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Gu C, Li Y, Cao D, Miao X, Paez AG, Sun Y, Cai J, Li W, Li X, Pillai JJ, Earley CJ, van Zijl PC, Hua J. On the optimization of 3D inflow-based vascular-space-occupancy (iVASO) MRI for the quantification of arterial cerebral blood volume (CBVa). Magn Reson Med 2024; 91:1893-1907. [PMID: 38115573 PMCID: PMC10950541 DOI: 10.1002/mrm.29971] [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: 11/01/2023] [Revised: 11/20/2023] [Accepted: 11/25/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE The inflow-based vascular-space-occupancy (iVASO) MRI was originally developed in a single-slice mode to measure arterial cerebral blood volume (CBVa). When vascular crushers are applied in iVASO, the signals can be sensitized predominantly to small pial arteries and arterioles. The purpose of this study is to perform a systematic optimization and evaluation of a 3D iVASO sequence on both 3 T and 7 T for the quantification of CBVa values in the human brain. METHODS Three sets of experiments were performed in three separate cohorts. (1) 3D iVASO MRI protocols were compared to single-slice iVASO, and the reproducibility of whole-brain 3D iVASO MRI was evaluated. (2) The effects from different vascular crushers in iVASO were assessed. (3) 3D iVASO MRI results were evaluated in arterial and venous blood vessels identified using ultrasmall-superparamagnetic-iron-oxides-enhanced MRI to validate its arterial origin. RESULTS 3D iVASO scans showed signal-to-noise ratio (SNR) and CBVa measures consistent with single-slice iVASO with reasonable intrasubject reproducibility. Among the iVASO scans performed with different vascular crushers, the whole-brain 3D iVASO scan with a motion-sensitized-driven-equilibrium preparation with two binomial refocusing pulses and an effective TE of 50 ms showed the best suppression of macrovascular signals, with a relatively low specific absorption rate. When no vascular crusher was applied, the CBVa maps from 3D iVASO scans showed large CBVa values in arterial vessels but well-suppressed signals in venous vessels. CONCLUSION A whole-brain 3D iVASO MRI scan was optimized for CBVa measurement in the human brain. When only microvascular signals are desired, a motion-sensitized-driven-equilibrium-based vascular crusher with binomial refocusing pulses can be applied in 3D iVASO.
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Affiliation(s)
- Chunming Gu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Yinghao Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Di Cao
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Xinyuan Miao
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Adrian G. Paez
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Yuanqi Sun
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Jitong Cai
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Wenbo Li
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Xu Li
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Jay J. Pillai
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Division of Neuroradiology, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Christopher J. Earley
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Peter C.M. van Zijl
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Jun Hua
- Neurosection, Division of MRI Research, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
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Wang Z, Gallegos J, Tippett D, Onyike CU, Desmond JE, Hillis AE, Frangakis CE, Caffo B, Tsapkini K. Baseline functional connectivity predicts who will benefit from neuromodulation: evidence from primary progressive aphasia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.19.24305354. [PMID: 38699365 PMCID: PMC11065007 DOI: 10.1101/2024.04.19.24305354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Background Identifying the characteristics of individuals who demonstrate response to an intervention allows us to predict who is most likely to benefit from certain interventions. Prediction is challenging in rare and heterogeneous diseases, such as primary progressive aphasia (PPA), that have varying clinical manifestations. We aimed to determine the characteristics of those who will benefit most from transcranial direct current stimulation (tDCS) of the left inferior frontal gyrus (IFG) using a novel heterogeneity and group identification analysis. Methods We compared the predictive ability of demographic and clinical patient characteristics (e.g., PPA variant and disease progression, baseline language performance) vs. functional connectivity alone (from resting-state fMRI) in the same cohort. Results Functional connectivity alone had the highest predictive value for outcomes, explaining 62% and 75% of tDCS effect of variance in generalization (semantic fluency) and in the trained outcome of the clinical trial (written naming), contrasted with <15% predicted by clinical characteristics, including baseline language performance. Patients with higher baseline functional connectivity between the left IFG (opercularis and triangularis), and between the middle temporal pole and posterior superior temporal gyrus, were most likely to benefit from tDCS. Conclusions We show the importance of a baseline 7-minute functional connectivity scan in predicting tDCS outcomes, and point towards a precision medicine approach in neuromodulation studies. The study has important implications for clinical trials and practice, providing a statistical method that addresses heterogeneity in patient populations and allowing accurate prediction and enrollment of those who will most likely benefit from specific interventions.
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Zachariou V, Pappas C, Bauer CE, Shao X, Liu P, Lu H, Wang DJJ, Gold BT. Regional differences in the link between water exchange rate across the blood-brain barrier and cognitive performance in normal aging. GeroScience 2024; 46:265-282. [PMID: 37713089 PMCID: PMC10828276 DOI: 10.1007/s11357-023-00930-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023] Open
Abstract
The blood-brain barrier (BBB) undergoes functional changes with aging which may contribute to cognitive decline. A novel, diffusion prepared arterial spin labeling-based MRI technique can measure the rate of water exchange across the BBB (kw) and may thus be sensitive to age-related alterations in water exchange at the BBB. However, studies investigating relationships between kw and cognition have reported different directions of association. Here, we begin to investigate the direction of associations between kw and cognition in different brain regions, and their possible underpinnings, by evaluating links between kw, cognitive performance, and MRI markers of cerebrovascular dysfunction and/or damage. Forty-seven healthy older adults (age range 61-84) underwent neuroimaging to obtain whole-brain measures of kw, cerebrovascular reactivity (CVR), and white matter hyperintensity (WMH) volumes. Additionally, participants completed uniform data set (Version 3) neuropsychological tests of executive function (EF) and episodic memory (MEM). Voxel-wise linear regressions were conducted to test associations between kw and cognitive performance, CVR, and WMH volumes. We found that kw in the frontoparietal brain regions was positively associated with cognitive performance but not with CVR or WMH volumes. Conversely, kw in the basal ganglia was negatively associated with cognitive performance and CVR and positively associated with regional, periventricular WMH volume. These regionally dependent associations may relate to different physiological underpinnings in the relationships between kw and cognition in neocortical versus subcortical brain regions in older adults.
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Affiliation(s)
- Valentinos Zachariou
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA.
| | - Colleen Pappas
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Christopher E Bauer
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Peiying Liu
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Danny J J Wang
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Brian T Gold
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
- Sanders-Brown Center On Aging, University of Kentucky, Lexington, KY, USA
- Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, USA
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Zhou G, Tward D, Lange K. A Majorization-Minimization Algorithm for Neuroimage Registration. SIAM JOURNAL ON IMAGING SCIENCES 2024; 17:273-300. [PMID: 38550750 PMCID: PMC10977051 DOI: 10.1137/22m1516907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
Intensity-based image registration is critical for neuroimaging tasks, such as 3D reconstruction, times-series alignment, and common coordinate mapping. The gradient-based optimization methods commonly used to solve this problem require a careful selection of step-length. This limitation imposes substantial time and computational costs. Here we propose a gradient-independent rigid-motion registration algorithm based on the majorization-minimization (MM) principle. Each iteration of our intensity-based MM algorithm reduces to a simple point-set rigid registration problem with a closed form solution that avoids the step-length issue altogether. The details of the algorithm are presented, and an error bound for its more practical truncated form is derived. The performance of the MM algorithm is shown to be more effective than gradient descent on simulated images and Nissl stained coronal slices of mouse brain. We also compare and contrast the similarities and differences between the MM algorithm and another gradient-free registration algorithm called the block-matching method. Finally, extensions of this algorithm to more complex problems are discussed.
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Affiliation(s)
- Gaiting Zhou
- Computational Medicine, UCLA, Los Angeles, CA 90024 USA
| | - Daniel Tward
- Computational Medicine, UCLA, Los Angeles, CA 90024 USA
| | - Kenneth Lange
- Computational Medicine, UCLA, Los Angeles, CA 90024 USA
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Sun F, Lyu J, Jian S, Qin Y, Tang X. Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline. Eur Radiol 2023; 33:8844-8853. [PMID: 37480547 DOI: 10.1007/s00330-023-09979-1] [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: 02/01/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 07/24/2023]
Abstract
OBJECTIVES This study aims at a fully automatic pipeline for measuring the magnetic resonance parkinsonism index (MRPI) using deep learning methods. METHODS MRPI is defined as the product of the pons area to the midbrain area ratio and the middle cerebellar peduncle (MCP) width to the superior cerebellar peduncle (SCP) width ratio. In our proposed pipeline, we first used nnUNet to segment the brainstem and then employed HRNet to identify two key boundary points so as to sub-divide the whole brainstem into midbrain and pons. HRNet was also employed to predict the MCP endpoints for measuring the MCP width. Finally, we segmented the SCP on an oblique coronal plane and calculated its width. A total of 400 T1-weighted magnetic resonance images (MRIs) were used to train the nnUNet and HRNet models. Five-fold cross-validation was conducted to evaluate our proposed pipeline's performance on the training dataset. We also evaluated the performance of our proposed pipeline on three external datasets. Two of them had two raters manually measuring the MRPI values, providing insights into automatic accuracy versus inter-rater variability. RESULTS We obtained average absolute percentage errors (APEs) of 17.21%, 18.17%, 20.83%, and 22.83% on the training dataset and the three external validation datasets, while the inter-rater average APE measured on the first two external validation datasets was 11.31%. Our proposed pipeline significantly improved the MRPI quantification accuracy over a representative state-of-the-art traditional approach (p < 0.001). CONCLUSION The proposed automatic pipeline can accurately predict MRPI that is comparable with manual measurement. CLINICAL RELEVANCE STATEMENT This study presents an automated magnetic resonance parkinsonism index measurement tool that can analyze large amounts of magnetic resonance images, enhance the efficiency of Parkinsonism-Plus syndrome diagnosis, reduce the workload of clinicians, and minimize the impact of human factors on diagnosis. KEY POINTS • We propose an automatic pipeline for measuring the magnetic resonance parkinsonism index from magnetic resonance images. • The effectiveness of the proposed pipeline is successfully established on multiple datasets and comparisons with inter-rater measurements. • The proposed pipeline significantly outperforms a state-of-the-art quantification approach, being much closer to ground truth.
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Affiliation(s)
- Fuhai Sun
- Department of Electronic and Electrical Engineering, College of Engineering, Southern University of Science and Technology, Xili, Nanshan, Shenzhen, 518055, People's Republic of China
| | - Junyan Lyu
- Department of Electronic and Electrical Engineering, College of Engineering, Southern University of Science and Technology, Xili, Nanshan, Shenzhen, 518055, People's Republic of China
| | - Si Jian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, Wuhan, 430030, People's Republic of China.
| | - Xiaoying Tang
- Department of Electronic and Electrical Engineering, College of Engineering, Southern University of Science and Technology, Xili, Nanshan, Shenzhen, 518055, People's Republic of China.
- Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, People's Republic of China.
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6
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Zhao Y, Wang B, Liu CF, Faria AV, Miller MI, Caffo BS, Luo X. Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors. Biometrics 2023; 79:2333-2345. [PMID: 36263865 PMCID: PMC10115907 DOI: 10.1111/biom.13775] [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: 12/07/2021] [Accepted: 10/03/2022] [Indexed: 11/30/2022]
Abstract
Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1 -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2 -norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Bingkai Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Chin-Fu Liu
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Andreia V. Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael I. Miller
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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7
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Liu CF, Younes L, Tong XJ, Hinkle JT, Wang M, Phatak S, Xu X, Bu X, Looi V, Bang J, Tabrizi SJ, Scahill RI, Paulsen JS, Georgiou-Karistianis N, Faria AV, Miller MI, Ratnanather JT, Ross CA. Longitudinal imaging highlights preferential basal ganglia circuit atrophy in Huntington's disease. Brain Commun 2023; 5:fcad214. [PMID: 37744022 PMCID: PMC10516592 DOI: 10.1093/braincomms/fcad214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 05/09/2023] [Accepted: 08/17/2023] [Indexed: 09/26/2023] Open
Abstract
Huntington's disease is caused by a CAG repeat expansion in the Huntingtin gene (HTT), coding for polyglutamine in the Huntingtin protein, with longer CAG repeats causing earlier age of onset. The variable 'Age' × ('CAG'-L), where 'Age' is the current age of the individual, 'CAG' is the repeat length and L is a constant (reflecting an approximation of the threshold), termed the 'CAG Age Product' (CAP) enables the consideration of many individuals with different CAG repeat expansions at the same time for analysis of any variable and graphing using the CAG Age Product score as the X axis. Structural MRI studies have showed that progressive striatal atrophy begins many years prior to the onset of diagnosable motor Huntington's disease, confirmed by longitudinal multicentre studies on three continents, including PREDICT-HD, TRACK-HD and IMAGE-HD. However, previous studies have not clarified the relationship between striatal atrophy, atrophy of other basal ganglia structures, and atrophy of other brain regions. The present study has analysed all three longitudinal datasets together using a single image segmentation algorithm and combining data from a large number of subjects across a range of CAG Age Product score. In addition, we have used a strategy of normalizing regional atrophy to atrophy of the whole brain, in order to determine which regions may undergo preferential degeneration. This made possible the detailed characterization of regional brain atrophy in relation to CAG Age Product score. There is dramatic selective atrophy of regions involved in the basal ganglia circuit-caudate, putamen, nucleus accumbens, globus pallidus and substantia nigra. Most other regions of the brain appear to have slower but steady degeneration. These results support (but certainly do not prove) the hypothesis of circuit-based spread of pathology in Huntington's disease, possibly due to spread of mutant Htt protein, though other connection-based mechanisms are possible. Therapeutic targets related to prion-like spread of pathology or other mechanisms may be suggested. In addition, they have implications for current neurosurgical therapeutic approaches, since delivery of therapeutic agents solely to the caudate and putamen may miss other structures affected early, such as nucleus accumbens and output nuclei of the striatum, the substantia nigra and the globus pallidus.
