modifiers, such as cardiovascular risk factors, APOE ε4 allele status and biological sex have not been clarified. We investigated 665 cognitively normal participants from the Baltimore Longitudinal Study of Aging (age 50-95, 56.7% female) with a total of 1384 DTI scans. WM microstructure was assessed by fractional anisotropy (FA) and mean diffusivity (MD). A vascular burden score was defined as the sum of five risk factors (hypertension, obesity, elevated cholesterol, diabetes and smoking status). Linear mixed effects models assessed the association of baseline vascular burden on baseline and on rates of change of FA and MD over a mean follow-up of 3.6 years, while controlling for age, race, and scanner type. We also compared DTI trajectories in APOE ε4 carriers vs. non-carriers and men vs. women. At baseline, higher vascular burden was associated with lower FA and higher MD in many WM structures including association, commissural, and projection fibers. Higher baseline vascular burden was also associated with greater longitudinal decline in FA in the hippocampal part of the cingulum and the fornix (crus)/stria terminalis and splenium of the corpus callosum, and with greater increases in MD in the splenium of the corpus callosum. APOE ε4 carriers did not differ from non-carriers in baseline DTI metrics but had greater decline in FA in the genu and splenium of the corpus callosum. Men had higher FA and lower MD in multiple WM regions at baseline but showed greater increase in MD in the genu of the corpus callosum. Women showed greater decreases over time in FA in the gyrus part of the cingulum, compared to men. Our findings show that modifiable vascular risk factors (1) have a negative impact on white matter microstructure and (2) are associated with faster microstructural deterioration of temporal WM regions and the splenium of the corpus callosum in cognitively normal adults. Reducing vascular burden in aging could modify the rate of WM deterioration and could decrease age-related cognitive decline and impairment.
Collapse
Affiliation(s)
- Owen A Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA.
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA
| | - Yuankai Huo
- School of Engineering, Vanderbilt University, Nashville, TN 37212, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
| | - Bennett A Landman
- School of Engineering, Vanderbilt University, Nashville, TN 37212, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA.
| |
Collapse
|
22
|
Lock C, Kwok J, Kumar S, Ahmad-Annuar A, Narayanan V, Ng ASL, Tan YJ, Kandiah N, Tan EK, Czosnyka Z, Czosnyka M, Pickard JD, Keong NC. DTI Profiles for Rapid Description of Cohorts at the Clinical-Research Interface. Front Med (Lausanne) 2019; 5:357. [PMID: 30687707 PMCID: PMC6335243 DOI: 10.3389/fmed.2018.00357] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 12/12/2018] [Indexed: 12/13/2022] Open
Abstract
Normal pressure hydrocephalus (NPH) is a syndrome comprising gait disturbance, cognitive decline and urinary incontinence that is an unique model of reversible brain injury, but it presents as a challenging spectrum of disease cohorts. Diffusion Tensor Imaging (DTI), with its ability to interrogate structural white matter patterns at a microarchitectural level, is a potentially useful tool for the confirmation and characterization of disease cohorts at the clinical-research interface. However, obstacles to its widespread use involve the need for consistent DTI analysis and interpretation tools across collaborator sites. We present the use of DTI profiles, a simplistic methodology to interpret white matter injury patterns based on the morphology of diffusivity parameters. We examined 13 patients with complex NPH, i.e., patients with NPH and overlay from multiple comorbidities, including vascular risk burden and neurodegenerative disease, undergoing extended CSF drainage, clinical assessments, and multi-modal MR imaging. Following appropriate exclusions, we compared the morphology of DTI profiles in such complex NPH patients (n = 12, comprising 4 responders and 8 non-responders) to exemplar DTI profiles from a cohort of classic NPH patients (n = 16) demonstrating responsiveness of white matter injury to ventriculo-peritoneal shunting. In the cohort of complex NPH patients, mean age was 71.3 ± 7.6 years (10 males, 2 females) with a mean MMSE score of 21.1. There were 5 age-matched healthy controls, mean age was 73.4 ± 7.2 years (1 male, 4 females) and mean MMSE score was 26.8. In the exemplar cohort of classic NPH patients, mean age was 74.7 ± 5.9 years (10 males, 6 females) and mean MMSE score was 24.1. There were 9 age-matched healthy controls, mean age was 69.4 ± 9.7 years (4 males, 5 females) and mean MMSE score was 28.6. We found that, despite the challenges of acquiring DTI metrics from differing scanners across collaborator sites and NPH patients presenting as differing cohorts along the spectrum of disease, DTI profiles for responsiveness to interventions were comparable. Distinct DTI characteristics were demonstrated for complex NPH responders vs. non-responders. The morphology of DTI profiles for complex NPH responders mimicked DTI patterns found in predominantly shunt-responsive patients undergoing intervention for classic NPH. However, DTI profiles for complex NPH non-responders was suggestive of atrophy. Our findings suggest that it is possible to use DTI profiles to provide a methodology for rapid description of differing cohorts of disease at the clinical-research interface. By describing DTI measures morphologically, it was possible to consistently compare white matter injury patterns across international collaborator datasets.
