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Smith DM, Parekh P, Kennedy J, Loughnan R, Frei O, Nichols TE, Andreassen OA, Jernigan TL, Dale AM. Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components. Cereb Cortex 2024; 34:bhae234. [PMID: 38850213 PMCID: PMC11161865 DOI: 10.1093/cercor/bhae234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 05/09/2024] [Accepted: 05/19/2024] [Indexed: 06/10/2024] Open
Abstract
The relative contributions of genetic variation and experience in shaping the morphology of the adolescent brain are not fully understood. Using longitudinal data from 11,665 subjects in the ABCD Study, we fit vertex-wise variance components including family effects, genetic effects, and subject-level effects using a computationally efficient framework. Variance in cortical thickness and surface area is largely attributable to genetic influence, whereas sulcal depth is primarily explained by subject-level effects. Our results identify areas with heterogeneous distributions of heritability estimates that have not been seen in previous work using data from cortical regions. We discuss the biological importance of subject-specific variance and its implications for environmental influences on cortical development and maturation.
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Affiliation(s)
- Diana M Smith
- Medical Scientist Training Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Neurosciences Graduate Program, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Problemveien 11, 0313 Oslo, Norway
| | - Joseph Kennedy
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Robert Loughnan
- Population Neuroscience and Genetics Lab, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Problemveien 11, 0313 Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Problemveien 11, 0313 Oslo, Norway
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7FZ, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Problemveien 11, 0313 Oslo, Norway
| | - Terry L Jernigan
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Cognitive Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Cognitive Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Radiology, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Psychiatry, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
- Department of Neuroscience, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093, USA
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Lawrence KE, Abaryan Z, Laltoo E, Hernandez LM, Gandal MJ, McCracken JT, Thompson PM. White matter microstructure shows sex differences in late childhood: Evidence from 6797 children. Hum Brain Mapp 2022; 44:535-548. [PMID: 36177528 PMCID: PMC9842921 DOI: 10.1002/hbm.26079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/29/2022] [Accepted: 08/19/2022] [Indexed: 02/01/2023] Open
Abstract
Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.
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Affiliation(s)
- Katherine E. Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Leanna M. Hernandez
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael J. Gandal
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA,Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA,Department of Human Genetics, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - James T. McCracken
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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Tuna EE, Poirot NL, Franson D, Bayona JB, Huang S, Seiberlich N, Griswold MA, Cavusoglu MC. MRI Distortion Correction and Robot-to-MRI Scanner Registration for an MRI-Guided Robotic System. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2022; 10:99205-99220. [PMID: 37041984 PMCID: PMC10085576 DOI: 10.1109/access.2022.3207156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Magnetic resonance imaging (MRI) guided robotic procedures require safe robotic instrument navigation and precise target localization. This depends on reliable tracking of the instrument from MR images, which requires accurate registration of the robot to the scanner. A novel differential image based robot-to-MRI scanner registration approach is proposed that utilizes a set of active fiducial coils, where background subtraction method is employed for coil detection. In order to use the presented preoperative registration approach jointly with the real-time high speed MRI image acquisition and reconstruction methods in real-time interventional procedures, the effects of the geometric MRI distortion in robot to scanner registration is analyzed using a custom distortion mapping algorithm. The proposed approach is validated by a set of target coils placed within the workspace, employing multi-planar capabilities of the scanner. Registration and validation errors are respectively 2.05 mm and 2.63 mm after the distortion correction showing an improvement of respectively 1.08 mm and 0.14 mm compared to the results without distortion correction.
