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Chaudhari NN, Imms PE, Chowdhury NF, Gatz M, Trumble BC, Mack WJ, Law EM, Sutherland ML, Sutherland JD, Rowan CJ, Wann LS, Allam AH, Thompson RC, Michalik DE, Miyamoto M, Lombardi G, Cummings DK, Seabright E, Alami S, Garcia AR, Rodriguez DE, Gutierrez RQ, Copajira AJ, Hooper PL, Buetow KH, Stieglitz J, Gurven MD, Thomas GS, Kaplan HS, Finch CE, Irimia A. Increases in regional brain volume across two native South American male populations. GeroScience 2024:10.1007/s11357-024-01168-2. [PMID: 38683289 DOI: 10.1007/s11357-024-01168-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 04/15/2024] [Indexed: 05/01/2024] Open
Abstract
Industrialized environments, despite benefits such as higher levels of formal education and lower rates of infections, can also have pernicious impacts upon brain atrophy. Partly for this reason, comparing age-related brain volume trajectories between industrialized and non-industrialized populations can help to suggest lifestyle correlates of brain health. The Tsimane, indigenous to the Bolivian Amazon, derive their subsistence from foraging and horticulture and are physically active. The Moseten, a mixed-ethnicity farming population, are physically active but less than the Tsimane. Within both populations (N = 1024; age range = 46-83), we calculated regional brain volumes from computed tomography and compared their cross-sectional trends with age to those of UK Biobank (UKBB) participants (N = 19,973; same age range). Surprisingly among Tsimane and Moseten (T/M) males, some parietal and occipital structures mediating visuospatial abilities exhibit small but significant increases in regional volume with age. UKBB males exhibit a steeper negative trend of regional volume with age in frontal and temporal structures compared to T/M males. However, T/M females exhibit significantly steeper rates of brain volume decrease with age compared to UKBB females, particularly for some cerebro-cortical structures (e.g., left subparietal cortex). Across the three populations, observed trends exhibit no interhemispheric asymmetry. In conclusion, the age-related rate of regional brain volume change may differ by lifestyle and sex. The lack of brain volume reduction with age is not known to exist in other human population, highlighting the putative role of lifestyle in constraining regional brain atrophy and promoting elements of non-industrialized lifestyle like higher physical activity.
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Affiliation(s)
- Nikhil N Chaudhari
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Phoebe E Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Nahian F Chowdhury
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
| | - Margaret Gatz
- Center for Economic and Social Research, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Benjamin C Trumble
- Center for Evolution & Medicine, School of Human Evolution and Social Change, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Wendy J Mack
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - E Meng Law
- iBRAIN Research Laboratory, Departments of Neuroscience, Computer Systems and Electrical Engineering, Monash University, Melbourne, VIC, Australia
- Department of Radiology, The Alfred Health Hospital, Melbourne, VIC, Australia
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | | | - Christopher J Rowan
- Renown Institute for Heart and Vascular Health, Reno, NV, USA
- School of Medicine, University of Nevada, Reno, NV, USA
| | - L Samuel Wann
- Division of Cardiology, University of New Mexico, Albuquerque, NM, USA
| | - Adel H Allam
- Department of Cardiology, School of Medicine, Al-Azhar University, Al Mikhaym Al Daem, Cairo, Egypt
| | - Randall C Thompson
- Saint Luke's Mid America Heart Institute, University of Missouri, Kansas City, MO, USA
| | - David E Michalik
- Department of Pediatrics, School of Medicine, University of California, Irvine, Orange, CA, USA
- MemorialCare Miller Children's & Women's Hospital, Long Beach Medical Center, Long Beach, CA, USA
| | - Michael Miyamoto
- Division of Cardiology, Mission Heritage Medical Group, Providence Health, Mission Viejo, CA, USA
| | | | - Daniel K Cummings
- Department of Anthropology, University of New Mexico, Albuquerque, NM, USA
- Economic Science Institute, Argyros School of Business and Economics, Chapman University, Orange, CA, USA
| | - Edmond Seabright
- Department of Anthropology, University of New Mexico, Albuquerque, NM, USA
| | - Sarah Alami
- Department of Anthropology, University of New Mexico, Albuquerque, NM, USA
| | - Angela R Garcia
- Center for Evolution & Medicine, School of Human Evolution and Social Change, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Daniel E Rodriguez
- Institute of Biomedical Research, San Simon University, Cochabamba, Bolivia
| | | | | | - Paul L Hooper
- Department of Anthropology, University of New Mexico, Albuquerque, NM, USA
| | - Kenneth H Buetow
- Center for Evolution & Medicine, School of Human Evolution and Social Change, School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Jonathan Stieglitz
- Institute for Advanced Study in Toulouse, Toulouse 1 Capitol University, Toulouse, France
| | - Michael D Gurven
- Department of Anthropology, University of California, Santa Barbara, USA
| | - Gregory S Thomas
- MemorialCare Health Systems, Fountain Valley, CA, USA
- Division of Cardiology, University of California, Irvine, Orange, CA, USA
| | - Hillard S Kaplan
- Economic Science Institute, Argyros School of Business and Economics, Chapman University, Orange, CA, USA
| | - Caleb E Finch
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Departments of Biological Sciences, Anthropology and Psychology, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Andrei Irimia
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
- Department of Quantitative and Computational Biology, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA.
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Joo Y, Namgung E, Jeong H, Kang I, Kim J, Oh S, Lyoo IK, Yoon S, Hwang J. Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms. Sci Rep 2023; 13:22388. [PMID: 38104173 PMCID: PMC10725434 DOI: 10.1038/s41598-023-49514-2] [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: 04/06/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer's disease groups by identifying variances in brain age gaps between them, highlighting the algorithm's potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.
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Affiliation(s)
- Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Eun Namgung
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Hyeonseok Jeong
- Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Ilhyang Kang
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Jinsol Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Sohyun Oh
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea.
| | - Jaeuk Hwang
- Department of Psychiatry, Soonchunhyang University College of Medicine, Seoul, South Korea.
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3
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Cen S, Gebregziabher M, Moazami S, Azevedo CJ, Pelletier D. Toward precision medicine using a "digital twin" approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis. Sci Rep 2023; 13:16279. [PMID: 37770560 PMCID: PMC10539386 DOI: 10.1038/s41598-023-43618-5] [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: 04/18/2023] [Accepted: 09/26/2023] [Indexed: 09/30/2023] Open
Abstract
Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a tenfold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset versus simultaneous onset of brain atrophy.
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Affiliation(s)
- Steven Cen
- Department of Radiology/Neurology, University of Southern California, Los Angeles, USA.
| | - Mulugeta Gebregziabher
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA
| | - Saeed Moazami
- Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, USA
| | - Christina J Azevedo
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Daniel Pelletier
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, USA
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4
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Parsaei M, Sanjari Moghaddam H, Aarabi MH. Sex differences in brain structures throughout the lifetime. AGING BRAIN 2023; 4:100098. [PMID: 37809276 PMCID: PMC10550774 DOI: 10.1016/j.nbas.2023.100098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Affiliation(s)
| | - Hossein Sanjari Moghaddam
- Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hadi Aarabi
- Department of Neuroscience (DNS), Padova Neuroscience Center, University of Padova, Padua, Italy
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5
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Kheloui S, Jacmin-Park S, Larocque O, Kerr P, Rossi M, Cartier L, Juster RP. Sex/gender differences in cognitive abilities. Neurosci Biobehav Rev 2023; 152:105333. [PMID: 37517542 DOI: 10.1016/j.neubiorev.2023.105333] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 07/09/2023] [Accepted: 07/27/2023] [Indexed: 08/01/2023]
Abstract
Sex/gender differences in cognitive sciences are riddled by conflicting perspectives. At the center of debates are clinical, social, and political perspectives. Front and center, evolutionary and biological perspectives have often focused on 'nature' arguments, while feminist and constructivist views have often focused on 'nurture arguments regarding cognitive sex differences. In the current narrative review, we provide a comprehensive overview regarding the origins and historical advancement of these debates while providing a summary of the results in the field of sexually polymorphic cognition. In so doing, we attempt to highlight the importance of using transdisciplinary perspectives which help bridge disciplines together to provide a refined understanding the specific factors that drive sex differences a gender diversity in cognitive abilities. To summarize, biological sex (e.g., birth-assigned sex, sex hormones), socio-cultural gender (gender identity, gender roles), and sexual orientation each uniquely shape the cognitive abilities reviewed. To date, however, few studies integrate these sex and gender factors together to better understand individual differences in cognitive functioning. This has potential benefits if a broader understanding of sex and gender factors are systematically measured when researching and treating numerous conditions where cognition is altered.
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Affiliation(s)
- Sarah Kheloui
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Silke Jacmin-Park
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Ophélie Larocque
- Department of Psychology, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Philippe Kerr
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Mathias Rossi
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Louis Cartier
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada
| | - Robert-Paul Juster
- Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada; Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Canada; Center on Sex⁎Gender, Allostasis and Resilience, Canada.
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6
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Weinstein SM, Davatzikos C, Doshi J, Linn KA, Shinohara RT. Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables. Biostatistics 2023; 24:653-668. [PMID: 35950944 PMCID: PMC10345990 DOI: 10.1093/biostatistics/kxac031] [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: 01/25/2022] [Revised: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.
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Affiliation(s)
- Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 108/109B, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jimit Doshi
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kristin A Linn
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 2nd Floor, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA and Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 2nd Floor, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA and Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
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Cen S, Gebregziabher M, Moazami S, Azevedo C, Pelletier D. Toward Precision Medicine Using a "Digital Twin" Approach: Modeling the Onset of Disease-Specific Brain Atrophy in Individuals with Multiple Sclerosis. RESEARCH SQUARE 2023:rs.3.rs-2833532. [PMID: 37205476 PMCID: PMC10187410 DOI: 10.21203/rs.3.rs-2833532/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a 10-fold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset vs. simultaneous onset of brain atrophy.
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8
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Bosco P, Lancione M, Retico A, Nigri A, Aquino D, Baglio F, Carne I, Ferraro S, Giulietti G, Napolitano A, Palesi F, Pavone L, Savini G, Tagliavini F, Bruzzone MG, Gandini Wheeler-Kingshott CAM, Tosetti M, Biagi L. Quality assessment, variability and reproducibility of anatomical measurements derived from T1-weighted brain imaging: The RIN-Neuroimaging Network case study. Phys Med 2023; 110:102577. [PMID: 37126963 DOI: 10.1016/j.ejmp.2023.102577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 03/01/2023] [Accepted: 04/05/2023] [Indexed: 05/03/2023] Open
Abstract
Initiatives for the collection of harmonized MRI datasets are growing continuously, opening questions on the reliability of results obtained in multi-site contexts. Here we present the assessment of the brain anatomical variability of MRI-derived measurements obtained from T1-weighted images, acquired according to the Standard Operating Procedures, promoted by the RIN-Neuroimaging Network. A multicentric dataset composed of 77 brain T1w acquisitions of young healthy volunteers (mean age = 29.7 ± 5.0 years), collected in 15 sites with MRI scanners of three different vendors, was considered. Parallelly, a dataset of 7 "traveling" subjects, each undergoing three acquisitions with scanners from different vendors, was also used. Intra-site, intra-vendor, and inter-site variabilities were evaluated in terms of the percentage standard deviation of volumetric and cortical thickness measures. Image quality metrics such as contrast-to-noise and signal-to-noise ratio in gray and white matter were also assessed for all sites and vendors. The results showed a measured global variability that ranges from 11% to 19% for subcortical volumes and from 3% to 10% for cortical thicknesses. Univariate distributions of the normalized volumes of subcortical regions, as well as the distributions of the thickness of cortical parcels appeared to be significantly different among sites in 8 subcortical (out of 17) and 21 cortical (out of 68) regions of i nterest in the multicentric study. The Bland-Altman analysis on "traveling" brain measurements did not detect systematic scanner biases even though a multivariate classification approach was able to classify the scanner vendor from brain measures with an accuracy of 0.60 ± 0.14 (chance level 0.33).
