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Zhu S, Shi Y, Chen Z, Long Z, Wan L, Chen D, Yuan X, Fu Y, Deng F, Long X, Du K, Qiu R, Tang B, Wang C, Jiang H. The characteristic and biomarker value of transcranial sonography in cerebellar ataxia. Ann Clin Transl Neurol 2024; 11:2100-2111. [PMID: 38924300 PMCID: PMC11330234 DOI: 10.1002/acn3.52131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 05/18/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVE Transcranial sonography (TCS) is a noninvasive neuroimaging technique, visualizing deep brain structures and the ventricular system. Although widely employed in diagnosing various movement disorders, such as Parkinson's disease and dystonia, by detecting disease-specific abnormalities, the specific characteristics of the TCS in cerebellar ataxia remain inconclusive. We aimed to assess the potential value of TCS in patients with cerebellar ataxias for disease diagnosis and severity assessment. METHODS TCS on patients with genetic and acquired cerebellar ataxia, including 94 with spinocerebellar ataxias (SCAs) containing 10 asymptomatic carriers, 95 with cerebellar subtype of multiple system atrophy (MSA-C), and 100 healthy controls (HC), was conducted. Assessments included third ventricle width, substantia nigra (SN) and lentiform nucleus (LN) echogenicity, along with comprehensive clinical evaluations and genetic testing. RESULTS The study revealed significant TCS abnormalities in patients with cerebellar ataxia, such as enlarged third ventricle widths and elevated rates of hyperechogenic SN and LN. TCS showed high accuracy in distinguishing patients with SCA or MSA-C from HC, with an AUC of 0.870 and 0.931, respectively. TCS abnormalities aided in identifying asymptomatic SCA carriers, effectively differentiating them from HC, with an AUC of 0.725. Furthermore, third ventricle width was significantly correlated with SARA and ICARS scores in patients with SCA3 and SCOPA-AUT scores in patients with MSA-C. The SN area and SARA or ICARS scores in patients with SCA3 were also positively correlated. INTERPRETATION Our findings illustrate remarkable TCS abnormalities in patients with cerebellar ataxia, serving as potential biomarkers for clinical diagnosis and progression assessment.
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Affiliation(s)
- Sudan Zhu
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
| | - Yuting Shi
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
- Key Laboratory of Hunan Province in Neurodegenerative DisordersCentral South UniversityChangshaChina
| | - Zhao Chen
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
- Key Laboratory of Hunan Province in Neurodegenerative DisordersCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaChina
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesChangshaChina
| | - Zhe Long
- Department of Neurology, The Second Xiangya HospitalCentral South UniversityChangshaChina
- Clinical Medical Research Center for Stroke Prevention and Treatment of Hunan Province, Department of Neurology, The Second Xiangya HospitalCentral South UniversityChangshaChina
| | - Linlin Wan
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
- Key Laboratory of Hunan Province in Neurodegenerative DisordersCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaChina
- Department of Radiology, Xiangya HospitalCentral South UniversityChangshaChina
- National International Collaborative Research Center for Medical MetabolomicsCentral South UniversityChangshaChina
| | - Daji Chen
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
| | - Xinrong Yuan
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
| | - You Fu
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
| | - Feiyan Deng
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
| | - Xiafei Long
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
| | - Kefang Du
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
| | - Rong Qiu
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Beisha Tang
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
- Key Laboratory of Hunan Province in Neurodegenerative DisordersCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaChina
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesChangshaChina
| | - Chunrong Wang
- Key Laboratory of Hunan Province in Neurodegenerative DisordersCentral South UniversityChangshaChina
- Department of Pathology, Xiangya HospitalCentral South UniversityChangshaChina
| | - Hong Jiang
- Department of Neurology, Xiangya HospitalCentral South UniversityChangshaChina
- Key Laboratory of Hunan Province in Neurodegenerative DisordersCentral South UniversityChangshaChina
- National Clinical Research Center for Geriatric Disorders, Xiangya HospitalCentral South UniversityChangshaChina
- Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic DiseasesChangshaChina
- National International Collaborative Research Center for Medical MetabolomicsCentral South UniversityChangshaChina
- Department of Neurology, The Third Xiangya HospitalCentral South UniversityChangshaChina
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Li Y, Zhang W, Wu Y, Yin L, Zhu C, Chen Y, Cetin-Karayumak S, Cho KIK, Zekelman LR, Rushmore J, Rathi Y, Makris N, O'Donnell LJ, Zhang F. A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations. Sci Data 2024; 11:787. [PMID: 39019877 PMCID: PMC11255335 DOI: 10.1038/s41597-024-03624-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/08/2024] [Indexed: 07/19/2024] Open
Abstract
The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data. This study presents a dMRI tractography atlas, namely the East-West WM Atlas, for concurrent WM mapping between Eastern and Western populations and creates a large, harmonized dMRI dataset (n=306) based on the Human Connectome Project and the Chinese Human Connectome Project. The curated WM atlas, as well as subject-specific data including the harmonized dMRI data, the whole brain tractography data, and parcellated WM fiber tracts and their diffusion measures, are publicly released. This resource is a valuable addition to facilitating the exploration of brain commonalities and differences across diverse cultural backgrounds.
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Affiliation(s)
- Yijie Li
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Li Yin
- West China Hospital of Medical Science, Sichuan University, Chengdu, China
| | - Ce Zhu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuqian Chen
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | | | - Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Leo R Zekelman
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Jarrett Rushmore
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Fan Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
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3
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Sultana OF, Bandaru M, Islam MA, Reddy PH. Unraveling the complexity of human brain: Structure, function in healthy and disease states. Ageing Res Rev 2024; 100:102414. [PMID: 39002647 DOI: 10.1016/j.arr.2024.102414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/29/2024] [Accepted: 07/05/2024] [Indexed: 07/15/2024]
Abstract
The human brain stands as an intricate organ, embodying a nexus of structure, function, development, and diversity. This review delves into the multifaceted landscape of the brain, spanning its anatomical intricacies, diverse functional capacities, dynamic developmental trajectories, and inherent variability across individuals. The dynamic process of brain development, from early embryonic stages to adulthood, highlights the nuanced changes that occur throughout the lifespan. The brain, a remarkably complex organ, is composed of various anatomical regions, each contributing uniquely to its overall functionality. Through an exploration of neuroanatomy, neurophysiology, and electrophysiology, this review elucidates how different brain structures interact to support a wide array of cognitive processes, sensory perception, motor control, and emotional regulation. Moreover, it addresses the impact of age, sex, and ethnic background on brain structure and function, and gender differences profoundly influence the onset, progression, and manifestation of brain disorders shaped by genetic, hormonal, environmental, and social factors. Delving into the complexities of the human brain, it investigates how variations in anatomical configuration correspond to diverse functional capacities across individuals. Furthermore, it examines the impact of neurodegenerative diseases on the structural and functional integrity of the brain. Specifically, our article explores the pathological processes underlying neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, shedding light on the structural alterations and functional impairments that accompany these conditions. We will also explore the current research trends in neurodegenerative diseases and identify the existing gaps in the literature. Overall, this article deepens our understanding of the fundamental principles governing brain structure and function and paves the way for a deeper understanding of individual differences and tailored approaches in neuroscience and clinical practice-additionally, a comprehensive understanding of structural and functional changes that manifest in neurodegenerative diseases.
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Affiliation(s)
- Omme Fatema Sultana
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Madhuri Bandaru
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Md Ariful Islam
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - P Hemachandra Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Nutritional Sciences Department, College of Human Sciences, Texas Tech University, Lubbock, TX 79409, USA; Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Neurology, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA 5. Department of Public Health, Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Speech, Language, and Hearing Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
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4
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Lee W, Lee S, Park Y, Kim GE, Bae JB, Han JW, Kim KW. Construction and validation of a brain magnetic resonance imaging template for normal older Koreans. BMC Neurol 2024; 24:222. [PMID: 38943101 PMCID: PMC11212263 DOI: 10.1186/s12883-024-03735-8] [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: 11/06/2023] [Accepted: 06/17/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Spatial normalization to a standardized brain template is a crucial step in magnetic resonance imaging (MRI) studies. Brain templates made from sufficient sample size have low brain variability, improving the accuracy of spatial normalization. Using population-specific template improves accuracy of spatial normalization because brain morphology varies according to ethnicity and age. METHODS We constructed a brain template of normal Korean elderly (KNE200) using MRI scans 100 male and 100 female aged over 60 years old with normal cognition. We compared the deformation after spatial normalization of the KNE200 template to that of the KNE96, constructed from 96 cognitively normal elderly Koreans and to that of the brain template (OCF), constructed from 434 non-demented older Caucasians to examine the effect of sample size and ethnicity on the accuracy of brain template, respectively. We spatially normalized the MRI scans of elderly Koreans and quantified the amount of deformations associated with spatial normalization using the magnitude of displacement and volumetric changes of voxels. RESULTS The KNE200 yielded significantly less displacement and volumetric change in the parahippocampal gyrus, medial and posterior orbital gyrus, fusiform gyrus, gyrus rectus, cerebellum and vermis than the KNE96. The KNE200 also yielded much less displacement in the cerebellum, vermis, hippocampus, parahippocampal gyrus and thalamus and much less volumetric change in the cerebellum, vermis, hippocampus and parahippocampal gyrus than the OCF. CONCLUSION KNE200 had the better accuracy than the KNE96 due to the larger sample size and was far accurate than the template constructed from elderly Caucasians in elderly Koreans.
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Grants
- HI09C1379 [A092077] Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea
- HI09C1379 [A092077] Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea
- HI09C1379 [A092077] Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea
- HI09C1379 [A092077] Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea
- HI09C1379 [A092077] Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea
- HI09C1379 [A092077] Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea
- HI09C1379 [A092077] Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea
- MSIT; 2018-2-00861 Institute for Information and Communications Technology Promotion
- MSIT; 2018-2-00861 Institute for Information and Communications Technology Promotion
- MSIT; 2018-2-00861 Institute for Information and Communications Technology Promotion
- MSIT; 2018-2-00861 Institute for Information and Communications Technology Promotion
- MSIT; 2018-2-00861 Institute for Information and Communications Technology Promotion
- MSIT; 2018-2-00861 Institute for Information and Communications Technology Promotion
- MSIT; 2018-2-00861 Institute for Information and Communications Technology Promotion
- Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea
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Affiliation(s)
- Wheesung Lee
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Subin Lee
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Yeseung Park
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Grace Eun Kim
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Jong Bin Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Ji Won Han
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Ki Woong Kim
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul, Republic of Korea.
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5
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Oh H, Kim J, Park S, Jang M, Kim M, Kwon JS. Constructing the KOR152 Korean Young Adult Brain Atlas Utilizing the State-of-the-Art Method for the Age-Specific Population. Psychiatry Investig 2024; 21:664-671. [PMID: 38960444 PMCID: PMC11222079 DOI: 10.30773/pi.2024.0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/31/2024] [Accepted: 04/29/2024] [Indexed: 07/05/2024] Open
Abstract
OBJECTIVE Spatial normalization is an essential process for comparative analyses that heavily depends on the standard brain template used. Brain morphological differences are observed in different populations due to genetic and environmental factors, causing mismatches in regions when the data are normalized to different population templates. Recent studies have indicated differences between Caucasian and East Asian populations as well as within East Asian populations, suggesting the necessity of population-specific brain templates. Thus, this study aimed to construct a Korean young adult age-specific brain template utilizing an advanced method of template construction to update the currently available Korean template. METHODS The KOR152 template was constructed via affine and nonlinear iterative procedures based on prior studies. We compared the morphological features of different population templates (MNI152, Indian_157, and CN200). The distance and volumetric changes before and after registering the data to these templates were calculated for registration accuracy. RESULTS The KOR152 global brain features revealed a shorter overall length than the other population templates. The registration accuracy by distance and volumetric change was significantly lower than that of the other population templates, implying that the KOR152 was more accurate than other templates for the young adult Korean population. CONCLUSION This study provided evidence for the need for a population-specific template that may be more appropriate for structural and functional studies in Korean populations.
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Affiliation(s)
- Harin Oh
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Jongrak Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Sunghyun Park
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Moonyoung Jang
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
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6
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Fu J, Zhang Q, Wang J, Wang M, Zhang B, Zhu W, Qiu S, Geng Z, Cui G, Yu Y, Liao W, Zhang H, Gao B, Xu X, Han T, Yao Z, Qin W, Liu F, Liang M, Wang S, Xu Q, Xu J, Zhang P, Li W, Shi D, Wang C, Lui S, Yan Z, Chen F, Zhang J, Li J, Shen W, Miao Y, Wang D, Xian J, Gao JH, Zhang X, Xu K, Zuo XN, Zhang L, Ye Z, Cheng J, Li MJ, Yu C. Cross-ancestry genome-wide association studies of brain imaging phenotypes. Nat Genet 2024; 56:1110-1120. [PMID: 38811844 DOI: 10.1038/s41588-024-01766-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 04/23/2024] [Indexed: 05/31/2024]
Abstract
Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and cross-ancestry genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese Han and 33,224 white British participants. We identified 38 new associations in Chinese-alone analyses and 486 additional new associations in cross-ancestry meta-analyses at P < 1.46 × 10-11 for discovery and P < 0.05 for replication. We pooled significant autosomal associations identified by single- or cross-ancestry analyses into 6,443 independent associations, which showed uneven distribution in the genome and the phenotype subgroups. We further divided them into 44 associations with different effect sizes and 3,557 associations with similar effect sizes between ancestries. Loci of these associations were shared with 15 brain-related non-imaging traits including cognition and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in more diverse populations.
