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Serio B, Hettwer MD, Wiersch L, Bignardi G, Sacher J, Weis S, Eickhoff SB, Valk SL. Sex differences in functional cortical organization reflect differences in network topology rather than cortical morphometry. Nat Commun 2024; 15:7714. [PMID: 39231965 PMCID: PMC11375086 DOI: 10.1038/s41467-024-51942-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 08/21/2024] [Indexed: 09/06/2024] Open
Abstract
Differences in brain size between the sexes are consistently reported. However, the consequences of this anatomical difference on sex differences in intrinsic brain function remain unclear. In the current study, we investigate whether sex differences in intrinsic cortical functional organization may be associated with differences in cortical morphometry, namely different measures of brain size, microstructure, and the geodesic distance of connectivity profiles. For this, we compute a low dimensional representation of functional cortical organization, the sensory-association axis, and identify widespread sex differences. Contrary to our expectations, sex differences in functional organization do not appear to be systematically associated with differences in total surface area, microstructural organization, or geodesic distance, despite these morphometric properties being per se associated with functional organization and differing between sexes. Instead, functional sex differences in the sensory-association axis are associated with differences in functional connectivity profiles and network topology. Collectively, our findings suggest that sex differences in functional cortical organization extend beyond sex differences in cortical morphometry.
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Affiliation(s)
- Bianca Serio
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany.
- Max Planck School of Cognition, Leipzig, Germany.
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Meike D Hettwer
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Leipzig, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Lisa Wiersch
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
- Brain-Based Predictive Modeling Lab, Feinstein Institutes for Medical Research, Glen Oaks, New York, NY, USA
| | - Giacomo Bignardi
- Max Planck School of Cognition, Leipzig, Germany
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Julia Sacher
- Max Planck School of Cognition, Leipzig, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Leipzig Center for Female Health & Gender Medicine, Medical Faculty, University Clinic Leipzig, Leipzig, Germany
- Clinic for Cognitive Neurology, University Medical Center Leipzig, Leipzig, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Leipzig, Germany
| | - Sofie L Valk
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany.
- Max Planck School of Cognition, Leipzig, Germany.
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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Pecheva D, Smith DM, Casey BJ, Woodward LJ, Dale AM, Filippi CG, Watts R. Sex and mental health are related to subcortical brain microstructure. Proc Natl Acad Sci U S A 2024; 121:e2403212121. [PMID: 39042688 PMCID: PMC11295051 DOI: 10.1073/pnas.2403212121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 06/14/2024] [Indexed: 07/25/2024] Open
Abstract
Some mental health problems such as depression and anxiety are more common in females, while others such as autism and attention deficit/hyperactivity (AD/H) are more common in males. However, the neurobiological origins of these sex differences are poorly understood. Animal studies have shown substantial sex differences in neuronal and glial cell structure, while human brain imaging studies have shown only small differences, which largely reflect overall body and brain size. Advanced diffusion MRI techniques can be used to examine intracellular, extracellular, and free water signal contributions and provide unique insights into microscopic cellular structure. However, the extent to which sex differences exist in these metrics of subcortical gray matter structures implicated in psychiatric disorders is not known. Here, we show large sex-related differences in microstructure in subcortical regions, including the hippocampus, thalamus, and nucleus accumbens in a large sample of young adults. Unlike conventional T1-weighted structural imaging, large sex differences remained after adjustment for age and brain volume. Further, diffusion metrics in the thalamus and amygdala were associated with depression, anxiety, AD/H, and antisocial personality problems. Diffusion MRI may provide mechanistic insights into the origin of sex differences in behavior and mental health over the life course and help to bridge the gap between findings from experimental, epidemiological, and clinical mental health research.