Collapse
Affiliation(s)
- Christine Lock
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Janell Kwok
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Sumeet Kumar
- Department of Neuroradiology, National Neuroscience Institute, Singapore, Singapore
| | - Azlina Ahmad-Annuar
- Department of Biomedical Science, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Vairavan Narayanan
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Adeline S L Ng
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Yi Jayne Tan
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Nagaendran Kandiah
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Eng-King Tan
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Zofia Czosnyka
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Marek Czosnyka
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - John D Pickard
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Nicole C Keong
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| |
Collapse
|
23
|
Cetin Karayumak S, Bouix S, Ning L, James A, Crow T, Shenton M, Kubicki M, Rathi Y. Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters. Neuroimage 2019; 184:180-200. [PMID: 30205206 PMCID: PMC6230479 DOI: 10.1016/j.neuroimage.2018.08.073] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 08/17/2018] [Accepted: 08/29/2018] [Indexed: 01/17/2023] Open
Abstract
A joint and integrated analysis of multi-site diffusion MRI (dMRI) datasets can dramatically increase the statistical power of neuroimaging studies and enable comparative studies pertaining to several brain disorders. However, dMRI data sets acquired on multiple scanners cannot be naively pooled for joint analysis due to scanner specific nonlinear effects as well as differences in acquisition parameters. Consequently, for joint analysis, the dMRI data has to be harmonized, which involves removing scanner-specific differences from the raw dMRI signal. In this work, we propose a dMRI harmonization method that is capable of removing scanner-specific effects, while accounting for minor differences in acquisition parameters such as b-value, spatial resolution and number of gradient directions. We validate our algorithm on dMRI data acquired from two sites: Philadelphia Neurodevelopmental Cohort (PNC) with 800 healthy adolescents (ages 8-22 years) and Brigham and Women's Hospital (BWH) with 70 healthy subjects (ages 14-54 years). In particular, we show that gender and age-related maturation differences in different age groups are preserved after harmonization, as measured using effect sizes (small, medium and large), irrespective of the test sample size. Since we use matched control subjects from different scanners to estimate scanner-specific effects, our goal in this work is also to determine the minimum number of well-matched subjects needed from each site to achieve best harmonization results. Our results indicate that at-least 16 to 18 well-matched healthy controls from each site are needed to reliably capture scanner related differences. The proposed method can thus be used for retrospective harmonization of raw dMRI data across sites despite differences in acquisition parameters, while preserving inter-subject anatomical variability.
Collapse
Affiliation(s)
- Suheyla Cetin Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA.