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Affiliation(s)
- Paolo Bosco
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Marta Lancione
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Alessandra Retico
- Pisa Division, INFN - National Institute for Nuclear Physics, Pisa, Italy
| | - Anna Nigri
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Stefania Ferraro
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy; MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Giovanni Giulietti
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; SAIMLAL Department, Sapienza University of Rome, Rome, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Fulvia Palesi
- Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Giovanni Savini
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square, Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Michela Tosetti
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy.
| | - Laura Biagi
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
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9
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Matusik PS, Zhong C, Matusik PT, Alomar O, Stein PK. Neuroimaging Studies of the Neural Correlates of Heart Rate Variability: A Systematic Review. J Clin Med 2023; 12:jcm12031016. [PMID: 36769662 PMCID: PMC9917610 DOI: 10.3390/jcm12031016] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/20/2023] [Accepted: 01/22/2023] [Indexed: 02/03/2023] Open
Abstract
Direct and indirect links between brain regions and cardiac function have been reported. We performed a systematic literature review to summarize current knowledge regarding the associations of heart rate variability (HRV) and brain region morphology, activity and connectivity involved in autonomic control at rest in healthy subjects. Both positive and negative correlations of cortical thickness and gray matter volumes of brain structures with HRV were observed. The strongest were found for a cluster located within the cingulate cortex. A decline in HRV, as well as cortical thickness with increasing age, especially in the orbitofrontal cortex were noted. When associations of region-specific brain activity with HRV were examined, HRV correlated most strongly with activity in the insula, cingulate cortex, frontal and prefrontal cortices, hippocampus, thalamus, striatum and amygdala. Furthermore, significant correlations, largely positive, between HRV and brain region connectivity (in the amygdala, cingulate cortex and prefrontal cortex) were observed. Notably, right-sided neural structures may be preferentially involved in heart rate and HRV control. However, the evidence for left hemispheric control of cardiac vagal function has also been reported. Our findings provide support for the premise that the brain and the heart are interconnected by both structural and functional networks and indicate complex multi-level interactions. Further studies of brain-heart associations promise to yield insights into their relationship to health and disease.
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Affiliation(s)
- Patrycja S. Matusik
- Department of Diagnostic Imaging, University Hospital, 30-688 Kraków, Poland
| | - Chuwen Zhong
- Center for Social Epidemiology and Population Health, Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Paweł T. Matusik
- Department of Electrocardiology, Institute of Cardiology, Faculty of Medicine, Jagiellonian University Medical College, 31-202 Kraków, Poland
- Department of Electrocardiology, The John Paul II Hospital, 31-202 Kraków, Poland
| | - Omar Alomar
- Department of Internal Medicine, Cardiovascular Division, Washington University School of Medicine, Saint Louis, MO 63110, USA
| | - Phyllis K. Stein
- Department of Internal Medicine, Cardiovascular Division, Washington University School of Medicine, Saint Louis, MO 63110, USA
- Correspondence:
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10
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Choi EY, Tian L, Su JH, Radovan MT, Tourdias T, Tran TT, Trelle AN, Mormino E, Wagner AD, Rutt BK. Thalamic nuclei atrophy at high and heterogenous rates during cognitively unimpaired human aging. Neuroimage 2022; 262:119584. [PMID: 36007822 PMCID: PMC9787236 DOI: 10.1016/j.neuroimage.2022.119584] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 08/09/2022] [Accepted: 08/21/2022] [Indexed: 02/02/2023] Open
Abstract
The thalamus is a central integration structure in the brain, receiving and distributing information among the cerebral cortex, subcortical structures, and the peripheral nervous system. Prior studies clearly show that the thalamus atrophies in cognitively unimpaired aging. However, the thalamus is comprised of multiple nuclei involved in a wide range of functions, and the age-related atrophy of individual thalamic nuclei remains unknown. Using a recently developed automated method of identifying thalamic nuclei (3T or 7T MRI with white-matter-nulled MPRAGE contrast and THOMAS segmentation) and a cross-sectional design, we evaluated the age-related atrophy rate for 10 thalamic nuclei (AV, CM, VA, VLA, VLP, VPL, pulvinar, LGN, MGN, MD) and an epithalamic nucleus (habenula). We also used T1-weighted images with the FreeSurfer SAMSEG segmentation method to identify and measure age-related atrophy for 11 extra-thalamic structures (cerebral cortex, cerebral white matter, cerebellar cortex, cerebellar white matter, amygdala, hippocampus, caudate, putamen, nucleus accumbens, pallidum, and lateral ventricle). In 198 cognitively unimpaired participants with ages spanning 20-88 years, we found that the whole thalamus atrophied at a rate of 0.45% per year, and that thalamic nuclei had widely varying age-related atrophy rates, ranging from 0.06% to 1.18% per year. A functional grouping analysis revealed that the thalamic nuclei involved in cognitive (AV, MD; 0.53% atrophy per year), visual (LGN, pulvinar; 0.62% atrophy per year), and auditory/vestibular (MGN; 0.64% atrophy per year) functions atrophied at significantly higher rates than those involved in motor (VA, VLA, VLP, and CM; 0.37% atrophy per year) and somatosensory (VPL; 0.32% atrophy per year) functions. A proximity-to-CSF analysis showed that the group of thalamic nuclei situated immediately adjacent to CSF atrophied at a significantly greater atrophy rate (0.59% atrophy per year) than that of the group of nuclei located farther from CSF (0.36% atrophy per year), supporting a growing hypothesis that CSF-mediated factors contribute to neurodegeneration. We did not find any significant hemispheric differences in these rates of change for thalamic nuclei. Only the CM thalamic nucleus showed a sex-specific difference in atrophy rates, atrophying at a greater rate in male versus female participants. Roughly half of the thalamic nuclei showed greater atrophy than all extra-thalamic structures examined (0% to 0.54% per year). These results show the value of white-matter-nulled MPRAGE imaging and THOMAS segmentation for measuring distinct thalamic nuclei and for characterizing the high and heterogeneous atrophy rates of the thalamus and its nuclei across the adult lifespan. Collectively, these methods and results advance our understanding of the role of thalamic substructures in neurocognitive and disease-related changes that occur with aging.
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Affiliation(s)
- Eun Young Choi
- Department of Neurosurgery, Stanford University, 300 Pasteur Drive, MC5327, Stanford, CA 94305, USA
| | - Lu Tian
- Department of Biomedical Data Science, 1265 Welch Road, MC5464, Stanford, CA 94305, USA
| | - Jason H. Su
- Department of Radiology, Stanford University, 300 Pasteur Drive, MC5488, Stanford, CA 94305, USA,Department of Electrical Engineering, Stanford University, 350 Jane Stanford Way, MC9505, Stanford, CA 94305, USA
| | - Matthew T. Radovan
- Department of Computer Science, Stanford University, 353 Jane Stanford Way, MC9025, Stanford, CA 94305, USA
| | - Thomas Tourdias
- Department of Neuroradiology, Bordeaux University Hospital, Bordeaux, France,INSERM U1215, Neurocentre Magendie, University of Bordeaux, France
| | - Tammy T. Tran
- Department of Psychology, Stanford University, Building 420, MC2130, Stanford, CA 94305, USA
| | - Alexandra N. Trelle
- Department of Psychology, Stanford University, Building 420, MC2130, Stanford, CA 94305, USA
| | - Elizabeth Mormino
- Department of Neurology and Neurological Sciences, Stanford, University, 300 Pasteur Drive, MC5235, Stanford, CA 94305, USA,Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, USA
| | - Anthony D. Wagner
- Department of Psychology, Stanford University, Building 420, MC2130, Stanford, CA 94305, USA,Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, USA
| | - Brian K. Rutt
- Department of Radiology, Stanford University, 300 Pasteur Drive, MC5488, Stanford, CA 94305, USA,Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, USA,Corresponding author. (B.K. Rutt)
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11
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Nerland S, Stokkan TS, Jørgensen KN, Wortinger LA, Richard G, Beck D, van der Meer D, Westlye LT, Andreassen OA, Agartz I, Barth C. A comparison of intracranial volume estimation methods and their cross-sectional and longitudinal associations with age. Hum Brain Mapp 2022; 43:4620-4639. [PMID: 35708198 PMCID: PMC9491281 DOI: 10.1002/hbm.25978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/28/2022] [Accepted: 05/30/2022] [Indexed: 11/05/2022] Open
Abstract
Intracranial volume (ICV) is frequently used in volumetric magnetic resonance imaging (MRI) studies, both as a covariate and as a variable of interest. Findings of associations between ICV and age have varied, potentially due to differences in ICV estimation methods. Here, we compared five commonly used ICV estimation methods and their associations with age. T1-weighted cross-sectional MRI data was included for 651 healthy individuals recruited through the NORMENT Centre (mean age = 46.1 years, range = 12.0-85.8 years) and 2410 healthy individuals recruited through the UK Biobank study (UKB, mean age = 63.2 years, range = 47.0-80.3 years), where longitudinal data was also available. ICV was estimated with FreeSurfer (eTIV and sbTIV), SPM12, CAT12, and FSL. We found overall high correlations across ICV estimation method, with the lowest observed correlations between FSL and eTIV (r = .87) and between FSL and CAT12 (r = .89). Widespread proportional bias was found, indicating that the agreement between methods varied as a function of head size. Body weight, age, sex, and mean ICV across methods explained the most variance in the differences between ICV estimation methods, indicating possible confounding for some estimation methods. We found both positive and negative cross-sectional associations with age, depending on dataset and ICV estimation method. Longitudinal ICV reductions were found for all ICV estimation methods, with annual percentage change ranging from -0.293% to -0.416%. This convergence of longitudinal results across ICV estimation methods offers strong evidence for age-related ICV reductions in mid- to late adulthood.
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Affiliation(s)
- Stener Nerland
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,NORMENT, University of Oslo, Oslo, Norway
| | - Therese S Stokkan
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,NORMENT, University of Oslo, Oslo, Norway
| | - Kjetil N Jørgensen
- NORMENT, University of Oslo, Oslo, Norway.,Department of Psychiatry, Telemark Hospital, Skien, Norway
| | - Laura A Wortinger
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,NORMENT, University of Oslo, Oslo, Norway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dani Beck
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,NORMENT, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,NORMENT, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,NORMENT, University of Oslo, Oslo, Norway.,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Claudia Barth
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,NORMENT, University of Oslo, Oslo, Norway
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12
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Sanchis-Segura C, Aguirre N, Cruz-Gómez ÁJ, Félix S, Forn C. Beyond "sex prediction": Estimating and interpreting multivariate sex differences and similarities in the brain. Neuroimage 2022; 257:119343. [PMID: 35654377 DOI: 10.1016/j.neuroimage.2022.119343] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/26/2022] [Accepted: 05/29/2022] [Indexed: 12/31/2022] Open
Abstract
Previous studies have shown that machine-learning (ML) algorithms can "predict" sex based on brain anatomical/ functional features. The high classification accuracy achieved by ML algorithms is often interpreted as revealing large differences between the brains of males and females and as confirming the existence of "male/female brains". However, classification and estimation are different concepts, and using classification metrics as surrogate estimates of between-group differences may result in major statistical and interpretative distortions. The present study avoids these distortions and provides a novel and detailed assessment of multivariate sex differences in gray matter volume (GMVOL) that does not rely on classification metrics. Moreover, appropriate regression methods were used to identify the brain areas that contribute the most to these multivariate differences, and clustering techniques and analyses of similarities (ANOSIM) were employed to empirically assess whether they assemble into two sex-typical profiles. Results revealed that multivariate sex differences in GMVOL: (1) are "large" if not adjusted for total intracranial volume (TIV) variation, but "small" when controlling for this variable; (2) differ in size between individuals and also depends on the ML algorithm used for their calculation (3) do not stem from two sex-typical profiles, and so describing them in terms of "male/female brains" is misleading.