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Affiliation(s)
- Chin-Fu Liu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Laurent Younes
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Xiao J Tong
- Division of Neurobiology, Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore MD 21287, USA
| | - Jared T Hinkle
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA
- Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Maggie Wang
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sanika Phatak
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Xin Xu
- Division of Magnetic Resonance, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Xuan Bu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Vivian Looi
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jee Bang
- Division of Neurobiology, Department of Psychiatry, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Sarah J Tabrizi
- HD Research Centre, University College London Queen Square Institute of Neurology, UCL, London, UK
| | - Rachael I Scahill
- HD Research Centre, University College London Queen Square Institute of Neurology, UCL, London, UK
| | - Jane S Paulsen
- Department of Neurology, University of Wisconsin, Madison, WI 53705, USA
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and The Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria 3800, Australia
| | - Andreia V Faria
- Division of Magnetic Resonance, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - J Tilak Ratnanather
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Christopher A Ross
- Division of Neurobiology, Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore MD 21287, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA
- Division of Neurobiology, Department of Psychiatry, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Pharmacology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
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Hu B, Younes L, Bu X, Liu CF, Ratnanather JT, Paulsen J, Georgiou-Karistianis N, Miller MI, Ross C, Faria AV. Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington's disease. Neuroimage Clin 2023; 39:103493. [PMID: 37582307 PMCID: PMC10448214 DOI: 10.1016/j.nicl.2023.103493] [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: 12/01/2022] [Revised: 04/29/2023] [Accepted: 08/07/2023] [Indexed: 08/17/2023]
Abstract
Changes in the brain of patients with Huntington's disease (HD) begin years before clinical onset, so it remains critical to identify biomarkers to track these early changes. Metrics derived from tensor modeling of diffusion-weighted MRIs (DTI), that indicate the microscopic brain structure, can add important information to regional volumetric measurements. This study uses two large-scale longitudinal, multicenter datasets, PREDICT-HD and IMAGE-HD, to trace changes in DTI of HD participants with a broad range of CAP scores (a product of CAG repeat expansion and age), including those with pre-manifest disease (i.e., prior to clinical onset). Utilizing a fully automated data-driven approach to study the whole brain divided in regions of interest, we traced changes in DTI metrics (diffusivity and fractional anisotropy) versus CAP scores, using sigmoidal and linear regression models. We identified points of inflection in the sigmoidal regression using change-point analysis. The deep gray matter showed more evident and earlier changes in DTI metrics over CAP scores, compared to the deep white matter. In the deep white matter, these changes were more evident and occurred earlier in superior and posterior areas, compared to anterior and inferior areas. The curves of mean diffusivity vs. age of HD participants within a fixed CAP score were different from those of controls, indicating that the disease has an additional effect to age on the microscopic brain structure. These results show the regional and temporal vulnerability of the white matter and deep gray matter in HD, with potential implications for experimental therapeutics.
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Affiliation(s)
- Beini Hu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Xuan Bu
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Chin-Fu Liu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - J Tilak Ratnanather
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jane Paulsen
- Department of Psychiatry, Neurology, Psychological Brain Sciences, University of Iowa, USA; Department Neurology, University of Wisconsin-Madison, USA
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and Turner Institute of Brain and Mental Health, Monash University, Australia
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Christopher Ross
- Department of Psychiatry, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Andreia V Faria
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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9
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Etyemez S, Narita Z, Mihaljevic M, Coughlin JM, Nestadt G, Nucifora FC, Sedlak TW, Cascella NG, Batt FD, Hua J, Faria A, Ishizuka K, Kamath V, Yang K, Sawa A. Brain regions associated with olfactory dysfunction in first episode psychosis patients. World J Biol Psychiatry 2023; 24:178-186. [PMID: 35678361 PMCID: PMC10503825 DOI: 10.1080/15622975.2022.2082526] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/03/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVES Olfactory dysfunction is reproducibly reported in psychotic disorders, particularly in association with negative symptoms. The superior frontal gyrus (SFG) has been frequently studied in patients with psychotic disorders, in particular with their associations with negative symptoms. The relationship between olfactory functions and brain structure has been studied in healthy controls (HCs). Nevertheless, the studies with patients with psychotic disorders are limited. Here we report the olfactory-brain relationship in a first episode psychosis (FEP) cohort through both hypothesis-driven (centred on the SFG) and data-driven approaches. METHODS Using data from 88 HCs and 76 FEP patients, we evaluated the correlation between olfactory functions and structural/resting-state functional magnetic resonance imaging (MRI) data. RESULTS We found a significant correlation between the left SFG volume and odour discrimination in FEP patients, but not in HCs. We also observed a significant correlation between rs-fMRI connectivity involving the left SFG and odour discrimination in FEP patients, but not in HCs. The data-driven approach didn't observe any significant correlations, possibly due to insufficient statistical power. CONCLUSION The left SFG may be a promising brain region in the context of olfactory dysfunction and negative symptoms in FEP.
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Affiliation(s)
- Semra Etyemez
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Zui Narita
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Marina Mihaljevic
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jennifer M. Coughlin
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Gerald Nestadt
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Frederick C. Nucifora
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Thomas W. Sedlak
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Nicola G. Cascella
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Finn-Davis Batt
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jun Hua
- Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
| | - Andreia Faria
- Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Koko Ishizuka
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Vidyulata Kamath
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kun Yang
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Akira Sawa
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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10
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Bu X, Gao Y, Liang K, Bao W, Chen Y, Guo L, Gong Q, Lu H, Caffo B, Mori S, Huang X. Multivariate associations between behavioural dimensions and white matter across children and adolescents with and without attention-deficit/hyperactivity disorder. J Child Psychol Psychiatry 2023; 64:244-253. [PMID: 36000340 PMCID: PMC10087687 DOI: 10.1111/jcpp.13689] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/11/2022] [Indexed: 01/17/2023]
Abstract
BACKGROUND Attention deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder. Integrity of white matter microstructure plays a key role in the neural mechanism of ADHD presentations. However, the relationships between specific behavioural dimensions and white matter microstructure are less well known. This study aimed to identify associations between white matter and a broad set of clinical features across children and adolescent with and without ADHD using a data-driven multivariate approach. METHOD We recruited a total of 130 children (62 controls and 68 ADHD) and employed regularized generalized canonical correlation analysis to characterize the associations between white matter and a comprehensive set of clinical measures covering three domains, including symptom, cognition and behaviour. We further applied linear discriminant analysis to integrate these associations to explore potential developmental effects. RESULTS We delineated two brain-behaviour dimensional associations in each domain resulting a total of six multivariate patterns of white matter microstructural alterations linked to hyperactivity-impulsivity and mild affected; executive functions and working memory; externalizing behaviour and social withdrawal, respectively. Apart from executive function and externalizing behaviour sharing similar white matter patterns, all other dimensions linked to a specific pattern of white matter microstructural alterations. The multivariate dimensional association scores showed an overall increase and normalization with age in ADHD group while remained stable in controls. CONCLUSIONS We found multivariate neurobehavioral associations exist across ADHD and controls, which suggested that multiple white matter patterns underlie ADHD heterogeneity and provided neural bases for more precise diagnosis and individualized treatment.
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Affiliation(s)
- Xuan Bu
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMDUSA
| | - Yingxue Gao
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Kaili Liang
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Weijie Bao
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Ying Chen
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
| | - Lanting Guo
- Department of PsychiatryWest China Hospital of Sichuan UniversityChengduChina
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceChengduChina
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMDUSA
| | - Brian Caffo
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMDUSA
| | - Xiaoqi Huang
- Department of Radiology, Huaxi MR Research CenterWest China Hospital of Sichuan UniversityChengduChina
- Research Unit of PsychoradiologyChinese Academy of Medical SciencesChengduChina
- Functional and Molecular Imaging Key Laboratory of Sichuan ProvinceChengduChina
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11
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Lee JK, Cho ACB, Andrews DS, Ozonoff S, Rogers SJ, Amaral DG, Solomon M, Nordahl CW. Default mode and fronto-parietal network associations with IQ development across childhood in autism. J Neurodev Disord 2022; 14:51. [PMID: 36109700 PMCID: PMC9479280 DOI: 10.1186/s11689-022-09460-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 09/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Intellectual disability affects approximately one third of individuals with autism spectrum disorder (autism). Yet, a major unresolved neurobiological question is what differentiates autistic individuals with and without intellectual disability. Intelligence quotients (IQs) are highly variable during childhood. We previously identified three subgroups of autistic children with different trajectories of intellectual development from early (2–3½ years) to middle childhood (9–12 years): (a) persistently high: individuals whose IQs remained in the normal range; (b) persistently low: individuals whose IQs remained in the range of intellectual disability (IQ < 70); and (c) changers: individuals whose IQs began in the range of intellectual disability but increased to the normal IQ range. The frontoparietal (FPN) and default mode (DMN) networks have established links to intellectual functioning. Here, we tested whether brain regions within the FPN and DMN differed volumetrically between these IQ trajectory groups in early childhood. Methods We conducted multivariate distance matrix regression to examine the brain regions within the FPN (11 regions x 2 hemispheres) and the DMN (12 regions x 2 hemispheres) in 48 persistently high (18 female), 108 persistently low (32 female), and 109 changers (39 female) using structural MRI acquired at baseline. FPN and DMN regions were defined using networks identified in Smith et al. (Proc Natl Acad Sci U S A 106:13040–5, 2009). IQ trajectory groups were defined by IQ measurements from up to three time points spanning early to middle childhood (mean age time 1: 3.2 years; time 2: 5.4 years; time 3: 11.3 years). Results The changers group exhibited volumetric differences in the DMN compared to both the persistently low and persistently high groups at time 1. However, the persistently high group did not differ from the persistently low group, suggesting that DMN structure may be an early predictor for change in IQ trajectory. In contrast, the persistently high group exhibited differences in the FPN compared to both the persistently low and changers groups, suggesting differences related more to concurrent IQ and the absence of intellectual disability. Conclusions Within autism, volumetric differences of brain regions within the DMN in early childhood may differentiate individuals with persistently low IQ from those with low IQ that improves through childhood. Structural differences in brain networks between these three IQ-based subgroups highlight distinct neural underpinnings of these autism sub-phenotypes. Supplementary Information The online version contains supplementary material available at 10.1186/s11689-022-09460-y.
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12
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Neurometabolic Dysfunction in SPG11 Hereditary Spastic Paraplegia. Nutrients 2022; 14:nu14224803. [PMID: 36432490 PMCID: PMC9693816 DOI: 10.3390/nu14224803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Pathogenic variants in SPG11 cause the most common autosomal recessive complicated hereditary spastic paraplegia. Besides the prototypical combination of spastic paraplegia with a thin corpus callosum, obesity has increasingly been reported in this multisystem neurodegenerative disease. However, a detailed analysis of the metabolic state is lacking. METHODS In order to characterize metabolic alterations, a cross-sectional analysis was performed comparing SPG11 patients (n = 16) and matched healthy controls (n = 16). We quantified anthropometric parameters, body composition as determined by bioimpedance spectroscopy, and serum metabolic biomarkers, and we measured hypothalamic volume by high-field MRI. RESULTS Compared to healthy controls, SPG11 patients exhibited profound changes in body composition, characterized by increased fat tissue index, decreased lean tissue index, and decreased muscle mass. The presence of lymphedema correlated with increased extracellular fluid. The serum levels of the adipokines leptin, resistin, and progranulin were significantly altered in SPG11 while adiponectin and C1q/TNF-related protein 3 (CTRP-3) were unchanged. MRI volumetry revealed a decreased hypothalamic volume in SPG11 patients. CONCLUSIONS Body composition, adipokine levels, and hypothalamic volume are altered in SPG11. Our data indicate a link between obesity and hypothalamic neurodegeneration in SPG11 and imply that specific metabolic interventions may prevent obesity despite severely impaired mobility in SPG11.
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13
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Tang B, Zhao Y, Venkataraman A, Tsapkini K, Lindquist MA, Pekar J, Caffo B. Differences in functional connectivity distribution after transcranial direct-current stimulation: A connectivity density point of view. Hum Brain Mapp 2022; 44:170-185. [PMID: 36371779 PMCID: PMC9783448 DOI: 10.1002/hbm.26112] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 09/09/2022] [Accepted: 10/02/2022] [Indexed: 11/14/2022] Open
Abstract
In this manuscript, we consider the problem of relating functional connectivity measurements viewed as statistical distributions to outcomes. We demonstrate the utility of using the distribution of connectivity on a study of resting-state functional magnetic resonance imaging association with an intervention. The method uses the estimated density of connectivity between nodes of interest as a functional covariate. Moreover, we demonstrate the utility of the procedure in an instance where connectivity is naturally considered an outcome by reversing the predictor/response relationship using case/control methodology. The method utilizes the density quantile, the density evaluated at empirical quantiles, instead of the empirical density directly. This improved the performance of the method by highlighting tail behavior, though we emphasize that by being flexible and non-parametric, the technique can detect effects related to the central portion of the density. To demonstrate the method in an application, we consider 47 primary progressive aphasia patients with various levels of language abilities. These patients were randomly assigned to two treatment arms, transcranial direct-current stimulation and language therapy versus sham (language therapy only), in a clinical trial. We use the method to analyze the effect of direct stimulation on functional connectivity. As such, we estimate the density of correlations among the regions of interest and study the difference in the density post-intervention between treatment arms. We discover that it is the tail of the density, rather than the mean or lower order moments of the distribution, that demonstrates a significant impact in the classification. The new approach has several benefits. Among them, it drastically reduces the number of multiple comparisons compared with edge-wise analysis. In addition, it allows for the investigation of the impact of functional connectivity on the outcomes where the connectivity is not geometrically localized.