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| | - Lipeng Ning
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| | - Anthony James
- Highfield Family and Adolescent Unit, Warneford Hospital, Oxford, UK
| | - Tim Crow
- Sane Powic, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Martha Shenton
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA; VA Boston Healthcare System, Brockton Division, Brockton, USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| |
Collapse
|
24
|
|
25
|
Scalar diffusion-MRI measures invariant to acquisition parameters: A first step towards imaging biomarkers. Magn Reson Imaging 2018; 54:194-213. [PMID: 30196167 DOI: 10.1016/j.mri.2018.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 01/18/2018] [Accepted: 03/07/2018] [Indexed: 11/23/2022]
Abstract
An imaging biomarker is a biologic feature in an image that is relevant to a patient's diagnosis or prognosis. In order to qualify as a biomarker, a measure must be robust and reproducible. However, the usual scalar measures derived from diffusion tensor imaging are known to be highly dependent on the variation of the acquisition parameters, which prevents their possible use as biomarkers. In this work, we propose a new set of quantitative measures based on diffusion magnetic resonance imaging from single-shell acquisitions that are designed to be robust to the variations of several acquisition parameters (number of gradient directions, b-value and SNR) while keeping a high discrimination power on differences in the diffusion characteristics of the tissue. These new scalar measures are analytically obtained from a generic diffusion function that does not require the calculation of a diffusion tensor. This way, on one hand, we avoid the use of a specific diffusion model and, on the other hand, we make easier the statistical characterization of the measures. Accordingly, the analysis of the measures bias is carried out and it is used to minimize their dependency with respect to the acquisition noise for different SNRs. The robustness and discrimination power of the measures are tested for different number of gradients, b-values and SNRs using a realistic phantom and three real datasets: (1) 13 control subjects and different acquisition parameters; (2) a public data set from a single subject acquired using multiple shells and (3) 32 schizophrenia patients and 32 age and sex-matched healthy controls with a varying number of gradient directions. The proposed quantitative measures exhibit low variability to the changes of the acquisition parameters, while at the same time they preserve a discrimination power that is able to detect significant changes in the anisotropy of the diffusion.
Collapse
|
26
|
Abstract
Diffusion MRI (dMRI) data acquired on different scanners varies significantly in its content throughout the brain even if the acquisition parameters are nearly identical. Thus, proper harmonization of such data sets is necessary to increase the sample size and thereby the statistical power of neuroimaging studies. In this paper, we present a novel approach to harmonize dMRI data (the raw signal, instead of dMRI derived measures such as fractional anisotropy) using rotation invariant spherical harmonic (RISH) features embedded within a multi-modal image registration framework. All dMRI data sets from all sites are registered to a common template and voxel-wise differences in RISH features between sites at a group level are used to harmonize the signal in a subject-specific manner. We validate our method on diffusion data acquired from seven different sites (two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across these sites before and after data harmonization. Validation was also done on a group oftest subjects, which were not used to "learn" the harmonization parameters. We also show results using TBSS before and after harmonization for independent validation of the proposed methodology. Using synthetic data, we show that any abnormality in diffusion measures due to disease is preserved during the harmonization process. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences in the signal can be removed using the proposed method in a model independent manner.
Collapse
|
27
|
Wilde EA, Provenzale JM, Taylor BA, Boss M, Zuccolotto A, Hachey R, Pathak S, Tate DF, Abildskov TJ, Schneider W. Assessment of quantitative magnetic resonance imaging metrics in the brain through the use of a novel phantom. Brain Inj 2018; 32:1266-1276. [PMID: 30169993 DOI: 10.1080/02699052.2018.1494855] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Multisite and longitudinal neuroimaging studies are important in uncovering trajectories of recovery and neurodegeneration following traumatic brain injury (TBI) and concussion through the use of diffusion tensor imaging (DTI) and other imaging modalities. This study assessed differences in anisotropic diffusion measurement across four scanners using a human and a novel phantom developed in conjunction with the Chronic Effects of Neurotrauma Consortium. METHOD Human scans provided measurement within biological tissue, and the novel physical phantom provided measures of anisotropic intra-tubular diffusion to serve as a model for intra-axonal water diffusion. Intra- and inter-scanner measurement variances were compared, and the impact on effect size was calculated. RESULTS Intra-scanner test-retest reliability estimates for fractional anisotropy (FA) demonstrated relative stability over testing intervals. The human tissue and phantom showed similar FA ranges, high linearity and large within-device effect sizes. However, inter-scanner measures of FA indicated substantial differences, some of which exceeded typical DTI effect sizes in mild TBI. CONCLUSION The diffusion phantom may be used to better elucidate inter-scanner variability in DTI-based measurement and provides an opportunity to better calibrate results obtained from scanners used in multisite and longitudinal studies. Novel solutions are being evaluated to understand and potentially overcome these differences.
