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Mao A, Flassbeck S, Marchetto E, Masurkar AV, Rusinek H, Assländer J. Sensitivity of unconstrained quantitative magnetization transfer MRI to Amyloid burden in preclinical Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305860. [PMID: 38699343 PMCID: PMC11065014 DOI: 10.1101/2024.04.15.24305860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Magnetization transfer MRI is sensitive to semi-solid macromolecules, including amyloid beta, and has previously been used to discriminate Alzheimer's disease (AD) patients from controls. Here, we fit an unconstrained 2-pool quantitative MT (qMT) model, i.e., without constraints on the longitudinal relaxation rateR 1 s of semi-solids, and investigate the sensitivity of the estimated parameters to amyloid accumulation in preclinical subjects. We scanned 15 cognitively normal volunteers, of which 9 were amyloid positive by [18F]Florbetaben PET. A 12 min hybrid-state qMT scan with an effective resolution of 1.24 mm isotropic and whole-brain coverage was acquired to estimate the unconstrained 2-pool qMT parameters. Group comparisons and correlations with Florbetaben PET standardized uptake value ratios were analyzed at the lobar level. We find that the exchange rate and semi-solid pool'sR 1 s were sensitive to the amyloid concentration, while morphometric measures of cortical thickness derived from structural MRI were not. Changes in the exchange rate are consistent with previous reports in clinical AD, while changes inR 1 s have not been reported previously as its value is typically constrained in the literature. Our results demonstrate that qMT MRI may be a promising surrogate marker of amyloid beta without the need for contrast agents or radiotracers.
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Affiliation(s)
- Andrew Mao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY, USA
| | - Sebastian Flassbeck
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Elisa Marchetto
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Arjun V. Masurkar
- Alzheimer’s Disease Research Center, Center for Cognitive Neurology, New York University Grossman School of Medicine, New York, NY, USA
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Henry Rusinek
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Alzheimer’s Disease Research Center, Center for Cognitive Neurology, New York University Grossman School of Medicine, New York, NY, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Jakob Assländer
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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Chen Y, Wang Y, Song Z, Fan Y, Gao T, Tang X. Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review. Ageing Res Rev 2023; 87:101911. [PMID: 36931328 DOI: 10.1016/j.arr.2023.101911] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 03/01/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
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Affiliation(s)
- Yu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yifei Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zeyu Song
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
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Duan W, Sehrawat P, Zhou TD, Becker JT, Lopez OL, Gach HM, Dai W. Pattern of Altered Magnetization Transfer Rate in Alzheimer's Disease. J Alzheimers Dis 2022; 88:693-705. [PMID: 35694929 PMCID: PMC9382719 DOI: 10.3233/jad-220335] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND Biomarkers for Alzheimer's disease (AD) are crucial for early diagnosis and treatment monitoring once disease modifying therapies become available. OBJECTIVE This study aims to quantify the forward magnetization transfer rate (kfor) map from brain tissue water to macromolecular protons and use it to identify the brain regions with abnormal kfor in AD and AD progression. METHODS From the Cardiovascular Health Study (CHS) cognition study, magnetization transfer imaging (MTI) was acquired at baseline from 63 participants, including 20 normal controls (NC), 18 with mild cognitive impairment (MCI), and 25 AD subjects. Of those, 53 participants completed a follow-up MRI scan and were divided into four groups: 15 stable NC, 12 NC-to-MCI, 12 stable MCI, and 14 MCI/AD-to-AD subjects. kfor maps were compared across NC, MCI, and AD groups at baseline for the cross-sectional study and across four longitudinal groups for the longitudinal study. RESULTS We found a lower kfor in the frontal gray matter (GM), parietal GM, frontal corona radiata (CR) white matter (WM) tracts, frontal and parietal superior longitudinal fasciculus (SLF) WM tracts in AD relative to both NC and MCI. Further, we observed progressive decreases of kfor in the frontal GM, parietal GM, frontal and parietal CR WM tracts, and parietal SLF WM tracts in stable MCI. In the parietal GM, parietal CR WM tracts, and parietal SLF WM tracts, we found trend differences between MCI/AD-to-AD and stable NC. CONCLUSION Forward magnetization transfer rate is a promising biomarker for AD diagnosis and progression.