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Affiliation(s)
- E Erdem Tuna
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Nate Lombard Poirot
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | | | - Juana Barrera Bayona
- School of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
| | - Sherry Huang
- General Electric Healthcare, Royal Oak, MI 48067, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann-Anbor, MI 48109, USA
| | - Mark A Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - M Cenk Cavusoglu
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
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Loughnan R, Ahern J, Tompkins C, Palmer CE, Iversen J, Thompson WK, Andreassen O, Jernigan T, Sugrue L, Dale A, Boyle MET, Fan CC. Association of Genetic Variant Linked to Hemochromatosis With Brain Magnetic Resonance Imaging Measures of Iron and Movement Disorders. JAMA Neurol 2022; 79:919-928. [PMID: 35913729 PMCID: PMC9344392 DOI: 10.1001/jamaneurol.2022.2030] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/26/2022] [Indexed: 12/31/2022]
Abstract
Importance Hereditary hemochromatosis (HH) is an autosomal recessive genetic disorder that leads to iron overload. Conflicting results from previous research has led some to believe the brain is spared the toxic effects of iron in HH. Objective To test the association of the strongest genetic risk variant for HH on brainwide measures sensitive to iron deposition and the rates of movement disorders in a substantially larger sample than previous studies of its kind. Design, Setting, and Participants This cross-sectional retrospective study included participants from the UK Biobank, a population-based sample. Genotype, health record, and neuroimaging data were collected from January 2006 to May 2021. Data analysis was conducted from January 2021 to April 2022. Disorders tested included movement disorders (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10], codes G20-G26), abnormalities of gait and mobility (ICD-10 codes R26), and other disorders of the nervous system (ICD-10 codes G90-G99). Exposures Homozygosity for p.C282Y, the largest known genetic risk factor for HH. Main Outcomes and Measures T2-weighted and T2* signal intensity from brain magnetic resonance imaging scans, measures sensitive to iron deposition, and clinical diagnosis of neurological disorders. Results The total cohort consisted of 488 288 individuals (264 719 female; ages 49-87 years, largely northern European ancestry), 2889 of whom were p.C282Y homozygotes. The neuroimaging analysis consisted of 836 individuals: 165 p.C282Y homozygotes (99 female) and 671 matched controls (399 female). A total of 206 individuals were excluded from analysis due to withdrawal of consent. Neuroimaging analysis showed that p.C282Y homozygosity was associated with decreased T2-weighted and T2* signal intensity in subcortical motor structures (basal ganglia, thalamus, red nucleus, and cerebellum; Cohen d >1) consistent with substantial iron deposition. Across the whole UK Biobank (2889 p.C282Y homozygotes, 485 399 controls), we found a significantly increased prevalence for movement disorders in male homozygotes (OR, 1.80; 95% CI, 1.28-2.55; P = .001) but not female individuals (OR, 1.09; 95% CI, 0.70-1.73; P = .69). Among the 31 p.C282Y male homozygotes with a movement disorder, only 10 had a concurrent HH diagnosis. Conclusions and Relevance These findings indicate increased iron deposition in subcortical motor circuits in p.C282Y homozygotes and confirm an increased association with movement disorders in male homozygotes. Early treatment in HH effectively prevents the negative consequences of iron overload in the liver and heart. Our work suggests that screening for p.C282Y homozygosity in high-risk individuals also has the potential to reduce brain iron accumulation and to reduce the risk of movement disorders among male individuals who are homozygous for this mutation.
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Affiliation(s)
- Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, La Jolla
- Population Neuroscience and Genetics, University of California, San Diego, La Jolla
| | - Jonathan Ahern
- Department of Cognitive Science, University of California, San Diego, La Jolla
| | - Cherisse Tompkins
- Department of Cognitive Science, University of California, San Diego, La Jolla
| | - Clare E. Palmer
- Center for Human Development, University of California, San Diego, La Jolla
| | - John Iversen
- Center for Human Development, University of California, San Diego, La Jolla
| | - Wesley K. Thompson
- Population Neuroscience and Genetics, University of California, San Diego, La Jolla
- Division of Biostatistics, Department of Radiology, University of California, San Diego, La Jolla
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Ole Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Terry Jernigan
- Department of Cognitive Science, University of California, San Diego, La Jolla
- Center for Human Development, University of California, San Diego, La Jolla
- Department of Radiology, University of California, San Diego School of Medicine, La Jolla
- Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla
| | - Leo Sugrue
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
- Department of Psychiatry, University of California, San Francisco
| | - Anders Dale
- Department of Cognitive Science, University of California, San Diego, La Jolla
- Department of Radiology, University of California, San Diego School of Medicine, La Jolla
- Department of Neuroscience, University of California, San Diego School of Medicine, La Jolla
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, La Jolla
| | - Mary E. T. Boyle
- Department of Cognitive Science, University of California, San Diego, La Jolla
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Chun Chieh Fan
- Population Neuroscience and Genetics, University of California, San Diego, La Jolla
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, La Jolla
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, Oklahoma
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Palmer CE, Pecheva D, Iversen JR, Hagler DJ, Sugrue L, Nedelec P, Fan CC, Thompson WK, Jernigan TL, Dale AM. Microstructural development from 9 to 14 years: Evidence from the ABCD Study. Dev Cogn Neurosci 2021; 53:101044. [PMID: 34896850 PMCID: PMC8671104 DOI: 10.1016/j.dcn.2021.101044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 11/23/2021] [Accepted: 12/02/2021] [Indexed: 02/03/2023] Open
Abstract
During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.