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Affiliation(s)
- Jilian Fu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, China
| | - Jianhua Wang
- Department of Bioinformatics, the Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital and Zhengzhou University People's Hospital, Zhengzhou, China
- Biomedical Institute, Henan Academy of Sciences, Zhengzhou, China
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shijun Qiu
- Department of Medical Imaging, the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Zuojun Geng
- Department of Medical Imaging, the Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province and Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Yongqiang Yu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- Molecular Imaging Research Center of Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Zhang
- Department of Radiology, the First Hospital of Shanxi Medical University, Taiyuan, China
| | - Bo Gao
- Department of Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Wen Qin
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Sijia Wang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Qiang Xu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiayuan Xu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
| | - Peng Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Dapeng Shi
- Department of Radiology, Henan Provincial People's Hospital and Zhengzhou University People's Hospital, Zhengzhou, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Su Lui
- Department of Radiology, Huaxi MR Research Center, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jiance Li
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin, China
| | - Yanwei Miao
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Xiaochu Zhang
- Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China
| | - Kai Xu
- Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center at IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Mulin Jun Li
- Department of Bioinformatics, the Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
| | - Chunshui Yu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging and State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
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7
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Skouras S, Kleinert ML, Lee EHM, Hui CLM, Suen YN, Camchong J, Chong CSY, Chang WC, Chan SKW, Lo WTL, Lim KO, Chen EYH. Aberrant connectivity in the hippocampus, bilateral insula and temporal poles precedes treatment resistance in first-episode psychosis: a prospective resting-state functional magnetic resonance imaging study with connectivity concordance mapping. Brain Commun 2024; 6:fcae094. [PMID: 38707706 PMCID: PMC11069118 DOI: 10.1093/braincomms/fcae094] [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: 03/05/2023] [Revised: 12/04/2023] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Functional connectivity resting-state functional magnetic resonance imaging has been proposed to predict antipsychotic treatment response in schizophrenia. However, only a few prospective studies have examined baseline resting-state functional magnetic resonance imaging data in drug-naïve first-episode schizophrenia patients with regard to subsequent treatment response. Data-driven approaches to conceptualize and measure functional connectivity patterns vary broadly, and model-free, voxel-wise, whole-brain analysis techniques are scarce. Here, we apply such a method, called connectivity concordance mapping to resting-state functional magnetic resonance imaging data acquired from an Asian sample (n = 60) with first-episode psychosis, prior to pharmaceutical treatment. Using a longitudinal design, 12 months after the resting-state functional magnetic resonance imaging, we measured and classified patients into two groups based on psychometric testing: treatment responsive and treatment resistant. Next, we compared the two groups' connectivity concordance maps that were derived from the resting-state functional magnetic resonance imaging data at baseline. We have identified consistently higher functional connectivity in the treatment-resistant group in a network including the left hippocampus, bilateral insula and temporal poles. These data-driven novel findings can help researchers to consider new regions of interest and facilitate biomarker development in order to identify treatment-resistant schizophrenia patients early, in advance of treatment and at the time of their first psychotic episode.
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Affiliation(s)
- Stavros Skouras
- Department of Fundamental Neurosciences, Faculty of Medicine, University of Geneva, CH-1211 Geneva, Switzerland
- Department of Neurology, Inselspital University Hospital Bern, CH3010 Bern, Switzerland
| | | | - Edwin H M Lee
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Christy L M Hui
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Yi Nam Suen
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Jazmin Camchong
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA
| | | | - Wing Chung Chang
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Sherry K W Chan
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - William T L Lo
- Department of Psychiatry, Kwai Chung Hospital, Hong Kong, China
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA
| | - Eric Y H Chen
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
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8
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Du F, Zheng S, Shao K, Yang Y, Chen W, Bai X, Hua Y. Specific-CT brain template construction and retrospective dosimetric comparison study in brain for nasopharyngeal carcinoma patients treated with IMRT or VMAT. Am J Cancer Res 2024; 14:1662-1674. [PMID: 38726278 PMCID: PMC11076250 DOI: 10.62347/hacn9549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/12/2024] [Indexed: 05/12/2024] Open
Abstract
The current Radiotherapy (RT) technology still inevitably irradiated normal brain tissue, causing implicit radiation-induced injury. This study investigates the precise localization and the corresponding radiation dosage of brain regions susceptible to damage in nasopharyngeal carcinoma (NPC) patients following RT. Utilizing the Advanced Normalization Tools (ANTs) package, a computed tomography (CT) brain template was created in the standard Montreal Neurological Institute (MNI) space, based on 803 Chinese NPC patients (T0~T4) who underwent RT. With this template, all patients' CT and RTdose data were registered to the MNI space, and the RTdose distribution characteristics in normal brain tissues were compared for NPC patients treated with Intensity-modulated radiotherapy (IMRT) or Volumetric Modulated Arc Therapy (VMAT), with patients' age and gender as covariates. Analysis of the average dosages indicated that certain areas within the Limbic, Temporal, and Posterior Lobes, the Brainstem, and the Cerebellum Posterior Lobe were exposed to doses exceeding 50 Gy. Inter-group analysis revealed that IMRT delivered higher doses than VMAT to brain regions anterior to the nasopharyngeal tumor, whereas VMAT affected the posterior regions more. Interestingly, VMAT showed a drawback in preserving the normal brain tissues for T4-stage patients. This revealed that the two treatment modalities have unique characteristics in preserving normal brain tissue, each with advantages. With better localization precision, the created CT brain template in MNI space may be beneficial for NPC patients' toxicity and dosimetric analyses.
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Affiliation(s)
- Fenglei Du
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhou 310022, Zhejiang, China
| | - Shuang Zheng
- School of Media and Design, Hangzhou Dianzi UniversityHangzhou 310018, Zhejiang, China
| | - Kainan Shao
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhou 310022, Zhejiang, China
- Department of Radiation Oncology, Zhejiang Provincial People’s HospitalHangzhou 314408, Zhejiang, China
| | - Yiwei Yang
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhou 310022, Zhejiang, China
| | - Wei Chen
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhou 310022, Zhejiang, China
- School of Nuclear Science and Technology, University of South ChinaHengyang 421001, Hunan, China
| | - Xue Bai
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhou 310022, Zhejiang, China
| | - Yonghong Hua
- Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhou 310022, Zhejiang, China
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9
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Barber N, Valoumas I, Leger KR, Chang YL, Huang CM, Goh JOS, Gutchess A. Culture, prefrontal volume, and memory. PLoS One 2024; 19:e0298235. [PMID: 38551909 PMCID: PMC10980194 DOI: 10.1371/journal.pone.0298235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 01/19/2024] [Indexed: 04/01/2024] Open
Abstract
Prior cross-cultural studies have demonstrated differences among Eastern and Western cultures in memory and cognition along with variation in neuroanatomy and functional engagement. We further probed cultural neuroanatomical variability in terms of its relationship with memory performance. Specifically, we investigated how memory performance related to gray matter volume in several prefrontal lobe structures, including across cultures. For 58 American and 57 Taiwanese young adults, memory performance was measured with the California Verbal Learning Test (CVLT) using performance on learning trial 1, on which Americans had higher scores than the Taiwanese, and the long delayed free recall task, on which groups performed similarly. MRI data were reconstructed using FreeSurfer. Across both cultures, we observed that larger volumes of the bilateral rostral anterior cingulate were associated with lower scores on both CVLT tasks. In terms of effects of culture, the relationship between learning trial 1 scores and gray matter volumes in the right superior frontal gyrus had a trend for a positive relationship in Taiwanese but not in Americans. In addition to the a priori analysis of select frontal volumes, an exploratory whole-brain analysis compared volumes-without considering CVLT performance-across the two cultural groups in order to assess convergence with prior research. Several cultural differences were found, such that Americans had larger volumes in the bilateral superior frontal and lateral occipital cortex, whereas Taiwanese had larger volumes in the bilateral rostral middle frontal and inferior temporal cortex, and the right precuneus.
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Affiliation(s)
- Nicolette Barber
- Department of Psychology, Brandeis University, Waltham, MA, United States of America
| | - Ioannis Valoumas
- Department of Psychology, Brandeis University, Waltham, MA, United States of America
| | - Krystal R. Leger
- Department of Psychology, Brandeis University, Waltham, MA, United States of America
| | - Yu-Ling Chang
- Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan
- Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan
- Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan
- Volen National Center for Complex Systems, Brandeis University, Waltham, MA, United States of America
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Joshua Oon Soo Goh
- Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan
- Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan
- Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan
- Department of Psychology, National Taiwan University, Taipei, Taiwan
| | - Angela Gutchess
- Department of Psychology, Brandeis University, Waltham, MA, United States of America
- Volen National Center for Complex Systems, Brandeis University, Waltham, MA, United States of America
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10
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Lafta MS, Mwinyi J, Affatato O, Rukh G, Dang J, Andersson G, Schiöth HB. Exploring sex differences: insights into gene expression, neuroanatomy, neurochemistry, cognition, and pathology. Front Neurosci 2024; 18:1340108. [PMID: 38449735 PMCID: PMC10915038 DOI: 10.3389/fnins.2024.1340108] [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: 11/17/2023] [Accepted: 02/09/2024] [Indexed: 03/08/2024] Open
Abstract
Increased knowledge about sex differences is important for development of individualized treatments against many diseases as well as understanding behavioral and pathological differences. This review summarizes sex chromosome effects on gene expression, epigenetics, and hormones in relation to the brain. We explore neuroanatomy, neurochemistry, cognition, and brain pathology aiming to explain the current state of the art. While some domains exhibit strong differences, others reveal subtle differences whose overall significance warrants clarification. We hope that the current review increases awareness and serves as a basis for the planning of future studies that consider both sexes equally regarding similarities and differences.
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Affiliation(s)
- Muataz S. Lafta
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Jessica Mwinyi
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
- Centre for Women’s Mental Health, Uppsala University, Uppsala, Sweden
| | - Oreste Affatato
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
- Centre for Women’s Mental Health, Uppsala University, Uppsala, Sweden
| | - Gull Rukh
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Junhua Dang
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Gerhard Andersson
- Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Helgi B. Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
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11
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Atilano-Barbosa D, Barrios FA. Brain morphological variability between whites and African Americans: the importance of racial identity in brain imaging research. Front Integr Neurosci 2023; 17:1027382. [PMID: 38192686 PMCID: PMC10773238 DOI: 10.3389/fnint.2023.1027382] [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: 08/24/2022] [Accepted: 04/14/2023] [Indexed: 01/10/2024] Open
Abstract
In a segregated society, marked by a historical background of inequalities, there is a consistent under-representation of ethnic and racial minorities in biomedical research, causing disparities in understanding genetic and acquired diseases as well as in the effectiveness of clinical treatments affecting different groups. The repeated inclusion of small and non-representative samples of the population in neuroimaging research has led to generalization bias in the morphological characterization of the human brain. A few brain morphometric studies between Whites and African Americans have reported differences in orbitofrontal volumetry and insula cortical thickness. Nevertheless, these studies are mostly conducted in small samples and populations with cognitive impairment. For this reason, this study aimed to identify brain morphological variability due to racial identity in representative samples. We hypothesized that, in neurotypical young adults, there are differences in brain morphometry between participants with distinct racial identities. We analyzed the Human Connectome Project (HCP) database to test this hypothesis. Brain volumetry, cortical thickness, and cortical surface area measures of participants identified as Whites (n = 338) or African Americans (n = 56) were analyzed. Non-parametrical permutation analysis of covariance between these racial identity groups adjusting for age, sex, education, and economic income was implemented. Results indicated volumetric differences in choroid plexus, supratentorial, white matter, and subcortical brain structures. Moreover, differences in cortical thickness and surface area in frontal, parietal, temporal, and occipital brain regions were identified between groups. In this regard, the inclusion of sub-representative minorities in neuroimaging research, such as African American persons, is fundamental for the comprehension of human brain morphometric diversity and to design personalized clinical brain treatments for this population.
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Affiliation(s)
| | - Fernando A. Barrios
- Institute of Neurobiology, National Autonomous University of Mexico, Juriquilla, Mexico
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12
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Perry JL, Gilbert IR, Xing F, Jin R, Kuehn DP, Shosted RK, Woo J, Liang ZP, Sutton BP. Preliminary Development of an MRI Atlas for Application to Cleft Care: Findings and Future Recommendations. Cleft Palate Craniofac J 2023:10556656231183385. [PMID: 37335134 DOI: 10.1177/10556656231183385] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE To introduce a highly innovative imaging method to study the complex velopharyngeal (VP) system and introduce the potential future clinical applications of a VP atlas in cleft care. DESIGN Four healthy adults participated in a 20-min dynamic magnetic resonance imaging scan that included a high-resolution T2-weighted turbo-spin-echo 3D structural scan and five custom dynamic speech imaging scans. Subjects repeated a variety of phrases when in the scanner as real-time audio was captured. SETTING Multisite institution and clinical setting. PARTICIPANTS Four adult subjects with normal anatomy were recruited for this study. MAIN OUTCOME Establishment of 4-D atlas constructed from dynamic VP MRI data. RESULTS Three-dimensional dynamic magnetic resonance imaging was successfully used to obtain high quality dynamic speech scans in an adult population. Scans were able to be re-sliced in various imaging planes. Subject-specific MR data were then reconstructed and time-aligned to create a velopharyngeal atlas representing the averaged physiological movements across the four subjects. CONCLUSIONS The current preliminary study examined the feasibility of developing a VP atlas for potential clinical applications in cleft care. Our results indicate excellent potential for the development and use of a VP atlas for assessing VP physiology during speech.