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Affiliation(s)
- Wenna Duan
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY
| | - Parshant Sehrawat
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY
| | - Tony D. Zhou
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, Washington University in St. Louis, Saint Louis, MO
| | - James T. Becker
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA
- Department of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA
| | - Oscar L. Lopez
- Department of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA
| | - H. Michael Gach
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, Washington University in St. Louis, Saint Louis, MO
| | - Weiying Dai
- Department of Computer Science, State University of New York at Binghamton, Binghamton, NY
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Orzyłowska A, Oakden W. Saturation Transfer MRI for Detection of Metabolic and Microstructural Impairments Underlying Neurodegeneration in Alzheimer's Disease. Brain Sci 2021; 12:53. [PMID: 35053797 PMCID: PMC8773856 DOI: 10.3390/brainsci12010053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/21/2021] [Accepted: 12/25/2021] [Indexed: 01/08/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most common causes of dementia and difficult to study as the pool of subjects is highly heterogeneous. Saturation transfer (ST) magnetic resonance imaging (MRI) methods are quantitative modalities with potential for non-invasive identification and tracking of various aspects of AD pathology. In this review we cover ST-MRI studies in both humans and animal models of AD over the past 20 years. A number of magnetization transfer (MT) studies have shown promising results in human brain. Increased computing power enables more quantitative MT studies, while access to higher magnetic fields improves the specificity of chemical exchange saturation transfer (CEST) techniques. While much work remains to be done, results so far are very encouraging. MT is sensitive to patterns of AD-related pathological changes, improving differential diagnosis, and CEST is sensitive to particular pathological processes which could greatly assist in the development and monitoring of therapeutic treatments of this currently incurable disease.
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Affiliation(s)
- Anna Orzyłowska
- Department of Neurosurgery and Paediatric Neurosurgery, Medical University of Lublin, Jaczewskiego 8 (SPSK 4), 20-090 Lublin, Poland
| | - Wendy Oakden
- Physical Sciences, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada;
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Zhao H, Cheng J, Liu T, Jiang J, Koch F, Sachdev PS, Basser PJ, Wen W. Orientational changes of white matter fibers in Alzheimer's disease and amnestic mild cognitive impairment. Hum Brain Mapp 2021; 42:5397-5408. [PMID: 34412149 PMCID: PMC8519856 DOI: 10.1002/hbm.25628] [Citation(s) in RCA: 3] [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: 07/02/2021] [Accepted: 08/07/2021] [Indexed: 12/13/2022] Open
Abstract
White matter abnormalities represent early neuropathological events in neurodegenerative diseases such as Alzheimer's disease (AD), investigating these white matter alterations would likely provide valuable insights into pathological changes over the course of AD. Using a novel mathematical framework called "Director Field Analysis" (DFA), we investigated the geometric microstructural properties (i.e., splay, bend, twist, and total distortion) in the orientation of white matter fibers in AD, amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) individuals from the Alzheimer's Disease Neuroimaging Initiative 2 database. Results revealed that AD patients had extensive orientational changes in the bilateral anterior thalamic radiation, corticospinal tract, inferior and superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus in comparison with CN. We postulate that these orientational changes of white matter fibers may be partially caused by the expansion of lateral ventricle, white matter atrophy, and gray matter atrophy in AD. In contrast, aMCI individuals showed subtle orientational changes in the left inferior longitudinal fasciculus and right uncinate fasciculus, which showed a significant association with the cognitive performance, suggesting that these regions may be preferential vulnerable to breakdown by neurodegenerative brain disorders, thereby resulting in the patients' cognitive impairment. To our knowledge, this article is the first to examine geometric microstructural changes in the orientation of white matter fibers in AD and aMCI. Our findings demonstrate that the orientational information of white matter fibers could provide novel insight into the underlying biological and pathological changes in AD and aMCI.
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Affiliation(s)
- Haichao Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Jian Cheng
- Beijing Advanced Innovation Center for Big Data‐Based Precision MedicineBeihang UniversityBeijingChina
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
- Beijing Advanced Innovation Center for Big Data‐Based Precision MedicineBeihang UniversityBeijingChina
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA)University of New South WalesSydneyNew South WalesAustralia
| | - Forrest Koch
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA)University of New South WalesSydneyNew South WalesAustralia
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA)University of New South WalesSydneyNew South WalesAustralia
| | - Peter J. Basser
- Section on Quantitative Imaging and Tissue SciencesNIBIB, NICHD, National Institutes of HealthBethesdaMaryland
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry (CHeBA)University of New South WalesSydneyNew South WalesAustralia
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Stone DB, Ryman SG, Hartman AP, Wertz CJ, Vakhtin AA. Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer's Disease. Front Aging Neurosci 2021; 13:711579. [PMID: 34366830 PMCID: PMC8343075 DOI: 10.3389/fnagi.2021.711579] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 06/28/2021] [Indexed: 11/24/2022] Open
Abstract
Identifying biomarkers that can assess the risk of developing Alzheimer's Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients.