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Affiliation(s)
- Carla Sanchis-Segura
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN., Castelló 12071, Spain.
| | - Naiara Aguirre
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN., Castelló 12071, Spain
| | - Álvaro Javier Cruz-Gómez
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN., Castelló 12071, Spain
| | - Sonia Félix
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN., Castelló 12071, Spain
| | - Cristina Forn
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN., Castelló 12071, Spain
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13
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López-Ojeda W, Hurley RA. Kisspeptin in the Limbic System: New Insights Into Its Neuromodulatory Roles. J Neuropsychiatry Clin Neurosci 2022; 34:190-195. [PMID: 35921618 DOI: 10.1176/appi.neuropsych.20220087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Wilfredo López-Ojeda
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education, and Clinical Center and Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Departments of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
| | - Robin A Hurley
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education, and Clinical Center and Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Departments of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
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14
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Comparing brain asymmetries independently of brain size. Neuroimage 2022; 254:119118. [PMID: 35318151 DOI: 10.1016/j.neuroimage.2022.119118] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 01/16/2023] Open
Abstract
Studies examining cerebral asymmetries typically divide the l-R Measure (e.g., Left-Right Volume) by the L + R Measure to obtain an Asymmetry Index (AI). However, contrary to widespread belief, such a division fails to render the AI independent from the L + R Measure and/or from total brain size. As a result, variations in brain size may bias correlation estimates with the AI or group differences in AI. We investigated how to analyze brain asymmetries in to distinguish global from regional effects, and report unbiased group differences in cerebral asymmetries in the UK Biobank (N = 40, 028). We used 306 global and regional brain measures provided by the UK Biobank. Global gray and white matter volumes were taken from Freesurfer ASEG, subcortical gray matter volumes from Freesurfer ASEG and subsegmentation, cortical gray matter volumes, mean thicknesses, and surface areas from the Destrieux atlas applied on T1-and T2-weighted images, cerebellar gray matter volumes from FAST FSL, and regional white matter volumes from Freesurfer ASEG. We analyzed the extent to which the L + R Measure, Total Cerebral Measure (TCM, e.g., Total Brain Volume), and l-R TCM predict regional asymmetries. As a case study, we assessed the consequences of omitting each of these predictors on the magnitude and significance of sex differences in asymmetries. We found that the L + R Measure, the TCM, and the l-R TCM predicted the AI of more than 89% of regions and that their relationships were generally linear. Removing any of these predictors changed the significance of sex differences in 33% of regions and the magnitude of sex differences across 13-42% of regions. Although we generally report similar sex and age effects on cerebral asymmetries to those of previous large-scale studies, properly adjusting for regional and global brain size revealed additional sex and age effects on brain asymmetry.
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15
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Wang H, Burles F, Subramaniapillai S, Pasvanis S, Rajah MN, Protzner AB. Sex differences in the relationship between age, performance, and BOLD signal variability during spatial context memory processing. Neurobiol Aging 2022; 118:77-87. [DOI: 10.1016/j.neurobiolaging.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 06/17/2022] [Accepted: 06/21/2022] [Indexed: 10/17/2022]
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16
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Mouches P, Wilms M, Rajashekar D, Langner S, Forkert ND. Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions. Hum Brain Mapp 2022; 43:2554-2566. [PMID: 35138012 PMCID: PMC9057090 DOI: 10.1002/hbm.25805] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 02/06/2023] Open
Abstract
Biological brain age predicted using machine learning models based on high-resolution imaging data has been suggested as a potential biomarker for neurological and cerebrovascular diseases. In this work, we aimed to develop deep learning models to predict the biological brain age using structural magnetic resonance imaging and angiography datasets from a large database of 2074 adults (21-81 years). Since different imaging modalities can provide complementary information, combining them might allow to identify more complex aging patterns, with angiography data, for instance, showing vascular aging effects complementary to the atrophic brain tissue changes seen in T1-weighted MRI sequences. We used saliency maps to investigate the contribution of cortical, subcortical, and arterial structures to the prediction. Our results show that combining T1-weighted and angiography MR data led to a significantly improved brain age prediction accuracy, with a mean absolute error of 3.85 years comparing the predicted and chronological age. The most predictive brain regions included the lateral sulcus, the fourth ventricle, and the amygdala, while the brain arteries contributing the most to the prediction included the basilar artery, the middle cerebral artery M2 segments, and the left posterior cerebral artery. Our study proposes a framework for brain age prediction using multimodal imaging, which gives accurate predictions and allows identifying the most predictive regions for this task, which can serve as a surrogate for the brain regions that are most affected by aging.
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Affiliation(s)
- Pauline Mouches
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Biomedical Engineering Program, University of Calgary, Calgary, Alberta, Canada
| | - Matthias Wilms
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Deepthi Rajashekar
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Biomedical Engineering Program, University of Calgary, Calgary, Alberta, Canada
| | - Sönke Langner
- Institute for Diagnostic Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Nils D Forkert
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
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17
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Blinkouskaya Y, Caçoilo A, Gollamudi T, Jalalian S, Weickenmeier J. Brain aging mechanisms with mechanical manifestations. Mech Ageing Dev 2021; 200:111575. [PMID: 34600936 PMCID: PMC8627478 DOI: 10.1016/j.mad.2021.111575] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 09/09/2021] [Accepted: 09/22/2021] [Indexed: 12/14/2022]
Abstract
Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain's (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.
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Affiliation(s)
- Yana Blinkouskaya
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Andreia Caçoilo
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Trisha Gollamudi
- Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Shima Jalalian
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States
| | - Johannes Weickenmeier
- Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States.
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18
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Sambuco N. Sex differences in the aging brain? A voxel-based morphometry analysis of the hippocampus and the amygdala. Neuroreport 2021; 32:1320-1324. [PMID: 34554939 DOI: 10.1097/wnr.0000000000001728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Volumetric reductions in the hippocampus and the amygdala are considered a hallmark for many psychiatric and neurodegenerative disorders. Because brain atrophy is often observed in disorders that have a higher prevalence in females than males, it has been proposed that sex differences in the aging brain represent a vulnerability factor for developing more severe psychiatric conditions. METHODS Sexual dimorphism was assessed in the amygdala volume and hippocampal volume in a large sample (N = 554) of healthy individuals ranging from 20 to 79 years old, using structural brain data available from a public dataset. RESULTS In both the hippocampus and the amygdala, a quadratic association was found between age and brain volume. Using uncorrected data for head size [total intracranial volume (TIV)], males clearly demonstrated larger amygdala and hippocampal volume across all ages, and an interaction between age and sex in the hippocampus supported the hypothesis of accelerated atrophy in the hippocampus in later life (60-79 years old). However, when volumetric data adjusted for TIV were used, sex differences were not observed in the hippocampus nor the amygdala. CONCLUSION These findings support the extensive series of studies suggesting that sex differences in brain volume are likely related to the confounding effect of head size. While continued effort is allocated to identify sex-related biomarkers, increasing evidence suggests that sexual dimorphism in the hippocampus or the amygdala does not appear to be the primary candidates for precision medicine to identify sex-related biomarkers that index potential vulnerabilities.
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Affiliation(s)
- Nicola Sambuco
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA
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19
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López-Ojeda W, Hurley RA. Sexual Dimorphism in Brain Development: Influence on Affective Disorders. J Neuropsychiatry Clin Neurosci 2021; 33:A485-89. [PMID: 34018811 DOI: 10.1176/appi.neuropsych.20100269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Wilfredo López-Ojeda
- The Veterans Affairs Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and the Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley). The Departments of Psychiatry and Behavioral Medicine (López-Ojeda) and Psychiatry and Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; and the Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
| | - Robin A Hurley
- The Veterans Affairs Mid-Atlantic Mental Illness Research, Education, and Clinical Center, and the Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley). The Departments of Psychiatry and Behavioral Medicine (López-Ojeda) and Psychiatry and Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; and the Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
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20
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Oughourlian TC, Wang C, Salamon N, Holly LT, Ellingson BM. Sex-Dependent Cortical Volume Changes in Patients with Degenerative Cervical Myelopathy. J Clin Med 2021; 10:jcm10173965. [PMID: 34501413 PMCID: PMC8432178 DOI: 10.3390/jcm10173965] [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: 07/25/2021] [Revised: 08/23/2021] [Accepted: 08/30/2021] [Indexed: 11/24/2022] Open
Abstract
Degenerative cervical myelopathy (DCM) is a progressive condition characterized by degeneration of osseocartilaginous structures within the cervical spine resulting in compression of the spinal cord and presentation of clinical symptoms. Compared to healthy controls (HCs), studies have shown DCM patients experience structural and functional reorganization in the brain; however, sex-dependent cortical differences in DCM patients remains largely unexplored. In the present study, we investigate the role of sex differences on the structure of the cerebral cortex in DCM and determine how structural differences may relate to clinical measures of neurological function. T1-weighted structural MRI scans were acquired in 85 symptomatic and asymptomatic patients with DCM and 90 age-matched HCs. Modified Japanese Orthopedic Association (mJOA) scores were obtained for patients. A general linear model was used to determine vertex-level significant differences in gray matter volume (GMV) between the following groups (1) male HCs and female HCs, (2) male patients and female patients, (3) male patients and male HCs, and (4) female patients and female HCs. Within patients, males exhibited larger GMV in motor, language, and vision related brain regions compared to female DCM patients. Males demonstrated a significant positive correlation between GMV and mJOA score, in which patients with worsening neurological symptoms exhibited decreasing GMV primarily across somatosensory and motor related cortical regions. Females exhibited a similar association, albeit across a broader range of cortical areas including those involved in pain processing. In sensorimotor regions, female patients consistently showed smaller GMV compared with male patients, independent of mJOA score. Results from the current study suggest strong sex-related differences in cortical volume in patients with DCM, which may reflect hormonal influence or differing compensation mechanisms.
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Affiliation(s)
- Talia C. Oughourlian
- UCLA Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90024, USA; (T.C.O.); (C.W.); (B.M.E.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA;
- Neuroscience Interdepartmental Graduate Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Chencai Wang
- UCLA Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90024, USA; (T.C.O.); (C.W.); (B.M.E.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - Langston T. Holly
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Correspondence: ; Tel.: +1-(310)-319-3475
| | - Benjamin M. Ellingson
- UCLA Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90024, USA; (T.C.O.); (C.W.); (B.M.E.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA;
- Neuroscience Interdepartmental Graduate Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
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21
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Tsuchida A, Laurent A, Crivello F, Petit L, Joliot M, Pepe A, Beguedou N, Gueye MF, Verrecchia V, Nozais V, Zago L, Mellet E, Debette S, Tzourio C, Mazoyer B. The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students. Brain Struct Funct 2021; 226:2057-2085. [PMID: 34283296 DOI: 10.1007/s00429-021-02334-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 06/11/2021] [Indexed: 01/04/2023]
Abstract
We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1870 young healthy adults, aged 18-35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility-weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early ageing.