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Affiliation(s)
- Jamie L Perry
- Department of Communication Sciences and Disorders, East Carolina University, Greenville, NC, USA
| | - Imani R Gilbert
- Department of Communication Sciences and Disorders, East Carolina University, Greenville, NC, USA
| | - Fangxu Xing
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Riwei Jin
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - David P Kuehn
- Department of Speech and Hearing Science, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Ryan K Shosted
- Department of Linguistics, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jonghye Woo
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Zhi-Pei Liang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Bradley P Sutton
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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13
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Taylor PA, Glen DR, Reynolds RC, Basavaraj A, Moraczewski D, Etzel JA. Editorial: Demonstrating quality control (QC) procedures in fMRI. Front Neurosci 2023; 17:1205928. [PMID: 37325035 PMCID: PMC10264898 DOI: 10.3389/fnins.2023.1205928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/12/2023] [Indexed: 06/17/2023] Open
Affiliation(s)
- Paul A. Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Daniel R. Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Richard C. Reynolds
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Arshitha Basavaraj
- Data Science and Sharing Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Dustin Moraczewski
- Data Science and Sharing Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Joset A. Etzel
- Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
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14
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Shao K, Zheng S, Wang Y, Bai X, Luo H, Du F. A detailed dosimetric comparative study of IMRT and VMAT in normal brain tissues for nasopharyngeal carcinoma patients treated with radiotherapy. FRONTIERS IN RADIOLOGY 2023; 3:1190763. [PMID: 37492390 PMCID: PMC10365280 DOI: 10.3389/fradi.2023.1190763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/27/2023] [Indexed: 07/27/2023]
Abstract
Background Radiotherapy (RT) is the primary treatment for nasopharyngeal carcinoma (NPC). However, it can cause implicit RT-induced injury by irradiating normal brain tissue. To date, there have been no detailed reports on the radiated exact location in the brain, the corresponding radiation dose, and their relationship. Methods We analyzed 803 Chinese NPC patients treated with RT and used a CT brain template in a Montreal Neurological Institute (MNI) space to compare the group differences in RT dose distribution for different RT technologies (IMRT or VMAT). Results Brain regions that received high doses (>50 Gy) of radiation were mainly located in parts of the temporal and limbic lobes, where radioactive damage often occurs. Brain regions that accepted higher doses with IMRT were mainly located near the anterior region of the nasopharyngeal tumor, while brain regions that accepted higher doses with VMAT were mainly located near the posterior region of the tumor. No significant difference was detected between IMRT and VMAT for T1 stage patients. For T2 stage patients, differences were widely distributed, with VMAT showing a significant dose advantage in protecting the normal brain tissue. For T3 stage patients, VMAT showed an advantage in the superior temporal gyrus and limbic lobe, while IMRT showed an advantage in the posterior cerebellum. For T4 stage patients, VMAT showed a disadvantage in protecting the normal brain tissue. These results indicate that IMRT and VMAT have their own advantages in sparing different organs at risk (OARs) in the brain for different T stages of NPC patients treated with RT. Conclusion Our approach for analyzing dosimetric characteristics in a standard MNI space for Chinese NPC patients provides greater convenience in toxicity and dosimetry analysis with superior localization accuracy. Using this method, we found interesting differences from previous reports: VMAT showed a disadvantage in protecting the normal brain tissue for T4 stage NPC patients.
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Affiliation(s)
- Kainan Shao
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou, China
| | - Shuang Zheng
- School of Media and Design, Hangzhou Dianzi University, Hangzhou, China
| | - Yajuan Wang
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou, China
| | - Xue Bai
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou, China
| | - Hongying Luo
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou, China
- Faculty of Nuclear Science and Technology, University of South China, Hengyang, China
| | - Fenglei Du
- Department of Radiation Physics, Zhejiang Cancer Hospital, Hangzhou, China
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15
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Linli Z, Rolls ET, Zhao W, Kang J, Feng J, Guo S. Smoking is associated with lower brain volume and cognitive differences: A large population analysis based on the UK Biobank. Prog Neuropsychopharmacol Biol Psychiatry 2023; 123:110698. [PMID: 36528239 DOI: 10.1016/j.pnpbp.2022.110698] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 11/25/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
The evidence about the association of smoking with both brain structure and cognitive functions remains inconsistent. Using structural magnetic resonance imaging from the UK Biobank (n = 33,293), we examined the relationships between smoking status, dosage, and abstinence with total and 166 regional brain gray matter volumes (GMV). The relationships between the smoking parameters with cognitive function, and whether this relationship was mediated by brain structure, were then investigated. Smoking was associated with lower total and regional GMV, with the extent depending on the frequency of smoking and on whether smoking had ceased: active regular smokers had the lowest GMV (Cohen's d = -0.362), and former light smokers had a slightly smaller GMV (Cohen's d = -0.060). The smaller GMV in smokers was most evident in the thalamus. Higher lifetime exposure (i.e., pack-years) was associated with lower total GMV (β = -311.84, p = 8.35 × 10-36). In those who ceased smoking, the duration of abstinence was associated with a larger total GMV (β = 139.57, p = 2.36 × 10-08). It was further found that reduced cognitive function was associated with smoker parameters and that the associations were partially mediated by brain structure. This is the largest scale investigation we know of smoking and brain structure, and these results are likely to be robust. The findings are of associations between brain structure and smoking, and in the future, it will be important to assess whether brain structure influences smoking status, or whether smoking influences brain structure, or both.
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Affiliation(s)
- Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China; School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, PR China.
| | - Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK; Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China
| | - Jujiao Kang
- Centre for Computational Systems Biology, Fudan University, Shanghai, PR China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry, UK; Centre for Computational Systems Biology, Fudan University, Shanghai, PR China.
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China.
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16
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Piredda GF, Caneschi S, Hilbert T, Bonanno G, Joseph A, Egger K, Peter J, Klöppel S, Jehli E, Grieder M, Slotboom J, Seiffge D, Goeldlin M, Hoepner R, Willems T, Vulliemoz S, Seeck M, Venkategowda PB, Corredor Jerez RA, Maréchal B, Thiran JP, Wiest R, Kober T, Radojewski P. Submillimeter T 1 atlas for subject-specific abnormality detection at 7T. Magn Reson Med 2023; 89:1601-1616. [PMID: 36478417 DOI: 10.1002/mrm.29540] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/14/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE Studies at 3T have shown that T1 relaxometry enables characterization of brain tissues at the single-subject level by comparing individual physical properties to a normative atlas. In this work, an atlas of normative T1 values at 7T is introduced with 0.6 mm isotropic resolution and its clinical potential is explored in comparison to 3T. METHODS T1 maps were acquired in two separate healthy cohorts scanned at 3T and 7T. Using transfer learning, a template-based brain segmentation algorithm was adapted to ultra-high field imaging data. After segmenting brain tissues, volumes were normalized into a common space, and an atlas of normative T1 values was established by modeling the T1 inter-subject variability. A method for single-subject comparisons restricted to white matter and subcortical structures was developed by computing Z-scores. The comparison was applied to eight patients scanned at both field strengths for proof of concept. RESULTS The proposed method for morphometry delivered segmentation masks without statistically significant differences from those derived with the original pipeline at 3T and achieved accurate segmentation at 7T. The established normative atlas allowed characterizing tissue alterations in single-subject comparisons at 7T, and showed greater anatomical details compared with 3T results. CONCLUSION A high-resolution quantitative atlas with an adapted pipeline was introduced and validated. Several case studies on different clinical conditions showed the feasibility, potential and limitations of high-resolution single-subject comparisons based on quantitative MRI atlases. This method in conjunction with 7T higher resolution broadens the range of potential applications of quantitative MRI in clinical practice.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland.,CIBM-AIT, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Samuele Caneschi
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Gabriele Bonanno
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Bern, Switzerland.,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Arun Joseph
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Bern, Switzerland.,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Karl Egger
- Department of Neuroradiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jessica Peter
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Elisabeth Jehli
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.,Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland
| | - Matthias Grieder
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - David Seiffge
- Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Martina Goeldlin
- Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Robert Hoepner
- Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Tom Willems
- Institute of Psychology, University of Bern, Bern, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | | | - Ricardo A Corredor Jerez
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Roland Wiest
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Piotr Radojewski
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
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17
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Approximating subject-specific brain injury models via scaling based on head-brain morphological relationships. Biomech Model Mechanobiol 2023; 22:159-175. [PMID: 36201071 DOI: 10.1007/s10237-022-01638-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 09/07/2022] [Indexed: 11/02/2022]
Abstract
Most human head/brain models represent a generic adult male head/brain. They may suffer in accuracy when investigating traumatic brain injury (TBI) on a subject-specific basis. Subject-specific models can be developed from neuroimages; however, neuroimages are not typically available in practice. In this study, we establish simple and elegant regression models between brain outer surface morphology and head dimensions measured from neuroimages along with age and sex information (N = 191; 141 males and 50 females with age ranging 14-25 years). The regression models are then used to approximate subject-specific brain models by scaling a generic counterpart, without using neuroimages. Model geometrical accuracy is assessed using adjusted [Formula: see text] and absolute percentage error (e.g., 0.720 and 3.09 ± 2.38%, respectively, for brain volume when incorporating tragion-to-top). For a subset of 11 subjects (from smallest to largest in brain volume), impact-induced brain strains are compared with those from "morphed models" derived from neuroimage-based mesh warping. We find that regional peak strains from the scaled subject-specific models are comparable to those of the morphed counterparts but could be considerably different from those of the generic model (e.g., linear regression slope of 1.01-1.03 for gray and white matter regions versus 1.16-1.19, or up to ~ 20% overestimation for the smallest brain studied). These results highlight the importance of incorporating brain morphological variations in impact simulation and demonstrate the feasibility of approximating subject-specific brain models without neuroimages using age, sex, and easily measurable head dimensions. The scaled models may improve subject specificity for future TBI investigations.
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18
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Yang G, Bozek J, Noble S, Han M, Wu X, Xue M, Kang J, Jia T, Fu J, Ge J, Cui Z, Li X, Feng J, Gao JH. Global diversity in individualized cortical network topography. Cereb Cortex 2023:6992941. [PMID: 36657772 DOI: 10.1093/cercor/bhad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 12/24/2022] [Accepted: 12/27/2022] [Indexed: 01/21/2023] Open
Abstract
Individualized cortical network topography (ICNT) varies between people and exhibits great variability in the association networks in the human brain. However, these findings were mainly discovered in Western populations. It remains unclear whether and how ICNT is shaped by the non-Western populations. Here, we leveraged a multisession hierarchical Bayesian model to define individualized functional networks in White American and Han Chinese populations with data from both US and Chinese Human Connectome Projects. We found that both the size and spatial topography of individualized functional networks differed between White American and Han Chinese groups, especially in the heteromodal association cortex (including the ventral attention, control, language, dorsal attention, and default mode networks). Employing a support vector machine, we then demonstrated that ethnicity-related ICNT diversity can be used to identify an individual's ethnicity with high accuracy (74%, pperm < 0.0001), with heteromodal networks contributing most to the classification. This finding was further validated through mass-univariate analyses with generalized additive models. Moreover, we reveal that the spatial heterogeneity of ethnic diversity in ICNT correlated with fundamental properties of cortical organization, including evolutionary cortical expansion, brain myelination, and cerebral blood flow. Altogether, this case study highlights a need for more globally diverse and publicly available neuroimaging datasets.