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Affiliation(s)
- Bohao Tang
- Department of BiostatisticsJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Yi Zhao
- Department of Biostatistics and Health Data ScienceIndiana University School of MedicineIndianapolisIndianaUSA
| | - Archana Venkataraman
- Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Kyrana Tsapkini
- Department of NeurologyJohns Hopkins MedicineBaltimoreMarylandUSA,Department of Cognitive ScienceJohns Hopkins MedicineBaltimoreMarylandUSA
| | | | - James Pekar
- F.M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA,Department of Radiology and Radiological ScienceJohns Hopkins University MedicineBaltimoreMarylandUSA
| | - Brian Caffo
- Department of BiostatisticsJohns Hopkins UniversityBaltimoreMarylandUSA
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14
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Endo H, Tagai K, Ono M, Ikoma Y, Oyama A, Matsuoka K, Kokubo N, Hirata K, Sano Y, Oya M, Matsumoto H, Kurose S, Seki C, Shimizu H, Kakita A, Takahata K, Shinotoh H, Shimada H, Tokuda T, Kawamura K, Zhang M, Oishi K, Mori S, Takado Y, Higuchi M. A Machine Learning-Based Approach to Discrimination of Tauopathies Using [ 18 F]PM-PBB3 PET Images. Mov Disord 2022; 37:2236-2246. [PMID: 36054492 PMCID: PMC9805085 DOI: 10.1002/mds.29173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/03/2022] [Accepted: 07/10/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND We recently developed a positron emission tomography (PET) probe, [18 F]PM-PBB3, to detect tau lesions in diverse tauopathies, including mixed three-repeat and four-repeat (3R + 4R) tau fibrils in Alzheimer's disease (AD) and 4R tau aggregates in progressive supranuclear palsy (PSP). For wider availability of this technology for clinical settings, bias-free quantitative evaluation of tau images without a priori disease information is needed. OBJECTIVE We aimed to establish tau PET pathology indices to characterize PSP and AD using a machine learning approach and test their validity and tracer capabilities. METHODS Data were obtained from 50 healthy control subjects, 46 patients with PSP Richardson syndrome, and 37 patients on the AD continuum. Tau PET data from 114 regions of interest were subjected to Elastic Net cross-validation linear classification analysis with a one-versus-the-rest multiclass strategy to obtain a linear function that discriminates diseases by maximizing the area under the receiver operating characteristic curve. We defined PSP- and AD-tau scores for each participant as values of the functions optimized for differentiating PSP (4R) and AD (3R + 4R), respectively, from others. RESULTS The discriminatory ability of PSP- and AD-tau scores assessed as the area under the receiver operating characteristic curve was 0.98 and 1.00, respectively. PSP-tau scores correlated with the PSP rating scale in patients with PSP, and AD-tau scores correlated with Mini-Mental State Examination scores in healthy control-AD continuum patients. The globus pallidus and amygdala were highlighted as regions with high weight coefficients for determining PSP- and AD-tau scores, respectively. CONCLUSIONS These findings highlight our technology's unbiased capability to identify topologies of 3R + 4R versus 4R tau deposits. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Hironobu Endo
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Kenji Tagai
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Maiko Ono
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Yoko Ikoma
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Asaka Oyama
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Kiwamu Matsuoka
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Naomi Kokubo
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Kosei Hirata
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Yasunori Sano
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Masaki Oya
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Hideki Matsumoto
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan,Department of Oral and Maxillofacial RadiologyTokyo Dental CollegeTokyoJapan
| | - Shin Kurose
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Chie Seki
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Hiroshi Shimizu
- Department of Pathology, Brain Research InstituteNiigata UniversityNiigataJapan
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research InstituteNiigata UniversityNiigataJapan
| | - Keisuke Takahata
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | | | - Hitoshi Shimada
- Department of Functional Neurology & Neurosurgery, Center for Integrated Human Brain Science, Brain Research InstituteNiigata UniversityNiigataJapan
| | - Takahiko Tokuda
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Kazunori Kawamura
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Ming‐Rong Zhang
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Yuhei Takado
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
| | - Makoto Higuchi
- Institute for Quantum Medical Science, Quantum Life and Medical Science DirectorateNational Institutes for Quantum Science and TechnologyChibaJapan
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15
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Lee JK, Andrews DS, Ozturk A, Solomon M, Rogers S, Amaral DG, Nordahl CW. Altered Development of Amygdala-Connected Brain Regions in Males and Females with Autism. J Neurosci 2022; 42:6145-6155. [PMID: 35760533 PMCID: PMC9351637 DOI: 10.1523/jneurosci.0053-22.2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/30/2022] [Accepted: 06/08/2022] [Indexed: 02/05/2023] Open
Abstract
Altered amygdala development is implicated in the neurobiology of autism, but little is known about the coordinated development of the brain regions directly connected with the amygdala. Here we investigated the volumetric development of an amygdala-connected network, defined as the set of brain regions with monosynaptic connections with the amygdala, in autism from early to middle childhood. A total of 950 longitudinal structural MRI scans were acquired from 282 children (93 female) with autism and 128 children with typical development (61 female) at up to four time points (mean ages: 39, 52, 64, and 137 months, respectively). Volumes from 32 amygdala-connected brain regions were examined using mixed effects multivariate distance matrix regression. The Social Responsiveness Scale-2 was administered to assess degree of autistic traits and social impairments. The amygdala-connected network exhibited persistent diagnostic differences (p values ≤ 0.03) that increased over time (p values ≤ 0.02). These differences were most prominent in autistics with more impacted social functioning at baseline. This pattern was not observed across regions without monosynaptic amygdala connection. We observed qualitative sex differences. In males, the bilateral subgenual anterior cingulate cortices were most affected, while in females the left fusiform and superior temporal gyri were most affected. In conclusion, (1) autism is associated with widespread alterations to the development of brain regions connected with the amygdala, which were associated with autistic social behaviors; and (2) autistic males and females exhibited different patterns of alterations, adding to a growing body of evidence of sex differences in the neurobiology of autism.SIGNIFICANCE STATEMENT Global patterns of development across brain regions with monosynaptic connection to the amygdala differentiate autism from typical development, and are modulated by social functioning in early childhood. Alterations to brain regions within the amygdala-connected network differed in males and females with autism. Results also indicate larger volumetric differences in regions having monosynaptic connection with the amygdala than in regions without monosynaptic connection.
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Affiliation(s)
- Joshua K Lee
- MIND Institute, University of California Davis School of Medicine, Sacramento, California 95817
- Department of Psychiatry and Behavioral Sciences
| | - Derek S Andrews
- MIND Institute, University of California Davis School of Medicine, Sacramento, California 95817
- Department of Psychiatry and Behavioral Sciences
| | - Arzu Ozturk
- Department of Radiology, University of California Davis School of Medicine, Sacramento, California 95817
| | - Marjorie Solomon
- MIND Institute, University of California Davis School of Medicine, Sacramento, California 95817
- Department of Psychiatry and Behavioral Sciences
| | - Sally Rogers
- MIND Institute, University of California Davis School of Medicine, Sacramento, California 95817
- Department of Psychiatry and Behavioral Sciences
| | - David G Amaral
- MIND Institute, University of California Davis School of Medicine, Sacramento, California 95817
- Department of Psychiatry and Behavioral Sciences
| | - Christine Wu Nordahl
- MIND Institute, University of California Davis School of Medicine, Sacramento, California 95817
- Department of Psychiatry and Behavioral Sciences
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16
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Peng SL, Chu LWL, Su FY. Cerebral hemodynamic response to caffeine: effect of dietary caffeine consumption. NMR IN BIOMEDICINE 2022; 35:e4727. [PMID: 35285102 DOI: 10.1002/nbm.4727] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/14/2022] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
Caffeine has a significant effect on cerebrovascular systems, and the dual action of caffeine on both neural and vascular responses leads to concerns for the interpretation of blood oxygenation level-dependent (BOLD) functional MRI. However, potential differences in the brain response to caffeine with regard to consumption habits have not been fully elucidated, as BOLD responses may vary with the dietary caffeine consumption history. The main aim of this study was to characterize the acute effect of caffeine on cerebral hemodynamic responses in participants with different patterns of caffeine consumption habits. Fifteen non-habitual and 11 habitual volunteers were included in this study. The cerebral blood flow (CBF) and cerebrovascular reactivity (CVR) to the breath-hold challenge were measured before and after 200 mg caffeine administration. The non-habitual individuals exhibited a pattern of progressive reduction in CBF with time. The CVR was diminished in the caffeinated condition (P < 0.05). In the habitual group, the pattern of CBF decrease was smaller and homogeneous across the brain, and reached steady state rapidly. The CVR was not affected in the presence of caffeine (P > 0.05). Our results demonstrated that the cerebral hemodynamic response to caffeine was subject to the habitual consumption patterns of the participants. The compromised CVR following caffeine administration in the non-habitual group may partially explain the suppressed BOLD response to a visual stimulation in low-caffeine-level users.
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Affiliation(s)
- Shin-Lei Peng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Lok Wang Lauren Chu
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Feng-Yi Su
- Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan
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17
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Urquia-Osorio H, Pimentel-Silva LR, Rezende TJR, Almendares-Bonilla E, Yasuda CL, Concha L, Cendes F. Superficial and deep white matter diffusion abnormalities in focal epilepsies. Epilepsia 2022; 63:2312-2324. [PMID: 35707885 DOI: 10.1111/epi.17333] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/11/2022] [Accepted: 06/14/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVE This study was undertaken to evaluate superficial-white matter (WM) and deep-WM magnetic resonance imaging diffusion tensor imaging (DTI) metrics and identify distinctive patterns of microstructural abnormalities in focal epilepsies of diverse etiology, localization, and response to antiseizure medication (ASM). METHODS We examined DTI data for 113 healthy controls and 113 patients with focal epilepsies: 51 patients with temporal lobe epilepsy (TLE) and hippocampal sclerosis (HS) refractory to ASM, 27 with pharmacoresponsive TLE-HS, 15 with temporal lobe focal cortical dysplasia (FCD), and 20 with frontal lobe FCD. To assess WM microstructure, we used a multicontrast multiatlas parcellation of DTI. We evaluated fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), and assessed within-group differences ipsilateral and contralateral to the epileptogenic lesion, as well as between-group differences, in regions of interest (ROIs). RESULTS The TLE-HS groups presented more widespread superficial- and deep-WM diffusion abnormalities than both FCD groups. Concerning superficial WM, TLE-HS groups showed multilobar ipsilateral and contralateral abnormalities, with less extensive distribution in pharmacoresponsive patients. Both the refractory TLE-HS and pharmacoresponsive TLE-HS groups also presented pronounced changes in ipsilateral frontotemporal ROIs (decreased FA and increased MD, RD, and AD). Conversely, FCD patients showed diffusion changes almost exclusively adjacent to epileptogenic areas. SIGNIFICANCE Our findings add further evidence of widespread abnormalities in WM diffusion metrics in patients with TLE-HS compared to other focal epilepsies. Notably, superficial-WM microstructural damage in patients with FCD is more restricted around the epileptogenic lesion, whereas TLE-HS groups showed diffuse WM damage with ipsilateral frontotemporal predominance. These findings suggest the potential of superficial-WM analysis for better understanding the biological mechanisms of focal epilepsies, and identifying dysfunctional networks and their relationship with the clinical-pathological phenotype. In addition, lobar superficial-WM abnormalities may aid in the diagnosis of subtle FCDs.
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Affiliation(s)
- Hebel Urquia-Osorio
- Department of Neurology, University of Campinas, São Paulo, Brazil.,Faculty of Medical Science, National Autonomous University of Honduras, Honduras
| | | | | | - Eimy Almendares-Bonilla
- Department of Neurology, University of Campinas, São Paulo, Brazil.,Faculty of Medical Science, National Autonomous University of Honduras, Honduras
| | | | - Luis Concha
- Institute of Neurobiology, National Autonomous University of Mexico, Queretaro, Mexico
| | - Fernando Cendes
- Department of Neurology, University of Campinas, São Paulo, Brazil
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18
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Pan Y, Liu S, Zeng Y, Ye C, Qiao H, Song T, Lv H, Chan P, Lu J, Ma T. A Multi-Atlas-Based [18F]9-Fluoropropyl-(+)-Dihydrotetrabenazine Positron Emission Tomography Image Segmentation Method for Parkinson's Disease Quantification. Front Aging Neurosci 2022; 14:902169. [PMID: 35769601 PMCID: PMC9234266 DOI: 10.3389/fnagi.2022.902169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives [18F]9-fluoropropyl-(+)-dihydrotetrabenazine ([18F]-FP-DTBZ) positron emission tomography (PET) provides reliable information for the diagnosis of Parkinson's disease (PD). In this study, we proposed a multi-atlas-based [18F]-FP-DTBZ PET image segmentation method for PD quantification assessment. Methods A total of 99 subjects from Xuanwu Hospital of Capital Medical University were included in this study, and both brain PET and magnetic resonance (MR) scans were conducted. Data from 20 subjects were used to generate atlases, based on which a multi-atlas-based [18F]-FP-DTBZ PET segmentation method was developed especially for striatum and its subregions. The proposed method was compared with the template-based method through striatal subregion parcellation performance and the standard uptake value ratio (SUVR) quantification accuracy. Discriminant analysis between healthy controls (HCs) and PD patients was further performed. Results Segmentation results of the multi-atlas-based method showed better consistency than the template-based method with the ground truth, yielding a dice coefficient of 0.81 over 0.73 on the full striatum. The SUVRs calculated by the multi-atlas-based method had an average interclass correlation coefficient (ICC) of 0.953 with the standardized result, whereas the template-based method only reached 0.815. The SUVRs of HCs were generally higher than that of patients with PD and showed significant differences in all of the striatal subregions (all p < 0.001). The median and posterior putamen performed best in discriminating patients with PD from HCs. Conclusion The proposed multi-atlas-based [18F]-FP-DTBZ PET image segmentation method achieved better performance than the template-based method, indicating great potential in improving accuracy and efficiency for PD diagnosis in clinical routine.