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Affiliation(s)
- Ami Tsuchida
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France
| | - Alexandre Laurent
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France
| | - Fabrice Crivello
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France
| | - Marc Joliot
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.,Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France
| | - Antonietta Pepe
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France
| | - Naka Beguedou
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France
| | - Marie-Fateye Gueye
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.,Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France
| | - Violaine Verrecchia
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.,Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France
| | - Victor Nozais
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France.,Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France
| | - Laure Zago
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France
| | - Emmanuel Mellet
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France
| | - Stéphanie Debette
- Université de Bordeaux, INSERM, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France.,Centre Hospitalier Universitaire Pellegrin, Bordeaux, France
| | - Christophe Tzourio
- Université de Bordeaux, INSERM, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France.,Centre Hospitalier Universitaire Pellegrin, Bordeaux, France
| | - Bernard Mazoyer
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, Université de Bordeaux, Bordeaux, France. .,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, Bordeaux, France. .,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CEA, Bordeaux, France. .,Ginesislab, Fealinx and Université de Bordeaux, Bordeaux, France. .,Centre Hospitalier Universitaire Pellegrin, Bordeaux, France.
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22
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Williams CM, Peyre H, Toro R, Ramus F. Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age. Hum Brain Mapp 2021; 42:4623-4642. [PMID: 34268815 PMCID: PMC8410561 DOI: 10.1002/hbm.25572] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 06/03/2021] [Accepted: 06/11/2021] [Indexed: 12/18/2022] Open
Abstract
Few neuroimaging studies are sufficiently large to adequately describe population‐wide variations. This study's primary aim was to generate neuroanatomical norms and individual markers that consider age, sex, and brain size, from 629 cerebral measures in the UK Biobank (N = 40,028). The secondary aim was to examine the effects and interactions of sex, age, and brain allometry—the nonlinear scaling relationship between a region and brain size (e.g., total brain volume)—across cerebral measures. Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and was largely stable across age and sex. Sex differences occurred in 67% of cerebral measures (median |β| = .13): 37% of regions were larger in males and 30% in females. Brain measures (49%) generally decreased with age, although aging effects varied across regions and sexes. While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. Finally, we contribute to the reproducibility of research on sex differences in the brain by replicating previous studies examining cerebral sex differences. This large‐scale study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.
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Affiliation(s)
- Camille Michèle Williams
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France
| | - Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France.,INSERM UMR 1141, Paris Diderot University, Paris, France.,Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France
| | - Roberto Toro
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR 3571 CNRS, Paris, France.,Center for Research and Interdisciplinarity (CRI), INSERM U1284, Paris, France.,Université de Paris, Paris, France
| | - Franck Ramus
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France
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23
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Dadar M, Potvin O, Camicioli R, Duchesne S. Beware of white matter hyperintensities causing systematic errors in FreeSurfer gray matter segmentations! Hum Brain Mapp 2021; 42:2734-2745. [PMID: 33783933 PMCID: PMC8127151 DOI: 10.1002/hbm.25398] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 02/19/2021] [Accepted: 02/19/2021] [Indexed: 12/11/2022] Open
Abstract
Volumetric estimates of subcortical and cortical structures, extracted from T1-weighted MRIs, are widely used in many clinical and research applications. Here, we investigate the impact of the presence of white matter hyperintensities (WMHs) on FreeSurfer gray matter (GM) structure volumes and its possible bias on functional relationships. T1-weighted images from 1,077 participants (4,321 timepoints) from the Alzheimer's Disease Neuroimaging Initiative were processed with FreeSurfer version 6.0.0. WMHs were segmented using a previously validated algorithm on either T2-weighted or Fluid-attenuated inversion recovery images. Mixed-effects models were used to assess the relationships between overlapping WMHs and GM structure volumes and overall WMH burden, as well as to investigate whether such overlaps impact associations with age, diagnosis, and cognitive performance. Participants with higher WMH volumes had higher overlaps with GM volumes of bilateral caudate, cerebral cortex, putamen, thalamus, pallidum, and accumbens areas (p < .0001). When not corrected for WMHs, caudate volumes increased with age (p < .0001) and were not different between cognitively healthy individuals and age-matched probable Alzheimer's disease patients. After correcting for WMHs, caudate volumes decreased with age (p < .0001), and Alzheimer's disease patients had lower caudate volumes than cognitively healthy individuals (p < .01). Uncorrected caudate volume was not associated with ADAS13 scores, whereas corrected lower caudate volumes were significantly associated with poorer cognitive performance (p < .0001). Presence of WMHs leads to systematic inaccuracies in GM segmentations, particularly for the caudate, which can also change clinical associations. While specifically measured for the Freesurfer toolkit, this problem likely affects other algorithms.
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Affiliation(s)
- Mahsa Dadar
- CERVO Brain Research CenterCentre intégré universitaire santé et services sociaux de la Capitale NationaleQuébecQuebecCanada
| | - Olivier Potvin
- CERVO Brain Research CenterCentre intégré universitaire santé et services sociaux de la Capitale NationaleQuébecQuebecCanada
| | - Richard Camicioli
- Department of Medicine, Division of NeurologyUniversity of AlbertaEdmontonAlbertaCanada
| | - Simon Duchesne
- CERVO Brain Research CenterCentre intégré universitaire santé et services sociaux de la Capitale NationaleQuébecQuebecCanada
- Department of Radiology and Nuclear Medicine, Faculty of MedicineUniversité LavalQuébecQuebecCanada
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24
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Evans index among adult Ghanaians on normal head computerized tomography scan. Heliyon 2021; 7:e06982. [PMID: 34027182 PMCID: PMC8129941 DOI: 10.1016/j.heliyon.2021.e06982] [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: 01/09/2021] [Revised: 03/03/2021] [Accepted: 04/29/2021] [Indexed: 11/19/2022] Open
Abstract
Introduction Normal-Pressure Hydrocephalus (NPH) is a neurological condition which is made up of a clinical triad of gait disturbance, dementia and urinary incontinence and can be reversed by ventricular shunting. Currently, some guidelines suggest the use of Evans' index (EI) for diagnosis of hydrocephalus radiologically. Most of the studies are based on the Western population data. None of these studies have been performed in the Ghanaian population setting yet. The aim of this study was to quantitatively establish normal borderline value for Evans Index in the Ghanaian adult population with respect to age and sex. Methods This study was retrospectively conducted on normal enhanced head CT scan images of 266 males and 241 females. EI was calculated as the linear ratio of Maximum Anterior Horn Width (MAHW) of the frontal horns of the lateral ventricles at the level of foramina of Monroe and the Maximum Intracranial Diameter (MICD) of the inner skull. Student T-test, ANOVA and Pearson's correlation were used to analyze the data. A test for a relationship was performed with a scatter plot and a linear regression was performed based on age, sex and different EI of ventricular size. Results The mean and median value of EI was 0.24 ± 0.02. There was no statistically significant difference in the EI values between males and females, (p-value = 0.61). A steady increase in EI with age was observed. There was a strong correlation coefficient r = 0.89 of EI and age, which suggested a strong linear relationship between EI and Age. The overall linear relationship model was EI = 0.1879 + 0.0011∗Age. Conclusions The mean EI of 0.24 ± 0.02 in our study agrees with adapted international guidelines cut-off value for normal adult patients of (<0.30) and can be a useful tool in determining ventricular enlargement particularly in resource limited settings.
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25
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Multimodal neuroimaging of sex differences in cognitively impaired patients on the Alzheimer's continuum: greater tau-PET retention in females. Neurobiol Aging 2021; 105:86-98. [PMID: 34049062 DOI: 10.1016/j.neurobiolaging.2021.04.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 04/03/2021] [Accepted: 04/05/2021] [Indexed: 12/23/2022]
Abstract
We assessed sex differences in amyloid- and tau-PET retention in 119 amyloid positive patients with mild cognitive impairment or Alzheimer's disease (AD) dementia. Patients underwent 3T-MRI, 11C-PIB amyloid-PET and 18F-Flortaucipir tau-PET. Linear ordinary least squares regression models tested sex differences in Flortaucipir-PET SUVR in a summary temporal region of interest as well as global PIB-PET. No sex differences were observed in demographics, Clinical Dementia Rating Sum of Boxes (CDR-SoB), Mini-Mental State Exam (MMSE), raw episodic memory scores, or cortical thickness. Females had higher global PIB SUVR (ηp²=.043, p=.025) and temporal Flortaucipir SUVR (ηp²=.070, p=.004), adjusting for age and CDR-SoB. Sex differences in temporal Flortaucipir-PET remained significant when controlling additionally for PIB SUVR and APOE4 status (ηp²=.055, p=.013), or when using partial volume-corrected data. No sex differences were present in areas of known Flortaucipir off-target binding. Overall, females demonstrated greater AD regional tau-PET burden than males despite clinical comparability. Further characterization of sex differences will provide insight into AD pathogenesis and support development of personalized therapeutic strategies.
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26
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Dump the "dimorphism": Comprehensive synthesis of human brain studies reveals few male-female differences beyond size. Neurosci Biobehav Rev 2021; 125:667-697. [PMID: 33621637 DOI: 10.1016/j.neubiorev.2021.02.026] [Citation(s) in RCA: 141] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 01/01/2021] [Accepted: 02/16/2021] [Indexed: 12/21/2022]
Abstract
With the explosion of neuroimaging, differences between male and female brains have been exhaustively analyzed. Here we synthesize three decades of human MRI and postmortem data, emphasizing meta-analyses and other large studies, which collectively reveal few reliable sex/gender differences and a history of unreplicated claims. Males' brains are larger than females' from birth, stabilizing around 11 % in adults. This size difference accounts for other reproducible findings: higher white/gray matter ratio, intra- versus interhemispheric connectivity, and regional cortical and subcortical volumes in males. But when structural and lateralization differences are present independent of size, sex/gender explains only about 1% of total variance. Connectome differences and multivariate sex/gender prediction are largely based on brain size, and perform poorly across diverse populations. Task-based fMRI has especially failed to find reproducible activation differences between men and women in verbal, spatial or emotion processing due to high rates of false discovery. Overall, male/female brain differences appear trivial and population-specific. The human brain is not "sexually dimorphic."
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27
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28
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Pandin P, Estruc I, Van Hecke D, Truong HN, Marullo L, Hublet S, Van Obbergh L. Brain Aging and Anesthesia. J Cardiothorac Vasc Anesth 2020; 33 Suppl 1:S58-S66. [PMID: 31279354 DOI: 10.1053/j.jvca.2019.03.042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Herein, the authors review the neuroanatomical and the neurophysiological aspects of the normal aging evolution based on the recent literature and briefly describe the difference between physiological and pathological brain aging, with consideration of the currently recommended anesthesia management of older patients. The population of elderly patients is growing drastically with advances in medicine that have prolonged the life span. One of the direct consequence has been a significant increase in the request for anesthesia care for older patients despite the type of surgery (cardiac vs noncardiac and mainly orthopedic). Because the brain of this category of patients undergoes a specific triple influence (immune, metabolic, and inflammatory), some particular physiological, anatomical, and structural modifications must be taken into account because they expose these patients more specifically to postoperative cognitive disturbances. To prevent type of adverse outcome, a better knowledge and understanding of these neurosciences must be promoted. The strategies developed to prevent such adverse outcomes include the determination and detection of significant at-risk patients and improvement in the titration of anesthesia to reduce exposure of anesthesia to these patients through an adapted anesthesia-induced unconsciousness that avoids, as much as possible, the risk of toxic overdose with an overly deep brain depression. To accomplish this, the unprocessed electroencephalogram (EEG) and its spectrogram may represent a significant improvement in monitoring, first by allowing for the rapid recognition of repetitive or persistent EEG suppression by the on-line reading of the raw EEG trace and second by allowing for the accurate determination of the adequate anesthetic-induced state, obtained in general in this category of patients by substantially lowered doses of anesthetic agents. This represents a new methodology for anesthesia titration that is adjusted on a more case-by-case basis and is related to the physiology of individual patients. A better understanding of aging-induced brain transformations remains the key regarding the improvement of the anesthetic management of the always growing population of elderly patients. The promotion of the unprocessed EEG may represent the best method of preventing the risk of anesthetic toxicity, including postoperative cognitive dysfunctions.