Collapse
Affiliation(s)
- Elisabeth A Wilde
- a Department of Veterans Affairs Medical Center, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.,b Department of Neurology, University of Utah , Salt Lake City , UT , USA.,c Departments of Physical Medicine and Rehabilitation, Neurology, and Radiology, Baylor College of Medicine , Houston , TX , USA
| | - James M Provenzale
- d Department of Radiology, Duke University Medical Center , Durham , NC , USA
| | - Brian A Taylor
- e Department of Biomedical Engineering, College of Engineering, Virginia Commonwealth University , Richmond , VA , USA
| | - Michael Boss
- f Department of Physics, University of Colorado Boulder , Boulder , CO , USA.,g National Institute of Standard and Technology , Boulder , CO , USA
| | - Anthony Zuccolotto
- h Phantom Metrics Division of Psychology Software Tools Inc , Pittsburgh, Pennsylvania, USA
| | - Rebecca Hachey
- i Learning Research and Development Center, University of Pittsburgh , Pittsburgh , PA , USA
| | - Sudhir Pathak
- i Learning Research and Development Center, University of Pittsburgh , Pittsburgh , PA , USA
| | - David F Tate
- j Missouri Institute of Mental Health, University of Missouri-St. Louis , St. Louis , MO , USA
| | - Tracy J Abildskov
- b Department of Neurology, University of Utah , Salt Lake City , UT , USA.,k Department of Psychology, Brigham Young University , Provo , UT , USA
| | - Walter Schneider
- h Phantom Metrics Division of Psychology Software Tools Inc , Pittsburgh, Pennsylvania, USA.,i Learning Research and Development Center, University of Pittsburgh , Pittsburgh , PA , USA.,l Departments of Psychology and Engineering, University of Pittsburgh Medical Center , Pittsburgh , PA , USA.,m Departments of Neurosurgery and Radiology, University of Pittsburgh Medical Center , Pittsburgh , PA , USA
| |
Collapse
|
28
|
Habes M, Sotiras A, Erus G, Toledo JB, Janowitz D, Wolk DA, Shou H, Bryan NR, Doshi J, Völzke H, Schminke U, Hoffmann W, Resnick SM, Grabe HJ, Davatzikos C. White matter lesions: Spatial heterogeneity, links to risk factors, cognition, genetics, and atrophy. Neurology 2018; 91:e964-e975. [PMID: 30076276 DOI: 10.1212/wnl.0000000000006116] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 06/04/2018] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVES To investigate spatial heterogeneity of white matter lesions or hyperintensities (WMH). METHODS MRI scans of 1,836 participants (median age 52.2 ± 13.16 years) encompassing a wide age range (22-84 years) from the cross-sectional Study of Health in Pomerania (Germany) were included as discovery set identifying spatially distinct components of WMH using a structural covariance approach. Scans of 307 participants (median age 73.8 ± 10.2 years, with 747 observations) from the Baltimore Longitudinal Study of Aging (United States) were included to examine differences in longitudinal progression of these components. The associations of these components with vascular risk factors, cortical atrophy, Alzheimer disease (AD) genetics, and cognition were then investigated using linear regression. RESULTS WMH were found to occur nonuniformly, with higher frequency within spatially heterogeneous patterns encoded by 4 components, which were consistent with common categorizations of deep and periventricular WMH, while further dividing the latter into posterior, frontal, and dorsal components. Temporal trends of the components differed both cross-sectionally and longitudinally. Frontal periventricular WMH were most distinctive as they appeared in the fifth decade of life, whereas the other components appeared later in life during the sixth decade. Furthermore, frontal WMH were associated with systolic blood pressure and with pronounced atrophy including AD-related regions. AD polygenic risk score was associated with the dorsal periventricular component in the elderly. Cognitive decline was associated with the dorsal component. CONCLUSIONS These results support the hypothesis that the appearance of WMH follows age and disease-dependent regional distribution patterns, potentially influenced by differential underlying pathophysiologic mechanisms, and possibly with a differential link to vascular and neurodegenerative changes.