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Affiliation(s)
- Yiwei Pan
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Shuying Liu
- Department of Neurology and Neurobiology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Chinese Institute for Brain Research (CIBR), Beijing, China
| | - Yao Zeng
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Chenfei Ye
- International Research Institute for Artificial Intelligence, Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Hongwen Qiao
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Tianbing Song
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Haiyan Lv
- Mindsgo Life Science Shenzhen Co. Ltd., Shenzhen, China
| | - Piu Chan
- Department of Neurology and Neurobiology, Xuanwu Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center of Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Capital Medical University, Beijing, China
| | - Ting Ma
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Peng Cheng Laboratory, Shenzhen, China
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19
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Oishi K, Soldan A, Pettigrew C, Hsu J, Mori S, Albert M, Oishi K. Dataset of relationship between longitudinal change in cognitive performance and functional connectivity in cognitively normal older individuals. Data Brief 2022; 42:108302. [PMID: 35669007 PMCID: PMC9163691 DOI: 10.1016/j.dib.2022.108302] [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: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/17/2022] [Indexed: 11/30/2022] Open
Abstract
The data show an association between measured and predicted changes in cognitive performance in older adults who are cognitively normal. Changes in cognitive performance over two years were assessed using the Cognitive Composite Score. The prediction of change in cognitive function was based on changes in pairwise functional connectivity between 80 gray matter regions examined by resting-state functional magnetic resonance imaging. A feature extraction process based on the Variable Importance Testing Approach (VITA) identified changes in 11 pairs of functional connections associated with the default mode network as features related to changes in cognitive performance. Linear and elastic net regression models were applied to these 11 features to predict changes in cognitive performance over two years. A relationship between the 11 features and the geriatric depression score was also shown. The dataset supplements the research findings in the "Changes in pairwise functional connectivity associated with changes in cognitive performance in cognitively normal older individuals: a two-year observational study" published in Oishi et al. (2022). The raw rs-fMRI correlation matrix and associated clinical data can be accessed upon request from the BIOCARD website (www.biocard-se.org) and can be reused for predictive model building.
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Affiliation(s)
- Kumiko Oishi
- Center for Imaging Science, Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Anja Soldan
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Corinne Pettigrew
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Johnny Hsu
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 208 Traylor Building, 720 Rutland Avenue, Baltimore, MD 21205, USA
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 208 Traylor Building, 720 Rutland Avenue, Baltimore, MD 21205, USA
| | - Marilyn Albert
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 208 Traylor Building, 720 Rutland Avenue, Baltimore, MD 21205, USA
- Corresponding author.
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20
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Wang B, Caffo BS, Luo X, Liu C, Faria AV, Miller MI, Zhao Y. Regularized regression on compositional trees with application to MRI analysis. J R Stat Soc Ser C Appl Stat 2022; 71:541-561. [DOI: 10.1111/rssc.12545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Bingkai Wang
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public Health BaltimoreMarylandUSA
| | - Brian S. Caffo
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public Health BaltimoreMarylandUSA
| | - Xi Luo
- Department of Biostatistics and Data ScienceThe University of Texas Health Science Center at Houston HoustonTexasUSA
| | - Chin‐Fu Liu
- Center for Imaging Science, Biomedical EngineeringJohns Hopkins University BaltimoreMarylandUSA
| | - Andreia V. Faria
- Department of RadiologyJohns Hopkins University School of Medicine BaltimoreMarylandUSA
| | - Michael I. Miller
- Center for Imaging Science, Biomedical EngineeringJohns Hopkins University BaltimoreMarylandUSA
| | - Yi Zhao
- Department of BiostatisticsIndiana University School of Medicine and for the Alzheimer's Disease Neuroimaging Initiative IndianapolisIndianaUSA
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21
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Afthinos A, Themistocleous C, Herrmann O, Fan H, Lu H, Tsapkini K. The Contribution of Working Memory Areas to Verbal Learning and Recall in Primary Progressive Aphasia. Front Neurol 2022; 13:698200. [PMID: 35250797 PMCID: PMC8892377 DOI: 10.3389/fneur.2022.698200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 01/04/2022] [Indexed: 11/13/2022] Open
Abstract
Recent evidence of domain-specific working memory (WM) systems has identified the areas and networks which are involved in phonological, orthographic, and semantic WM, as well as in higher level domain-general WM functions. The contribution of these areas throughout the process of verbal learning and recall is still unclear. In the present study, we asked, what is the contribution of domain-specific specialized WM systems in the course of verbal learning and recall? To answer this question, we regressed the perfusion data from pseudo-continuous arterial spin labeling (pCASL) MRI with all the immediate, consecutive, and delayed recall stages of the Rey Auditory Verbal Learning Test (RAVLT) from a group of patients with Primary Progressive Aphasia (PPA), a neurodegenerative syndrome in which language is the primary deficit. We found that the early stages of verbal learning involve the areas with subserving phonological processing (left superior temporal gyrus), as well as semantic WM memory (left angular gyrus, AG_L). As learning unfolds, areas with subserving semantic WM (AG_L), as well as lexical/semantic (inferior temporal and fusiform gyri, temporal pole), and episodic memory (hippocampal complex) become more involved. Finally, a delayed recall depends entirely on semantic and episodic memory areas (hippocampal complex, temporal pole, and gyri). Our results suggest that AG_L subserving domain-specific (semantic) WM is involved only during verbal learning, but a delayed recall depends only on medial and cortical temporal areas.
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Affiliation(s)
- Alexandros Afthinos
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | | | - Olivia Herrmann
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Hongli Fan
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, United States
| | - Kyrana Tsapkini
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, United States
- *Correspondence: Kyrana Tsapkini
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22
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Wang JY, Grigsby J, Placido D, Wei H, Tassone F, Kim K, Hessl D, Rivera SM, Hagerman RJ. Clinical and Molecular Correlates of Abnormal Changes in the Cerebellum and Globus Pallidus in Fragile X Premutation. Front Neurol 2022; 13:797649. [PMID: 35211082 PMCID: PMC8863211 DOI: 10.3389/fneur.2022.797649] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/12/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Fragile X premutation carriers (55-200 CGG triplets) may develop a progressive neurodegenerative disorder, fragile X-associated tremor/ataxia syndrome (FXTAS), after the age of 50. The neuroradiologic markers of FXTAS are hyperintense T2-signals in the middle cerebellar peduncle-the MCP sign. We recently noticed abnormal T2-signals in the globus pallidus in male premutation carriers and controls but the prevalence and clinical significance were unknown. METHODS We estimated the prevalence of the MCP sign and pallidal T2-abnormalities in 230 male premutation carriers and 144 controls (aged 8-86), and examined the associations with FXTAS symptoms, CGG repeat length, and iron content in the cerebellar dentate nucleus and globus pallidus. RESULTS Among participants aged ≥45 years (175 premutation carriers and 82 controls), MCP sign was observed only in premutation carriers (52 vs. 0%) whereas the prevalence of pallidal T2-abnormalities approached significance in premutation carriers compared with controls after age-adjustment (25.1 vs. 13.4%, p = 0.069). MCP sign was associated with impaired motor and executive functioning, and the additional presence of pallidal T2-abnormalities was associated with greater impaired executive functioning. Among premutation carriers, significant iron accumulation was observed in the dentate nucleus, and neither pallidal or MCP T2-abnormalities affected measures of the dentate nucleus. While the MCP sign was associated with CGG repeat length >75 and dentate nucleus volume correlated negatively with CGG repeat length, pallidal T2-abnormalities did not correlate with CGG repeat length. However, pallidal signal changes were associated with age-related accelerated iron depletion and variability and having both MCP and pallidal signs further increased iron variability in the globus pallidus. CONCLUSIONS Only the MCP sign, not pallidal abnormalities, revealed independent associations with motor and cognitive impairment; however, the occurrence of combined MCP and pallidal T2-abnormalities may present a risk for greater cognitive impairment and increased iron variability in the globus pallidus.
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Affiliation(s)
- Jun Yi Wang
- Center for Mind and Brain, University of California, Davis, Davis, CA, United States
| | - Jim Grigsby
- Departments of Psychology and Medicine, University of Colorado Denver, Denver, CO, United States
| | - Diego Placido
- Department of Psychology, University of California, Davis, Davis, CA, United States
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute for Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Flora Tassone
- Department of Biochemistry and Molecular Medicine, University of California Davis School of Medicine, Sacramento, CA, United States
- The MIND Institute, University of California Davis Medical Center, Sacramento, CA, United States
| | - Kyoungmi Kim
- Department of Public Health Sciences, University of California Davis School of Medicine, Sacramento, CA, United States
| | - David Hessl
- The MIND Institute, University of California Davis Medical Center, Sacramento, CA, United States
- Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA, United States
| | - Susan M. Rivera
- Center for Mind and Brain, University of California, Davis, Davis, CA, United States
- Departments of Psychology and Medicine, University of Colorado Denver, Denver, CO, United States
- The MIND Institute, University of California Davis Medical Center, Sacramento, CA, United States
| | - Randi J. Hagerman
- The MIND Institute, University of California Davis Medical Center, Sacramento, CA, United States
- Department of Pediatrics, University of California Davis School of Medicine, Sacramento, CA, United States
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23
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A multimodal study of a first episode psychosis cohort: potential markers of antipsychotic treatment resistance. Mol Psychiatry 2022; 27:1184-1191. [PMID: 34642460 PMCID: PMC9001745 DOI: 10.1038/s41380-021-01331-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 09/17/2021] [Accepted: 09/29/2021] [Indexed: 11/13/2022]
Abstract
Treatment resistant (TR) psychosis is considered to be a significant cause of disability and functional impairment. Numerous efforts have been made to identify the clinical predictors of TR. However, the exploration of molecular and biological markers is still at an early stage. To understand the TR condition and identify potential molecular and biological markers, we analyzed demographic information, clinical data, structural brain imaging data, and molecular brain imaging data in 7 Tesla magnetic resonance spectroscopy from a first episode psychosis cohort that includes 136 patients. Age, gender, race, smoking status, duration of illness, and antipsychotic dosages were controlled in the analyses. We found that TR patients had a younger age at onset, more hospitalizations, more severe negative symptoms, a reduction in the volumes of the hippocampus (HP) and superior frontal gyrus (SFG), and a reduction in glutathione (GSH) levels in the anterior cingulate cortex (ACC), when compared to non-TR patients. The combination of multiple markers provided a better classification between TR and non-TR patients compared to any individual marker. Our study shows that ACC-GSH, HP and SFG volumes, and age at onset, could potentially be biomarkers for TR diagnosis, while hospitalization and negative symptoms could be used to evaluate the progression of the disease. Multimodal cohorts are essential in obtaining a comprehensive understanding of brain disorders.
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24
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Yan Y, Balbastre Y, Brudfors M, Ashburner J. Factorisation-Based Image Labelling. Front Neurosci 2022; 15:818604. [PMID: 35110992 PMCID: PMC8801908 DOI: 10.3389/fnins.2021.818604] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 12/10/2021] [Indexed: 12/21/2022] Open
Abstract
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.
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Affiliation(s)
- Yu Yan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Yaël Balbastre
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Mikael Brudfors
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - John Ashburner
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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25
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Keator LM, Yourganov G, Faria AV, Hillis AE, Tippett DC. Application of the dual stream model to neurodegenerative disease: Evidence from a multivariate classification tool in primary progressive aphasia. APHASIOLOGY 2022; 36:618-647. [PMID: 35493273 PMCID: PMC9053317 DOI: 10.1080/02687038.2021.1897079] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 02/19/2021] [Indexed: 05/20/2023]
Abstract
BACKGROUND A clinical diagnosis of primary progressive aphasia relies on behavioral characteristics and patterns of atrophy to determine a variant: logopenic; nonfluent/agrammatic; or semantic. The dual stream model (Hickok & Poeppel, 2000; 2004; 2007; 2015) is a contemporary paradigm that has been applied widely to understand brain-behavior relationships; however, applications to neurodegenerative diseases like primary progressive aphasia are limited. AIMS The primary aim of this study is to determine if the dual stream model can be applied to a neurodegenerative disease, such as primary progressive aphasia, using both behavioral and neuroimaging data. METHODS & PROCEDURES We analyzed behavioral and neuroimaging data to apply a multivariate classification tool (support vector machines) to determine if the dual stream model extends to primary progressive aphasia. Sixty-four individuals with primary progressive aphasia were enrolled (26 logopenic variant, 20 nonfluent/agrammatic variant, and 18 semantic variant) and administered four behavioral tasks to assess three linguistic domains (naming, repetition, and semantic knowledge). We used regions of interest from the dual stream model and calculated the cortical volume for gray matter regions and white matter structural volumes and fractional anisotropy. We applied a multivariate classification tool (support vector machines) to distinguish variants based on behavioral performance and patterns of atrophy. OUTCOMES & RESULTS Behavioral performance discriminates logopenic from semantic variant and nonfluent/agrammatic from semantic variant. Cortical volume distinguishes all three variants. White matter structural volumes and fractional anisotropy primarily distinguish nonfluent/agrammatic from semantic variant. Regions of interest that contribute to each classification in cortical and white matter analyses demonstrate alignment of logopenic and nonfluent/agrammatic variants to the dorsal stream, while the semantic variant aligns with the ventral stream. CONCLUSIONS A novel implementation of an automated multivariate classification suggests that the dual stream model can be extended to primary progressive aphasia. Variants are distinguished by behavioral and neuroanatomical patterns and align to the dorsal and ventral streams of the dual stream model.