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Affiliation(s)
- Pierre Pandin
- Department of Anesthesia and Critical Care, Erasmus Academic Hospital, Université Libre de Bruxelles, Brussels, Belgium.
| | - Isabel Estruc
- Department of Anesthesia and Critical Care, Erasmus Academic Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Delphine Van Hecke
- Department of Anesthesia and Critical Care, Erasmus Academic Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Ha-Nam Truong
- Department of Anesthesia and Critical Care, Erasmus Academic Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Lucia Marullo
- Department of Anesthesia and Critical Care, Erasmus Academic Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Stephane Hublet
- Department of Anesthesia and Critical Care, Erasmus Academic Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Luc Van Obbergh
- Department of Anesthesia and Critical Care, Erasmus Academic Hospital, Université Libre de Bruxelles, Brussels, Belgium
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29
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Fjell AM, Chen CH, Sederevicius D, Sneve MH, Grydeland H, Krogsrud SK, Amlien I, Ferschmann L, Ness H, Folvik L, Beck D, Mowinckel AM, Tamnes CK, Westerhausen R, Håberg AK, Dale AM, Walhovd KB. Continuity and Discontinuity in Human Cortical Development and Change From Embryonic Stages to Old Age. Cereb Cortex 2020; 29:3879-3890. [PMID: 30357317 DOI: 10.1093/cercor/bhy266] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 08/28/2018] [Indexed: 11/12/2022] Open
Abstract
The human cerebral cortex is highly regionalized, and this feature emerges from morphometric gradients in the cerebral vesicles during embryonic development. We tested if this principle of regionalization could be traced from the embryonic development to the human life span. Data-driven fuzzy clustering was used to identify regions of coordinated longitudinal development of cortical surface area (SA) and thickness (CT) (n = 301, 4-12 years). The principal divide for the developmental SA clusters extended from the inferior-posterior to the superior-anterior cortex, corresponding to the major embryonic morphometric anterior-posterior (AP) gradient. Embryonic factors showing a clear AP gradient were identified, and we found significant differences in gene expression of these factors between the anterior and posterior clusters. Further, each identified developmental SA and CT clusters showed distinguishable life span trajectories in a larger longitudinal dataset (4-88 years, 1633 observations), and the SA and CT clusters showed differential relationships to cognitive functions. This means that regions that developed together in childhood also changed together throughout life, demonstrating continuity in regionalization of cortical changes. The AP divide in SA development also characterized genetic patterning obtained in an adult twin sample. In conclusion, the development of cortical regionalization is a continuous process from the embryonic stage throughout life.
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Affiliation(s)
- Anders M Fjell
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.,Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Chi-Hua Chen
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Donatas Sederevicius
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Markus H Sneve
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Håkon Grydeland
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Stine K Krogsrud
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Inge Amlien
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Lia Ferschmann
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Hedda Ness
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Line Folvik
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Dani Beck
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Athanasia M Mowinckel
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Christian K Tamnes
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - René Westerhausen
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Asta K Håberg
- Department of Medical Imaging, St. Olav's Hospital, Trondheim, Norway.,Department of Neuroscience, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA.,Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Kristine B Walhovd
- Department of Psychology, Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway.,Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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30
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Sanchis-Segura C, Ibañez-Gual MV, Aguirre N, Cruz-Gómez ÁJ, Forn C. Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction. Sci Rep 2020; 10:12953. [PMID: 32737332 PMCID: PMC7395772 DOI: 10.1038/s41598-020-69361-9] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 07/08/2020] [Indexed: 12/22/2022] Open
Abstract
Sex differences in 116 local gray matter volumes (GMVOL) were assessed in 444 males and 444 females without correcting for total intracranial volume (TIV) or after adjusting the data with the scaling, proportions, power-corrected proportions (PCP), and residuals methods. The results confirmed that only the residuals and PCP methods completely eliminate TIV-variation and result in sex-differences that are "small" (∣d∣ < 0.3). Moreover, as assessed using a totally independent sample, sex differences in PCP and residuals adjusted-data showed higher replicability ([Formula: see text] 93%) than scaling and proportions adjusted-data [Formula: see text] 68%) or raw data ([Formula: see text] 45%). The replicated effects were meta-analyzed together and confirmed that, when TIV-variation is adequately controlled, volumetric sex differences become "small" (∣d∣ < 0.3 in all cases). Finally, we assessed the utility of TIV-corrected/ TIV-uncorrected GMVOL features in predicting individuals' sex with 12 different machine learning classifiers. Sex could be reliably predicted (> 80%) when using raw local GMVOL, but also when using scaling or proportions adjusted-data or TIV as a single predictor. Conversely, after properly controlling TIV variation with the PCP and residuals' methods, prediction accuracy dropped to [Formula: see text] 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GMVOL.
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Affiliation(s)
- Carla Sanchis-Segura
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN, 12071, Castelló, Spain.
| | | | - Naiara Aguirre
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN, 12071, Castelló, Spain
| | - Álvaro Javier Cruz-Gómez
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN, 12071, Castelló, Spain
| | - Cristina Forn
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN, 12071, Castelló, Spain
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31
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van Eijk L, Hansell NK, Strike LT, Couvy-Duchesne B, de Zubicaray GI, Thompson PM, McMahon KL, Zietsch BP, Wright MJ. Region-specific sex differences in the hippocampus. Neuroimage 2020; 215:116781. [DOI: 10.1016/j.neuroimage.2020.116781] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 02/12/2020] [Accepted: 03/27/2020] [Indexed: 01/11/2023] Open
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Zong X, Lian C, Jimenez J, Yamashita K, Shen D, Lin W. Morphology of perivascular spaces and enclosed blood vessels in young to middle-aged healthy adults at 7T: Dependences on age, brain region, and breathing gas. Neuroimage 2020; 218:116978. [PMID: 32447015 PMCID: PMC7485170 DOI: 10.1016/j.neuroimage.2020.116978] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 05/16/2020] [Accepted: 05/18/2020] [Indexed: 12/30/2022] Open
Abstract
Perivascular spaces (PVSs) are fluid-filled spaces surrounding penetrating blood vessels in the brain and are an integral pathway of the glymphatic system. A PVS and the enclosed blood vessel are commonly visualized as a single vessel-like complex (denoted as PVSV) in high-resolution MRI images. Quantitative characterization of the PVSV morphology in MRI images in healthy subjects may serve as a reference for detecting disease related PVS and/or blood vessel alterations in patients with brain diseases. To this end, we evaluated the age dependences, spatial heterogeneities, and dynamic properties of PVSV morphological features in 45 healthy subjects (21–55 years old), using an ultra-high-resolution three-dimensional transverse relaxation time weighted MRI sequence (0.41 × 0.41 × 0.4 mm3) at 7T. Quantitative PVSV parameters, including apparent diameter, count, volume fraction (VF), and relative contrast to noise ratio (rCNR) were calculated in the white matter and subcortical structures. Dynamic changes were induced by carbogen breathing which are known to induce vasodilation and increase the blood oxygenation level in the brain. PVSV count and VF significantly increased with age in basal ganglia (BG), so did rCNR in BG, midbrain, and white matter (WM). Apparent PVSV diameter also showed a positive association with age in the three brain regions, although it did not reach statistical significance. The PVSV VF and count showed large inter-subject variations, with coefficients of variation ranging from 0.17 to 0.74 after regressing out age and gender effects. Both apparent diameter and VF exhibited significant spatial heterogeneity, which cannot be explained solely by radio-frequency field inhomogeneities. Carbogen breathing significantly increased VF in BG and WM, and rCNR in thalamus, BG, and WM compared to air breathing. Our results are consistent with gradual dilation of PVSs with age in healthy adults. The PVSV morphology exhibited spatial heterogeneity and large inter-subject variations and changed during carbogen breathing compared to air breathing.
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Affiliation(s)
- Xiaopeng Zong
- Biomedical Research Imaging Center, Chapel Hill, NC, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Chunfeng Lian
- Biomedical Research Imaging Center, Chapel Hill, NC, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jordan Jimenez
- Biomedical Research Imaging Center, Chapel Hill, NC, USA
| | - Koji Yamashita
- Biomedical Research Imaging Center, Chapel Hill, NC, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Biomedical Research Imaging Center, Chapel Hill, NC, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Biomedical Research Imaging Center, Chapel Hill, NC, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Cespedes MI, McGree JM, Drovandi CC, Mengersen K, Fripp J, Doecke JD. Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration. Stat Med 2020; 39:2695-2713. [PMID: 32419227 DOI: 10.1002/sim.8568] [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: 05/11/2018] [Revised: 04/15/2020] [Accepted: 04/16/2020] [Indexed: 11/11/2022]
Abstract
The degeneration of the human brain is a complex process, which often affects certain brain regions due to healthy aging or disease. This degeneration can be evaluated on regions of interest (ROI) in the brain through probabilistic networks and morphological estimates. Current approaches for finding such networks are limited to analyses at discrete neuropsychological stages, which cannot appropriately account for connectivity dynamics over the onset of cognitive deterioration, and morphological changes are seldom unified with connectivity networks, despite known dependencies. To overcome these limitations, a probabilistic wombling model is proposed to simultaneously estimate ROI cortical thickness and covariance networks contingent on rates of change in cognitive decline. This proposed model was applied to analyze longitudinal data from healthy control (HC) and Alzheimer's disease (AD) groups and found connection differences pertaining to regions, which play a crucial role in lasting cognitive impairment, such as the entorhinal area and temporal regions. Moreover, HC cortical thickness estimates were significantly higher than those in the AD group across all ROIs. The analyses presented in this work will help practitioners jointly analyze brain tissue atrophy at the ROI-level conditional on neuropsychological networks, which could potentially allow for more targeted therapeutic interventions.
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Affiliation(s)
- Marcela I Cespedes
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Herston, Queensland, Australia
| | - James M McGree
- ARC Centre for Mathematical and Statistical Frontiers and School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Christopher C Drovandi
- ARC Centre for Mathematical and Statistical Frontiers and School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie Mengersen
- ARC Centre for Mathematical and Statistical Frontiers and School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Herston, Queensland, Australia
| | - James D Doecke
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Herston, Queensland, Australia
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Fluid intelligence is associated with cortical volume and white matter tract integrity within multiple-demand system across adult lifespan. Neuroimage 2020; 212:116576. [PMID: 32105883 DOI: 10.1016/j.neuroimage.2020.116576] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 12/23/2019] [Accepted: 01/19/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Fluid intelligence (Gf) is the innate ability of an individual to respond to complex and unexpected situations. Although some studies have considered that the multiple-demand (MD) system of the brain was the biological foundation for Gf, further characterization of their relationships in the context of aging is limited. The present study hypothesized that the structural metrics of the MD system, including cortical thickness, cortical volumes, and white matter (WM) tract integrity, was the brain correlates for Gf across the adult life span. Partial correlation analysis was performed to investigate whether the MD system could still explain Gf independent of the age effect. Moreover, the partial correlations between Gf and left/right structural metrics within the MD regions were compared to test whether the correlations displayed distinct lateralization. METHODS The participants were recruited from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) databank, comprising the images of 603 healthy participants aged 18-88 years acquired on a 3-T system. The MRI data included high-resolution T1-weighted and diffusion-weighted images, from which gray matter and WM structural metrics of the MD system were analyzed, respectively. The structural metrics of gray matter were quantified in terms of cortical volume/thickness of five pairs of cortical regions, and those of WM were quantified in terms of the mean axial diffusivity (DA), radial diffusivity (DR), mean diffusivity (DM), and generalized fractional anisotropy (GFA) on five pairs of tracts. Partial correlation controlling for age and sex effects, was performed to investigate the associations of Gf scores with the mean DA, DR, DM and GFA of all tracts in the MD system, those of left and right hemispheric tracts, and those of each tract. Fisher's exact test was used to compare the partial correlations between left and right MD regions. RESULTS The linear relationship between cortical volumes and Gf was evident across all levels of the MD system even after controlling for age and sex. For the WM integrity, diffusion indices including DA, DR, DM and GFA displayed linear relationships with Gf scores at various levels of the MD system. Among the 10 WM tracts connecting the MD regions, bilateral superior longitudinal fasciculus I and bilateral frontal aslant tracts exhibited the strongest and significant associations. Our results did not show significant inter-hemispheric differences in the associations between structural metrics of the MD system and Gf. CONCLUSION Our results demonstrate significant associations between Gf and both cortical volumes and tract integrity of the MD system across the adult lifespan in a population-based cohort. We found that the association remained significant in the entire adult lifespan despite simultaneous decline of Gf and the MD system. Our results suggest that the MD system might be a structural underpinning of Gf and support the fronto-parietal model of cognitive aging. However, we did not find hemispheric differences in the Gf-MD correlations, not supporting the hemi-aging hypothesis.