Collapse
Affiliation(s)
- Mohamad Habes
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD.
| | - Aristeidis Sotiras
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Guray Erus
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Jon B Toledo
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Deborah Janowitz
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - David A Wolk
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Haochang Shou
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Nick R Bryan
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Jimit Doshi
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Henry Völzke
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Ulf Schminke
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Wolfgang Hoffmann
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Susan M Resnick
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Hans J Grabe
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| | - Christos Davatzikos
- From the Center for Biomedical Image Computing and Analytics (M.H., A.S., G.E., N.R.B., J.D., C.D.), Department of Neurology and Penn Memory Center (M.H., D.A.W.), and Department of Biostatistics and Epidemiology (H.S.), University of Pennsylvania, Philadelphia; Department of Psychiatry (M.H., D.J., H.J.G.), Institute for Community Medicine (M.H., H.V., W.H.), and Department of Neurology (U.S.), University of Greifswald, Germany; Department of Neurology (J.B.T.), Houston Methodist Hospital, TX; German Center for Neurodegenerative Diseases (W.H., H.J.G.), Rostock/Greifswald, Germany; and Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD
| |
Collapse
|
29
|
Zhou X, Sakaie KE, Debbins JP, Narayanan S, Fox RJ, Lowe MJ. Scan-rescan repeatability and cross-scanner comparability of DTI metrics in healthy subjects in the SPRINT-MS multicenter trial. Magn Reson Imaging 2018; 53:105-111. [PMID: 30048675 DOI: 10.1016/j.mri.2018.07.011] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 06/08/2018] [Accepted: 07/21/2018] [Indexed: 12/14/2022]
Abstract
PURPOSE To assess intrascanner repeatability and cross-scanner comparability for diffusion tensor imaging (DTI) metrics in a multicenter clinical trial. METHODS DTI metrics (including longitudinal diffusivity [LD], fractional anisotropy [FA], mean diffusivity [MD], and transverse diffusivity [TD]) from pyramidal tracts for healthy controls were calculated from images acquired on twenty-seven 3T MR scanners (Siemens and GE) with 6 different scanner models and 7 different software versions as part of the NN102/SPRINT-MS clinical trial. Each volunteer underwent two scanning sessions on the same scanner. Signal-to-noise ratio (SNR) and signal-to-noise floor ratio (SNFR) were also assessed. RESULTS DTI metrics showed good scan-rescan repeatability. There were no significant differences between scans and rescans in LD, FA, MD, or TD values. Although the cross-scanner coefficient of variation (CV) values for all DTI metrics were <5.7%, significant differences were observed for LD (p < 3.3e-5) and FA (p < 0.0024) when GE scanners were compared with Siemens scanners. Significant differences were also observed for SNR when comparing GE scanners and Siemens Skyra scanners (p < 1.4e-7) and when comparing Siemens Skyra scanners and TIM Trio scanners (p < 1.0e-10). Analysis of background signal also demonstrated differences between GE and Siemens scanners in terms of signal statistics. The measured signal intensity from a background noise region of interest was significantly higher for GE scanners than for Siemens scanners (p < 1.2e-12). Significant differences were also observed for SNFR when comparing GE scanners and Siemens Skyra scanners (p < 2.5e-11), GE scanners and Siemens Trio scanners (p < 7.5e-11), and Siemens Skyra scanners and TIM Trio scanners (p < 2.5e-9). CONCLUSIONS The good repeatability of the DTI metrics among the 27 scanners used in this study confirms the feasibility of combining DTI data from multiple centers using high angular resolution sequences. Our observations support the feasibility of longitudinal multicenter clinical trials using DTI outcome measures. The noise floor level and SNFR are important parameters that must be assessed when comparing studies that used different scanner models.