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Affiliation(s)
- Lynsey M. Keator
- Department of Neurology, Johns Hopkins University School of Medicine, Phipps 446, 600 N. Wolfe Street, Baltimore, MD 21287
| | - Grigori Yourganov
- Department of Psychology, McCausland Center for Brain Imaging, 6 Medical Park Road, University of South Carolina, Columbia, South Carolina 29201
| | - Andreia V. Faria
- The Russell H. Morgan Department of Radiology and Radiological Science, 1800 Orleans Street, Johns Hopkins University, Baltimore, MD 21287
| | - Argye E. Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Phipps 446, 600 N. Wolfe Street, Baltimore, MD 21287
- Department of Physical Medicine and Rehabilitation, 600 N. Wolfe Street, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Department of Cognitive Science, Krieger School of Arts and Sciences, 3400 N. Charles Street, Johns Hopkins University, Baltimore, MD 21218
| | - Donna C. Tippett
- Department of Neurology, Johns Hopkins University School of Medicine, Phipps 446, 600 N. Wolfe Street, Baltimore, MD 21287
- Department of Physical Medicine and Rehabilitation, 600 N. Wolfe Street, Johns Hopkins University School of Medicine, Baltimore, MD 21287
- Department of Otolaryngology—Head and Neck Surgery, 601 N. Caroline Street, 6 floor, Johns Hopkins University School of Medicine, Baltimore, MD 21287
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Liu G, Wu J, Dang C, Tan S, Peng K, Guo Y, Xing S, Xie C, Zeng J, Tang X. Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes. Neurorehabil Neural Repair 2021; 36:38-48. [PMID: 34724851 DOI: 10.1177/15459683211054178] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Background. Neuroimaging biomarkers are valuable predictors of motor improvement after stroke, but there is a gap between published evidence and clinical usage. Objective. In this work, we aimed to investigate whether machine learning techniques, when applied to a combination of baseline whole brain volumes and clinical data, can accurately predict individual motor outcome after stroke. Methods. Upper extremity Fugl-Meyer Assessments (FMA-UE) were conducted 1 week and 12 weeks, and structural MRI was performed 1 week, after onset in 56 patients with subcortical infarction. Proportional recovery model residuals were employed to assign patients to proportional and poor recovery groups (34 vs 22). A sophisticated machine learning scheme, consisting of conditional infomax feature extraction, synthetic minority over-sampling technique for nominal and continuous, and bagging classification, was employed to predict motor outcomes, with the input features being a combination of baseline whole brain volumes and clinical data (FMA-UE scores). Results. The proposed machine learning scheme yielded an overall balanced accuracy of 87.71% in predicting proportional vs poor recovery outcomes, a sensitivity of 93.77% in correctly identifying poor recovery outcomes, and a ROC AUC of 89.74%. Compared with only using clinical data, adding whole brain volumes can significantly improve the classification performance, especially in terms of the overall balanced accuracy (from 80.88% to 87.71%) and the sensitivity (from 92.23% to 93.77%). Conclusions. Experimental results suggest that a combination of baseline whole brain volumes and clinical data, when equipped with appropriate machine learning techniques, may provide valuable information for personalized rehabilitation planning after subcortical infarction.
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Affiliation(s)
- Gang Liu
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 26469Sun Yat-Sen University, Guangzhou, China.,Guangdong-HongKong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China
| | - Jiewei Wu
- Department of Electrical and Electronic Engineering, 255310Southern University of Science and Technology, Shenzhen, China.,School of Electronics and Information Technology, 26469Sun Yat-Sen University, Guangzhou, China
| | - Chao Dang
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 26469Sun Yat-Sen University, Guangzhou, China
| | - Shuangquan Tan
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 26469Sun Yat-Sen University, Guangzhou, China
| | - Kangqiang Peng
- Department of Medical Imaging, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, 71067Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yaomin Guo
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 26469Sun Yat-Sen University, Guangzhou, China
| | - Shihui Xing
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 26469Sun Yat-Sen University, Guangzhou, China
| | - Chuanmiao Xie
- Department of Medical Imaging, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, 71067Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jinsheng Zeng
- Department of Neurology, The First Affiliated Hospital, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, 26469Sun Yat-Sen University, Guangzhou, China
| | - Xiaoying Tang
- Department of Electrical and Electronic Engineering, 255310Southern University of Science and Technology, Shenzhen, China
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Wu J, Lyu G, Wang K, Tang X. A Hybrid Learning Pipeline for Automated Diagnosis of First-Episode Schizophrenia Utilizing T1-weighted Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2761-2764. [PMID: 34891821 DOI: 10.1109/embc46164.2021.9630313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In this work, we proposed and validated a hybrid learning pipeline for automated diagnosis of first-episode schizophrenia (FES) utilizing T1-weighted images. Amygdalar and hippocampal shape abnormalities in FES have been observed in previous studies. In this work, we jointly made use of two types of features, together with advanced machine learning techniques, for an automated discrimination of FES and healthy control (96 versus 102). Specifically, we first employed a ResNet34 model to extract convolutional neural network (CNN) features. We then combined these CNN features with shape features of the bilateral hippocampi and the bilateral amygdalas, before being inputted to advanced classification algorithms such as the Gradient Boosting Decision Tree (GBDT) for classifying between FES and healthy control. Shape features were represented using log Jacobian determinants, through a well-established statistical shape analysis pipeline. When combining CNN with hippocampal shape, the best results came from utilizing GBDT as the classifier, with an overall accuracy of 75.15%, a sensitivity of 69.35%, a specificity of 80.19%, an F1 of 72.16%, and an AUC of 79.68%. When combing CNN and amygdalar shape, the best results came from utilizing Bagging as the classifier, with an overall accuracy of 74.39%, a sensitivity of 67.93%, a specificity of 80%, an F1 of 71.11%, and an AUC of 80.98%. Compared with using each single set of features, either CNN or shape, significant improvements have been observed, in terms of FES discrimination. To the best of our knowledge, this is the first work that has tried to combine CNN features and hippocampal/amygdalar shape features for automated FES identification.
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28
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Huang W, Tang X. Down-sampling template curve to accelerate LDDMM-curve with application to shape analysis of the corpus callosum. Healthc Technol Lett 2021; 8:78-83. [PMID: 34035928 PMCID: PMC8136766 DOI: 10.1049/htl2.12011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/05/2021] [Accepted: 03/16/2021] [Indexed: 11/19/2022] Open
Abstract
Large deformation diffeomorphic metric mapping for curve (LDDMM-curve) has been widely used in deformation based statistical shape analysis of the mid-sagittal corpus callosum. A main limitation of LDDMM-curve is that it is time-consuming and computationally complex. In this study, down-sampling strategies for accelerating LDDMM-curve are investigated and tested on two large datasets, one on Alzheimer's disease (155 Alzheimer's disease, 325 mild cognitive impairment and 185 healthy controls) and the other on first-episode schizophrenia (92 first-episode schizophrenia and 106 healthy controls). For both datasets a variety of down-sampling factors are tested in terms of registration accuracy, registration speed, and most importantly disease-related patterns. Experimental results revealed that down-sampling template curve by a factor of 2 can significantly reduce the running time of LDDMM-curve without sacrificing the registration accuracy. Also, the disease-induced patterns, or more specifically the group comparison results, were almost identical before and after down-sampling. It is also shown that there was no need to down-sample the target population curves but only the single template curve of the study of interest. Comprehensive analyses were conducted.
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Affiliation(s)
- Weikai Huang
- Department of Electrical and Electronic EngineeringSouthern University of Science and TechnologyShenzhenGuangdongChina
| | - Xiaoying Tang
- Department of Electrical and Electronic EngineeringSouthern University of Science and TechnologyShenzhenGuangdongChina
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Yousef A, Hinkley LB, Nagarajan SS, Cheung SW. Neuroanatomic Volume Differences in Tinnitus and Hearing Loss. Laryngoscope 2021; 131:1863-1868. [PMID: 33811641 DOI: 10.1002/lary.29549] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 03/12/2021] [Accepted: 03/22/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVES To investigate neuroanatomic volume differences in tinnitus and hearing loss. STUDY DESIGN Cross-sectional. METHODS Sixteen regions of interest (ROIs) in adults (43 male, 29 female) were examined using 3Tesla structural magnetic resonance imaging in four cohorts: 1) tinnitus with moderate hearing loss (N = 31), 2) moderate hearing loss only (N = 15), 3) tinnitus with normal hearing (N = 17), and 4) normal hearing only (N = 13). ROI volumes were corrected for brain size, age, and sex variations. Analysis of covariance (ANCOVA) and post hoc Tukey's test were used to isolate the effects of tinnitus and hearing loss on volume differences. Effect sizes were calculated as the fraction of total variance (η2 ) in ANCOVA models and percent of mean volume difference relative to mean total volume. RESULTS The four cohort ANCOVA revealed tinnitus and hearing loss cohorts to have increased volume in the corona radiata (η2 = 0.192; P = .0018) and decreased volume in the nucleus accumbens (η2 = 0.252; P < .0001), caudate nucleus (η2 = 0.188; P = .002), and inferior fronto-occipital fasciculus (η2 = 0.250; P = .0001). Tinnitus with normal hearing showed decreased volume in the nucleus accumbens (22.0%; P = .001) and inferior fronto-occipital fasciculus (18.1%; P = .002), and hearing loss only showed increased volume in the corona radiata (10.7%; P = .01) and decreased volume in the nucleus accumbens (22.1%; P = .001), caudate nucleus (16.1%; P = .004), and inferior fronto-occipital fasciculus (18.3%; P = .003). CONCLUSION Tinnitus and hearing loss have overlapping effects on neurovolumetric alterations, especially impacting the nucleus accumbens and inferior fronto-occipital fasciculus. Neurovolumetric studies on tinnitus or hearing loss can be more complete by accounting for those two clinical dimensions separately and jointly. LEVEL OF EVIDENCE 3 Laryngoscope, 131:1863-1868, 2021.
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Affiliation(s)
- Andrew Yousef
- Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California, U.S.A
| | - Leighton B Hinkley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, U.S.A
| | - Srikantan S Nagarajan
- Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California, U.S.A.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, U.S.A
| | - Steven W Cheung
- Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California, U.S.A
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30
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Kawasaki Y, Oishi K, Hernandez A, Ernst T, Wu D, Otsuka Y, Ceritoglu C, Chang L. Brain-derived neurotrophic factor Val66Met variant on brain volumes in infants. Brain Struct Funct 2021; 226:919-925. [PMID: 33474578 DOI: 10.1007/s00429-020-02207-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 12/19/2020] [Indexed: 10/22/2022]
Abstract
The brain-derived neurotrophic factor (BDNF) has many important roles in neurogenesis and neuronal health. BDNF is also involved in learning and memory. Individuals with BDNF-Val66Met variant (Met +) are at higher risk for neuropsychiatric disorders and have smaller hippocampi and amgydalae compared to those without this variant (Met -). Whether these smaller brain volumes are already present at birth is unknown and were evaluated. 66 newborn infants were genotyped for BDNF-rs6265 and had brain MRI scans. The T1-weighted images were automatically parcellated for hippocampus and amygdala, as well as the intracranial volume (ICV), total brain volume, total gray and white matter, using a multi-atlas label fusion method implemented in the MRICloud ( https://braingps.anatomyworks.org ). The segmented brain volumes were normalized to the ICV for group comparisons. The two infant groups were not different in their demographics and birth characteristics. However, compared to Met - infants, the Met + infants had smaller hippocampi (p = 0.013), smaller amygdalae (p = 0.041), and less steep age-related declines in total brain volume and % white matter volume. The smaller relative hippocampal and amygdala volumes in Met + infants suggest that the Met + genotype affected prenatal developmental processes. In addition, the slower age-dependent declines in the relative total brain and white matter volumes of the Met + group in this cross-sectional dataset suggest the BDNF-Val66Met variant might have an ongoing negative influence on the postnatal developmental processes.
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Affiliation(s)
- Yukako Kawasaki
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Neonatology, Maternal and Perinatal Center, Toyama University Hospital, Toyama, Japan
| | - Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Antonette Hernandez
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
| | - Thomas Ernst
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA
- Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University of School of Medicine, Baltimore, MD, USA
| | - Dan Wu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yoshihisa Otsuka
- Department of Neurology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Can Ceritoglu
- Center for Imaging Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Linda Chang
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, USA.
- Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins University of School of Medicine, Baltimore, MD, USA.
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31
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Wei Y, Huang N, Liu Y, Zhang X, Wang S, Tang X. Hippocampal and Amygdalar Morphological Abnormalities in Alzheimer's Disease Based on Three Chinese MRI Datasets. Curr Alzheimer Res 2021; 17:1221-1231. [PMID: 33602087 DOI: 10.2174/1567205018666210218150223] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/12/2020] [Accepted: 12/22/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Early detection of Alzheimer's disease (AD) and its early stage, the mild cognitive impairment (MCI), has important scientific, clinical and social significance. Magnetic resonance imaging (MRI) based statistical shape analysis provides an opportunity to detect regional structural abnormalities of brain structures caused by AD and MCI. OBJECTIVE In this work, we aimed to employ a well-established statistical shape analysis pipeline, in the framework of large deformation diffeomorphic metric mapping, to identify and quantify the regional shape abnormalities of the bilateral hippocampus and amygdala at different prodromal stages of AD, using three Chinese MRI datasets collected from different domestic hospitals. METHODS We analyzed the region-specific shape abnormalities at different stages of the neuropathology of AD by comparing the localized shape characteristics of the bilateral hippocampi and amygdalas between healthy controls and two disease groups (MCI and AD). In addition to group comparison analyses, we also investigated the association between the shape characteristics and the Mini Mental State Examination (MMSE) of each structure of interest in the disease group (MCI and AD combined) as well as the discriminative power of different morphometric biomarkers. RESULTS We found the strongest disease pathology (regional atrophy) at the subiculum and CA1 subregions of the hippocampus and the basolateral, basomedial as well as centromedial subregions of the amygdala. Furthermore, the shape characteristics of the hippocampal and amygdalar subregions exhibiting the strongest AD related atrophy were found to have the most significant positive associations with the MMSE. Employing the shape deformation marker of the hippocampus or the amygdala for automated MCI or AD detection yielded a significant accuracy boost over the corresponding volume measurement. CONCLUSION Our results suggested that the amygdalar and hippocampal morphometrics, especially those of shape morphometrics, can be used as auxiliary indicators for monitoring the disease status of an AD patient.
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Affiliation(s)
- Yuanyuan Wei
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Nianwei Huang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Zhang
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital; National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Silun Wang
- YIWEI Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
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32
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Huang W, Chen M, Lyu G, Tang X. A Deformation-Based Shape Study of the Corpus Callosum in First Episode Schizophrenia. Front Psychiatry 2021; 12:621515. [PMID: 34149469 PMCID: PMC8211893 DOI: 10.3389/fpsyt.2021.621515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 05/04/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Previous first-episode schizophrenia (FES) studies have reported abnormalities in the volume and mid-sagittal size of the corpus callosum (CC), but findings have been inconsistent. Besides, the CC shape has rarely been analyzed in FES. Therefore, in this study, we investigated FES-related CC shape abnormalities using 198 participants [92 FES patients and 106 healthy controls (HCs)]. Methods: We conducted statistical shape analysis of the mid-sagittal CC curve in a large deformation diffeomorphic metric mapping framework. The CC was divided into the genu, body, and splenium (gCC, bCC, and sCC) to target the key CC sub-regions affected by the FES pathology. Gender effects have been investigated. Results: There were significant area differences between FES and HC in the entire CC and gCC but not in bCC nor sCC. In terms of the localized shape morphometrics, significant region-specific shape inward-deformations were detected in the superior portion of gCC and the anterosuperior portion of bCC in FES. These global area and local shape morphometric abnormalities were restricted to female FES but not male FES. Conclusions: gCC was significantly affected in the neuropathology of FES and this finding was specific to female FES. This study suggests that gCC may be a key sub-region that is vulnerable to the neuropathology of FES, specifically in female patients. The morphometrics of gCC may serve as novel and efficient biomarkers for screening female FES patients.
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Affiliation(s)
- Weikai Huang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Minhua Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Guiwen Lyu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
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33
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Athey TL, Ceritoglu C, Tward DJ, Kutten KS, DePaulo JR, Glazer K, Goes FS, Kelsoe JR, Mondimore F, Nievergelt CM, Rootes-Murdy K, Zandi PP, Ratnanather JT, Mahon PB. A 7 Tesla Amygdalar-Hippocampal Shape Analysis of Lithium Response in Bipolar Disorder. Front Psychiatry 2021; 12:614010. [PMID: 33664682 PMCID: PMC7920967 DOI: 10.3389/fpsyt.2021.614010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 01/19/2021] [Indexed: 11/15/2022] Open
Abstract
Research to discover clinically useful predictors of lithium response in patients with bipolar disorder has largely found them to be elusive. We demonstrate here that detailed neuroimaging may have the potential to fill this important gap in mood disorder therapeutics. Lithium treatment and bipolar disorder have both been shown to affect anatomy of the hippocampi and amygdalae but there is no consensus on the nature of their effects. We aimed to investigate structural surface anatomy changes in amygdala and hippocampus correlated with treatment response in bipolar disorder. Patients with bipolar disorder (N = 14) underwent lithium treatment, were classified by response status at acute and long-term time points, and scanned with 7 Tesla structural MRI. Large Deformation Diffeomorphic Metric Mapping was applied to detect local differences in hippocampal and amygdalar anatomy between lithium responders and non-responders. Anatomy was also compared to 21 healthy comparison participants. A patch of the ventral surface of the left hippocampus was found to be significantly atrophied in non-responders as compared to responders at the acute time point and was associated at a trend-level with long-term response status. We did not detect an association between response status and surface anatomy of the right hippocampus or amygdala. To the best of our knowledge, this is the first shape analysis of hippocampus and amygdala in bipolar disorder using 7 Tesla MRI. These results can inform future work investigating possible neuroimaging predictors of lithium response in bipolar disorder.
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Affiliation(s)
- Thomas L Athey
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Can Ceritoglu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
| | - Daniel J Tward
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Kwame S Kutten
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
| | - J Raymond DePaulo
- Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Kara Glazer
- Department of Occupational Therapy, Boston University, Boston, MA, United States
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - John R Kelsoe
- Department of Psychiatry, VA San Diego Healthcare System, La Jolla, CA, United States.,Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Francis Mondimore
- Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Caroline M Nievergelt
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States
| | - Peter P Zandi
- Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - J Tilak Ratnanather
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Pamela B Mahon
- Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Department of Psychiatry, Brigham & Women's Hospital, Boston, MA, United States.,Department of Psychiatry, Harvard School of Medicine, Boston, MA, United States
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34
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Faria AV, Zhao Y, Ye C, Hsu J, Yang K, Cifuentes E, Wang L, Mori S, Miller M, Caffo B, Sawa A. Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup. Hum Brain Mapp 2020; 42:1034-1053. [PMID: 33377594 PMCID: PMC7856640 DOI: 10.1002/hbm.25276] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/29/2020] [Accepted: 10/18/2020] [Indexed: 02/06/2023] Open
Abstract
Multi‐institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure‐based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI—rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure‐based analysis showed widespread DTI abnormalities in FEP and rs‐fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof‐of‐concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub‐groups.
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Affiliation(s)
- Andreia V Faria
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yi Zhao
- Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, Indiana, USA
| | - Chenfei Ye
- Department of Electronics and Information, Harbin Institute of Technology Shenzhen Graduate School, Guangdong, China
| | - Johnny Hsu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kun Yang
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Elizabeth Cifuentes
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences and Radiology, Northwestern University, Evanston, Illinois, USA
| | - Susumu Mori
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael Miller
- Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA
| | - Brian Caffo
- Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Akira Sawa
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA.,Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Mental Health, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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35
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Shi H, Liang Z, Chen J, Li W, Zhu J, Li Y, Ye J, Zhang J, Xue J, Liu W, Wang F, Wang W, Li Q, He X. Gray matter alteration in heroin-dependent men: An atlas-based magnetic resonance imaging study. Psychiatry Res Neuroimaging 2020; 304:111150. [PMID: 32717665 PMCID: PMC8170872 DOI: 10.1016/j.pscychresns.2020.111150] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/18/2020] [Accepted: 07/20/2020] [Indexed: 12/28/2022]
Abstract
Previous imaging studies on heroin addiction have reported brain morphological alterations. However, the effects of heroin exposure on gray matter volume varied among different studies due to different factors such as substitution treatment or mandatory abstinence. Meanwhile, the relationship between gray matter and heroin use history remains unknown. Thirty-three male heroin-dependent (HD) individuals who are not under any substitution treatment or mandatory abstinence and 40 male healthy controls (HC) were included in this structural magnetic resonance imaging study. With an atlas-based approach, gray matter structures up to individual functional area were delineated, and the differences in their volumes between the HD and HC groups were analyzed. In addition, the relationship between gray matter volume and duration of heroin use was explored. The HD group demonstrated significantly lower cortical volume mainly in the prefrontal cortex and mesolimbic dopaminergic regions across different parcellation levels, whereas several visual and somatosensory cortical regions in the HD group had greater volume relative to the HC group at a more detailed parcellation level. The duration of heroin use was negatively correlated with the gray matter volume of prefrontal cortex. These findings suggest that heroin addiction be related to gray matter alteration and might be related to damage/maladaption of the inhibitory control, reward, visual, and somatosensory functions of the brain, although cognitive correlates are warranted in future study. In addition, the atlas-based morphology analysis is a potential tool to help researchers search biomarkers of heroin addiction.
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Affiliation(s)
- Hong Shi
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Zifei Liang
- Department of Radiology, New York University, New York, NY, USA; Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; College of Electronic and Information Engineering, Sichuan University, Chengdu, 610065, China
| | - Jiajie Chen
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Wei Li
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Jia Zhu
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Yongbin Li
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Jianjun Ye
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Jiangyang Zhang
- Department of Radiology, New York University, New York, NY, USA; Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jiuhua Xue
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Wei Liu
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Fan Wang
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Wei Wang
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Qiang Li
- Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China; Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Xiaohai He
- College of Electronic and Information Engineering, Sichuan University, Chengdu, 610065, China.
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36
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Zhang Y, Wu J, Liu Y, Chen Y, Chen W, Wu EX, Li C, Tang X. A deep learning framework for pancreas segmentation with multi-atlas registration and 3D level-set. Med Image Anal 2020; 68:101884. [PMID: 33246228 DOI: 10.1016/j.media.2020.101884] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 06/14/2020] [Accepted: 10/16/2020] [Indexed: 12/21/2022]
Abstract
In this paper, we propose and validate a deep learning framework that incorporates both multi-atlas registration and level-set for segmenting pancreas from CT volume images. The proposed segmentation pipeline consists of three stages, namely coarse, fine, and refine stages. Firstly, a coarse segmentation is obtained through multi-atlas based 3D diffeomorphic registration and fusion. After that, to learn the connection feature, a 3D patch-based convolutional neural network (CNN) and three 2D slice-based CNNs are jointly used to predict a fine segmentation based on a bounding box determined from the coarse segmentation. Finally, a 3D level-set method is used, with the fine segmentation being one of its constraints, to integrate information of the original image and the CNN-derived probability map to achieve a refine segmentation. In other words, we jointly utilize global 3D location information (registration), contextual information (patch-based 3D CNN), shape information (slice-based 2.5D CNN) and edge information (3D level-set) in the proposed framework. These components form our cascaded coarse-fine-refine segmentation framework. We test the proposed framework on three different datasets with varying intensity ranges obtained from different resources, respectively containing 36, 82 and 281 CT volume images. In each dataset, we achieve an average Dice score over 82%, being superior or comparable to other existing state-of-the-art pancreas segmentation algorithms.
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Affiliation(s)
- Yue Zhang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Jiong Wu
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China; School of Computer and Electrical Engineering, Hunan University of Arts and Science, Hunan, China
| | - Yilong Liu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Yifan Chen
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Chen
- Department of Radiology, Third Military Medical University Southwest Hospital, Chongqing, China
| | - Ed X Wu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
| | - Chunming Li
- Department of Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.
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37
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Song Y, Zou L, Zhao J, Zhou X, Huang Y, Qiu H, Han H, Yang Z, Li X, Tang X, Chu J. Whole brain volume and cortical thickness abnormalities in Wilson's disease: a clinical correlation study. Brain Imaging Behav 2020; 15:1778-1787. [PMID: 33052506 DOI: 10.1007/s11682-020-00373-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Wilson's disease (WD) is an inherited autosomal recessive disorder of copper metabolism, and its neurological and neuropsychiatric manifestations are associated with copper accumulation in brain. A few neuroimaging studies have shown that gray matter atrophy in WD affects both subcortical structures and cortex. This study aims to quantitatively evaluate the morphometric brain abnormalities in patients with WD in terms of whole brain volume and cortical thickness and their associations with clinical severity of WD. Thirty patients clinically diagnosed as WD with neurological manifestations and 25 healthy controls (HC) were recruited. 3D T1-weighted images were segmented into 276 whole-brain regions of interest (ROIs) and 68 cortical ROIs. WD-vs-HC group comparisons were then conducted for each ROI. The associations between those morphometric measurements and the Global Assessment Scale (GAS) score for WD were analyzed. Compared with HC, significant WD-related volumetric decreases were found in the bilateral subcortical nuclei (putamen, globus pallidus, caudate nucleus, substantia nigra, red nucleus and thalamus), diffuse white matter and several gray matter regions. WD patients showed reduced cortical thickness in the left precentral gyrus and the left insula. Further, the volumes of the right globus pallidus, bilateral putamen, right external capsule and left superior longitudinal fasciculus were negatively correlated with GAS. Our results indicated that significant WD-related morphometric abnormalities were quantified in terms of whole-brain volumes and cortical thicknesses, some of which correlated significantly to the clinical severity of WD. Those morphometrics may provide a potentially effective biomarker of WD.