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Currà A, Pierelli F, Gasbarrone R, Mannarelli D, Nofroni I, Matone V, Marinelli L, Trompetto C, Fattapposta F, Missori P. The Ventricular System Enlarges Abnormally in the Seventies, Earlier in Men, and First in the Frontal Horn: A Study Based on More Than 3,000 Scans. Front Aging Neurosci 2019; 11:294. [PMID: 31749695 PMCID: PMC6848156 DOI: 10.3389/fnagi.2019.00294] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 10/14/2019] [Indexed: 11/29/2022] Open
Abstract
Objectives: To detect on computed tomography (CT) brain scans the trajectories of normal and abnormal ventricular enlargement during aging. Methods: For each 1-year age cohort, we assessed in 3,193 axial CT scans the Evans’ index (EI) in the anterior frontal horns and the parieto-occipital (POR) and temporal ratio (TR) in the posterior and inferior horns. Cut-off values for abnormal enlargement were based on previous clinical studies. Results: The mean age associated with normal linear measures was 71 years. Values for all three measures increased with age, showing a linear relationship below—but not above—each cut-off value. The mean age of participants with abnormal enlargement on CT progressed from 79 years for EI to 83 years for POR to 87 years for TR. These results suggested that ventricular dilatation progresses in an age–location relationship. First comes enlargement of the frontal horns (13.8% of scans), followed by the parieto-occipital horns (15.1% of scans) and then temporal horn enlargement (6.8% of scans). Scans from men displayed abnormal values earlier than scans from women (on average 6 years). Risk increased 5.1% annually for abnormal EI, 9.0% for abnormal POR, and 11% for abnormal TR (all p < 0.001). The most frequent agreement between categories (normal–abnormal) for values of neuroimaging measures was identified for POR–TR. Conclusion: The results of this large radiological study suggest that the ventricular system enlarges progressively during aging, and in a subset of patients follows an abnormal consecutive geometric dilatation, influenced by age and sex.
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Affiliation(s)
- Antonio Currà
- Academic Neurology Unit, A. Fiorini Hospital, Terracina, (LT), Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome-Polo Pontino, Rome, Italy
| | - Francesco Pierelli
- Academic Neuro-Rehabilitation Unit, ICOT, Latina, IRCCS Neuromed and Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome-Polo Pontino, Rome, Italy
| | - Riccardo Gasbarrone
- Department of Chemical Engineering, Materials & Environment, Sapienza University of Rome, Rome, Italy
| | - Daniela Mannarelli
- Neurology Unit, Department of Human Neurosciences, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Italo Nofroni
- Department of Public Health and Infectious Diseases, Medical Statistics and Biometry, Sapienza University of Rome, Rome, Italy
| | - Vittoria Matone
- Neurosurgery Unit, Department of Human Neurosciences, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Lucio Marinelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy.,Department of Neuroscience, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Carlo Trompetto
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy.,Department of Neuroscience, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Francesco Fattapposta
- Neurology Unit, Department of Human Neurosciences, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Paolo Missori
- Neurosurgery Unit, Department of Human Neurosciences, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
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Fyllingen EH, Hansen TI, Jakola AS, Håberg AK, Salvesen Ø, Solheim O. Does risk of brain cancer increase with intracranial volume? A population-based case control study. Neuro Oncol 2019; 20:1225-1230. [PMID: 29554311 DOI: 10.1093/neuonc/noy043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Background Glioma is the most common primary brain tumor and is believed to arise from glial stem cells. Despite large efforts, there are limited established risk factors. It has been suggested that tissue with more stem cell divisions may exhibit higher risk of cancer due to chance alone. Assuming a positive correlation between the number of stem cell divisions in an organ and size of the same organ, we hypothesized that variation in intracranial volume, as a proxy for brain size, may be linked to risk of high-grade glioma. Methods Intracranial volume was calculated from pretreatment 3D T1-weighted MRI brain scans from 124 patients with high-grade glioma and 995 general population-based controls. Binomial logistic regression analyses were performed to ascertain the effect of intracranial volume and sex on the likelihood that participants had high-grade glioma. Results An increase in intracranial volume of 100 mL was associated with an odds ratio of high-grade glioma of 1.69 (95% CI: 1.44‒1.98; P < 0.001). After adjusting for intracranial volume, female sex emerged as a risk factor for high-grade glioma (odds ratio for male sex = 0.56, 95% CI: 0.33‒0.93; P = 0.026). Conclusions Intracranial volume is strongly associated with risk of high-grade glioma. After correcting for intracranial volume, risk of high-grade glioma was higher in women. The development of glioma is correlated to brain size and may to a large extent be a stochastic event related to the number of cells at risk.
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Affiliation(s)
- Even Hovig Fyllingen
- Department of Neurosurgery, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Tor Ivar Hansen
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Asgeir Store Jakola
- Department of Neurosurgery, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden.,Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Asta Kristine Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Øyvind Salvesen
- Unit for Applied Clinical Research, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ole Solheim
- Department of Neurosurgery, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,National Competence Centre for Ultrasound and Image Guided Surgery, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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37
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Tullo S, Patel R, Devenyi GA, Salaciak A, Bedford SA, Farzin S, Wlodarski N, Tardif CL, Breitner JCS, Chakravarty MM. MR-based age-related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan. Hum Brain Mapp 2019; 40:5269-5288. [PMID: 31452289 PMCID: PMC6864890 DOI: 10.1002/hbm.24771] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 07/17/2019] [Accepted: 08/05/2019] [Indexed: 01/18/2023] Open
Abstract
While numerous studies have used magnetic resonance imaging (MRI) to elucidate normative age‐related trajectories in subcortical structures across the human lifespan, there exists substantial heterogeneity among different studies. Here, we investigated the normative relationships between age and morphology (i.e., volume and shape), and microstructure (using the T1‐weighted/T2‐weighted [T1w/T2w] signal ratio as a putative index of myelin and microstructure) of the striatum, globus pallidus, and thalamus across the adult lifespan using a dataset carefully quality controlled, yielding a final sample of 178 for the morphological analyses, and 162 for the T1w/T2w analyses from an initial dataset of 253 healthy subjects, aged 18–83. In accordance with previous cross‐sectional studies of adults, we observed age‐related volume decrease that followed a quadratic relationship between age and bilateral striatal and thalamic volumes, and a linear relationship in the globus pallidus. Our shape indices consistently demonstrated age‐related posterior and medial areal contraction bilaterally across all three structures. Beyond morphology, we observed a quadratic inverted U‐shaped relationship between T1w/T2w signal ratio and age, with a peak value occurring in middle age (at around 50 years old). After permutation testing, the Akaike information criterion determined age relationships remained significant for the bilateral globus pallidus and thalamus, for both the volumetric and T1w/T2w analyses. Our findings serve to strengthen and expand upon previous volumetric analyses by providing a normative baseline of morphology and microstructure of these structures to which future studies investigating patients with various disorders can be compared.
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Affiliation(s)
- Stephanie Tullo
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Alyssa Salaciak
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Saashi A Bedford
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Sarah Farzin
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Nancy Wlodarski
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Christine L Tardif
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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- Centre for the Studies on the Prevention of AD, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - John C S Breitner
- Centre for the Studies on the Prevention of AD, Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - M Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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Sanchis-Segura C, Ibañez-Gual MV, Adrián-Ventura J, Aguirre N, Gómez-Cruz ÁJ, Avila C, Forn C. Sex differences in gray matter volume: how many and how large are they really? Biol Sex Differ 2019; 10:32. [PMID: 31262342 PMCID: PMC6604149 DOI: 10.1186/s13293-019-0245-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 06/06/2019] [Indexed: 11/17/2022] Open
Abstract
Background Studies assessing volumetric sex differences have provided contradictory results. Total intracranial volume (TIV) is a major confounding factor when estimating local volumes of interest (VOIs). We investigated how the number, size, and direction of sex differences in gray matter volume (GMv) vary depending on how TIV variation is statistically handled. Methods Sex differences in the GMv of 116 VOIs were assessed in 356 participants (171 females) without correcting for TIV variation or after adjusting the data with 5 different methods (VBM8 non-linear-only modulation, proportions, power-corrected-proportions, covariation, and the residuals method). The outcomes obtained with these procedures were compared to each other and to those obtained in three criterial subsamples, one comparing female-male pairs matched on their TIV and two others comparing groups of either females or males with large/small TIVs. Linear regression was used to quantify TIV effects on raw GMv and the efficacy of each method in controlling for them. Results Males had larger raw GMv than females in all brain areas, but these differences were driven by direct TIV-VOIs relationships and more closely resembled the differences observed between individuals with large/small TIVs of sex-specific subsamples than the sex differences observed in the TIV-matched subsample. All TIV-adjustment methods reduced the number of sex differences but their results were very different. The VBM8- and the proportions-adjustment methods inverted TIV-VOIs relationships and resulted in larger adjusted volumes in females, promoting sex differences largely attributable to TIV variation and very distinct from those observed in the TIV-matched subsample. The other three methods provided results unrelated to TIV and very similar to those of the TIV-matched subsample. In these datasets, sex differences were bidirectional and achieved satisfactory replication rates in 19 VOIs, but they were “small” (d < ∣0.38∣) and most of them faded away after correcting for multiple comparisons. Conclusions There is not just one answer to the question of how many and how large the sex differences in GMv are, but not all the possible answers are equally valid. When TIV effects are ruled out using appropriate adjustment methods, few sex differences (if any) remain statistically significant, and their size is quite reduced. Electronic supplementary material The online version of this article (10.1186/s13293-019-0245-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Carla Sanchis-Segura
- Departament de Psicologia bàsica, clínica i psicobiologia, Universitat Jaume I, Castelló, Spain.
| | | | - Jesús Adrián-Ventura
- Departament de Psicologia bàsica, clínica i psicobiologia, Universitat Jaume I, Castelló, Spain
| | - Naiara Aguirre
- Departament de Psicologia bàsica, clínica i psicobiologia, Universitat Jaume I, Castelló, Spain
| | | | - César Avila
- Departament de Psicologia bàsica, clínica i psicobiologia, Universitat Jaume I, Castelló, Spain
| | - Cristina Forn
- Departament de Psicologia bàsica, clínica i psicobiologia, Universitat Jaume I, Castelló, Spain
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Armstrong NM, An Y, Beason-Held L, Doshi J, Erus G, Ferrucci L, Davatzikos C, Resnick SM. Sex differences in brain aging and predictors of neurodegeneration in cognitively healthy older adults. Neurobiol Aging 2019; 81:146-156. [PMID: 31280118 DOI: 10.1016/j.neurobiolaging.2019.05.020] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 05/04/2019] [Accepted: 05/30/2019] [Indexed: 01/15/2023]
Abstract
We evaluated sex differences in MRI-based volume loss and differences in predictors of this neurodegeneration in cognitively healthy older adults. Mixed-effects regression was used to compare regional brain volume trajectories of 295 male and 328 female cognitively healthy Baltimore Longitudinal Study of Aging participants, aged 55-92 years, with up to 20 years of follow-up and to assess sex differences in the associations of age, hypertension, obesity, APOE e4 carrier status, and high-density lipoprotein cholesterol with regional brain volume trajectories. For both sexes, older age was associated with steeper volumetric declines in many brain regions, with sex differences in volume loss observed in frontal, temporal, and parietal regions. In males, hypertension and higher high-density lipoprotein cholesterol were protective against volume loss in the hippocampus, entorhinal cortex, and parahippocampal gyrus. In females, hypertension was associated with steeper volumetric decline in gray matter, and obesity was protective against volume loss in temporal gray matter. Predictors of volume change may affect annual rates of volume change differently between men and women.