Collapse
Affiliation(s)
- Xiaopeng Zhou
- School of Health Sciences, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA
| | - Ken E Sakaie
- Imaging Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195, USA
| | - Josef P Debbins
- Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, 350 W Thomas Rd, Phoenix, AZ 85013, USA
| | - Sridar Narayanan
- McConnell Brain Imaging Centre, Montreal Neurological Institute, 3801 University St., Montreal, QC H3A2B4, Canada; NeuroRx Research, 3575 Avenue du Parc, Suite 5322, Montreal, QC, H2X 3P9, Canada
| | - Robert J Fox
- Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195, USA
| | - Mark J Lowe
- Imaging Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195, USA.
| |
Collapse
|
30
|
Palacios EM, Martin AJ, Boss MA, Ezekiel F, Chang YS, Yuh EL, Vassar MJ, Schnyer DM, MacDonald CL, Crawford KL, Irimia A, Toga AW, Mukherjee P. Toward Precision and Reproducibility of Diffusion Tensor Imaging: A Multicenter Diffusion Phantom and Traveling Volunteer Study. AJNR Am J Neuroradiol 2016; 38:537-545. [PMID: 28007768 DOI: 10.3174/ajnr.a5025] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Accepted: 10/10/2016] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Precision medicine is an approach to disease diagnosis, treatment, and prevention that relies on quantitative biomarkers that minimize the variability of individual patient measurements. The aim of this study was to assess the intersite variability after harmonization of a high-angular-resolution 3T diffusion tensor imaging protocol across 13 scanners at the 11 academic medical centers participating in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury multisite study. MATERIALS AND METHODS Diffusion MR imaging was acquired from a novel isotropic diffusion phantom developed at the National Institute of Standards and Technology and from the brain of a traveling volunteer on thirteen 3T MR imaging scanners representing 3 major vendors (GE Healthcare, Philips Healthcare, and Siemens). Means of the DTI parameters and their coefficients of variation across scanners were calculated for each DTI metric and white matter tract. RESULTS For the National Institute of Standards and Technology diffusion phantom, the coefficients of variation of the apparent diffusion coefficient across the 13 scanners was <3.8% for a range of diffusivities from 0.4 to 1.1 × 10-6 mm2/s. For the volunteer, the coefficients of variations across scanners of the 4 primary DTI metrics, each averaged over the entire white matter skeleton, were all <5%. In individual white matter tracts, large central pathways showed good reproducibility with the coefficients of variation consistently below 5%. However, smaller tracts showed more variability, with the coefficients of variation of some DTI metrics reaching 10%. CONCLUSIONS The results suggest the feasibility of standardizing DTI across 3T scanners from different MR imaging vendors in a large-scale neuroimaging research study.
Collapse
Affiliation(s)
- E M Palacios
- From the Departments of Radiology and Biomedical Imaging (E.M.P., A.J.M., F.E., Y.S.C., E.L.Y., P.M.)
| | - A J Martin
- From the Departments of Radiology and Biomedical Imaging (E.M.P., A.J.M., F.E., Y.S.C., E.L.Y., P.M.)
| | - M A Boss
- National Institute of Standards and Technology (M.A.B.), Boulder, Colorado
| | - F Ezekiel
- From the Departments of Radiology and Biomedical Imaging (E.M.P., A.J.M., F.E., Y.S.C., E.L.Y., P.M.)
| | - Y S Chang
- From the Departments of Radiology and Biomedical Imaging (E.M.P., A.J.M., F.E., Y.S.C., E.L.Y., P.M.)