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Affiliation(s)
- Clare E. Palmer
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA,Corresponding author.
| | - Diliana Pecheva
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA,Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - John R. Iversen
- Institute for Neural Computation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Donald J. Hagler
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA,Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - Leo Sugrue
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Pierre Nedelec
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Chun Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA
| | - Wesley K. Thompson
- Division of Biostatistics, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Terry L. Jernigan
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA,Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA,Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA,Department of Psychiatry, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA,Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA,Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA,Department of Neuroscience, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA
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6
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Hagler DJ, Hatton SN, Cornejo MD, Makowski C, Fair DA, Dick AS, Sutherland MT, Casey BJ, Barch DM, Harms MP, Watts R, Bjork JM, Garavan HP, Hilmer L, Pung CJ, Sicat CS, Kuperman J, Bartsch H, Xue F, Heitzeg MM, Laird AR, Trinh TT, Gonzalez R, Tapert SF, Riedel MC, Squeglia LM, Hyde LW, Rosenberg MD, Earl EA, Howlett KD, Baker FC, Soules M, Diaz J, de Leon OR, Thompson WK, Neale MC, Herting M, Sowell ER, Alvarez RP, Hawes SW, Sanchez M, Bodurka J, Breslin FJ, Morris AS, Paulus MP, Simmons WK, Polimeni JR, van der Kouwe A, Nencka AS, Gray KM, Pierpaoli C, Matochik JA, Noronha A, Aklin WM, Conway K, Glantz M, Hoffman E, Little R, Lopez M, Pariyadath V, Weiss SRB, Wolff-Hughes DL, DelCarmen-Wiggins R, Ewing SWF, Miranda-Dominguez O, Nagel BJ, Perrone AJ, Sturgeon DT, Goldstone A, Pfefferbaum A, Pohl KM, Prouty D, Uban K, Bookheimer SY, Dapretto M, Galvan A, Bagot K, Giedd J, Infante MA, Jacobus J, Patrick K, Shilling PD, Desikan R, Li Y, Sugrue L, Banich MT, Friedman N, Hewitt JK, Hopfer C, Sakai J, Tanabe J, Cottler LB, Nixon SJ, Chang L, Cloak C, Ernst T, Reeves G, Kennedy DN, Heeringa S, Peltier S, Schulenberg J, Sripada C, Zucker RA, Iacono WG, Luciana M, Calabro FJ, Clark DB, Lewis DA, Luna B, Schirda C, Brima T, Foxe JJ, Freedman EG, Mruzek DW, Mason MJ, Huber R, McGlade E, Prescot A, Renshaw PF, Yurgelun-Todd DA, Allgaier NA, Dumas JA, Ivanova M, Potter A, Florsheim P, Larson C, Lisdahl K, Charness ME, Fuemmeler B, Hettema JM, Maes HH, Steinberg J, Anokhin AP, Glaser P, Heath AC, Madden PA, Baskin-Sommers A, Constable RT, Grant SJ, Dowling GJ, Brown SA, Jernigan TL, Dale AM. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. Neuroimage 2019; 202:116091. [PMID: 31415884 PMCID: PMC6981278 DOI: 10.1016/j.neuroimage.2019.116091] [Citation(s) in RCA: 429] [Impact Index Per Article: 85.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 08/01/2019] [Accepted: 08/08/2019] [Indexed: 01/29/2023] Open
Abstract
The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Feng Xue
- University of California, San Diego
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Megan Herting
- University of Southern California & Children’s Hospital Los Angeles
| | | | - Ruben P Alvarez
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Yi Li
- University of California, San Francisco
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Michael E Charness
- VA Boston Healthcare System; Harvard Medical School; Boston University School of Medicine
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7
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Polimeni JR, Renvall V, Zaretskaya N, Fischl B. Analysis strategies for high-resolution UHF-fMRI data. Neuroimage 2018; 168:296-320. [PMID: 28461062 PMCID: PMC5664177 DOI: 10.1016/j.neuroimage.2017.04.053] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Revised: 04/21/2017] [Accepted: 04/22/2017] [Indexed: 12/22/2022] Open
Abstract