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Affiliation(s)
- Guoyuan Yang
- Advanced Research Institute of Multidisciplinary Sciences, School of Medical Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb 10000, Croatia
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Meizhen Han
- McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xinyu Wu
- Advanced Research Institute of Multidisciplinary Sciences, School of Medical Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Mufan Xue
- Advanced Research Institute of Multidisciplinary Sciences, School of Medical Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai 200433, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai 200433, China.,Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London SE5 8AF, United Kingdom
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300203, China
| | - Jianqiao Ge
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Xuesong Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai 200433, China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China.,Beijing City Key Laboratory for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China
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19
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Spatiotemporal Atlas of the Fetal Brain Depicts Cortical Developmental Gradient. J Neurosci 2022; 42:9435-9449. [PMID: 36323525 PMCID: PMC9794379 DOI: 10.1523/jneurosci.1285-22.2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/17/2022] [Accepted: 10/25/2022] [Indexed: 11/12/2022] Open
Abstract
The fetal brains experience rapid and complex development in utero during the second and third trimesters. In utero MRI of the fetal brain in this period enables us to quantify normal fetal brain development in the spatiotemporal domain. In this study, we established a high-quality spatiotemporal atlas between 23 and 38 weeks gestational age (GA) from 90 healthy Chinese human fetuses of both sexes using a pairwise and groupwise registration pipeline. We quantified the fetal cortical morphology indices and characterized their spatiotemporal developmental pattern. The cortical thickness exhibited a biphasic pattern that first increased and then decreased; the curvature fitted well into the Gompertz growth model; sulcal depth increased linearly, while surface area expanded exponentially. The cortical thickness and curvature trajectories consistently pointed to a characteristic time point around GA of 31 weeks. The characteristic GA and growth rate obtained from individual cortical regions suggested a central-to-peripheral developmental gradient, with the earliest development in the parietal lobe, and we also observed a superior-to-inferior gradient within the temporal lobe. These findings may be linked to biophysical events, such as dendritic arborization and thalamocortical fibers ingrowth. The proposed atlas was also compared with an existing fetal atlas from a white/mixed population. Finally, we examined the structural asymmetry of the fetal brains and found extensive asymmetry that dynamically changed with development. The current study depicted a comprehensive profile of fetal cortical development, and the established atlas could be used as a normative reference for neurodevelopmental and diagnostic purposes, especially in the Chinese population.SIGNIFICANCE STATEMENT We generated a high-quality 4D spatiotemporal atlas of the normal fetal brain development from 23 to 38 gestational weeks in a Chinese population and characterized the spatiotemporal developmental pattern of cortical morphology. According to the cortical development trajectories, the fetal cerebral cortex development follows a central-to-peripheral developmental gradient that may be related to the underlying cellular events. The majority of cortical regions already exhibit significant asymmetry during the fetal period.
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20
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Chen R, Sun C, Liu T, Liao Y, Wang J, Sun Y, Zhang Y, Wang G, Wu D. Deciphering the developmental order and microstructural patterns of early white matter pathways in a diffusion MRI based fetal brain atlas. Neuroimage 2022; 264:119700. [PMID: 36270621 DOI: 10.1016/j.neuroimage.2022.119700] [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: 04/17/2022] [Revised: 10/14/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022] Open
Abstract
White matter (WM) of the fetal brain undergoes rapid development to form early structural connections. Diffusion magnetic resonance imaging (dMRI) has shown to be a useful tool to depict fetal brain WM in utero, and many studies have observed increasing fractional anisotropy and decreasing diffusivity in the fetal brain during the second-to-third trimester, whereas others reported non-monotonic changes. Unbiased dMRI atlases of the fetal brain are important for characterizing the developmental trajectories of WM and providing normative references for in utero diagnosis of prenatal abnormalities. To date, the sole fetal brain dMRI atlas was collected from a Caucasian/mixed population and was constructed based on the diffusion tensor model with limited spatial resolution. In this work, we proposed a fiber orientation distribution (FOD) based pipeline for generating fetal brain dMRI atlases, which showed better registration accuracy than a diffusion tensor based pipeline. Based on the FOD-based pipeline, we constructed the first Chinese fetal brain dMRI atlas using 89 dMRI scans of normal fetuses at gestational age between 24 and 38 weeks. Complex non-monotonic trends of tensor- and FOD-derived microstructural parameters in eight WM tracts were observed, which jointly pointed to different phases of microstructural development. Specifically, we speculated that the turning point of the diffusivity trajectory may correspond to the starting point of pre-myelination, based on which, the developmental order of WM tracts can be mapped and the order was in agreement with the order of myelination from histological studies. The normative atlas also provided a reference for the detection of abnormal WM development, such as that in congenital heart disease. Therefore, the established high-order fetal brain dMRI atlas depicted the spatiotemporal pattern of early WM development, and findings may help decipher the distinct microstructural events in utero.
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Affiliation(s)
- Ruike Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Cong Sun
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yuhao Liao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | | | - Yi Sun
- MR Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
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21
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Gu D, Shi F, Hua R, Wei Y, Li Y, Zhu J, Zhang W, Zhang H, Yang Q, Huang P, Jiang Y, Bo B, Li Y, Zhang Y, Zhang M, Wu J, Shi H, Liu S, He Q, Zhang Q, Zhang X, Wei H, Liu G, Xue Z, Shen D. An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images. Hum Brain Mapp 2022; 44:861-875. [PMID: 36269199 PMCID: PMC9875934 DOI: 10.1002/hbm.26126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/23/2022] [Accepted: 10/01/2022] [Indexed: 01/28/2023] Open
Abstract
It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.
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Affiliation(s)
- Dongdong Gu
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Rui Hua
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Ying Wei
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Yufei Li
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
- School of Mathematics and Computer ScienceChifeng UniversityChifengChina
| | - Jiayu Zhu
- Shanghai United Imaging Healthcare Co., Ltd.ShanghaiChina
| | - Weijun Zhang
- Shanghai United Imaging Healthcare Co., Ltd.ShanghaiChina
| | - Han Zhang
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Institute of Brain‐Intelligence TechnologyZhangjiang Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai Center of Brain‐Intelligence EngineeringShanghaiChina
| | - Qing Yang
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Institute of Brain‐Intelligence TechnologyZhangjiang Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai Center of Brain‐Intelligence EngineeringShanghaiChina
| | - Peiyu Huang
- Department of RadiologyThe Second Affiliated Hospital, Zhejiang University School of MedicineHangzhouChina
| | - Yi Jiang
- Institute of Brain‐Intelligence TechnologyZhangjiang Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai Center of Brain‐Intelligence EngineeringShanghaiChina
| | - Bin Bo
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Yao Li
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Yaoyu Zhang
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Minming Zhang
- Department of RadiologyThe Second Affiliated Hospital, Zhejiang University School of MedicineHangzhouChina
| | - Jinsong Wu
- Glioma Surgery Division, Neurologic Surgery DepartmentHuashan HospitalShanghaiChina
- Medical CollegeFudan UniversityShanghaiChina
| | - Hongcheng Shi
- Department of Nuclear MedicineZhongshan Hospital, Fudan UniversityShanghaiChina
| | - Siwei Liu
- Department of Nuclear MedicineZhongshan Hospital, Fudan UniversityShanghaiChina
| | - Qiang He
- Shanghai United Imaging Healthcare Co., Ltd.ShanghaiChina
- United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
| | - Qiang Zhang
- Shanghai United Imaging Healthcare Co., Ltd.ShanghaiChina
| | - Xu Zhang
- Institute of Brain‐Intelligence TechnologyZhangjiang Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai Center of Brain‐Intelligence EngineeringShanghaiChina
| | - Hongjiang Wei
- School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | | | - Zhong Xue
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Dinggang Shen
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Shanghai Clinical Research and Trial CenterShanghaiChina
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22
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Rushmore RJ, Sunderland K, Carrington H, Chen J, Halle M, Lasso A, Papadimitriou G, Prunier N, Rizzoni E, Vessey B, Wilson-Braun P, Rathi Y, Kubicki M, Bouix S, Yeterian E, Makris N. Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach. Front Neuroanat 2022; 16:894606. [PMID: 36249866 PMCID: PMC9562126 DOI: 10.3389/fnana.2022.894606] [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: 03/11/2022] [Accepted: 07/15/2022] [Indexed: 11/27/2022] Open
Abstract
Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.
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Affiliation(s)
- R. Jarrett Rushmore
- Department of Psychiatry, Department of Neurology, Center for Morphometric Analysis, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
| | - Kyle Sunderland
- School of Computing, Queen’s University, Kingston, ON, Canada
| | - Holly Carrington
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Justine Chen
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Michael Halle
- Surgical Planning Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Andras Lasso
- School of Computing, Queen’s University, Kingston, ON, Canada
| | - G. Papadimitriou
- Department of Psychiatry, Department of Neurology, Center for Morphometric Analysis, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - N. Prunier
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Elizabeth Rizzoni
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Brynn Vessey
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Peter Wilson-Braun
- Department of Psychiatry, Department of Neurology, Center for Morphometric Analysis, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Yogesh Rathi
- Department of Psychiatry, Department of Neurology, Center for Morphometric Analysis, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Marek Kubicki
- Department of Psychiatry, Department of Neurology, Center for Morphometric Analysis, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
| | - Edward Yeterian
- Department of Psychiatry, Department of Neurology, Center for Morphometric Analysis, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Department of Psychology, Colby College, Waterville, ME, United States
| | - Nikos Makris
- Department of Psychiatry, Department of Neurology, Center for Morphometric Analysis, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- *Correspondence: Nikos Makris,
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23
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Demographic reporting across a decade of neuroimaging: a systematic review. Brain Imaging Behav 2022; 16:2785-2796. [DOI: 10.1007/s11682-022-00724-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2022] [Indexed: 11/25/2022]
Abstract
Abstract
Diversity of participants in biomedical research with respect to race, ethnicity, and biological sex is crucial, particularly given differences in disease prevalence, recovery, and survival rates between demographic groups. The objective of this systematic review was to report on the demographics of neuroimaging studies using magnetic resonance imaging (MRI). The Web of Science database was used and data collection was performed between June 2021 to November 2021; all articles were reviewed independently by at least two researchers. Articles utilizing MR data acquired in the United States, with n ≥ 10 human subjects, and published between 2010–2020 were included. Non-primary research articles and those published in journals that did not meet a quality control check were excluded. Of the 408 studies meeting inclusion criteria, approximately 77% report sex, 10% report race, and 4% report ethnicity. Demographic reporting also varied as function of disease studied, participant age range, funding, and publisher. We anticipate quantitative data on the extent, or lack, of reporting will be necessary to ensure inclusion of diverse populations in biomedical research.
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24
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Pirazzoli L, Sullivan E, Xie W, Richards JE, Bulgarelli C, Lloyd-Fox S, Shama T, Kakon SH, Haque R, Petri WAJ, Nelson CA. Association of psychosocial adversity and social information processing in children raised in a low-resource setting: an fNIRS study. Dev Cogn Neurosci 2022; 56:101125. [PMID: 35763916 PMCID: PMC9241055 DOI: 10.1016/j.dcn.2022.101125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 06/02/2022] [Accepted: 06/13/2022] [Indexed: 01/01/2023] Open
Abstract
Social cognition skills and socioemotional development are compromised in children growing up in low SES contexts, however, the mechanisms underlying this association remain unknown. Exposure to psychosocial risk factors early in life alters the child's social milieu and in turn, could lead to atypical processing of social stimuli. In this study, we used functional Near Infrared Spectroscopy (fNIRS) to measure cortical responses to a social discrimination task in children raised in a low-resource setting at 6, 24, and 36 months. In addition, we assessed the relation between cortical responses to social and non-social information with psychosocial risk factors assessed using the Childhood Psychosocial Adversity Scale (CPAS). In line with previous findings, we observed specialization to social stimuli in cortical regions in all age groups. In addition, we found that risk factors were associated with social discrimination at 24 months (intimate partner violence and verbal abuse and family conflict) and 36 months (verbal abuse and family conflict and maternal depression) but not at 6 months. Overall, the results show that exposure to psychosocial adversity has more impact on social information processing in toddlerhood than earlier in infancy.
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Affiliation(s)
- Laura Pirazzoli
- Labs of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Eileen Sullivan
- Labs of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Graduate School of Education, Cambridge, MA, USA
| | - Wanze Xie
- School of Psychological and Cognitive Sciences, Peking University, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, China
| | | | - Chiara Bulgarelli
- Birkbeck, University of London, UK; University College London, London, UK
| | | | | | | | | | | | - Charles A Nelson
- Labs of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard Graduate School of Education, Cambridge, MA, USA
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25
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Sandu AL, Waiter GD, Staff RT, Nazlee N, Habota T, McNeil CJ, Chapko D, Williams JH, Fall CHD, Chandak GR, Pene S, Krishna M, McIntosh AM, Whalley HC, Kumaran K, Krishnaveni GV, Murray AD. Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study. Sci Rep 2022; 12:11025. [PMID: 35773463 PMCID: PMC9247090 DOI: 10.1038/s41598-022-15208-4] [Citation(s) in RCA: 4] [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: 06/24/2021] [Accepted: 06/20/2022] [Indexed: 01/20/2023] Open
Abstract
Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.
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Affiliation(s)
- Anca-Larisa Sandu
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK.
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Roger T Staff
- Aberdeen Royal Infirmary, NHS Grampian, Aberdeen, UK
| | - Nafeesa Nazlee
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Tina Habota
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Chris J McNeil
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
| | - Dorota Chapko
- School of Public Health, Imperial College London, London, UK
| | | | - Caroline H D Fall
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases, CSIR - Centre for Cellular and Molecular Biology, Hyderabad, India
| | - Shailesh Pene
- Department of Imaging and Interventional Radiology, Narayana Multispecialty Hospital, Mysore, India
| | - Murali Krishna
- Foundation for Research and Advocacy in Mental Health, Mysore, India
| | - Andrew M McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kalyanaraman Kumaran
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | | | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Lilian Sutton Building, Foresterhill, Aberdeen, AB25 2ZD, UK
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26
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Zhu J, Qiu A. Chinese adult brain atlas with functional and white matter parcellation. Sci Data 2022; 9:352. [PMID: 35725852 PMCID: PMC9209432 DOI: 10.1038/s41597-022-01476-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/14/2022] [Indexed: 12/03/2022] Open
Abstract
Brain atlases play important roles in studying anatomy and function of the brain. As increasing interests in multi-modal magnetic resonance imaging (MRI) approaches, such as combining structural MRI, diffusion weighted imaging (DWI), and resting-state functional MRI (rs-fMRI), there is a need to construct integrated brain atlases based on these three imaging modalities. This study constructed a multi-modal brain atlas for a Chinese aging population (n = 180, age: 22-79 years), which consists of a T1 atlas showing the brain morphology, a high angular resolution diffusion imaging (HARDI) atlas delineating the complex fiber architecture, and a rs-fMRI atlas reflecting brain intrinsic functional organization in one stereotaxic coordinate. We employed large deformation diffeomorphic metric mapping (LDDMM) and unbiased diffeomorphic atlas generation to simultaneously generate the T1 and HARDI atlases. Using spectral clustering, we generated 20 brain functional networks from rs-fMRI data. We demonstrated the use of the atlas to explore the coherent markers among the brain morphology, functional networks, and white matter tracts for aging and gender using joint independent component analysis.