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Affiliation(s)
- Diliana Pecheva
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA92093
| | - Diana M. Smith
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA92093
- Medical Scientist Training Program, University of California, San Diego, La Jolla, CA92093
| | - B. J. Casey
- Department of Neuroscience and Behavior, Barnard College, New York, NY10027
| | - Lianne J. Woodward
- Faculty of Health, University of Canterbury, Christchurch8140, New Zealand
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA92093
- Department of Radiology, University of California, San Diego, La Jolla, CA92093
- Department of Neurosciences, University of California, San Diego, La Jolla, CA92093
- Department of Psychiatry, University of California, San Diego, La Jolla, CA92093
| | - Christopher G. Filippi
- Department of Radiology, The Hospital for Sick Children and the SickKids Research Institute, Toronto, ON M5G 1E8, Canada
| | - Richard Watts
- Faculty of Health, University of Canterbury, Christchurch8140, New Zealand
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Luders E, Gaser C, Spencer D, Thankamony A, Hughes I, Simpson H, Srirangalingam U, Gleeson H, Hines M, Kurth F. Cortical gyrification in women and men and the (missing) link to prenatal androgens. Eur J Neurosci 2024; 60:3995-4003. [PMID: 38733283 PMCID: PMC11260240 DOI: 10.1111/ejn.16391] [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: 01/29/2024] [Revised: 04/13/2024] [Accepted: 05/01/2024] [Indexed: 05/13/2024]
Abstract
Previous studies have reported sex differences in cortical gyrification. Since most cortical folding is principally defined in utero, sex chromosomes as well as gonadal hormones are likely to influence sex-specific aspects of local gyrification. Classic congenital adrenal hyperplasia (CAH) causes high levels of androgens during gestation in females, whereas levels in males are largely within the typical male range. Therefore, CAH provides an opportunity to study the possible effects of prenatal androgens on cortical gyrification. Here, we examined the vertex-wise absolute mean curvature-a common estimate for cortical gyrification-in individuals with CAH (33 women and 20 men) and pair-wise matched controls (33 women and 20 men). There was no significant main effect of CAH and no significant CAH-by-sex interaction. However, there was a significant main effect of sex in five cortical regions, where gyrification was increased in women compared to men. These regions were located on the lateral surface of the brain, specifically left middle frontal (rostral and caudal), right inferior frontal, left inferior parietal, and right occipital. There was no cortical region where gyrification was increased in men compared to women. Our findings do not only confirm prior reports of increased cortical gyrification in female brains but also suggest that cortical gyrification is not significantly affected by prenatal androgen exposure. Instead, cortical gyrification might be determined by sex chromosomes either directly or indirectly-the latter potentially by affecting the underlying architecture of the cortex or the size of the intracranial cavity, which is smaller in women.
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Affiliation(s)
- Eileen Luders
- Department of Women’s and Children’s Health, Uppsala University, Uppsala 75237, Sweden
- Swedish Collegium for Advanced Study (SCAS), Uppsala 75238, Sweden
- School of Psychology, University of Auckland, Auckland 1010, New Zealand
- Laboratory of Neuro Imaging, School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Christian Gaser
- Department of Neurology, Jena University Hospital, Jena 07747, Germany
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena 07747, Germany
- German Center for Mental Health (DZPG), Germany
| | - Debra Spencer
- Department of Psychology, University of Cambridge, Cambridge CB23RQ, UK
| | - Ajay Thankamony
- Department of Paediatrics, Addenbrooke’s Hospital, University of Cambridge, Cambridge CB20QQ, UK
- Weston Centre for Paediatric Endocrinology & Diabetes, Addenbrooke’s Hospital, University of Cambridge, Cambridge CB20QQ, UK
| | - Ieuan Hughes
- Department of Paediatrics, Addenbrooke’s Hospital, University of Cambridge, Cambridge CB20QQ, UK
| | - Helen Simpson
- Department of Endocrinology and Diabetes, University College Hospital London, London NW12BU, UK
| | | | | | - Melissa Hines
- Department of Psychology, University of Cambridge, Cambridge CB23RQ, UK
| | - Florian Kurth
- School of Psychology, University of Auckland, Auckland 1010, New Zealand
- Departments of Neuroradiology and Radiology, Jena University Hospital, Jena 07747, Germany
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Hu M, Lou Y, Zhu C, Chen J, Liu S, Liang Y, Liu S, Tang Y. Evaluating the Impact of Intracranial Volume Correction Approaches on the Quantification of Intracranial Structures in MRI: A Systematic Analysis. J Magn Reson Imaging 2024; 59:2164-2177. [PMID: 37702125 DOI: 10.1002/jmri.28974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/10/2023] [Accepted: 08/10/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND In neuroscience, accurately quantifying individual brain regions in large cohorts is a challenge. Differences in intracranial structures can suggest functional differences, but they also reflect the effects of other factors. However, there is currently no standardized method for the correction of intracranial structure measurements. PURPOSE To identify the optimal method to counteract the influence of total intracranial volume (TIV) and gender on the measurement of intracranial structures. STUDY TYPE Prospective. POPULATION/SUBJECTS One hundred forty-one healthy adult volunteers (70 male, mean age 21.8 ± 1.7 years). FIELD STRENGTH/SEQUENCE T1-weighted 3D gradient-echo sequence at 3.0 T. ASSESSMENT A radiologist with 5 years of work experience screened the raw images to exclude poor-quality images. Freesurfer then performed automated segmentation to obtain measurements of intracranial structures. Male-only, female-only, and TIV-matched sub-samples were created separately. Comparisons between the original data