| | - E L Yuh
- From the Departments of Radiology and Biomedical Imaging (E.M.P., A.J.M., F.E., Y.S.C., E.L.Y., P.M.).,Brain and Spinal Cord Injury Center (E.L.Y., M.J.V., P.M.), San Francisco General Hospital and Trauma Center, San Francisco, California
| | - M J Vassar
- Neurological Surgery and Brain and Spinal Injury Center (M.J.V.).,Brain and Spinal Cord Injury Center (E.L.Y., M.J.V., P.M.), San Francisco General Hospital and Trauma Center, San Francisco, California
| | - D M Schnyer
- Department of Psychology (D.M.S.), University of Texas, Austin, Texas
| | - C L MacDonald
- Department of Neurological Surgery (C.L.M.), University of Washington, Seattle, Washington
| | - K L Crawford
- Mark and Mary Stevens Neuroimaging and Informatics Institute (K.L.C., A.I., A.W.T.), University of Southern California, Los Angeles, California
| | - A Irimia
- Mark and Mary Stevens Neuroimaging and Informatics Institute (K.L.C., A.I., A.W.T.), University of Southern California, Los Angeles, California
| | - A W Toga
- Mark and Mary Stevens Neuroimaging and Informatics Institute (K.L.C., A.I., A.W.T.), University of Southern California, Los Angeles, California
| | - P Mukherjee
- From the Departments of Radiology and Biomedical Imaging (E.M.P., A.J.M., F.E., Y.S.C., E.L.Y., P.M.) .,Bioengineering and Therapeutic Sciences (P.M.), University of California, San Francisco, San Francisco, California.,Brain and Spinal Cord Injury Center (E.L.Y., M.J.V., P.M.), San Francisco General Hospital and Trauma Center, San Francisco, California
| | | |
Collapse
|
31
|
Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, Galluzzi S, Marizzoni M, Frisoni GB. Brain atrophy in Alzheimer's Disease and aging. Ageing Res Rev 2016; 30:25-48. [PMID: 26827786 DOI: 10.1016/j.arr.2016.01.002] [Citation(s) in RCA: 438] [Impact Index Per Article: 54.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 01/15/2016] [Accepted: 01/20/2016] [Indexed: 01/22/2023]
Abstract
Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.
Collapse
Affiliation(s)
- Lorenzo Pini
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Michela Pievani
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Martina Bocchetta
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | - Daniele Altomare
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Bosco
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Enrica Cavedo
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Institut du Cerveau et de la Moelle épinière (ICM), UMR S 1127, Hôpital de la Pitié-Salpétrière Paris & CATI Multicenter Neuroimaging Platform, France
| | - Samantha Galluzzi
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Moira Marizzoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Giovanni B Frisoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.
| |
Collapse
|
32
|
Mirzaalian H, Ning L, Savadjiev P, Pasternak O, Bouix S, Michailovich O, Grant G, Marx CE, Morey RA, Flashman LA, George MS, McAllister TW, Andaluz N, Shutter L, Coimbra R, Zafonte RD, Coleman MJ, Kubicki M, Westin CF, Stein MB, Shenton ME, Rathi Y. Inter-site and inter-scanner diffusion MRI data harmonization. Neuroimage 2016; 135:311-23. [PMID: 27138209 DOI: 10.1016/j.neuroimage.2016.04.041] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 03/15/2016] [Accepted: 04/18/2016] [Indexed: 11/17/2022] Open
Abstract
We propose a novel method to harmonize diffusion MRI data acquired from multiple sites and scanners, which is imperative for joint analysis of the data to significantly increase sample size and statistical power of neuroimaging studies. Our method incorporates the following main novelties: i) we take into account the scanner-dependent spatial variability of the diffusion signal in different parts of the brain; ii) our method is independent of compartmental modeling of diffusion (e.g., tensor, and intra/extra cellular compartments) and the acquired signal itself is corrected for scanner related differences; and iii) inter-subject variability as measured by the coefficient of variation is maintained at each site. We represent the signal in a basis of spherical harmonics and compute several rotation invariant spherical harmonic features to estimate a region and tissue specific linear mapping between the signal from different sites (and scanners). We validate our method on diffusion data acquired from seven different sites (including two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. Since the extracted rotation invariant spherical harmonic features depend on the accuracy of the brain parcellation provided by Freesurfer, we propose a feature based refinement of the original parcellation such that it better characterizes the anatomy and provides robust linear mappings to harmonize the dMRI data. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across multiple sites before and after data harmonization. We also show results using tract-based spatial statistics before and after harmonization for independent validation of the proposed methodology. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.