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Affiliation(s)
- Jingwen Zhu
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore.
- NUS (Suzhou) Research Institute, National University of Singapore, Suzhou, China.
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
- Institute of Data Science, National University of Singapore, Singapore, Singapore.
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, USA.
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27
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Gao P, Dong HM, Liu SM, Fan XR, Jiang C, Wang YS, Margulies D, Li HF, Zuo XN. A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping. Sci Data 2022; 9:286. [PMID: 35680932 PMCID: PMC9184635 DOI: 10.1038/s41597-022-01413-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 05/24/2022] [Indexed: 11/09/2022] Open
Abstract
The big-data use is becoming a standard practice in the neuroimaging field through data-sharing initiatives. It is important for the community to realize that such open science effort must protect personal, especially facial information when raw neuroimaging data are shared. An ideal tool for the face anonymization should not disturb subsequent brain tissue extraction and further morphological measurements. Using the high-resolution head images from magnetic resonance imaging (MRI) of 215 healthy Chinese, we discovered and validated a template effect on the face anonymization. Improved facial anonymization was achieved when the Chinese head templates but not the Western templates were applied to obscure the faces of Chinese brain images. This finding has critical implications for international brain imaging data-sharing. To facilitate the further investigation of potential culture-related impacts on and increase diversity of data-sharing for the human brain mapping, we released the 215 Chinese multi-modal MRI data into a database for imaging Chinese young brains, namely'I See your Brains (ISYB)', to the public via the Science Data Bank ( https://doi.org/10.11922/sciencedb.00740 ).
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Affiliation(s)
- Peng Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- National Basic Science Data Center, Beijing, 100109, China
| | - Si-Man Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xue-Ru Fan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Chao Jiang
- School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Daniel Margulies
- Centre National de la Recherche Scientifique, Frontlab, Brain and Spinal Cord Institute, Paris, UMR 7225, France
| | - Hai-Fang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- National Basic Science Data Center, Beijing, 100109, China.
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, 530001, China.
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28
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Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Epicardial and pericardial adipose tissues (EAT and PAT), which are located around the heart, have been linked to coronary atherosclerosis, cardiomyopathy, coronary artery disease, and other cardiovascular diseases. Additionally, the volume and thickness of EAT are good predictors of CVD risk levels. Manual quantification of these tissues is a tedious and error-prone process. This paper presents a comprehensive and critical overview of research on the epicardial and pericardial adipose tissue segmentation and quantification methods, evaluates their effectiveness in terms of segmentation time and accuracy, provides a critical comparison of the methods, and presents ongoing and future challenges in the field. Described methods are classified into pericardial adipose tissue segmentation, direct epicardial adipose tissue segmentation, and epicardial adipose tissue segmentation via pericardium delineation. A comprehensive categorization of the underlying methods is conducted with insights into their evolution from traditional image processing methods to recent deep learning-based methods. The paper also provides an overview of the research on the clinical significance of epicardial and pericardial adipose tissues as well as the terminology and definitions used in the medical literature.
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29
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Zhang R, Zhang Y, Liu Y, Guo Y, Shen Y, Deng D, Qiu YJ, Dinov ID. Kimesurface Representation and Tensor Linear Modeling of Longitudinal Data. Neural Comput Appl 2022; 34:6377-6396. [PMID: 35936508 PMCID: PMC9355340 DOI: 10.1007/s00521-021-06789-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 11/21/2021] [Indexed: 11/25/2022]
Abstract
Many modern techniques for analyzing time-varying longitudinal data rely on parametric models to interrogate the time-courses of univariate or multivariate processes. Typical analytic objectives include utilizing retrospective observations to model current trends, predict prospective trajectories, derive categorical traits, or characterize various relations. Among the many mathematical, statistical, and computational strategies for analyzing longitudinal data, tensor-based linear modeling offers a unique algebraic approach that encodes different characterizations of the observed measurements in terms of state indices. This paper introduces a new method of representing, modeling, and analyzing repeated-measurement longitudinal data using a generalization of event order from the positive reals to the complex plane. Using complex time (kime), we transform classical time-varying signals as 2D manifolds called kimesurfaces. This kime characterization extends the classical protocols for analyzing time-series data and offers unique opportunities to design novel inference, prediction, classification, and regression techniques based on the corresponding kimesurface manifolds. We define complex time and illustrate alternative time-series to kimesurface transformations. Using the Laplace transform and its inverse, we demonstrate the bijective mapping between time-series and kimesurfaces. A proposed general tensor regression based linear model is validated using functional Magnetic Resonance Imaging (fMRI) data. This kimesurface representation method can be used with a wide range of machine learning algorithms, artificial intelligence tools, analytical approaches, and inferential techniques to interrogate multivariate, complex-domain, and complex-range longitudinal processes.
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Affiliation(s)
- Rongqian Zhang
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yupeng Zhang
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yuyao Liu
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yunjie Guo
- Electrical Computer Engineering Division, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yueyang Shen
- Electrical Computer Engineering Division, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daxuan Deng
- Electrical Computer Engineering Division, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yongkai Joshua Qiu
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ivo D. Dinov
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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30
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Hu H, Jiang Y, Xia M, Tang Y, Zhang T, Cui H, Wang J, Xu L, Curtin A, Sheng J, Cao X, Guo Q, Jia Y, Li C, Wang Z, Luo C, Wang J. Functional reconfiguration of cerebellum-cerebral neural loop in schizophrenia following electroconvulsive therapy. Psychiatry Res Neuroimaging 2022; 320:111441. [PMID: 35085957 DOI: 10.1016/j.pscychresns.2022.111441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 11/15/2021] [Accepted: 01/17/2022] [Indexed: 11/26/2022]
Abstract
Recent evidence highlights the role of the cerebellum-cerebral loop in the pathophysiology of schizophrenia (SZ). Electroconvulsive therapy (ECT) is clinically applied to augment the effect of antipsychotic drugs. The study aims to address whether the cerebellum-cerebral loop is involved in the mechanisms of ECT's augmentation effect. Forty-two SZ patients and 23 healthy controls (HC) were recruited and scanned using resting-state functional MRI (rs-fMRI). Twenty-one patients received modified ECT plus antipsychotics (MSZ group), and 21 patients took antipsychotics only (DSZ group). All patients were re-scanned four weeks later. Brain functional network was constructed according to the graph theory. The sub-network exhibited longitudinal changes after ECT or medications were constructed. For the MSZ group, a sub-network involving default-mode network and cerebellum showed significant longitudinal changes. For the DSZ group, a different sub-network involving the thalamus, frontal and occipital cortex was found to be altered in the follow-up scan. In addition, the changing FC of the left cerebellar crus2 region was correlated with the changing scores of the psychotic symptoms only in the MSZ group but not in the DSZ group. In conclusion, the cerebral-cerebellum loop is possibly involved in the antipsychotic mechanisms of ECT for schizophrenia.
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Affiliation(s)
- Hao Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Yuchao Jiang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Mengqing Xia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Huiru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Junjie Wang
- Institute of Mental Health, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu, 215137, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Adrian Curtin
- School of Biomedical Engineering & Health Sciences, Drexel University, Philadelphia, PA 19104, United States; Med-X Institute, Shanghai Jiao Tong University, Shanghai 200300, China
| | - Jianhua Sheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Xinyi Cao
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Qian Guo
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Yuping Jia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China; CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhen Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China.
| | - Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao tong University School of Medicine, Shanghai 200030, China; CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China.
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31
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Tanner JJ, Cardoso J, Terry EL, Booker SQ, Glover TL, Garvan C, Deshpande H, Deutsch G, Lai S, Staud R, Addison A, Redden D, Goodin BR, Price CC, Fillingim RB, Sibille KT. Chronic Pain Severity and Sociodemographics: An Evaluation of the Neurobiological Interface. THE JOURNAL OF PAIN 2022; 23:248-262. [PMID: 34425249 PMCID: PMC8828699 DOI: 10.1016/j.jpain.2021.07.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 07/19/2021] [Accepted: 07/27/2021] [Indexed: 02/03/2023]
Abstract
Chronic pain is variably associated with brain structure. Phenotyping based on pain severity may address inconsistencies. Sociodemographic groups also differ in the experience of chronic pain severity. Whether differences by chronic pain severity and/or sociodemographic groups are indicated in pain-related areas of the brain is unknown. Relations between 2 measures of chronic pain severity and brain structure via T1-weighted MRI were investigated and sociodemographic group differences explored. The observational study included 142 community-dwelling (68 non-Hispanic Black [NHB] and 74 non-Hispanic White [NHW]) adults with/at risk for knee osteoarthritis. Relationships between chronic pain severity, sociodemographic groups, and a priori selected brain structures (postcentral gyrus, insula, medial orbitofrontal, anterior cingulate, rostral middle frontal gyrus, hippocampus, amygdala, thalamus) were explored. Chronic pain severity associated with cortical thickness. NHB participants reported lower sociodemographic protective factors and greater clinical pain compared to NHWs who reported higher sociodemographic protective factors and lower clinical pain. Greater chronic pain severity was associated with smaller amygdala volumes in the NHB group and larger amygdala volumes in the NHW group. Brain structure by chronic pain stage differed between and within sociodemographic groups. Overall, chronic pain severity and sociodemographic factors are associated with pain-related brain structures. Our findings highlight the importance of further investigating social and environmental contributions in the experience of chronic pain to unravel the complex array of factors contributing to disparities. PERSPECTIVE: The study presents data demonstrating structural brain relationships with clinical pain severity, characteristic pain intensity and chronic pain stage, differ by sociodemographic groups. Findings yield insights into potential sources of previous inconsistent pain-brain relationships and highlights the need for future investigations to address social and environmental factors in chronic pain disparities research.
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Affiliation(s)
- Jared J Tanner
- Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida.
| | - Josue Cardoso
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida
| | - Ellen L Terry
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida; Department of Behavioral Nursing Science, University of Florida, Gainesville, Florida
| | - Staja Q Booker
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida; Department of Behavioral Nursing Science, University of Florida, Gainesville, Florida
| | - Toni L Glover
- School of Nursing, Oakland University, Rochester, Michigan
| | - Cynthia Garvan
- Department of Anesthesiology, University of Florida, Gainesville, Florida
| | - Hrishikesh Deshpande
- Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Georg Deutsch
- Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Song Lai
- Department of Radiation Oncology, University of Florida, Gainesville, Florida
| | - Roland Staud
- Department of Medicine, University of Florida, Gainesville, Florida
| | - Adrianna Addison
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama
| | - David Redden
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama; Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Burel R Goodin
- Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Catherine C Price
- Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida
| | - Roger B Fillingim
- Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida; Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida
| | - Kimberly T Sibille
- Pain Research and Intervention Center of Excellence, University of Florida, Gainesville, Florida; Department of Aging & Geriatric Research, University of Florida, Gainesville, Florida
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32
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Kemenczky P, Vakli P, Somogyi E, Homolya I, Hermann P, Gál V, Vidnyánszky Z. Effect of head motion-induced artefacts on the reliability of deep learning-based whole-brain segmentation. Sci Rep 2022; 12:1618. [PMID: 35102199 PMCID: PMC8803940 DOI: 10.1038/s41598-022-05583-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 12/30/2021] [Indexed: 11/21/2022] Open
Abstract
Due to their robustness and speed, recently developed deep learning-based methods have the potential to provide a faster and hence more scalable alternative to more conventional neuroimaging analysis pipelines in terms of whole-brain segmentation based on magnetic resonance (MR) images. These methods were also shown to have higher test-retest reliability, raising the possibility that they could also exhibit superior head motion tolerance. We investigated this by comparing the effect of head motion-induced artifacts in structural MR images on the consistency of segmentation performed by FreeSurfer and recently developed deep learning-based methods to a similar extent. We used state-of-the art neural network models (FastSurferCNN and Kwyk) and developed a new whole-brain segmentation pipeline (ReSeg) to examine whether reliability depends on choice of deep learning method. Structural MRI scans were collected from 110 participants under rest and active head motion and were evaluated for image quality by radiologists. Compared to FreeSurfer, deep learning-based methods provided more consistent segmentations across different levels of image quality, suggesting that they also have the advantage of providing more reliable whole-brain segmentations of MR images corrupted by motion-induced artifacts, and provide evidence for their practical applicability in the study of brain structural alterations in health and disease.