and these sub-samples were used to assess the effects of gender and TIV. Comparison the consistency between TIV-matched sample and corrected data that corrected by four methods: Proportion method, power-corrected proportion method, covariate regression method, and residual method. STATISTICAL TESTS Cohen's d for examining group distribution disparities, t-tests for probing mean differences, correlation coefficients to assess the relationships between intracranial substructure measurements and TIV. Multiple comparison corrections were applied to the results. RESULTS The correlation coefficients between TIV and the volumes of intracranial structures ranged from 0.033 to 0.883, with an average of 0.467. Thirty significant volume differences were found among 36 structures in the original sample, while no differences were observed in the TIV-matched sample. Among the four correction methods, the residual method had highest consistency (similarity 94.4%) with the TIV-matched group. DATA CONCLUSION The variation in intracranial structure sizes between genders was largely attributable to TIV. The residual method offers a more accurate and effective approach for correcting the effects of TIV on intracranial structures. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Minqi Hu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, China
| | - Yunxia Lou
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, China
- Department of Ultrasound, Cheeloo Hospital, Shandong University, Jinan, Shandong, China
| | - Caiting Zhu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, China
| | - Jiachen Chen
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, China
| | - Shizhou Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, China
| | - Yongfeng Liang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Radiology, Cheeloo Hospital, Shandong University, Jinan, Shandong, China
| | - Shuwei Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, China
| | - Yuchun Tang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, China
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
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Matte Bon G, Kraft D, Comasco E, Derntl B, Kaufmann T. Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders. Biol Sex Differ 2024; 15:42. [PMID: 38750598 PMCID: PMC11097569 DOI: 10.1186/s13293-024-00615-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. METHODS We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV). RESULTS Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction. CONCLUSIONS Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.
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Affiliation(s)
- Gloria Matte Bon
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany.
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Dominik Kraft
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany
| | - Erika Comasco
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany
- German Center for Mental Health (DZPG), Partner Site Tübingen, Tübingen, Germany
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Calwerstraße 14, 72076, Tübingen, Germany.
- German Center for Mental Health (DZPG), Partner Site Tübingen, Tübingen, Germany.
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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Coverdale NS, Champagne AA, Allen MD, Tremblay JC, Ethier TS, Fernandez-Ruiz J, Marshall RA, MacPherson REK, Pyke KE, Cook DJ, Olver TD. Brain atrophy, reduced cerebral perfusion, arterial stiffening, and wall thickening with aging coincide with stimulus-specific changes in fMRI-BOLD responses. Am J Physiol Regul Integr Comp Physiol 2024; 326:R346-R356. [PMID: 38406844 DOI: 10.1152/ajpregu.00270.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/07/2024] [Accepted: 02/16/2024] [Indexed: 02/27/2024]
Abstract
The aim of this study was to investigate how aging affects blood flow and structure of the brain. It was hypothesized older individuals would have lower gray matter volume (GMV), resting cerebral blood flow (CBF0), and depressed responses to isometabolic and neurometabolic stimuli. In addition, increased carotid-femoral pulse-wave velocity (PWV), carotid intima-media thickness (IMT), and decreased brachial flow-mediated dilation (FMD) would be associated with lower CBF0, cerebrovascular reactivity (CVR), and GMV. Brain scans (magnetic resonance imaging) and cardiovascular examinations were conducted in young (age = 24 ± 3 yr, range = 22-28 yr; n = 13) and old (age = 71 ± 4 yr; range = 67-82 yr, n = 14) participants, and CBF0, CVR [isometabolic % blood oxygen level-dependent (BOLD) in response to a breath hold (BH)], brain activation patterns during a working memory task (neurometabolic %BOLD response to N-back trial), GMV, PWV, IMT, and FMD were measured. CBF0 and to a lesser extent CVRBH were lower in the old group (P ≤ 0.050); however, the increase in the %BOLD response to the memory task was not blunted (P ≥ 0.2867). Age-related differential activation patterns during the working memory task were characterized by disinhibition of the default mode network in the old group (P < 0.0001). Linear regression analyses revealed PWV, and IMT were negatively correlated with CBF0, CVRBH, and GMV across age groups, but within the old group alone only the relationships between PWV-CVRBH and IMT-GMV remained significant (P ≤ 0.0183). These findings suggest the impacts of age on cerebral %BOLD responses are stimulus specific, brain aging involves alterations in cerebrovascular and possibly neurocognitive control, and arterial stiffening and wall thickening may serve a role in cerebrovascular aging.NEW & NOTEWORTHY Cerebral perfusion was lower in old versus young adults. %Blood oxygen level-dependent (BOLD) responses to an isometabolic stimulus and gray matter volume were decreased in old versus young adults and associated with arterial stiffening and wall thickening. The increased %BOLD response to a neurometabolic stimulus appeared unaffected by age; however, the old group displayed disinhibition of the default mode network during the stimulus. Thus, age-related alterations in cerebral %BOLD responses were stimulus specific and related to arterial remodeling.