Collapse
Affiliation(s)
- H Mirzaalian
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA.
| | - L Ning
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - P Savadjiev
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - O Pasternak
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - S Bouix
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | | | - G Grant
- Stanford University Medical Center, Palo Alto, CA, USA (Previously Duke University)
| | - C E Marx
- Duke University Medical Center and VA Mid-Atlantic MIRECC, NC, USA
| | - R A Morey
- Duke University Medical Center and VA Mid-Atlantic MIRECC, NC, USA
| | - L A Flashman
- Dartmouth University, Hanover and Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - M S George
- Medical University of South Carolina, Charleston, SC, USA, Ralph H. Johnson VA Medical Center, Charleston
| | - T W McAllister
- Geisel School of Medicine at Dartmouth (original) and Indiana University School of Medicine (current)
| | - N Andaluz
- Department of Neurosurgery, University of Cincinnati (UC) College of Medicine; Neurotrauma Center at UC Neuroscience Institute; and Mayfield Clinic, Cincinnati, OH
| | - L Shutter
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA (Previously Duke University)
| | - R Coimbra
- Department of Surgery, University of California, San Diego
| | - R D Zafonte
- Spaulding Rehabilitation Hospital and Harvard Medical School, Boston, USA
| | - M J Coleman
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - M Kubicki
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - C F Westin
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| | - M B Stein
- University of California, San Diego, San Diego, CA, USA
| | - M E Shenton
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA; VA Boston Healthcare System, Boston, MA, USA
| | - Y Rathi
- Harvard Medical School and Brigham and Women's Hospital, Boston, USA
| |
Collapse
|
33
|
Gonzalez CE, Venkatraman VK, An Y, Landman BA, Davatzikos C, Ratnam Bandaru VV, Haughey NJ, Ferrucci L, Mielke MM, Resnick SM. Peripheral sphingolipids are associated with variation in white matter microstructure in older adults. Neurobiol Aging 2016; 43:156-63. [PMID: 27255825 DOI: 10.1016/j.neurobiolaging.2016.04.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 04/01/2016] [Accepted: 04/12/2016] [Indexed: 12/29/2022]
Abstract
Sphingolipids serve important structural and functional roles in cellular membranes and myelin sheaths. Plasma sphingolipids have been shown to predict cognitive decline and Alzheimer's disease. However, the association between plasma sphingolipid levels and brain white matter (WM) microstructure has not been examined. We investigated whether plasma sphingolipids (ceramides and sphingomyelins) were associated with magnetic resonance imaging-based diffusion measures, fractional anisotropy (FA), and mean diffusivity, 10.5 years later in 17 WM regions of 150 cognitively normal adults (mean age 67.2). Elevated ceramide species (C20:0, C22:0, C22:1, and C24:1) were associated with lower FA in multiple WM regions, including total cerebral WM, anterior corona radiata, and the cingulum of the cingulate gyrus. Higher sphingomyelins (C18:1 and C20:1) were associated with lower FA in regions such as the anterior corona radiata and body of the corpus callosum. Furthermore, lower sphingomyelin to ceramide ratios (C22:0, C24:0, and C24:1) were associated with lower FA or higher mean diffusivity in regions including the superior and posterior corona radiata. However, although these associations were significant at the a priori p < 0.05, only associations with some regional diffusion measures for ceramide C22:0 and sphingomyelin C18:1 survived correction for multiple comparisons. These findings suggest plasma sphingolipids are associated with variation in WM microstructure in cognitively normal aging.
Collapse
Affiliation(s)
- Christopher E Gonzalez
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Vijay K Venkatraman
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bennett A Landman
- Institute of Imaging Science and Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | | | - Norman J Haughey
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Luigi Ferrucci
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Michelle M Mielke
- Department of Health Science Research, Mayo Clinic, Rochester, MN, USA; Department of Neurology, Mayo Clinic, Rochester, MN, USA.
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| |
Collapse
|