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Affiliation(s)
- Nicole M Armstrong
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jimit Doshi
- Department of Radiology, Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Department of Radiology, Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Christos Davatzikos
- Department of Radiology, Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
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Kaczkurkin AN, Raznahan A, Satterthwaite TD. Sex differences in the developing brain: insights from multimodal neuroimaging. Neuropsychopharmacology 2019; 44:71-85. [PMID: 29930385 PMCID: PMC6235840 DOI: 10.1038/s41386-018-0111-z] [Citation(s) in RCA: 193] [Impact Index Per Article: 38.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 05/21/2018] [Accepted: 05/23/2018] [Indexed: 12/20/2022]
Abstract
Youth (including both childhood and adolescence) is a period when the brain undergoes dramatic remodeling and is also a time when neuropsychiatric conditions often emerge. Many of these illnesses have substantial sex differences in prevalence, suggesting that sex differences in brain development may underlie differential risk for psychiatric symptoms between males and females. Substantial evidence documents sex differences in brain structure and function in adults, and accumulating data suggests that these sex differences may be present or emerge during development. Here we review the evidence for sex differences in brain structure, white matter organization, and perfusion during development. We then use these normative differences as a framework to understand sex differences in brain development associated with psychopathology. In particular, we focus on sex differences in the brain as they relate to anxiety, depression, psychosis, and attention-deficit/hyperactivity symptoms. Finally, we highlight existing limitations, gaps in knowledge, and fertile avenues for future research.
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Affiliation(s)
- Antonia N Kaczkurkin
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, 20814, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Kurth F, Cherbuin N, Luders E. Age but no sex effects on subareas of the amygdala. Hum Brain Mapp 2018; 40:1697-1704. [PMID: 30549129 DOI: 10.1002/hbm.24481] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 10/23/2018] [Accepted: 11/07/2018] [Indexed: 01/05/2023] Open
Abstract
The amygdala, an anatomical composite of several nuclei that have been grouped anatomically and functionally into three major subareas, has been reported to decrease in size with increasing age and to differ in size between male and female brains. However, findings are rather inconsistent across existing studies, possibly reflecting differences in the cohorts examined or the approaches chosen to define and measure the dimensions of the amygdala. Here, we investigated possible effects of age and sex on the amygdala as well as age-by-sex interactions in 100 healthy subjects (50 men/50 women) aged 18-69 years. For this purpose, we enhanced conventional imaging-based information with microscopically defined cytoarchitectonic probabilities to discriminate between different subareas. We observed significant negative correlations between age and all subareas of the amygdala indicating decreases over time, but with subarea-specific trajectories. In addition, we detected a significant quadratic association with age for the left superficial subarea suggesting an accelerating volume loss over time. Such regional information may serve as a frame of reference in future studies, not only for normative samples but also potentially for clinical populations known to present with an atypical atrophy of the amygdala. There were no sex differences and no interactions between sex and age, suggesting that the size of the amygdala is similar in male and female brains (at least when properly accounting for total intracranial volume) and that its age-related decline follows a similar trajectory in both sexes.
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Affiliation(s)
- Florian Kurth
- School of Psychology, University of Auckland, Auckland, New Zealand
| | - Nicolas Cherbuin
- Centre for Research on Ageing Health and Wellbeing, Australian National University, Canberra, Australia
| | - Eileen Luders
- School of Psychology, University of Auckland, Auckland, New Zealand.,Centre for Research on Ageing Health and Wellbeing, Australian National University, Canberra, Australia.,Laboratory of Neuro Imaging, School of Medicine, University of Southern California, Los Angeles, California
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Aghamohammadi-Sereshki A, Hrybouski S, Travis S, Huang Y, Olsen F, Carter R, Camicioli R, Malykhin NV. Amygdala subnuclei and healthy cognitive aging. Hum Brain Mapp 2018; 40:34-52. [PMID: 30291764 DOI: 10.1002/hbm.24353] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 07/13/2018] [Accepted: 08/02/2018] [Indexed: 12/25/2022] Open
Abstract
Amygdala is a group of nuclei involved in the neural circuits of fear, reward learning, and stress. The main goal of this magnetic resonance imaging (MRI) study was to investigate the relationship between age and the amygdala subnuclei volumes in a large cohort of healthy individuals. Our second goal was to determine effects of the apolipoprotein E (APOE) and brain-derived neurotrophic factor (BDNF) polymorphisms on the amygdala structure. One hundred and twenty-six healthy participants (18-85 years old) were recruited for this study. MRI datasets were acquired on a 4.7 T system. Amygdala was manually segmented into five major subdivisions (lateral, basal, accessory basal nuclei, and cortical, and centromedial groups). The BDNF (methionine and homozygous valine) and APOE genotypes (ε2, homozygous ε3, and ε4) were obtained using single nucleotide polymorphisms. We found significant nonlinear negative associations between age and the total amygdala and its lateral, basal, and accessory basal nuclei volumes, while the cortical amygdala showed a trend. These age-related associations were found only in males but not in females. Centromedial amygdala did not show any relationship with age. We did not observe any statistically significant effects of APOE and BDNF polymorphisms on the amygdala subnuclei volumes. In contrast to APOE ε2 allele carriers, both older APOE ε4 and ε3 allele carriers had smaller lateral, basal, accessory basal nuclei volumes compared to their younger counterparts. This study indicates that amygdala subnuclei might be nonuniformly affected by aging and that age-related association might be gender specific.
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Affiliation(s)
| | - Stanislau Hrybouski
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Scott Travis
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Yushan Huang
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Fraser Olsen
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Rawle Carter
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Richard Camicioli
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada.,Division of Neurology, University of Alberta, Edmonton, Alberta, Canada
| | - Nikolai V Malykhin
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada.,Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
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The Lifespan Trajectory of the Encoding-Retrieval Flip: A Multimodal Examination of Medial Parietal Cortex Contributions to Episodic Memory. J Neurosci 2018; 38:8666-8679. [PMID: 30143571 DOI: 10.1523/jneurosci.1702-17.2018] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 06/21/2018] [Accepted: 06/23/2018] [Indexed: 11/21/2022] Open
Abstract
The formation of episodic memories is associated with deactivation during encoding and activation during retrieval in the posteromedial cortex (PMC). We hypothesized that the encoding/retrieval (E/R) flip is a critical component of episodic memory across the lifespan because structural and metabolic changes in the PMC coincide with the fine tuning of the episodic memory system in development and the reductions of memory performance in aging. The aims of the present study were, first, to describe lifespan trajectories of PMC encoding and retrieval activity in 270 human participants (167 females) from 6 to 80 years of age. Our second goal was to construct a model for episodic memory development in which contributions from brain activity, cortical thickness (CT), and structural connectivity are accounted for. We found that modulation of neural activity in response to memory encoding and retrieval demands was not fully developed until adolescence and decreased from adulthood through old age. The magnitude of the E/R flip was related to source memory and 55% of the age-related variance in source memory performance during childhood and adolescence could be accounted for by the E/R flip, CT, and mean diffusivity together. However, only CT and the E/R flip provided unique contributions with which to explain memory performance. The results suggest that neural dynamics in the PMC is related to the development of episodic memory during childhood and adolescence. The similar trajectories of the E/R flip and episodic memory emergence and decline through development and aging further suggests that a lifelong relationship exists.SIGNIFICANCE STATEMENT Modulation of neural activity in the posteromedial cortex (PMC) in response to memory encoding/retrieval (E/R) demands (E/R flip) does not reach its peak until adolescence and decreases from adulthood through old age. The magnitude of the E/R flip is related to source memory and 55% of the age-related variance in source memory performance during childhood and adolescence can be accounted for by the E/R flip and brain structure together. The results suggest that neural dynamics in the PMC is related to the development of episodic memory function during childhood and adolescence and the similar trajectories of the E/R flip and episodic memory performance through development and aging suggests that a lifelong relationship exists.
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Ørbo MC, Vangberg TR, Tande PM, Anke A, Aslaksen PM. Memory performance, global cerebral volumes and hippocampal subfield volumes in long-term survivors of Out-of-Hospital Cardiac Arrest. Resuscitation 2018; 126:21-28. [DOI: 10.1016/j.resuscitation.2018.02.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 02/07/2018] [Accepted: 02/13/2018] [Indexed: 10/18/2022]
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Opfer R, Ostwaldt AC, Sormani MP, Gocke C, Walker-Egger C, Manogaran P, De Stefano N, Schippling S. Estimates of age-dependent cutoffs for pathological brain volume loss using SIENA/FSL-a longitudinal brain volumetry study in healthy adults. Neurobiol Aging 2017; 65:1-6. [PMID: 29407463 DOI: 10.1016/j.neurobiolaging.2017.12.024] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 12/19/2017] [Accepted: 12/21/2017] [Indexed: 01/01/2023]
Abstract
Brain volume loss (BVL) has gained increasing interest for monitoring tissue damage in neurodegenerative diseases including multiple sclerosis (MS). In this longitudinal study, 117 healthy participants (age range 37.3-82.6 years) received at least 2 magnetic resonance imaging examinations. BVL (in %) was determined with the Structural Image Evaluation using Normalisation of Atrophy/FMRIB Software Library and annualized. Mean BVL per year was 0.15%, 0.30%, 0.46%, and 0.61% at ages 45, 55, 65, and 75 years, respectively. The corresponding BVL per year values of the age-dependent 95th percentiles were 0.52%, 0.77%, 1.05% and 1.45%. Pathological BVL can be assumed if an individual BVL per year exceeds these thresholds for a given age. The mean BVL per year determined in this longitudinal study was consistent with results from a cross-sectional study that was published recently. The cut-off for a pathological BVL per year at the age of 45 years (0.52%) was consistent with the cut-off suggested previously to distinguish between physiological and pathological BVL in MS patients. Different cut-off values, however, need to be considered when interpreting BVL assessed in cohorts of higher ages.