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Mole JP, Fasano F, Evans J, Sims R, Kidd E, Aggleton JP, Metzler-Baddeley C. APOE-ε4-related differences in left thalamic microstructure in cognitively healthy adults. Sci Rep 2020; 10:19787. [PMID: 33188215 PMCID: PMC7666117 DOI: 10.1038/s41598-020-75992-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/15/2020] [Indexed: 01/05/2023] Open
Abstract
APOE-ε4 is a main genetic risk factor for developing late onset Alzheimer's disease (LOAD) and is thought to interact adversely with other risk factors on the brain. However, evidence regarding the impact of APOE-ε4 on grey matter structure in asymptomatic individuals remains mixed. Much attention has been devoted to characterising APOE-ε4-related changes in the hippocampus, but LOAD pathology is known to spread through the whole of the Papez circuit including the limbic thalamus. Here, we tested the impact of APOE-ε4 and two other risk factors, a family history of dementia and obesity, on grey matter macro- and microstructure across the whole brain in 165 asymptomatic individuals (38-71 years). Microstructural properties of apparent neurite density and dispersion, free water, myelin and cell metabolism were assessed with Neurite Orientation Density and Dispersion (NODDI) and quantitative magnetization transfer (qMT) imaging. APOE-ε4 carriers relative to non-carriers had a lower macromolecular proton fraction (MPF) in the left thalamus. No risk effects were present for cortical thickness, subcortical volume, or NODDI indices. Reduced thalamic MPF may reflect inflammation-related tissue swelling and/or myelin loss in APOE-ε4. Future prospective studies should investigate the sensitivity and specificity of qMT-based MPF as a non-invasive biomarker for LOAD risk.
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Affiliation(s)
- Jilu P Mole
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Fabrizio Fasano
- Siemens Healthcare, Henkestrasse 127, 91052, Erlangen, Germany
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Rebecca Sims
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Haydn Ellis Building, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Emma Kidd
- School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Redwood Building, King Edward VII Avenue,, Cardiff, CF10 3NB, UK
| | - John P Aggleton
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Claudia Metzler-Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK.
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Xue C, Yuan B, Yue Y, Xu J, Wang S, Wu M, Ji N, Zhou X, Zhao Y, Rao J, Yang W, Xiao C, Chen J. Distinct Disruptive Patterns of Default Mode Subnetwork Connectivity Across the Spectrum of Preclinical Alzheimer's Disease. Front Aging Neurosci 2019; 11:307. [PMID: 31798440 PMCID: PMC6863958 DOI: 10.3389/fnagi.2019.00307] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 10/25/2019] [Indexed: 12/28/2022] Open
Abstract
Background: The early progression continuum of Alzheimer’s disease (AD) has been considered to advance through subjective cognitive decline (SCD), non-amnestic mild cognitive impairment (naMCI), and amnestic mild cognitive impairment (aMCI). Altered functional connectivity (FC) in the default mode network (DMN) is regarded as a hallmark of AD. Furthermore, the DMN can be divided into two subnetworks, the anterior and posterior subnetworks. However, little is known about distinct disruptive patterns in the subsystems of the DMN across the preclinical AD spectrum. This study investigated the connectivity patterns of anterior DMN (aDMN) and posterior DMN (pDMN) across the preclinical AD spectrum. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in the DMN subnetworks in 20 healthy controls (HC), eight SCD, 11 naMCI, and 28 aMCI patients. Moreover, a correlation analysis was used to examine associations between the altered connectivity of the DMN subnetworks and the neurocognitive performance. Results: Compared to the HC, SCD patients showed increased FC in the bilateral superior frontal gyrus (SFG), naMCI patients showed increased FC in the left inferior parietal lobule (IPL), and aMCI patients showed increased FC in the bilateral IPL in the aDMN; while SCD patients showed decreased FC in the precuneus, naMCI patients showed increased FC in the left middle temporal gyrus (MTG), and aMCI patients also showed increased FC in the right middle frontal gyrus (MFG) in the pDMN. Notably, the FC between the ventromedial prefrontal cortex (vmPFC) and the left MFG and the IPL in the aDMN was associated with episodic memory in the SCD and aMCI groups. Interestingly, the FC between the posterior cingulated cortex (PCC) and several regions in the pDMN was associated with other cognitive functions in the SCD and naMCI groups. Conclusions: This study demonstrates that the three preclinical stages of AD exhibit distinct FC alternations in the DMN subnetworks. Furthermore, the patient group data showed that the altered FC involves cognitive function. These findings can provide novel insights for tailored clinical intervention across the preclinical AD spectrum.
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Affiliation(s)
- Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Baoyu Yuan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yingying Yue
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jiani Xu
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Siyu Wang
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Meilin Wu
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Nanxi Ji
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Xingzhi Zhou
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Yilin Zhao
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenjie Yang
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
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