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Affiliation(s)
- Péter Kemenczky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
| | - Pál Vakli
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
| | - Eszter Somogyi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
- Institute of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Viktor Gál
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
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33
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Ottom MA, Rahman HA, Dinov ID. Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:1800508. [PMID: 35774412 PMCID: PMC9236306 DOI: 10.1109/jtehm.2022.3176737] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 05/12/2022] [Accepted: 05/16/2022] [Indexed: 11/22/2022]
Abstract
Background: Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans. Deep learning approaches have attracted researchers in medical imaging due to their capacity, performance, and potential to assist in accurate diagnosis, prognosis, and medical treatment technologies. Methods and procedures: This paper presents a novel framework for segmenting 2D brain tumors in MR images using deep neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (Znet) is based on the idea of skip-connection, encoder-decoder architectures, and data amplification to propagate the intrinsic affinities of a relatively smaller number of expert delineated tumors, e.g., hundreds of patients of the low-grade glioma (LGG), to many thousands of synthetic cases. Results: Our experimental results showed high values of the mean dice similarity coefficient (dice = 0.96 during model training and dice = 0.92 for the independent testing dataset). Other evaluation measures were also relatively high, e.g., pixel accuracy = 0.996, F1 score = 0.81, and Matthews Correlation Coefficient, MCC = 0.81. The results and visualization of the DNN-derived tumor masks in the testing dataset showcase the ZNet model’s capability to localize and auto-segment brain tumors in MR images. This approach can further be generalized to 3D brain volumes, other pathologies, and a wide range of image modalities. Conclusion: We can confirm the ability of deep learning methods and the proposed Znet framework to detect and segment tumors in MR images. Furthermore, pixel accuracy evaluation may not be a suitable evaluation measure for semantic segmentation in case of class imbalance in MR images segmentation. This is because the dominant class in ground truth images is the background. Therefore, a high value of pixel accuracy can be misleading in some computer vision applications. On the other hand, alternative evaluation metrics, such as dice and IoU (Intersection over Union), are more factual for semantic segmentation. Clinical impact: Artificial intelligence (AI) applications in medicine are advancing swiftly, however, there is a lack of deployed techniques in clinical practice. This research demonstrates a practical example of AI applications in medical imaging, which can be deployed as a tool for auto-segmentation of tumors in MR images.
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Affiliation(s)
| | - Hanif Abdul Rahman
- Departments of Health Behavior and Biological Sciences and Computational Medicine and Bioinformatics, Statistics Online Computational Resource, University of Michigan, Ann Arbor, MI, USA
| | - Ivo D. Dinov
- Departments of Health Behavior and Biological Sciences and Computational Medicine and Bioinformatics, Statistics Online Computational Resource, University of Michigan, Ann Arbor, MI, USA
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34
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Rabin RA, Mackey S, Parvaz MA, Cousijn J, Li C, Pearlson G, Schmaal L, Sinha R, Stein E, Veltman D, Thompson PM, Conrod P, Garavan H, Alia‐Klein N, Goldstein RZ. Common and gender-specific associations with cocaine use on gray matter volume: Data from the ENIGMA addiction working group. Hum Brain Mapp 2022; 43:543-554. [PMID: 32857473 PMCID: PMC8675419 DOI: 10.1002/hbm.25141] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 06/26/2020] [Accepted: 07/10/2020] [Indexed: 11/30/2022] Open
Abstract
Gray matter volume (GMV) in frontal cortical and limbic regions is susceptible to cocaine-associated reductions in cocaine-dependent individuals (CD) and is negatively associated with duration of cocaine use. Gender differences in CD individuals have been reported clinically and in the context of neural responses to cue-induced craving and stress reactivity. The variability of GMV in select brain areas between men and women (e.g., limbic regions) underscores the importance of exploring interaction effects between gender and cocaine dependence on brain structure. Therefore, voxel-based morphometry data derived from the ENIGMA Addiction Consortium were used to investigate potential gender differences in GMV in CD individuals compared to matched controls (CTL). T1-weighted MRI scans and clinical data were pooled from seven sites yielding 420 gender- and age-matched participants: CD men (CDM, n = 140); CD women (CDW, n = 70); control men (CTLM, n = 140); and control women (CTLW, n = 70). Differences in GMV were assessed using a 2 × 2 ANCOVA, and voxelwise whole-brain linear regressions were conducted to explore relationships between GMV and duration of cocaine use. All analyses were corrected for age, total intracranial volume, and site. Diagnostic differences were predominantly found in frontal regions (CD < CTL). Interestingly, gender × diagnosis interactions in the left anterior insula and left lingual gyrus were also documented, driven by differences in women (CDW < CTLW). Further, lower right hippocampal GMV was associated with greater cocaine duration in CDM. Given the importance of the anterior insula to interoception and the hippocampus to learning contextual associations, results may point to gender-specific mechanisms in cocaine addiction.
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Affiliation(s)
- Rachel A. Rabin
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Scott Mackey
- Departments of Psychiatry and PsychologyUniversity of VermontBurlingtonVermontUSA
| | - Muhammad A. Parvaz
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Janna Cousijn
- Department of PsychologyUniversity of AmsterdamAmsterdamThe Netherlands
| | - Chiang‐shan Li
- Department of PsychiatryYale University School of MedicineNew HavenConnecticutUSA
| | - Godfrey Pearlson
- Department of PsychiatryYale University School of MedicineNew HavenConnecticutUSA
| | - Lianne Schmaal
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia and Centre for Youth Mental HealthThe University of MelbourneMelbourneAustralia
| | - Rajita Sinha
- Department of PsychiatryYale University School of MedicineNew HavenConnecticutUSA
| | - Elliot Stein
- Intramural Research Program—Neuroimaging Research BranchNational Institute on Drug AbuseBaltimoreMarylandUSA
| | - Dick Veltman
- Department of PsychiatryVU University Medical CenterAmsterdamThe Netherlands
| | - Paul M. Thompson
- Imaging Genetics Center, Department of Neurology Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Patricia Conrod
- Department of PsychiatryUniversité de Montreal, CHU Ste Justine HospitalMontrealQuebecCanada
| | - Hugh Garavan
- Departments of Psychiatry and PsychologyUniversity of VermontBurlingtonVermontUSA
| | - Nelly Alia‐Klein
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Rita Z. Goldstein
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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Population-specific brain [ 18F]-FDG PET templates of Chinese subjects for statistical parametric mapping. Sci Data 2021; 8:305. [PMID: 34836985 PMCID: PMC8626451 DOI: 10.1038/s41597-021-01089-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 11/06/2021] [Indexed: 11/14/2022] Open
Abstract
Statistical Parametric Mapping (SPM) is a computational approach for analysing functional brain images like Positron Emission Tomography (PET). When performing SPM analysis for different patient populations, brain PET template images representing population-specific brain morphometry and metabolism features are helpful. However, most currently available brain PET templates were constructed using the Caucasian data. To enrich the family of publicly available brain PET templates, we created Chinese-specific template images based on 116 [18F]-fluorodeoxyglucose ([18F]-FDG) PET images of normal participants. These images were warped into a common averaged space, in which the mean and standard deviation templates were both computed. We also developed the SPM analysis programmes to facilitate easy use of the templates. Our templates were validated through the SPM analysis of Alzheimer’s and Parkinson’s patient images. The resultant SPM t-maps accurately depicted the disease-related brain regions with abnormal [18F]-FDG uptake, proving the templates’ effectiveness in brain function impairment analysis. Measurement(s) | brain metabolism measurement | Technology Type(s) | FDG-Positron Emission Tomography | Factor Type(s) | age • sex | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | China |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16382418
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Borys D, Kijonka M, Psiuk-Maksymowicz K, Gorczewski K, Zarudzki L, Sokol M, Swierniak A. Non-parametric MRI Brain Atlas for the Polish Population. Front Neuroinform 2021; 15:684759. [PMID: 34690731 PMCID: PMC8526931 DOI: 10.3389/fninf.2021.684759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: The application of magnetic resonance imaging (MRI) to acquire detailed descriptions of the brain morphology in vivo is a driving force in brain mapping research. Most atlases are based on parametric statistics, however, the empirical results indicate that the population brain tissue distributions do not exhibit exactly a Gaussian shape. Our aim was to verify the population voxel-wise distribution of three main tissue classes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), and to construct the brain templates for the Polish (Upper Silesian) healthy population with the associated non-parametric tissue probability maps (TPMs) taking into account the sex and age influence. Material and Methods: The voxel-wise distributions of these tissues were analyzed using the Shapiro-Wilk test. The non-parametric atlases were generated from 96 brains of the ethnically homogeneous, neurologically healthy, and radiologically verified group examined in a 3-Tesla MRI system. The standard parametric tissue proportion maps were also calculated for the sake of comparison. The maps were compared using the Wilcoxon signed-rank test and Kolmogorov-Smirnov test. The volumetric results segmented with the parametric and non-parametric templates were also analyzed. Results: The results confirmed that in each brain structure (regardless of the studied sub-population) the data distribution is skewed and apparently not Gaussian. The determined non-parametric and parametric templates were statistically compared, and significant differences were found between the maps obtained using both measures (the maps of GM, WM, and CSF). The impacts of applying the parametric and non-parametric TPMs on the segmentation process were also compared. The GM volumes are significantly greater when using the non-parametric atlas in the segmentation procedure, while the CSF volumes are smaller. Discussion and Conclusion: To determine the population atlases the parametric measures are uncritically and widely used. However, our findings suggest that the mean and parametric measures of such skewed distribution may not be the most appropriate summary statistic to find the best spatial representations of the structures in a standard space. The non-parametric methodology is more relevant and universal than the parametric approach in constructing the MRI brain atlases.
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Affiliation(s)
- Damian Borys
- Faculty of Automatic Control, Electronics and Computer Science, Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland.,Biotechnology Center, Silesian University of Technology, Gliwice, Poland
| | - Marek Kijonka
- Department of Medical Physics, Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Krzysztof Psiuk-Maksymowicz
- Faculty of Automatic Control, Electronics and Computer Science, Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland.,Biotechnology Center, Silesian University of Technology, Gliwice, Poland
| | - Kamil Gorczewski
- Faculty of Automatic Control, Electronics and Computer Science, Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Lukasz Zarudzki
- Department of Radiology, Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Maria Sokol
- Department of Medical Physics, Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Andrzej Swierniak
- Faculty of Automatic Control, Electronics and Computer Science, Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland.,Biotechnology Center, Silesian University of Technology, Gliwice, Poland
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Ciolac D, Gonzalez-Escamilla G, Radetz A, Fleischer V, Person M, Johnen A, Landmeyer NC, Krämer J, Muthuraman M, Meuth SG, Groppa S. Sex-specific signatures of intrinsic hippocampal networks and regional integrity underlying cognitive status in multiple sclerosis. Brain Commun 2021; 3:fcab198. [PMID: 34514402 PMCID: PMC8417841 DOI: 10.1093/braincomms/fcab198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/27/2021] [Accepted: 07/15/2021] [Indexed: 11/24/2022] Open
Abstract
The hippocampus is an anatomically compartmentalized structure embedded in highly wired networks that are essential for cognitive functions. The hippocampal vulnerability has been postulated in acute and chronic neuroinflammation in multiple sclerosis, while the patterns of occurring inflammation, neurodegeneration or compensation have not yet been described. Besides focal damage to hippocampal tissue, network disruption is an important contributor to cognitive decline in multiple sclerosis patients. We postulate sex-specific trajectories in hippocampal network reorganization and regional integrity and address their relationship to markers of neuroinflammation, cognitive/memory performance and clinical severity. In a large cohort of multiple sclerosis patients (n = 476; 337 females, age 35 ± 10 years, disease duration 16 ± 14 months) and healthy subjects (n = 110, 54 females; age 34 ± 15 years), we utilized MRI at baseline and at 2-year follow-up to quantify regional hippocampal volumetry and reconstruct single-subject hippocampal networks. Through graph analytical tools we assessed the clustered topology of the hippocampal networks. Mixed-effects analyses served to model sex-based differences in hippocampal network and subfield integrity between multiple sclerosis patients and healthy subjects at both time points and longitudinally. Afterwards, hippocampal network and subfield integrity were related to clinical and radiological variables in dependency of sex attribution. We found a more clustered network architecture in both female and male patients compared to their healthy counterparts. At both time points, female patients displayed a more clustered network topology in comparison to male patients. Over time, multiple sclerosis patients developed an even more clustered network architecture, though with a greater magnitude in females. We detected reduced regional volumes in most of the addressed hippocampal subfields in both female and male patients compared to healthy subjects. Compared to male patients, females displayed lower volumes of para- and presubiculum but higher volumes of the molecular layer. Longitudinally, volumetric alterations were more pronounced in female patients, which showed a more extensive regional tissue loss. Despite a comparable cognitive/memory performance between female and male patients over the follow-up period, we identified a strong interrelation between hippocampal network properties and cognitive/memory performance only in female patients. Our findings evidence a more clustered hippocampal network topology in female patients with a more extensive subfield volume loss over time. A stronger relation between cognitive/memory performance and the network topology in female patients suggests greater entrainment of the brain’s reserve. These results may serve to adapt sex-targeted neuropsychological interventions.