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Affiliation(s)
- Nicole S Coverdale
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
| | - Allen A Champagne
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
- School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Matti D Allen
- Department of Physical Medicine and Rehabilitation, Queen's University, Kingston, Ontario, Canada
| | - Joshua C Tremblay
- School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom
| | - Tarrah S Ethier
- School of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada
| | - Juan Fernandez-Ruiz
- Departamento de Fisiología, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, México
| | - Rory A Marshall
- Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
- Department of Biomedical Sciences, Western College of Veterinary Medicine, the University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Rebecca E K MacPherson
- Department of Health Sciences, Faculty of Applied Health Sciences, Brock University, St. Catharines, Ontario, Canada
| | - Kyra E Pyke
- School of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada
| | - Douglas J Cook
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
- Department of Surgery, Queen's University, Kingston, Ontario, Canada
| | - T Dylan Olver
- Department of Biomedical Sciences, Western College of Veterinary Medicine, the University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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Park B, Kim Y, Park J, Choi H, Kim SE, Ryu H, Seo K. Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study. J Med Internet Res 2024; 26:e54538. [PMID: 38631021 PMCID: PMC11063880 DOI: 10.2196/54538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/29/2023] [Accepted: 03/09/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. OBJECTIVE We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. METHODS The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. RESULTS The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). CONCLUSIONS The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.
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Affiliation(s)
- Bogyeom Park
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Yuwon Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Jinseok Park
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Hojin Choi
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Hokyoung Ryu
- Graduate School of Technology and Innovation Management, Hanyang University, Seoul, Republic of Korea
| | - Kyoungwon Seo
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
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Hu M, Xu F, Liu S, Yao Y, Xia Q, Zhu C, Zhang X, Tang H, Qaiser Z, Liu S, Tang Y. Aging pattern of the brainstem based on volumetric measurement and optimized surface shape analysis. Brain Imaging Behav 2024; 18:396-411. [PMID: 38155336 DOI: 10.1007/s11682-023-00840-z] [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] [Accepted: 12/11/2023] [Indexed: 12/30/2023]
Abstract
The brainstem, a small and crucial structure, is connected to the cerebrum, spinal cord, and cerebellum, playing a vital role in regulating autonomic functions, transmitting motor and sensory information, and modulating cognitive processes, emotions, and consciousness. While previous research has indicated that changes in brainstem anatomy can serve as a biomarker for aging and neurodegenerative diseases, the structural changes that occur in the brainstem during normal aging remain unclear. This study aimed to examine the age- and sex-related differences in the global and local structural measures of the brainstem in 187 healthy adults (ranging in age from 18 to 70 years) using structural magnetic resonance imaging. The findings showed a significant negative age effect on the volume of the two major components of the brainstem: the medulla oblongata and midbrain. The shape analysis revealed that atrophy primarily occurs in specific structures, such as the pyramid, cerebral peduncle, superior and inferior colliculi. Surface area and shape analysis showed a trend of flattening in the aging brainstem. There were no significant differences between the sexes or sex-by-age interactions in brainstem structural measures. These findings provide a systematic description of age associations with brainstem structures in healthy adults and may provide a reference for future research on brain aging and neurodegenerative diseases.
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Affiliation(s)
- Minqi Hu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Feifei Xu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Shizhou Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Yuan Yao
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Qing Xia
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Caiting Zhu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Xinwen Zhang
- Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Haiyan Tang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Zubair Qaiser
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Shuwei Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China
| | - Yuchun Tang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, 250012, Shandong, China.
- Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
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9
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Luckhoff HK, Smit R, Phahladira L, du Plessis, Emsley R, Asmal L. Sex versus gender associations with brain structure. J Clin Neurosci 2024; 122:103-109. [PMID: 38493700 DOI: 10.1016/j.jocn.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/22/2024] [Accepted: 03/13/2024] [Indexed: 03/19/2024]
Abstract
In contrast to sex (a biological distinction), little is known about the associations between gender (a societal construct) and brain structure in the general population. In response to this knowledge gap, we examined the associations of sex vs. gender with FreeSurfer-generated cortical thickness and proportion-adjusted subcortical brain volume regions-of-interest (ROIs) in healthy adults (n = 88) screened for general medical conditions, mental illness, substance abuse, and intracranial pathologies. Gender role endorsement was assessed using the well-established and validated Bem Sex Role Inventory. For our main objectives, we calculated a continuum score as a composite measure of gender. For our secondary objectives, we examined sex-specific associations of the masculine vs. feminine gender role endorsement domains with brain structural outcomes. We found that female sex, independent of continuum scores, was associated with larger proportion-adjusted volumes for the basal ganglia, hippocampus, and ventral diencephalon. Higher continuum scores, independent of sex, were associated with thicker cortical thickness for the left and right superior frontal cortex, caudal and rostral middle frontal cortex, and right pars orbitalis. Female sex and higher continuum scores were independently associated with larger corpus callosum volumes. Post-hoc testing showed sex-specific associations between higher femininity scores and thicker prefrontal cortical thickness for the ROIs in females, but not in males. In conclusion, sex and gender showed semi-independent associations with brain structure in a general population sample. Our research supports the disaggregation of sex and gender to provide a more nuanced perspective on brain structural differences between men and women.
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Affiliation(s)
- H K Luckhoff
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
| | - R Smit
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - L Phahladira
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - du Plessis
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - R Emsley
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - L Asmal
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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10
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Gandia-Ferrero MT, Adrián-Ventura J, Cháfer-Pericás C, Alvarez-Sanchez L, Ferrer-Cairols I, Martinez-Sanchis B, Torres-Espallardo I, Baquero-Toledo M, Marti-Bonmati L. Relationship between neuroimaging and emotion recognition in mild cognitive impairment patients. Behav Brain Res 2024; 461:114844. [PMID: 38176615 DOI: 10.1016/j.bbr.2023.114844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/27/2023] [Accepted: 12/29/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE Dementia is a major public health problem with high needs for early detection, efficient treatment, and prognosis evaluation. Social cognition impairment could be an early dementia indicator and can be assessed with emotion recognition evaluation tests. The purpose of this study is to investigate the link between different brain imaging modalities and cognitive status in Mild Cognitive Impairment (MCI) patients, with the goal of uncovering potential physiopathological mechanisms based on social cognition performance. METHODS The relationship between the Reading the Mind in the Eyes Test (RMET) and some clinical and biochemical variables ([18 F]FDG PET-CT and anatomical MR parameters, neuropsychological evaluation, and CSF biomarkers) was studied in 166 patients with MCI by using a correlational approach. RESULTS The RMET correlated with neuropsychological variables, as well as with structural and functional brain parameters obtained from the MR and FDG-PET imaging evaluation. However, significant correlations between the RMET and CSF biomarkers were not found. DISCUSSION Different neuroimaging parameters were found to be related to an emotion recognition task in MCI. This analysis identified potential minimally-invasive biomarkers providing some knowledge about the physiopathological mechanisms in MCI.
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Affiliation(s)
- Maria Teresa Gandia-Ferrero
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Jesús Adrián-Ventura
- Department of Psychology and Sociology, University of Zaragoza, Atarazanas 4, 44003 Teruel, Spain
| | - Consuelo Cháfer-Pericás
- Grupo de investigación en Enfermedad de Alzheimer (GINEA), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain.
| | - Lourdes Alvarez-Sanchez
- Grupo de investigación en Enfermedad de Alzheimer (GINEA), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain; Neurology Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Inés Ferrer-Cairols
- Grupo de investigación en Enfermedad de Alzheimer (GINEA), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Begoña Martinez-Sanchis
- Nuclear Medicine Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Irene Torres-Espallardo
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain; Nuclear Medicine Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Miquel Baquero-Toledo
- Grupo de investigación en Enfermedad de Alzheimer (GINEA), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain; Neurology Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Luis Marti-Bonmati
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain; Radiology Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
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11
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Alex AM, Aguate F, Botteron K, Buss C, Chong YS, Dager SR, Donald KA, Entringer S, Fair DA, Fortier MV, Gaab N, Gilmore JH, Girault JB, Graham AM, Groenewold NA, Hazlett H, Lin W, Meaney MJ, Piven J, Qiu A, Rasmussen JM, Roos A, Schultz RT, Skeide MA, Stein DJ, Styner M, Thompson PM, Turesky TK, Wadhwa PD, Zar HJ, Zöllei L, de Los Campos G, Knickmeyer RC. A global multicohort study to map subcortical brain development and cognition in infancy and early childhood. Nat Neurosci 2024; 27:176-186. [PMID: 37996530 PMCID: PMC10774128 DOI: 10.1038/s41593-023-01501-6] [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: 04/11/2022] [Accepted: 10/16/2023] [Indexed: 11/25/2023]
Abstract
The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.