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Affiliation(s)
- Yukun Song
- Department of Radiology, The First Affiliated Hospital of Xiamen University, Xiamen, 361001, Fujian Province, China
| | - Lin Zou
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518052, Guangdong Province, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Guangdong Province, China
| | - Xiangxue Zhou
- Department of Neurology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Guangdong Province, China
| | - Yingqian Huang
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Guangdong Province, China
| | - Haishan Qiu
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Guangdong Province, China
| | - Haiwei Han
- Department of Radiology, The First Affiliated Hospital of Xiamen University, Xiamen, 361001, Fujian Province, China
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Guangdong Province, China
| | - Xunhua Li
- Department of Neurology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Guangdong Province, China
| | - Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518052, Guangdong Province, China.
| | - Jianping Chu
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Guangdong Province, China.
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De Groote S, Goudman L, Van Schuerbeek P, Peeters R, Sunaert S, Linderoth B, De Andrés J, Rigoard P, De Jaeger M, Moens M. Effects of spinal cord stimulation on voxel-based brain morphometry in patients with failed back surgery syndrome. Clin Neurophysiol 2020; 131:2578-2587. [PMID: 32927213 DOI: 10.1016/j.clinph.2020.07.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/30/2020] [Accepted: 07/26/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Despite the clinical effectiveness of Spinal Cord Stimulation (SCS), potential structural brain modifications have not been explored. Our aim was to identify structural volumetric changes during subsensory SCS, in patients with Failed Back Surgery Syndrome (FBSS). METHODS In this cohort study, twenty-two FBSS patients underwent a magnetic resonance imaging protocol before SCS and 3 months after SCS. Clinical parameters were correlated with volumetric changes, calculated with voxel-based morphometry. RESULTS After 3 months, a significant volume decrease was found in the inferior frontal gyrus, precuneus, cerebellar posterior lobe and middle temporal gyrus. Significant increases were found in the inferior temporal gyrus, precentral gyrus and the middle frontal gyrus after SCS. Additionally, significant increases in volume of superior frontal and parietal white matter and a significant decrease in volume of white matter underlying the premotor/middle frontal gyrus were revealed after SCS. A significant correlation was highlighted between white matter volume underlying premotor/middle frontal gyrus and leg pain relief. CONCLUSIONS This study revealed for the first time that SCS is able to induce volumetric changes in gray and white matter, suggesting the reversibility of brain alterations after chronic pain treatment. SIGNIFICANCE Volumetric brain alterations are observable after 3 months of subsensory SCS in FBSS patients.
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Affiliation(s)
- Sander De Groote
- Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium
| | - Lisa Goudman
- Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium; STIMULUS consortium (reSearch and TeachIng neuroModULation Uz bruSsel), Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium.; Pain in Motion International Research Group, Belgium; Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium
| | - Peter Van Schuerbeek
- Department of Radiology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium
| | - Ronald Peeters
- Department of Radiology, Universitair Ziekenhuis Leuven, UZ Herestraat 49-bus 7003 54, 3000 Leuven, Belgium
| | - Stefan Sunaert
- Department of Radiology, Universitair Ziekenhuis Leuven, UZ Herestraat 49-bus 7003 54, 3000 Leuven, Belgium
| | - Bengt Linderoth
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jose De Andrés
- Surgical Specialties Department Valencia University Medical School, and Department of Anesthesiology Critical Care and Pain Management, General University Hospital, Valencia, Spain
| | - Philippe Rigoard
- Department of Neurosurgery, Poitiers University Hospital, Poitiers, France; Institut Pprime UPR 3346, CNRS, University of Poitiers, Poitiers, ISAE-ENSMA, France; PRISMATICS Lab (Predictive Research in Spine/Neuromodulation Management and Thoracic Innovation/Cardiac Surgery), Poitiers University Hospital, Poitiers, France
| | - Mats De Jaeger
- Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium
| | - Maarten Moens
- Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium; STIMULUS consortium (reSearch and TeachIng neuroModULation Uz bruSsel), Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium.; Department of Radiology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium; Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium.
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39
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Cardenas R, Curiale AH, Mato G. Left ventricle segmentation using a Bayesian approach with distance dependent shape priors. Biomed Phys Eng Express 2020; 6:045013. [PMID: 33444274 DOI: 10.1088/2057-1976/ab9556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
We propose a method for segmentation of the left ventricle in magnetic resonance cardiac images. The framework consists of an initial Bayesian segmentation of the central slice of the volume. This segmentation is used to locate a shape prior for the LV myocardial tissue. This shape prior is determined using the fact that the myocardium is approximately annular as seen in the short-axis. Then a second Bayesian segmentation is performed to obtain the final result. This procedure is repeated for the rest of the slices. An extrapolation of the area of the LV is used to determine a stopping criterion. The method was evaluated on the databases of the Cardiac Atlas project. Our results demonstrate a suitable accuracy for myocardial segmentation (≈0.8 Dice's coefficient). For the endocardium and the epicardium the Dice's coefficients are 0.94 and 0.9 respectively. The accuracy was also evaluated in terms of the Hausdorff distance and the average distance. For the myocardium we obtain 8 mm and 2 mm respectively. Our results demonstrate the capability and merits of the proposed method to estimate the structure of the LV. The method requires minimal user input and generates results with quality comparable to more complex approaches. This paper suggests a new efficient approach for automatic LV quantification based on a Bayesian technique with shape priors with errors comparable to state-of-the-art techniques.
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Affiliation(s)
- Rodrigo Cardenas
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina. Centro Atómico Bariloche, Av. Bustillo 9500, R8402AGP S. C. de Bariloche, Río Negro, Argentina
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40
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Peng SL, Yang HC, Chen CM, Shih CT. Short- and long-term reproducibility of BOLD signal change induced by breath-holding at 1.5 and 3 T. NMR IN BIOMEDICINE 2020; 33:e4195. [PMID: 31885110 DOI: 10.1002/nbm.4195] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 09/09/2019] [Accepted: 09/09/2019] [Indexed: 06/10/2023]
Abstract
Cerebrovascular reactivity (CVR) can give insight into the cerebrovascular function. CVR can be estimated by measuring a blood-oxygen-level-dependent (BOLD) response combined with breath-holding (BH). The reproducibility of this technique has been addressed and existing studies have focused on short-term reproducibility using a 3 T magnetic resonance imaging (MRI) system. However, little is known about the long-term reproducibility of this procedure and the corresponding reproducibility using a 1.5 T MRI system. Here, we systematically examined the short- and long-term reproducibility of BOLD responses to BH across field strengths. Nine subjects participated in three MRI sessions separated by 30 minutes (sessions 1 and 2: short term) and 68-92 days (sessions 1 and 3, long term) at both 1.5 and 3 T MRI. Our findings revealed that significant differences between field strengths were detected in the activated gray matter volume and BOLD signal change (both P < 0.001), with smaller magnitudes at 1.5 T. However, activation patterns were reproducible, independent of the time interval, brain region or field strength. All interscan coefficient of variation values were below the 33% fiducial limit, and the intraclass correlation coefficient values were above 0.4, which is usually considered the acceptability limit in functional studies. These findings suggest that the response of BOLD signal to BH for assessing CVR is reproducible over time at 1.5 and 3 T. This technique can be considered a tool for monitoring longitudinal changes in patients with cerebrovascular diseases, and its use should be encouraged for clinical 1.5 T MRI systems.
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Affiliation(s)
- Shin-Lei Peng
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Hui-Chieh Yang
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Chun-Ming Chen
- Department of Radiology, China Medical University Hospital, Taichung, Taiwan
| | - Cheng-Ting Shih
- Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan
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41
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Tward D, Brown T, Kageyama Y, Patel J, Hou Z, Mori S, Albert M, Troncoso J, Miller M. Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease. Front Neurosci 2020; 14:52. [PMID: 32116503 PMCID: PMC7027169 DOI: 10.3389/fnins.2020.00052] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 01/14/2020] [Indexed: 12/15/2022] Open
Abstract
This paper examines the problem of diffeomorphic image registration in the presence of differing image intensity profiles and sparsely sampled, missing, or damaged tissue. Our motivation comes from the problem of aligning 3D brain MRI with 100-micron isotropic resolution to histology sections at 1 × 1 × 1,000-micron resolution with multiple varying stains. We pose registration as a penalized Bayesian estimation, exploiting statistical models of image formation where the target images are modeled as sparse and noisy observations of the atlas. In this injective setting, there is no assumption of symmetry between atlas and target. Cross-modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity. Missing data is accommodated via a multiple atlas selection procedure where several atlas images may be of homogeneous intensity and correspond to "background" or "artifact." The two concepts are combined within an Expectation-Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively and polynomial coefficients are computed in closed form. We validate our method with simulated images, examples from neuropathology, and a standard benchmarking dataset. Finally, we apply it to reconstructing digital pathology and MRI in standard atlas coordinates. By using a standard convolutional neural network to detect tau tangles in histology slices, this registration method enabled us to quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer's disease postmortem specimen.
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Affiliation(s)
- Daniel Tward
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
| | - Timothy Brown
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
| | - Yusuke Kageyama
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jaymin Patel
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Zhipeng Hou
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Juan Troncoso
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Michael Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States
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42
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Wang JY, Hessl D, Tassone F, Kim K, Hagerman RJ, Rivera SM. Interaction between ventricular expansion and structural changes in the corpus callosum and putamen in males with FMR1 normal and premutation alleles. Neurobiol Aging 2020; 86:27-38. [PMID: 31733943 PMCID: PMC6995416 DOI: 10.1016/j.neurobiolaging.2019.09.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 08/10/2019] [Accepted: 09/13/2019] [Indexed: 12/23/2022]
Abstract
Ventricular enlargement (VE) is commonly observed in aging and fragile X-associated tremor/ataxia syndrome (FXTAS), a late-onset neurodegenerative disorder. VE may generate a mechanical force causing structural deformation. In this longitudinal study, we examined the relationships between VE and structural changes in the corpus callosum (CC) and putamen. MRI scans (2-7/person over 0.2-7.5 years) were acquired from 22 healthy controls, 26 unaffected premutation carriers (PFX-), and 39 carriers affected with FXTAS (PFX+). Compared with controls, PFX- demonstrated enlarged fourth ventricles, whereas PFX+ displayed enlargement in both third and fourth ventricles, CC thinning, putamen atrophy/deformation (thinning and increased distance), and accelerated expansions in lateral ventricles. Common for all groups, baseline VE predicted accelerated CC thinning and putamen atrophy/deformation and conversely, baseline CC and putamen atrophy/deformation and enlarged third and fourth ventricles predicted accelerated lateral ventricular expansion. The results suggest a progressive VE within the 4 ventricles as FXTAS develops and a deleterious cycle between VE and brain deformation that may commonly occur during aging and FXTAS progression but become accelerated in FXTAS.
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Affiliation(s)
- Jun Yi Wang
- Center for Mind and Brain, University of California-Davis, Davis, CA, USA; MIND Institute, University of California-Davis Medical Center, Sacramento, CA, USA; Department of Biochemistry and Molecular Medicine, University of California-Davis, School of Medicine, Sacramento, CA, USA.
| | - David Hessl
- MIND Institute, University of California-Davis Medical Center, Sacramento, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California-Davis, School of Medicine, Sacramento, CA, USA
| | - Flora Tassone
- MIND Institute, University of California-Davis Medical Center, Sacramento, CA, USA; Department of Biochemistry and Molecular Medicine, University of California-Davis, School of Medicine, Sacramento, CA, USA
| | - Kyoungmi Kim
- MIND Institute, University of California-Davis Medical Center, Sacramento, CA, USA; Department of Public Health Sciences, University of California-Davis, School of Medicine, Sacramento, CA, USA
| | - Randi J Hagerman
- MIND Institute, University of California-Davis Medical Center, Sacramento, CA, USA; Department of Pediatrics, University of California-Davis, School of Medicine, Sacramento, CA, USA
| | - Susan M Rivera
- Center for Mind and Brain, University of California-Davis, Davis, CA, USA; MIND Institute, University of California-Davis Medical Center, Sacramento, CA, USA; Department of Psychology, University of California-Davis, Davis, CA, USA
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43
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Santos E, Emeka‐Nwonovo C, Wang JY, Schneider A, Tassone F, Hagerman P, Hagerman R. Developmental aspects of FXAND in a man with the FMR1 premutation. Mol Genet Genomic Med 2020; 8:e1050. [PMID: 31899609 PMCID: PMC7005639 DOI: 10.1002/mgg3.1050] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 10/23/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Fragile X mental retardation 1 (FMR1) premutation can cause developmental problems including autism spectrum disorder (ASD), social anxiety, depression, and attention deficit hyperactivity disorder (ADHD). These problems fall under an umbrella term of Fragile X-associated Neuropsychiatric Disorders (FXAND) and is separate from Fragile X-associated Tremor/Ataxia syndrome (FXTAS), a neurodegenerative disorder. METHODS/CLINICAL CASE A 26-year-old Caucasian male with the Fragile X premutation who presented with multiple behavior and emotional problems including depression and anxiety at 10 years of age. He was evaluated at 13, 18, and 26 years old with age-appropriate cognitive assessments, psychiatric evaluations, and an MRI of the brain. RESULTS The Autism Diagnostic Observation Scale (ADOS) was done at 13 years old and showed the patient has autism spectrum disorder (ASD). An evaluation at 18 years old showed a full-scale IQ of 64. A Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) performed at 26 years old confirmed the previous impression of social anxiety disorder, agoraphobia disorder, and selective mutism. His MRI acquired at 26 years old showed enlarged ventricles, increased frontal subarachnoid spaces, and hypergyrification. CONCLUSION This is an exemplary case of an FMR1 premutation carrier with significant psychiatric and cognitive issues that demonstrates Fragile X-associated Neuropsychiatric Disorders (FXAND) as separate from the other well-known premutation disorders.