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Affiliation(s)
- Dumitru Ciolac
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany.,Department of Neurology, Institute of Emergency Medicine, Chisinau 2004, Moldova.,Laboratory of Neurobiology and Medical Genetics, Nicolae Testemitanu State University of Medicine and Pharmacy, Chisinau 2004, Moldova
| | - Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Angela Radetz
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Maren Person
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Andreas Johnen
- Department of Neurology with Institute of Translational Neurology, University Hospital of Münster, Münster 48149, Germany
| | - Nils C Landmeyer
- Department of Neurology with Institute of Translational Neurology, University Hospital of Münster, Münster 48149, Germany
| | - Julia Krämer
- Department of Neurology with Institute of Translational Neurology, University Hospital of Münster, Münster 48149, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Sven G Meuth
- Department of Neurology, Heinrich Heine University, Düsseldorf 40225, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55131, Germany
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Choi YY, Lee JJ, Choi KY, Choi US, Seo EH, Choo IH, Kim H, Song MK, Choi SM, Cho SH, Choe Y, Kim BC, Lee KH. Multi-Racial Normative Data for Lobar and Subcortical Brain Volumes in Old Age: Korean and Caucasian Norms May Be Incompatible With Each Other †. Front Aging Neurosci 2021; 13:675016. [PMID: 34413763 PMCID: PMC8369368 DOI: 10.3389/fnagi.2021.675016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/15/2021] [Indexed: 11/17/2022] Open
Abstract
Brain aging is becoming an increasingly important topic, and the norms of brain structures are essential for diagnosing neurodegenerative diseases. However, previous studies of the aging brain have mostly focused on Caucasians, not East Asians. The aim of this paper was to examine ethnic differences in the aging process of brain structures or to determine to what extent ethnicity affects the normative values of lobar and subcortical volumes in clinically normal elderly and the diagnosis in multi-racial patients with Alzheimer's disease (AD). Lobar and subcortical volumes were measured using FreeSurfer from MRI data of 1,686 normal Koreans (age range 59–89) and 851 Caucasian, non-Hispanic subjects in the ADNI and OASIS datasets. The regression models were designed to predict brain volumes, including ethnicity, age, sex, intracranial volume (ICV), magnetic field strength (MFS), and MRI scanner manufacturers as independent variables. Ethnicity had a significant effect for all lobar (|β| > 0.20, p < 0.001) and subcortical regions (|β| > 0.08, p < 0.001) except left pallidus and bilateral ventricles. To demonstrate the validity of the z-score for AD diagnosis, 420 patients and 420 normal controls were selected evenly from the Korean and Caucasian datasets. The four validation groups divided by race and diagnosis were matched on age and sex using a propensity score matching. We analyzed whether and to what extent the ethnicity adjustment improved the diagnostic power of the logistic regression model that was built using the only z-scores of six regions: bilateral temporal cortices, hippocampi, and amygdalae. The performance of the classifier after ethnicity adjustment was significantly improved compared with the classifier before ethnicity adjustment (ΔAUC = 0.10, D = 7.80, p < 0.001; AUC comparison test using bootstrap). Korean AD dementia patients may not be classified by Caucasian norms of brain volumes because the brain regions vulnerable to AD dementia are bigger in normal Korean elderly peoples. Therefore, ethnicity is an essential factor in establishing normative data for regional volumes in brain aging and applying it to the diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Yu Yong Choi
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea.,Biomedical Technology Center, Chosun University Hospital, Gwangju, South Korea
| | - Jang Jae Lee
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea
| | - Kyu Yeong Choi
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea
| | - Uk-Su Choi
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea
| | - Eun Hyun Seo
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea
| | - Il Han Choo
- Department of Neuropsychiatry, Chosun University School of Medicine and Hospital, Gwangju, South Korea
| | - Hoowon Kim
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea.,Biomedical Technology Center, Chosun University Hospital, Gwangju, South Korea.,Department of Neurology, Chosun University School of Medicine and Hospital, Gwangju, South Korea
| | - Min-Kyung Song
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Seong-Min Choi
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Soo Hyun Cho
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | | | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's Disease and Related Dementia Cohort Research Center, Chosun University, Gwangju, South Korea.,Korea Brain Research Institute, Daegu, South Korea.,Department of Biomedical Science, Chosun University, Gwangju, South Korea.,Neurozen Inc., Seoul, South Korea
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Personalized prediction of transcranial magnetic stimulation clinical response in patients with treatment-refractory depression using neuroimaging biomarkers and machine learning. J Affect Disord 2021; 290:261-271. [PMID: 34010751 DOI: 10.1016/j.jad.2021.04.081] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/13/2021] [Accepted: 04/23/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Functional connectivity between the left dorsolateral prefrontal cortex (DLPFC) and subgenual cingulate (sgACC) may serve as a biomarker for transcranial magnetic stimulation (rTMS) treatment response. The first aim was to establish whether this finding is veridical or artifactually induced by the pre-processing method. Furthermore, alternative biomarkers were identified and the clinical utility for personalized medicine was examined. METHODS Resting-state fMRI data were collected in medication-refractory depressed patients (n = 70, 16 males) before undergoing neuronavigated left DLPFC rTMS. Seed-based analyses were performed with and without global signal regression pre-processing to identify biomarkers of short-term and long-term treatment response. Receiver Operating Characteristic curve and supervised machine learning analyses were applied to assess the clinical utility of these biomarkers for the classification of categorical rTMS response. RESULTS Regardless of the pre-processing method, DLPFC-sgACC connectivity was not associated with treatment outcome. Instead, poorer connectivity between the sgACC and three clusters (peak locations: frontal pole, superior parietal lobule, occipital cortex) and DLPFC-central opercular cortex were observed in long-term nonresponders. The identified connections could serve as acceptable to excellent markers. Combining the features using supervised machine learning reached accuracy rates of 95.35% (CI=82.94-100.00) and 88.89% (CI=63.96-100.00) in the cross-validation and test dataset, respectively. LIMITATIONS The sample size was moderate, and features for machine learning were based on group differences. CONCLUSIONS Long-term nonresponders showed greater disrupted connectivity in regions involving the central executive network. Our findings may aid the development of personalized medicine for medication-refractory depression.
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Yarach U, Saekho S, Setsompop K, Suwannasak A, Boonsuth R, Wantanajittikul K, Angkurawaranon S, Angkurawaranon C, Sangpin P. Feasibility of accelerated 3D T1-weighted MRI using compressed sensing: application to quantitative volume measurements of human brain structures. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 34:915-927. [PMID: 34181119 DOI: 10.1007/s10334-021-00939-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 06/09/2021] [Accepted: 06/23/2021] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Scan time reduction is necessary for volumetric acquisitions to improve workflow productivity and to reduce motion artifacts during MRI procedures. We explored the possibility that Compressed Sensing-4 (CS-4) can be employed with 3D-turbo-field-echo T1-weighted (3D-TFE-T1W) sequence without compromising subcortical measurements on clinical 1.5 T MRI. MATERIALS AND METHODS Thirty-three healthy volunteers (24 females, 9 males) underwent imaging scans on a 1.5 T MRI equipped with a 12-channel head coil. 3D-TFE-T1W for whole-brain coverage was performed with different acceleration factors, including SENSE-2, SENSE-4, CS-4. Freesurfer, FSL's FIRST, and volBrain packages were utilized for subcortical segmentation. All processed data were assessed using the Wilcoxon signed-rank test. RESULTS The results obtained from SENSE-2 were considered as references. For SENSE-4, the maximum signal-to-noise ratio (SNR) drop was detected in the Accumbens (51.96%). For CS-4, the maximum SNR drop was detected in the Amygdala (10.55%). Since the SNR drop in CS-4 is relatively small, the SNR in all of the subcortical volumes obtained from SENSE-2 and CS-4 are not statistically different (P > 0.05), and their Pearson's correlation coefficients are larger than 0.90. The maximum biases of SENSE-4 and CS-4 were found in the Thalamus with the mean of differences of 1.60 ml and 0.18 ml, respectively. CONCLUSION CS-4 provided sufficient quality of 3D-TFE-T1W images for 1.5 T MRI equipped with a 12-channel receiver coil. Subcortical volumes obtained from the CS-4 images are consistent among different post-processing packages.
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Affiliation(s)
- Uten Yarach
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, 110 Intavaroros Rd. Sripoom, Chiang Mai, 50200, Thailand.
| | - Suwit Saekho
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, 110 Intavaroros Rd. Sripoom, Chiang Mai, 50200, Thailand
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Atita Suwannasak
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, 110 Intavaroros Rd. Sripoom, Chiang Mai, 50200, Thailand
| | - Ratthaporn Boonsuth
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, 110 Intavaroros Rd. Sripoom, Chiang Mai, 50200, Thailand
| | - Kittichai Wantanajittikul
- Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, 110 Intavaroros Rd. Sripoom, Chiang Mai, 50200, Thailand
| | - Salita Angkurawaranon
- Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Chaisiri Angkurawaranon
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
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Guo XY, Chang Y, Kim Y, Rhee HY, Cho AR, Park S, Ryu CW, San Lee J, Lee KM, Shin W, Park KC, Kim EJ, Jahng GH. Development and evaluation of a T1 standard brain template for Alzheimer disease. Quant Imaging Med Surg 2021; 11:2224-2244. [PMID: 34079697 DOI: 10.21037/qims-20-710] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) have high variability in brain tissue loss, making it difficult to use a disease-specific standard brain template. The objective of this study was to develop an AD-specific three-dimensional (3D) T1 brain tissue template and to evaluate the characteristics of the populations used to form the template. Methods We obtained 3D T1-weighted images from 294 individuals, including 101 AD, 96 amnestic MCI, and 97 cognitively normal (CN) elderly individuals, and segmented them into different brain tissues to generate AD-specific brain tissue templates. Demographic data and clinical outcome scores were compared between the three groups. Voxel-based analyses and regions-of-interest-based analyses were performed to compare gray matter volume (GMV) and white matter volume (WMV) between the three participant groups and to evaluate the relationship of GMV and WMV loss with age, years of education, and Mini-Mental State Examination (MMSE) scores. Results We created high-resolution AD-specific tissue probability maps (TPMs). In the AD and MCI groups, losses of both GMV and WMV were found with respect to the CN group in the hippocampus (F >44.60, P<0.001). GMV was lower with increasing age in all individuals in the left (r=-0.621, P<0.001) and right (r=-0.632, P<0.001) hippocampi. In the left hippocampus, GMV was positively correlated with years of education in the CN groups (r=0.345, P<0.001) but not in the MCI (r=0.223, P=0.0293) or AD (r=-0.021, P=0.835) groups. WMV of the corpus callosum was not significantly correlated with years of education in any of the three subject groups (r=0.035 and P=0.549 for left, r=0.013 and P=0.821 for right). In all individuals, GMV of the hippocampus was significantly correlated with MMSE scores (left, r=0.710 and P<0.001; right, r=0.680 and P<0.001), while WMV of the corpus callosum showed a weak correlation (left, r=0.142 and P=0.015; right, r=0.123 and P=0.035). Conclusions A 3D, T1 brain tissue template was created using imaging data from CN, MCI, and AD participants considering the participants' age, sex, and years of education. Our disease-specific template can help evaluate brains to promote early diagnosis of MCI individuals and aid treatment of MCI and AD individuals.
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Affiliation(s)
- Xiao-Yi Guo
- Department of Medicine, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Yunjung Chang
- Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, Gyeonggi-do, Republic of Korea
| | - Yehee Kim
- Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, Gyeonggi-do, Republic of Korea
| | - Hak Young Rhee
- Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Ah Rang Cho
- Department of Psychiatry, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Soonchan Park
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Chang-Woo Ryu
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Kyung Mi Lee
- Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Wonchul Shin
- Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Key-Chung Park
- Department of Neurology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Eui Jong Kim
- Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
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Li H, Yan G, Luo W, Liu T, Wang Y, Liu R, Zheng W, Zhang Y, Li K, Zhao L, Limperopoulos C, Zou Y, Wu D. Mapping fetal brain development based on automated segmentation and 4D brain atlasing. Brain Struct Funct 2021; 226:1961-1972. [PMID: 34050792 DOI: 10.1007/s00429-021-02303-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022]
Abstract
Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23 and 36 weeks of gestation. The proposed pipeline enabled the fully automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for the quantitative characterization of fetal brain development, especially in the Chinese population.
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Affiliation(s)
- Haotian Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Guohui Yan
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wanrong Luo
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yan Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ruibin Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Neurology, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Kui Li
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Li Zhao
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Catherine Limperopoulos
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Yu Zou
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
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Razavi F, Raminfard S, Kalantar Hormozi H, Sisakhti M, Batouli SAH. A Probabilistic Atlas of the Pineal Gland in the Standard Space. Front Neuroinform 2021; 15:554229. [PMID: 34079447 PMCID: PMC8165226 DOI: 10.3389/fninf.2021.554229] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 04/20/2021] [Indexed: 12/15/2022] Open
Abstract
Pineal gland (PG) is a structure located in the midline of the brain, and is considered as a main part of the epithalamus. There are numerous reports on the facilitatory role of this area for brain function; hormone secretion and its role in sleep cycle are the major reports. However, reports are rarely available on the direct role of this structure in brain cognition and in information processing. A suggestion for the limited number of such studies is the lack of a standard atlas for the PG; none of the available MRI templates and atlases has provided parcellations for this structure. In this study, we used the three-dimensional (3D) T1-weighted MRI data of 152 healthy young volunteers, and provided a probabilistic map of the PG in the standard Montreal Neurologic Institute (MNI) space. The methods included collecting the data using a 64-channel head coil on a 3-Tesla Prisma MRI Scanner, manual delineation of the PG by two experts, and robust template and atlas construction algorithms. This atlas is freely accessible, and we hope importing this atlas in the well-known neuroimaging software packages would help to identify other probable roles of the PG in brain function. It could also be used to study pineal cysts, for volumetric analyses, and to test any associations between the cognitive abilities of the human and the structure of the PG.