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Affiliation(s)
- Ann M Alex
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
| | - Fernando Aguate
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
- Departments of Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, USA
| | - Kelly Botteron
- Mallinickrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Claudia Buss
- Department of Medical Psychology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Yap-Seng Chong
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
| | - Stephen R Dager
- Department of Radiology, University of Washington Medical Center, Seattle, WA, USA
| | - Kirsten A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sonja Entringer
- Department of Medical Psychology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Marielle V Fortier
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore
| | - Nadine Gaab
- Harvard Graduate School of Education, Harvard University, Cambridge, MA, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Nynke A Groenewold
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council (SA-MRC) Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Heather Hazlett
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Weili Lin
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael J Meaney
- Department of Radiology, University of Washington Medical Center, Seattle, WA, USA
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- NUS (Suzhou) Research Institute, National University of Singapore, Suzhou, China
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, China
| | - Jerod M Rasmussen
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Annerine Roos
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Robert T Schultz
- Center for Autism Research, Children's Hospital of Philadelphia and the University of Pennsylvania, Philadelphia, PA, USA
| | - Michael A Skeide
- Research Group Learning in Early Childhood, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Dan J Stein
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Martin Styner
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of Southern California, Marina del Rey, CA, USA
| | - Ted K Turesky
- Harvard Graduate School of Education, Harvard University, Cambridge, MA, USA
| | - Pathik D Wadhwa
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
- Departments of Psychiatry and Human Behavior, Obstetrics & Gynecology, Epidemiology, University of California, Irvine, Irvine, CA, USA
| | - Heather J Zar
- South African Medical Research Council (SA-MRC) Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Gustavo de Los Campos
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
- Departments of Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, USA
- Department of Statistics & Probability, Michigan State University, East Lansing, MI, USA
| | - Rebecca C Knickmeyer
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA.
- Department of Pediatrics and Human Development, Michigan State University, East Lansing, MI, USA.
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12
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Sanchis-Segura C, Wilcox RR, Cruz-Gómez AJ, Félix-Esbrí S, Sebastián-Tirado A, Forn C. Univariate and multivariate sex differences and similarities in gray matter volume within essential language-processing areas. Biol Sex Differ 2023; 14:90. [PMID: 38129916 PMCID: PMC10740309 DOI: 10.1186/s13293-023-00575-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Sex differences in language-related abilities have been reported. It is generally assumed that these differences stem from a different organization of language in the brains of females and males. However, research in this area has been relatively scarce, methodologically heterogeneous and has yielded conflicting results. METHODS Univariate and multivariate sex differences and similarities in gray matter volume (GMVOL) within 18 essential language-processing brain areas were assessed in a sex-balanced sample (N = 588) of right-handed young adults. Univariate analyses involved location, spread, and shape comparisons of the females' and males' distributions and were conducted with several robust statistical methods able to quantify the size of sex differences and similarities in a complementary way. Multivariate sex differences and similarities were estimated by the same methods in the continuous scores provided by two distinct multivariate procedures (logistic regression and a multivariate analog of the Wilcoxon-Mann-Whitney test). Additional analyses were addressed to compare the outcomes of these two multivariate analytical strategies and described their structure (that is, the relative contribution of each brain area to the multivariate effects). RESULTS When not adjusted for total intracranial volume (TIV) variation, "large" univariate sex differences (males > females) were found in all 18 brain areas considered. In contrast, "small" differences (females > males) in just two of these brain areas were found when controlling for TIV. The two multivariate methods tested provided very similar results. Multivariate sex differences surpassed univariate differences, yielding "large" differences indicative of larger volumes in males when calculated from raw GMVOL estimates. Conversely, when calculated from TIV-adjusted GMVOL, multivariate differences were "medium" and indicative of larger volumes in females. Despite their distinct size and direction, multivariate sex differences in raw and TIV-adjusted GMVOL shared a similar structure and allowed us to identify the components of the SENT_CORE network which more likely contribute to the observed effects. CONCLUSIONS Our results confirm and extend previous findings about univariate sex differences in language-processing areas, offering unprecedented evidence at the multivariate level. We also observed that the size and direction of these differences vary quite substantially depending on whether they are estimated from raw or TIV-adjusted GMVOL measurements.