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Affiliation(s)
- Roland Opfer
- Neuroimmunology and Multiple Sclerosis Research, Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland; Jung diagnostics GmbH, Hamburg, Germany.
| | | | - Maria Pia Sormani
- Biostatistics Unit, Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Carola Gocke
- Medical Prevention Center Hamburg (MPCH), Hamburg, Germany
| | - Christine Walker-Egger
- Neuroimmunology and Multiple Sclerosis Research, Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Praveena Manogaran
- Neuroimmunology and Multiple Sclerosis Research, Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland; Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Sven Schippling
- Neuroimmunology and Multiple Sclerosis Research, Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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Malykhin NV, Huang Y, Hrybouski S, Olsen F. Differential vulnerability of hippocampal subfields and anteroposterior hippocampal subregions in healthy cognitive aging. Neurobiol Aging 2017; 59:121-134. [DOI: 10.1016/j.neurobiolaging.2017.08.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 08/01/2017] [Accepted: 08/02/2017] [Indexed: 11/29/2022]
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Chen JC, Wang X, Serre M, Cen S, Franklin M, Espeland M. Particulate Air Pollutants, Brain Structure, and Neurocognitive Disorders in Older Women. Res Rep Health Eff Inst 2017; 2017:1-65. [PMID: 31898881 PMCID: PMC7266369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023] Open
Abstract
Introduction An increasing number of studies have suggested that exposure to particulate matter (PM) may represent a novel - and potentially amendable - environmental determinant of brain aging. The current longitudinal environmental epidemiological study addressed some important knowledge gaps in this emerging field, which combines the study of air pollution and neuroepidemiology. The investigators hypothesized that long-term PM exposure adversely influences global brain volume and brain regions (e.g., frontal lobe or hippocampus) that are critical to memory and complex cognitive processing or that are affected by neuropathological changes in dementia. It was also hypothesized that long-term PM exposure results in neurovascular damage and may increase the risk of mild cognitive impairment (MCI) and -dementia. Methods The investigators selected a well-characterized and geographically diverse population of older women (N = 7,479; average age = 71.0 ± 3.8 years at baseline) in the Women's Health Initiative (WHI) Memory Study (WHIMS) cohort (1996-2007), which included a subcohort (n = 1,403) enrolled in the WHIMS-Magnetic Resonance Imaging (WHIMS-MRI) study (2005-2006). Residence-specific yearly exposures to PM ≤ 2.5 µm in aerodynamic diameter (PM₂.₅) were estimated using a Bayesian maximum entropy spatiotemporal model of annual monitoring data (1999-2007) recorded in the U.S. Environmental Protection Agency (U.S. EPA) Air Quality System (AQS). Annual exposures (1996-2005) to diesel PM (DPM) were assigned to each residential census tract in a nationwide spatiotemporal mapping, based on a generalized additive model (GAM), to conduct census tract-specific temporal interpolation of DPM on-road estimates given by the U.S. EPA National-Scale Air Toxics Assessment Program. Multiple linear regression and multicovariate-adjusted Cox models were used to examine the associations, with statistical adjustment for multiple potential confounders. Results The investigators found that participants had smaller brain volumes, especially in the normal-appearing white matter (WM), if they lived in locations with higher levels of cumulative exposure (1999-2006) to PM ₂.₅ before the brain MRI scans were performed. The associations were not explained by sociodemographic factors, socioeconomic status, lifestyle factors, or other clinical characteristics. Analyses showed that the adverse effect on brain structure in the participants was driven primarily by the smaller WM volumes associated with cumulative PM₂.₅ exposures, which were present in the WM divisions of the association brain area (frontal, parietal, and temporal lobes) and corpus callosum. Increased DPM exposures were associated with larger ventricular volume, suggesting an overall atrophic effect on the aging brains. The participants tended to have smaller gray matter (GM) volumes if they lived in areas with the highest (i.e., fourth quartile) estimated cumulative DPM exposure in the 10 years before the brain MRI scans, compared with women in the first to third quartiles. This observed association was present in the total brain GM and in the association brain cortices. The associations with normal-appearing WM varied by DPM exposure range. For women with estimated cumulative exposure below that of the fourth quartile, increased DPM estimates were associated with smaller WM volumes. However, for women with increased cumulative DPM exposures estimates in the fourth quartile, WM volumes were larger. This pattern of association was found consistently in the association brain area; no measurable difference was found in the volume of the corpus callosum. These observed adverse effects of cumulative exposure to PM₂.₅ (linking exposure with smaller WM volumes) and to DPM (linking exposure in the highest quartile with smaller GM volumes) were not significantly modified by existing cardiovascular diseases, diabetes mellitus, obesity, or measured white blood cell (WBC) count. MRI measurements of the structural brain showed no differences in small-vessel ischemic diseases (SVID) in participants with varying levels of cumulative exposure to PM₂.₅ (1999-2006) or DPM (1996-2005), and no associations between PM exposures and SVID volumes were noted for total brain, association brain area, GM, or WM. For neurocognitive outcomes followed until 2007, the investigators found no evidence for increased risk of MCI/dementia associated with long-term PM exposures. Although exploratory secondary analyses showed different patterns of associations linking PM exposures separately with MCI and dementia, none of the -results was statistically significant. A similar lack of associations between PM exposures and MCI/dementia was found across the subgroups, with no strong indications for effect modification by cardiovascular diseases, diabetes mellitus, obesity, or WBC count. Conclusions The investigators concluded that their study findings support the hypothesized brain-structure neurotoxicity associated with PM exposures, a result that is in line with emerging neurotoxicological data. However, the investigators found no evidence of increased risk of MCI/dementia associated with long-term PM exposures. To better test the neurovascular effect hypothesis in PM-associated neurotoxic effects on the aging brain, the investigators recommend that future studies pay greater attention to selecting optimal populations with repeated measurements of cerebrovascular damage and address the possibility of selection biases accordingly. To further investigate the long-term consequence of brain-structure neurotoxicity on pathological brain aging, future researchers should take the pathobiologically heterogeneous neurocognitive outcomes into account and design adequately powered prospective cohort studies with improved exposure estimation and valid outcome ascertainment to assess whether PM-associated neurotoxicity increases the risks of pathological brain aging, including MCI and dementia.
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Affiliation(s)
- J-C Chen
- Keck School of Medicine, University of Southern California, Los Angeles
| | - X Wang
- Keck School of Medicine, University of Southern California, Los Angeles
| | - M Serre
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill
| | - S Cen
- Keck School of Medicine, University of Southern California, Los Angeles
| | - M Franklin
- Keck School of Medicine, University of Southern California, Los Angeles
| | - M Espeland
- Wake Forest University School of Medicine, Winston-Salem, North Carolina
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Sex in Context: Limitations of Animal Studies for Addressing Human Sex/Gender Neurobehavioral Health Disparities. J Neurosci 2017; 36:11823-11830. [PMID: 27881769 DOI: 10.1523/jneurosci.1391-16.2016] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Revised: 09/02/2016] [Accepted: 09/15/2016] [Indexed: 01/10/2023] Open
Abstract
Many brain and behavioral disorders differentially affect men and women. The new National Institutes of Health requirement to include both male and female animals in preclinical studies aims to address such health disparities, but we argue that the mandate is not the best solution to this problem. Sex differences are highly species-specific, tied to the mating system and social ecology of a given species or even strain of animal. In many cases, animals poorly replicate male-female differences in brain-related human diseases. Sex/gender disparities in human health have a strong sociocultural component that is intimately entangled with biological sex and challenging to model in animals. We support research that investigates sex-related variables in hypothesis-driven studies of animal brains and behavior. However, institutional policies that require sex analysis and give it special salience over other sources of biological variance can distort research. We caution that the costly imposition of sex analysis on nearly all animal research entrenches the presumption that human brain and behavioral differences are largely biological in origin and overlooks the potentially more powerful social, psychological, and cultural contributors to male-female neurobehavioral health gaps.
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Savjani RR, Taylor BA, Acion L, Wilde EA, Jorge RE. Accelerated Changes in Cortical Thickness Measurements with Age in Military Service Members with Traumatic Brain Injury. J Neurotrauma 2017; 34:3107-3116. [PMID: 28657432 DOI: 10.1089/neu.2017.5022] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Finding objective and quantifiable imaging markers of mild traumatic brain injury (TBI) has proven challenging, especially in the military population. Changes in cortical thickness after injury have been reported in animals and in humans, but it is unclear how these alterations manifest in the chronic phase, and it is difficult to characterize accurately with imaging. We used cortical thickness measures derived from Advanced Normalization Tools (ANTs) to predict a continuous demographic variable: age. We trained four different regression models (linear regression, support vector regression, Gaussian process regression, and random forests) to predict age from healthy control brains from publicly available datasets (n = 762). We then used these models to predict brain age in military Service Members with TBI (n = 92) and military Service Members without TBI (n = 34). Our results show that all four models overpredicted age in Service Members with TBI, and the predicted age difference was significantly greater compared with military controls. These data extend previous civilian findings and show that cortical thickness measures may reveal an association of accelerated changes over time with military TBI.
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Affiliation(s)
- Ricky R Savjani
- 1 Michael E. DeBakey Veterans Affairs Medical Center , Houston, Texas.,2 Department of Neuroscience, Baylor College of Medicine , Houston, Texas.,7 Texas A&M Health Science Center College of Medicine , Bryan, Texas
| | - Brian A Taylor
- 1 Michael E. DeBakey Veterans Affairs Medical Center , Houston, Texas.,3 Department of Radiology, Baylor College of Medicine , Houston, Texas.,4 Department of Physical Medicine and Rehabilitation, Baylor College of Medicine , Houston, Texas
| | - Laura Acion
- 6 Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine , Houston, Texas.,8 Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires-CONICET , Buenos Aires, Argentina
| | - Elisabeth A Wilde
- 1 Michael E. DeBakey Veterans Affairs Medical Center , Houston, Texas.,3 Department of Radiology, Baylor College of Medicine , Houston, Texas.,4 Department of Physical Medicine and Rehabilitation, Baylor College of Medicine , Houston, Texas.,5 Department of Neurology, Baylor College of Medicine , Houston, Texas
| | - Ricardo E Jorge
- 1 Michael E. DeBakey Veterans Affairs Medical Center , Houston, Texas.,6 Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine , Houston, Texas
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Narvacan K, Treit S, Camicioli R, Martin W, Beaulieu C. Evolution of deep gray matter volume across the human lifespan. Hum Brain Mapp 2017; 38:3771-3790. [PMID: 28548250 DOI: 10.1002/hbm.23604] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 03/24/2017] [Accepted: 03/26/2017] [Indexed: 12/11/2022] Open
Abstract
Magnetic resonance imaging of subcortical gray matter structures, which mediate behavior, cognition and the pathophysiology of several diseases, is crucial for establishing typical maturation patterns across the human lifespan. This single site study examines T1-weighted MPRAGE images of 3 healthy cohorts: (i) a cross-sectional cohort of 406 subjects aged 5-83 years; (ii) a longitudinal neurodevelopment cohort of 84 subjects scanned twice approximately 4 years apart, aged 5-27 years at first scan; and (iii) a longitudinal aging cohort of 55 subjects scanned twice approximately 3 years apart, aged 46-83 years at first scan. First scans from longitudinal subjects were included in the cross-sectional analysis. Age-dependent changes in thalamus, caudate, putamen, globus pallidus, nucleus accumbens, hippocampus, and amygdala volumes were tested with Poisson, quadratic, and linear models in the cross-sectional cohort, and quadratic and linear models in the longitudinal cohorts. Most deep gray matter structures best fit to Poisson regressions in the cross-sectional cohort and quadratic curves in the young longitudinal cohort, whereas the volume of all structures except the caudate and globus pallidus decreased linearly in the longitudinal aging cohort. Males had larger volumes than females for all subcortical structures, but sex differences in trajectories of change with age were not significant. Within subject analysis showed that 65%-80% of 13-17 year olds underwent a longitudinal decrease in volume between scans (∼4 years apart) for the putamen, globus pallidus, and hippocampus, suggesting unique developmental processes during adolescence. This lifespan study of healthy participants will form a basis for comparison to neurological and psychiatric disorders. Hum Brain Mapp 38:3771-3790, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Karl Narvacan
- Neuroscience and Mental Health Institute, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Sarah Treit
- Neuroscience and Mental Health Institute, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada.,Department of Biomedical Engineering, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Richard Camicioli
- Division of Neurology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Wayne Martin
- Division of Neurology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Christian Beaulieu
- Neuroscience and Mental Health Institute, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada.,Department of Biomedical Engineering, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
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