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Affiliation(s)
- Foroogh Razavi
- Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Samira Raminfard
- Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Hadis Kalantar Hormozi
- Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Minoo Sisakhti
- Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.,Department of Cognitive Psychology, Institute for Cognitive Sciences Studies, Tehran, Iran
| | - Seyed Amir Hossein Batouli
- Neuroimaging and Analysis Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.,Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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44
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Schwarz CG. Uses of Human MR and PET Imaging in Research of Neurodegenerative Brain Diseases. Neurotherapeutics 2021; 18:661-672. [PMID: 33723751 PMCID: PMC8423895 DOI: 10.1007/s13311-021-01030-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 01/18/2023] Open
Abstract
In the past decades, many neuroimaging studies have aimed to improve the scientific understanding of human neurodegenerative diseases using MRI and PET. This article is designed to provide an overview of the major classes of brain imaging and how/why they are used in this line of research. It is intended as a primer for individuals who are relatively unfamiliar with the methods of neuroimaging research to gain a better understanding of the vocabulary and overall methodologies. It is not intended to describe or review any research findings for any disease or biology, but rather to broadly describe the imaging methodologies that are used in conducting this neurodegeneration research. We will also review challenges and strategies for analyzing neuroimaging data across multiple sites and studies, i.e., harmonization and standardization of imaging data for multi-site and meta-analyses.
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Princich JP, Donnelly-Kehoe PA, Deleglise A, Vallejo-Azar MN, Pascariello GO, Seoane P, Veron Do Santos JG, Collavini S, Nasimbera AH, Kochen S. Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm. Front Neurol 2021; 12:613967. [PMID: 33692740 PMCID: PMC7937810 DOI: 10.3389/fneur.2021.613967] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/18/2021] [Indexed: 01/07/2023] Open
Abstract
Introduction: Several methods offer free volumetry services for MR data that adequately quantify volume differences in the hippocampus and its subregions. These methods are frequently used to assist in clinical diagnosis of suspected hippocampal sclerosis in temporal lobe epilepsy. A strong association between severity of histopathological anomalies and hippocampal volumes was reported using MR volumetry with a higher diagnostic yield than visual examination alone. Interpretation of volumetry results is challenging due to inherent methodological differences and to the reported variability of hippocampal volume. Furthermore, normal morphometric differences are recognized in diverse populations that may need consideration. To address this concern, we highlighted procedural discrepancies including atlas definition and computation of total intracranial volume that may impact volumetry results. We aimed to quantify diagnostic performance and to propose reference values for hippocampal volume from two well-established techniques: FreeSurfer v.06 and volBrain-HIPS. Methods: Volumetry measures were calculated using clinical T1 MRI from a local population of 61 healthy controls and 57 epilepsy patients with confirmed unilateral hippocampal sclerosis. We further validated the results by a state-of-the-art machine learning classification algorithm (Random Forest) computing accuracy and feature relevance to distinguish between patients and controls. This validation process was performed using the FreeSurfer dataset alone, considering morphometric values not only from the hippocampus but also from additional non-hippocampal brain regions that could be potentially relevant for group classification. Mean reference values and 95% confidence intervals were calculated for left and right hippocampi along with hippocampal asymmetry degree to test diagnostic accuracy. Results: Both methods showed excellent classification performance (AUC:> 0.914) with noticeable differences in absolute (cm3) and normalized volumes. Hippocampal asymmetry was the most accurate discriminator from all estimates (AUC:1~0.97). Similar results were achieved in the validation test with an automatic classifier (AUC:>0.960), disclosing hippocampal structures as the most relevant features for group differentiation among other brain regions. Conclusion: We calculated reference volumetry values from two commonly used methods to accurately identify patients with temporal epilepsy and hippocampal sclerosis. Validation with an automatic classifier confirmed the principal role of the hippocampus and its subregions for diagnosis.
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Affiliation(s)
- Juan Pablo Princich
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital de Pediatría J.P Garrahan, Departamento de Neuroimágenes, Buenos Aires, Argentina
| | - Patricio Andres Donnelly-Kehoe
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Grupo de Procesamiento de Señales Multimedia - División Neuroimágenes, Universidad Nacional de Rosario, Rosario, Argentina
| | - Alvaro Deleglise
- Instituto de Fisiología y Biofísica B. Houssay (IFIBIO), Consejo Nacional de Investigaciones Científicas y Técnicas, Departamento de Fisiología y Biofísica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Mariana Nahir Vallejo-Azar
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
| | - Guido Orlando Pascariello
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas (CIFASIS) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Grupo de Procesamiento de Señales Multimedia - División Neuroimágenes, Universidad Nacional de Rosario, Rosario, Argentina
| | - Pablo Seoane
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital J.M Ramos Mejía, Centro de Epilepsia, Buenos Aires, Argentina
| | - Jose Gabriel Veron Do Santos
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
| | - Santiago Collavini
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Instituto de investigación en Electrónica, Control y Procesamiento de Señales (LEICI), Universidad Nacional de La Plata-Consejo Nacional de Investigaciones Científicas y Técnicas, La Plata, Argentina.,Instituto de Ingeniería y Agronomía, Universidad Nacional Arturo Jauretche, Florencio Varela, Argentina
| | - Alejandro Hugo Nasimbera
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina.,Hospital J.M Ramos Mejía, Centro de Epilepsia, Buenos Aires, Argentina
| | - Silvia Kochen
- ENyS (Estudios en Neurociencias y Sistemas Complejos), Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional Arturo Jauretche y Hospital El Cruce, Florencio Varela, Argentina
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Abstract
Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski, Woycickiego 1/3, Block 12, room 1220, 01-938, Warsaw, Poland.
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47
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Bae JB, Lee S, Jung W, Park S, Kim W, Oh H, Han JW, Kim GE, Kim JS, Kim JH, Kim KW. Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging. Sci Rep 2020; 10:22252. [PMID: 33335244 PMCID: PMC7746752 DOI: 10.1038/s41598-020-79243-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 12/07/2020] [Indexed: 11/09/2022] Open
Abstract
The classification of Alzheimer's disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a convolutional neural network (CNN)-based AD classification algorithm using magnetic resonance imaging (MRI) scans from AD patients and age/gender-matched cognitively normal controls from two populations that differ in ethnicity and education level. These populations come from the Seoul National University Bundang Hospital (SNUBH) and Alzheimer's Disease Neuroimaging Initiative (ADNI). For each population, we trained CNNs on five subsets using coronal slices of T1-weighted images that cover the medial temporal lobe. We evaluated the models on validation subsets from both the same population (within-dataset validation) and other population (between-dataset validation). Our models achieved average areas under the curves of 0.91-0.94 for within-dataset validation and 0.88-0.89 for between-dataset validation. The mean processing time per person was 23-24 s. The within-dataset and between-dataset performances were comparable between the ADNI-derived and SNUBH-derived models. These results demonstrate the generalizability of our models to different patients with different ethnicities and education levels, as well as their potential for deployment as fast and accurate diagnostic support tools for AD.
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Affiliation(s)
- Jong Bin Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Subin Lee
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea
| | | | | | | | | | - Ji Won Han
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Grace Eun Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea
| | - Jun Sung Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Ki Woong Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea.
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea.
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48
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Holla B, Taylor PA, Glen DR, Lee JA, Vaidya N, Mehta UM, Venkatasubramanian G, Pal PK, Saini J, Rao NP, Ahuja CK, Kuriyan R, Krishna M, Basu D, Kalyanram K, Chakrabarti A, Orfanos DP, Barker GJ, Cox RW, Schumann G, Bharath RD, Benegal V. A series of five population-specific Indian brain templates and atlases spanning ages 6-60 years. Hum Brain Mapp 2020; 41:5164-5175. [PMID: 32845057 PMCID: PMC7670651 DOI: 10.1002/hbm.25182] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/27/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022] Open
Abstract
Anatomical brain templates are commonly used as references in neurological MRI studies, for bringing data into a common space for group‐level statistics and coordinate reporting. Given the inherent variability in brain morphology across age and geography, it is important to have templates that are as representative as possible for both age and population. A representative‐template increases the accuracy of alignment, decreases distortions as well as potential biases in final coordinate reports. In this study, we developed and validated a new set of T1w Indian brain templates (IBT) from a large number of brain scans (total n = 466) acquired across different locations and multiple 3T MRI scanners in India. A new tool in AFNI, make_template_dask.py, was created to efficiently make five age‐specific IBTs (ages 6–60 years) as well as maximum probability map (MPM) atlases for each template; for each age‐group's template–atlas pair, there is both a “population‐average” and a “typical” version. Validation experiments on an independent Indian structural and functional‐MRI dataset show the appropriateness of IBTs for spatial normalization of Indian brains. The results indicate significant structural differences when comparing the IBTs and MNI template, with these differences being maximal along the Anterior–Posterior and Inferior–Superior axes, but minimal Left–Right. For each age‐group, the MPM brain atlases provide reasonably good representation of the native‐space volumes in the IBT space, except in a few regions with high intersubject variability. These findings provide evidence to support the use of age and population‐specific templates in human brain mapping studies.
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Affiliation(s)
- Bharath Holla
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Paul A Taylor
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, Maryland, USA
| | - Daniel R Glen
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, Maryland, USA
| | - John A Lee
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, Maryland, USA
| | - Nilakshi Vaidya
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India.,Centre for Population Neuroscience and Stratified Medicine (PONS), SGDP Centre, IoPPN, KCL, London, UK
| | | | | | - Pramod Kumar Pal
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Jitender Saini
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Naren P Rao
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Chirag K Ahuja
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Rebecca Kuriyan
- St. John's Medical College and Research Institute, Bengaluru, India
| | - Murali Krishna
- CSI Holdsworth Memorial Hospital, Mysore, India.,Foundation for Research and Advocacy in Mental Health, Mysore, India
| | - Debashish Basu
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | | | | | | | - Gareth J Barker
- Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), London, UK
| | - Robert W Cox
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, Maryland, USA
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), SGDP Centre, IoPPN, KCL, London, UK
| | - Rose Dawn Bharath
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Vivek Benegal
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
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49
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Charting brain growth in tandem with brain templates at school age. Sci Bull (Beijing) 2020; 65:1924-1934. [PMID: 36738058 DOI: 10.1016/j.scib.2020.07.027] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 04/30/2020] [Accepted: 06/09/2020] [Indexed: 02/07/2023]
Abstract
Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental trajectories. The present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging protocol. Two accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and China. Anatomical magnetic resonance imaging (MRI) of typically developing school-age children (TDC) was obtained up to three times at nominal intervals of 1.25 years. The protocol generated and compared population- and age-specific brain templates and growth charts, respectively. A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American TDC. Population- and age-specific brain templates were used to quantify warp cost, the differences between individual brains and brain templates. Volumetric growth charts for labeled brain network areas were generated. Shape analyses of cost functions supported the necessity of age-specific and ethnicity-matched brain templates, which was confirmed by growth chart analyses. These analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas, regions which are most variable across individuals in regard to their structure and function. Age- and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts, indicating the necessity of the brain charts and brain templates generated in tandem. These templates and growth charts as well as related codes have been made freely available to the public for open neuroscience (https://github.com/zuoxinian/CCS/tree/master/H3/GrowthCharts).
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50
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Avinun R, Israel S, Knodt AR, Hariri AR. Little evidence for associations between the Big Five personality traits and variability in brain gray or white matter. Neuroimage 2020; 220:117092. [PMID: 32599267 PMCID: PMC7593529 DOI: 10.1016/j.neuroimage.2020.117092] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 06/18/2020] [Accepted: 06/23/2020] [Indexed: 12/12/2022] Open
Abstract
Attempts to link the Big Five personality traits of Openness-to-Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism with variability in trait-like features of brain structure have produced inconsistent results. Small sample sizes and heterogeneous methodology have been suspected in driving these inconsistencies. Here, using data collected from 1,107 university students (636 women, mean age 19.69 ± 1.24 years), representing the largest sample to date of unrelated individuals, we tested for associations between the Big Five personality traits and measures of cortical thickness and surface area, subcortical volume, and white matter microstructural integrity. In addition to replication analyses based on a prior study, we conducted exploratory whole-brain analyses. Four supplementary analyses were also conducted to examine 1) possible associations with lower-order facets of personality; 2) modulatory effects of sex; 3) effect of controlling for non-target personality traits; and 4) parcellation scheme effects. Our analyses failed to identify significant associations between the Big Five personality traits and brain morphometry, except for a weak association between greater surface area of the superior temporal gyrus and lower conscientiousness scores. As the latter association is not supported by previous studies, it should be treated with caution. Our supplementary analyses mirrored these predominantly null findings, suggesting they were not substantively biased by our analytic choices. Collectively, these results indicate that if there are associations between the Big Five personality traits and brain structure, they are likely of very small effect size and will require very large samples for reliable detection.
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Affiliation(s)
- Reut Avinun
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA; Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Salomon Israel
- Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Annchen R Knodt
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
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