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Affiliation(s)
- Carla Sanchis-Segura
- Departament de Psicologia Bàsica Clinica I Psicobiología, Facultat de Ciències de La Salut, Universitat Jaume I, Avda Sos Baynat, SN, 12071, Castelló, Spain.
| | - Rand R Wilcox
- Department of Psychology, University of Southern California, Los Angeles, USA
| | | | - Sonia Félix-Esbrí
- Departament de Psicologia Bàsica Clinica I Psicobiología, Facultat de Ciències de La Salut, Universitat Jaume I, Avda Sos Baynat, SN, 12071, Castelló, Spain
| | - Alba Sebastián-Tirado
- Departament de Psicologia Bàsica Clinica I Psicobiología, Facultat de Ciències de La Salut, Universitat Jaume I, Avda Sos Baynat, SN, 12071, Castelló, Spain
| | - Cristina Forn
- Departament de Psicologia Bàsica Clinica I Psicobiología, Facultat de Ciències de La Salut, Universitat Jaume I, Avda Sos Baynat, SN, 12071, Castelló, Spain
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13
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Fiscone C, Rundo L, Lugaresi A, Manners DN, Allinson K, Baldin E, Vornetti G, Lodi R, Tonon C, Testa C, Castelli M, Zaccagna F. Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. Sci Rep 2023; 13:16239. [PMID: 37758804 PMCID: PMC10533494 DOI: 10.1038/s41598-023-42914-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.
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Affiliation(s)
- Cristiana Fiscone
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Alessandra Lugaresi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - David Neil Manners
- Department for Life Quality Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Kieren Allinson
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Elisa Baldin
- Epidemiology and Statistics Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Gianfranco Vornetti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy.
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
| | - Fulvio Zaccagna
- Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Investigative Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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14
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Dhamala E, Rong Ooi LQ, Chen J, Ricard JA, Berkeley E, Chopra S, Qu Y, Zhang XH, Lawhead C, Yeo BTT, Holmes AJ. Brain-Based Predictions of Psychiatric Illness-Linked Behaviors Across the Sexes. Biol Psychiatry 2023; 94:479-491. [PMID: 37031778 PMCID: PMC10524434 DOI: 10.1016/j.biopsych.2023.03.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/11/2023]
Abstract
BACKGROUND Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined. METHODS In this work, we used predictive modeling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development Study (N = 5260; 2571 females). RESULTS We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females but predict internalizing behaviors only in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks, including default, limbic, control, and dorsal attention networks, are associated with internalizing and externalizing behaviors. CONCLUSIONS Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. Based on these results, we recommend that both clinicians and researchers carefully consider how sex may influence the presentation of psychiatric illnesses, especially those along the internalizing-externalizing spectrum.
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Affiliation(s)
- Elvisha Dhamala
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York; Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - Leon Qi Rong Ooi
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore
| | - Jocelyn A Ricard
- Department of Psychology, Yale University, New Haven, Connecticut
| | | | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Yueyue Qu
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Xi-Han Zhang
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Connor Lawhead
- Department of Psychology, Yale University, New Haven, Connecticut
| | - B T Thomas Yeo
- Centre for Sleep and Cognition and Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut; Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, New Jersey.
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15
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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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16
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Zelco A, Wapeesittipan P, Joshi A. Insights into Sex and Gender Differences in Brain and Psychopathologies Using Big Data. Life (Basel) 2023; 13:1676. [PMID: 37629533 PMCID: PMC10455614 DOI: 10.3390/life13081676] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/30/2023] [Accepted: 07/15/2023] [Indexed: 08/27/2023] Open
Abstract
The societal implication of sex and gender (SG) differences in brain are profound, as they influence brain development, behavior, and importantly, the presentation, prevalence, and therapeutic response to diseases. Technological advances have enabled speed up identification and characterization of SG differences during development and in psychopathologies. The main aim of this review is to elaborate on new technological advancements, such as genomics, imaging, and emerging biobanks, coupled with bioinformatics analyses of data generated from these technologies have facilitated the identification and characterization of SG differences in the human brain through development and psychopathologies. First, a brief explanation of SG concepts is provided, along with a developmental and evolutionary context. We then describe physiological SG differences in brain activity and function, and in psychopathologies identified through imaging techniques. We further provide an overview of insights into SG differences using genomics, specifically taking advantage of large cohorts and biobanks. We finally emphasize how bioinformatics analyses of big data generated by emerging technologies provides new opportunities to reduce SG disparities in health outcomes, including major challenges.
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Affiliation(s)
| | | | - Anagha Joshi
- Department of Clinical Science, Computational Biology Unit, University of Bergen, 5020 Bergen, Norway; (A.Z.); (P.W.)