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Affiliation(s)
- Ellery Santos
- MIND InstituteUniversity of California Davis School of MedicineSacramentoCAUSA
- Department of PediatricsUniversity of California Davis School of MedicineSacramentoCAUSA
| | | | - Jun Yi Wang
- MIND InstituteUniversity of California Davis School of MedicineSacramentoCAUSA
- Center for Mind and BrainUniversity of California DavisSacramentoCAUSA
- Department of Biochemistry and Molecular MedicineUniversity of California Davis School of MedicineSacramentoCAUSA
| | - Andrea Schneider
- MIND InstituteUniversity of California Davis School of MedicineSacramentoCAUSA
- Department of PediatricsUniversity of California Davis School of MedicineSacramentoCAUSA
| | - Flora Tassone
- MIND InstituteUniversity of California Davis School of MedicineSacramentoCAUSA
- Department of Biochemistry and Molecular MedicineUniversity of California Davis School of MedicineSacramentoCAUSA
| | - Paul Hagerman
- MIND InstituteUniversity of California Davis School of MedicineSacramentoCAUSA
- Department of Biochemistry and Molecular MedicineUniversity of California Davis School of MedicineSacramentoCAUSA
| | - Randi Hagerman
- MIND InstituteUniversity of California Davis School of MedicineSacramentoCAUSA
- Department of PediatricsUniversity of California Davis School of MedicineSacramentoCAUSA
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Faria AV, Meyer A, Friedman R, Tippett DC, Hillis AE. Baseline MRI associates with later naming status in primary progressive aphasia. BRAIN AND LANGUAGE 2020; 201:104723. [PMID: 31864209 PMCID: PMC7282486 DOI: 10.1016/j.bandl.2019.104723] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 07/29/2019] [Accepted: 11/13/2019] [Indexed: 06/10/2023]
Abstract
Advanced imaging studies in neurodegenerative disease have yielded new insights into subtypes of disease, progression of disease in various brain regions, and changes in structural and functional connectivity between brain regions related to symptom progression. However, few studies have revealed imaging markers at baseline that correlate with rate or degree of decline in function. Here we tested the hypothesis that imaging features at baseline correlate with outcome of naming in primary progressive aphasia. We obtained longitudinal multimodal imaging in 15 individuals with primary progressive aphasia at the same time points as assessment of naming. We found that functional connectivity between particular brain regions (measured with resting state functional connectivity magnetic resonance imaging) is strongly associated with accuracy of naming 21 months later, independently of baseline severity of naming impairment. These data indicate that functional connectivity may carry information about later performance in naming, and is potentially useful for refining prognosis.
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Affiliation(s)
- Andreia V Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Aaron Meyer
- Department of Neurology, Georgetown University School of Medicine, Washington, USA
| | - Rhonda Friedman
- Department of Neurology, Georgetown University School of Medicine, Washington, USA
| | - Donna C Tippett
- Department of Otolaryngology & Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Argye E Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Physical Medicine & Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA
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Ferre CL, Carmel JB, Flamand VH, Gordon AM, Friel KM. Anatomical and Functional Characterization in Children With Unilateral Cerebral Palsy: An Atlas-Based Analysis. Neurorehabil Neural Repair 2020; 34:148-158. [PMID: 31983314 DOI: 10.1177/1545968319899916] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background. Variability in hand function among children with unilateral cerebral palsy (UCP) might reflect the type of brain injury and resulting anatomical sequelae. Objective. We used atlas-based analysis of structural images to determine whether children with periventricular (PV) versus middle cerebral artery (MCA) injuries might exhibit unique anatomical characteristics that account for differences in hand function. Methods. Forty children with UCP underwent structural brain imaging using 3-T magnetic resonance imaging. Brain lesions were classified as PV or MCA. A group of 40 typically developing (TD) children served as comparison controls. Whole brains were parcellated into 198 structures (regions of interest) to obtain volume estimates. Dexterity and bimanual hand function were assessed. Unbiased, differential expression analysis was performed to determine volumetric differences between PV and MCA groups. Principal component analysis (PCA) was performed and the top 3 components were extracted to perform regression on hand function. Results. Children with PV had significantly better hand function than children with MCA. Multidimensional scaling analysis of volumetric data revealed separate clustering of children with MCA, PV, and TD children. PCA extracted anatomical components that comprised the 2 types of brain injury. In the MCA group, reductions of volume were concentrated in sensorimotor structures of the injured hemisphere. Models using PCA predicted hand function with greater accuracy than models based on qualitative brain injury type. Conclusions. Our results highlight unique quantitative differences in children with UCP that also predict differences in hand function. The systematic discrimination between groups found in our study reveals future questions about the potential prognostic utility of this approach.
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Affiliation(s)
| | - Jason B Carmel
- Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Véronique H Flamand
- Université Laval, Quebec City, Quebec, Canada.,Center for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec City, Quebec, Canada
| | | | - Kathleen M Friel
- Burke Neurological Institute, White Plains, NY, USA.,Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
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Tang X, Lyu G, Chen M, Huang W, Lin Y. Amygdalar and Hippocampal Morphometry Abnormalities in First-Episode Schizophrenia Using Deformation-Based Shape Analysis. Front Psychiatry 2020; 11:677. [PMID: 32765318 PMCID: PMC7379331 DOI: 10.3389/fpsyt.2020.00677] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 06/29/2020] [Indexed: 11/14/2022] Open
Abstract
In this study, we investigated and quantified the amygdalar and hippocampal morphometry abnormalities exerted by first-episode schizophrenia using a total of 92 patients and 106 healthy control participants. Magnetic resonance imaging (MRI) based automated segmentation was conducted to obtain the amygdalar and hippocampal segmentations. Disease-versus-control volume differences of the bilateral amygdalas and hippocampi were quantified. In addition, deformation-based statistical shape analysis was employed to quantify the region-specific shape abnormalities of each structure of interest. To better identify the key relevant areas in the pathology of first-episode schizophrenia, each structure was divided into four subregions; CA1, CA2, CA3 combined with dentate gyrus for the hippocampus in each hemisphere and basolateral, basomedial, centromedial, and lateral nucleus for the amygdala in each hemisphere. We observed significant global volume reduction and localized shape atrophy in each of the four structures of interest. The amygdalar shape abnormalities mainly occurred at the basolateral and centromedial subregions, whereas the hippocampal shape abnormalities mainly concentrated on the CA1 and CA2 subregions. For the same structure, the one on the right hemisphere was affected more by the disease pathology than that on the left hemisphere. To conclude, we have successfully quantified the global and local morphometric abnormalities of the bilateral amygdalas and hippocampi using a sophisticated statistical analysis pipeline and high-field subregion segmentations, with MRI data of a considerable sample size. This study is one of the very first of such kind in first-episode schizophrenia analyses.
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Affiliation(s)
- Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Guiwen Lyu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Minhua Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.,Department of Electrical and Electronic Engineering, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
| | - Weikai Huang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yin Lin
- Department of Psychology, Shenzhen Children's Hospital, Shenzhen, China
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de Aguiar V, Zhao Y, Faria A, Ficek B, Webster KT, Wendt H, Wang Z, Hillis AE, Onyike CU, Frangakis C, Caffo B, Tsapkini K. Brain volumes as predictors of tDCS effects in primary progressive aphasia. BRAIN AND LANGUAGE 2020; 200:104707. [PMID: 31704518 PMCID: PMC7709910 DOI: 10.1016/j.bandl.2019.104707] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 10/01/2019] [Accepted: 10/08/2019] [Indexed: 05/28/2023]
Abstract
The current study aims to determine the brain areas critical for response to anodal transcranial direct current stimulation (tDCS) in PPA. Anodal tDCS and sham were administered over the left inferior frontal gyrus (IFG), combined with written naming/spelling therapy. Thirty people with PPA were included in this study, and assessed immediately, 2 weeks, and 2 months post-therapy. We identified anatomical areas whose volumes significantly predicted the additional tDCS effects. For trained words, the volumes of the left Angular Gyrus and left Posterior Cingulate Cortex predicted the additional tDCS gain. For untrained words, the volumes of the left Middle Frontal Gyrus, left Supramarginal Gyrus, and right Posterior Cingulate Cortex predicted the additional tDCS gain. These findings show that areas involved in language, attention and working memory contribute to the maintenance and generalization of stimulation effects. The findings highlight that tDCS possibly affects areas anatomically or functionally connected to stimulation targets.
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Affiliation(s)
- Vânia de Aguiar
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States; Center for Language and Cognition Groningen (CLCG), University of Groningen, Netherlands.
| | - Yi Zhao
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, United States
| | - Andreia Faria
- Department of Radiology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Bronte Ficek
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Kimberly T Webster
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Haley Wendt
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Zeyi Wang
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, United States
| | - Argye E Hillis
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States; Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, United States; Department of Physical Medicine & Rehabilitation, Johns Hopkins University, Baltimore, MD, United States
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins Medicine, Baltimore, MD, United States
| | - Constantine Frangakis
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, United States; Department of Radiology, Johns Hopkins School of Medicine, Baltimore, MD, United States; Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Brian Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, United States
| | - Kyrana Tsapkini
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States; Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, United States
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Liu CF, Padhy S, Ramachandran S, Wang VX, Efimov A, Bernal A, Shi L, Vaillant M, Ratnanather JT, Faria AV, Caffo B, Albert M, Miller MI. Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment. Magn Reson Imaging 2019; 64:190-199. [PMID: 31319126 PMCID: PMC6874905 DOI: 10.1016/j.mri.2019.07.003] [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: 01/04/2019] [Revised: 07/01/2019] [Accepted: 07/02/2019] [Indexed: 01/12/2023]
Abstract
In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD dementia. In this study, a deep learning framework utilizing Siamese neural networks trained on paired lateral inter-hemispheric regions is used to harness the discriminative power of whole-brain volumetric asymmetry. The method uses the MRICloud pipeline to yield low-dimensional volumetric features of pre-defined atlas brain structures, and a novel non-linear kernel trick to normalize these features to reduce batch effects across datasets and populations. By working with the low-dimensional features, Siamese networks were shown to yield comparable performance to studies that utilize whole-brain MR images, with the advantage of reduced complexity and computational time, while preserving the biological information density. Experimental results also show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetry, on the ADNI and BIOCARD datasets.
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Affiliation(s)
- Chin-Fu Liu
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Shreyas Padhy
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Sandhya Ramachandran
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Victor X Wang
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew Efimov
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Alonso Bernal
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Linyuan Shi
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | | | - J Tilak Ratnanather
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Andreia V Faria
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Brian Caffo
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Michael I Miller
- Center for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
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Arai H, Chayama Y, Iyatomi H, Oishi K. Significant Dimension Reduction of 3D Brain MRI using 3D Convolutional Autoencoders. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:5162-5165. [PMID: 30441502 DOI: 10.1109/embc.2018.8513469] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Content-based image retrieval (CBIR) is a technology designed to retrieve images from a database based on visual features. While the CBIR is highly desired, it has not been applied to clinical neuroradiology, because clinically relevant neuroradiological features are swamped by a huge number of noisy and unrelated voxel information. Thus, effective dimension reduction is the key to successful CBIR. We propose a novel dimensional compression method based on 3D convolutional autoencoders (3D-CAE), which was applied to the ADNI2 3D brain MRI dataset. Our method succeeded in compressing 5 million voxel information to only 150 dimensions, while preserving clinically relevant neuroradiological features. The RMSE per voxel was as low as 8.4%, suggesting a promise of our method toward the application to the CBIR.
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Rezende TJR, Campos BM, Hsu J, Li Y, Ceritoglu C, Kutten K, França Junior MC, Mori S, Miller MI, Faria AV. Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI. Brain Behav 2019; 9:e01363. [PMID: 31483562 PMCID: PMC6790328 DOI: 10.1002/brb3.1363] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 06/07/2019] [Accepted: 06/24/2019] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION The increasing use of large sample sizes for population and personalized medicine requires high-throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification. Exploring the reproducibility of these tools reveals the specific strengths and weaknesses that heavily influence the interpretation of results, contributing to transparence in science. METHODS We tested-retested the reproducibility of MRICloud, a free automated method for whole-brain, multimodal MRI segmentation and quantification, on two public, independent datasets of healthy adults. RESULTS The reproducibility was extremely high for T1-volumetric analysis, high for diffusion tensor images (DTI) (however, regionally variable), and low for resting-state fMRI. CONCLUSION In general, the reproducibility of the different modalities was slightly superior to that of widely used software. This analysis serves as a normative reference for planning samples and for the interpretation of structure-based MRI studies.
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Affiliation(s)
| | - Brunno M Campos
- Department of Neurology, University of Campinas, Campinas, Brazil
| | - Johnny Hsu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Yue Li
- AnatomyWorks LLC, Baltimore, Maryland
| | - Can Ceritoglu
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Kwame Kutten
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland
| | | | - Susumu Mori
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Michael I Miller
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Andreia V Faria
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
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