Functional MRI (fMRI) benefits from both increased sensitivity and specificity with increasing magnetic field strength, making it a key application for Ultra-High Field (UHF) MRI scanners. Most UHF-fMRI studies utilize the dramatic increases in sensitivity and specificity to acquire high-resolution data reaching sub-millimeter scales, which enable new classes of experiments to probe the functional organization of the human brain. This review article surveys advanced data analysis strategies developed for high-resolution fMRI at UHF. These include strategies designed to mitigate distortion and artifacts associated with higher fields in ways that attempt to preserve spatial resolution of the fMRI data, as well as recently introduced analysis techniques that are enabled by these extremely high-resolution data. Particular focus is placed on anatomically-informed analyses, including cortical surface-based analysis, which are powerful techniques that can guide each step of the analysis from preprocessing to statistical analysis to interpretation and visualization. New intracortical analysis techniques for laminar and columnar fMRI are also reviewed and discussed. Prospects for single-subject individualized analyses are also presented and discussed. Altogether, there are both specific challenges and opportunities presented by UHF-fMRI, and the use of proper analysis strategies can help these valuable data reach their full potential.
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Affiliation(s)
- Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States.
| | - Ville Renvall
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Natalia Zaretskaya
- Centre for Integrative Neuroscience, Department of Psychology, University of Tübingen, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, United States; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
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8
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Blumhagen JO, Ladebeck R, Fenchel M, Scheffler K. MR-based field-of-view extension in MR/PET:B0homogenization using gradient enhancement (HUGE). Magn Reson Med 2012. [DOI: 10.1002/mrm.24555] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Jan O. Blumhagen
- Magnetic Resonance; Healthcare Sector; Siemens AG; Erlangen Germany
- Division of Radiological Physics; University of Basel Hospital; Basel Switzerland
| | - Ralf Ladebeck
- Magnetic Resonance; Healthcare Sector; Siemens AG; Erlangen Germany
| | - Matthias Fenchel
- Magnetic Resonance; Healthcare Sector; Siemens AG; Erlangen Germany
| | - Klaus Scheffler
- Division of Radiological Physics; University of Basel Hospital; Basel Switzerland
- MRC Department; Max Planck Institute for Biological Cybernetics; Tuebingen Germany
- Department of Biomedical Magnetic Resonance; University Hospital Tuebingen; Tuebingen Germany
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9
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Roche A, Ribes D, Bach-Cuadra M, Krüger G. On the convergence of EM-like algorithms for image segmentation using Markov random fields. Med Image Anal 2011; 15:830-9. [PMID: 21621449 DOI: 10.1016/j.media.2011.05.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2010] [Revised: 04/20/2011] [Accepted: 05/04/2011] [Indexed: 11/19/2022]
Abstract
Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.
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Affiliation(s)
- Alexis Roche
- CIBM-Siemens, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland.
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Holland D, Dale AM. Nonlinear registration of longitudinal images and measurement of change in regions of interest. Med Image Anal 2011; 15:489-97. [PMID: 21388857 DOI: 10.1016/j.media.2011.02.005] [Citation(s) in RCA: 95] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2010] [Revised: 02/08/2011] [Accepted: 02/14/2011] [Indexed: 01/18/2023]
Abstract
We describe here a method, Quarc, for accurately quantifying structural changes in organs, based on serial MRI scans. The procedure can be used to measure deformations globally or in regions of interest (ROIs), including large-scale changes in the whole organ, and subtle changes in small-scale structures. We validate the method with model studies, and provide an illustrative analysis using the brain. We apply the method to the large, publicly available ADNI database of serial brain scans, and calculate Cohen's d effect sizes for several ROIs. Using publicly available derived-data, we directly compare effect sizes from Quarc with those from four existing methods that quantify cerebral structural change. Quarc produced a slightly improved, though not significantly different, whole brain effect size compared with the standard KN-BSI method, but in all other cases it produced significantly larger effect sizes.
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Affiliation(s)
- Dominic Holland
- Multimodal Imaging Laboratory, The University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92037, USA.
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