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17
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Li G, Li Y, Zhang Z, Chen Y, Li B, Hao D, Yang L, Yang Y, Li X, Li CSR. Sex differences in externalizing and internalizing traits and ventral striatal responses to monetary loss. J Psychiatr Res 2023; 162:11-20. [PMID: 37062201 PMCID: PMC10225357 DOI: 10.1016/j.jpsychires.2023.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/26/2023] [Accepted: 04/08/2023] [Indexed: 04/18/2023]
Abstract
Ventral striatum (VS) processes rewarding and punishing stimuli. Women and men vary in externalizing and internalizing traits, which may influence neural responses to reward and punishment. To investigate sex differences in how individual traits influence VS responses to reward and punishment, we curated the data of the Human Connectome Project and identified 981 (473 men) subjects evaluated by the Achenbach Adult Self-Report Syndrome Scales. We processed the imaging data with published routines and extracted VS response (β) to win and to loss vs. baseline in a gambling task for correlation with externalizing and internalizing symptom severity. Men vs. women showed more severe externalizing symptoms and higher VS response to monetary losses (VS-loss β) but not to wins. Men but not women showed a significant, positive correlation between VS-loss β and externalizing traits, and the sex difference was confirmed by a slope test. The correlations of VS-loss vs. externalizing and of VS-win vs. externalizing and those of VS-loss vs. externalizing and of VS-loss vs. internalizing traits both differed significantly in slope, confirming its specificity, in men. Further, the sex-specific relationship between VS-loss β and externalizing trait did not extend to activities during exposure to negative emotion in the face matching task. To conclude, VS responses to loss but not to win and their correlation with externalizing rather than internalizing symptom severity showed sex differences in young adults. The findings highlight the relationship of externalizing traits and VS response to monetary loss and may have implications for psychological models of externalizing behaviors in men.
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Affiliation(s)
- Guangfei Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China; Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China.
| | - Yashuang Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Zhao Zhang
- Department of Biomedical Engineering, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Bao Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Dongmei Hao
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China; Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China
| | - Lin Yang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China; Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China
| | - Yimin Yang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China; Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China
| | - Xuwen Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing, China; Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA.
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18
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Clunie DA, Flanders A, Taylor A, Erickson B, Bialecki B, Brundage D, Gutman D, Prior F, Seibert JA, Perry J, Gichoya JW, Kirby J, Andriole K, Geneslaw L, Moore S, Fitzgerald TJ, Tellis W, Xiao Y, Farahani K, Luo J, Rosenthal A, Kandarpa K, Rosen R, Goetz K, Babcock D, Xu B, Hsiao J. Report of the Medical Image De-Identification (MIDI) Task Group - Best Practices and Recommendations. ARXIV 2023:arXiv:2303.10473v2. [PMID: 37033463 PMCID: PMC10081345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Affiliation(s)
| | | | | | | | | | | | | | - Fred Prior
- University of Arkansas for Medical Sciences
| | | | | | | | - Justin Kirby
- Frederick National Laboratory for Cancer Research
| | | | | | | | | | | | - Ying Xiao
- University of Pennsylvania Health System
| | | | - James Luo
- National Heart, Lung, and Blood Institute (NHLBI)
| | - Alex Rosenthal
- National Institute of Allergy and Infectious Diseases (NIAID)
| | - Kris Kandarpa
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)
| | - Rebecca Rosen
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
| | | | - Debra Babcock
- National Institute of Neurological Disorders and Stroke (NINDS)
| | - Ben Xu
- National Institute on Alcohol Abuse and Alcoholism (NIAAA)
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19
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Li G, Tong R, Zhang M, Gillen KM, Jiang W, Du Y, Wang Y, Li J. Age-dependent changes in brain iron deposition and volume in deep gray matter nuclei using quantitative susceptibility mapping. Neuroimage 2023; 269:119923. [PMID: 36739101 DOI: 10.1016/j.neuroimage.2023.119923] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/10/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Microstructural changes in deep gray matter (DGM) nuclei are related to physiological behavior, cognition, and memory. Therefore, it is critical to study age-dependent trajectories of biomarkers in DGM nuclei for understanding brain development and aging, as well as predicting cognitive or neurodegenerative diseases. OBJECTIVES We aimed to (1) characterize age-dependent trajectories of mean susceptibility, adjusted volume, and total iron content simultaneously in DGM nuclei using quantitative susceptibility mapping (QSM); (2) examine potential contributions of sex related effects to the different age-dependence trajectories of volume and iron deposition; and (3) evaluate the ability of brain age prediction by combining mean magnetic susceptibility and volume of DGM nuclei. METHODS Magnetic susceptibilities and volumetric values of DGM nuclei were obtained from 220 healthy participants (aged 10-70 years) scanned on a 3T MRI system. Regions of interest (ROIs) were drawn manually on the QSM images. Univariate regression analysis between age and each of the MRI measurements in a single ROI was performed. Pearson correlation coefficients were calculated between magnetic susceptibility and adjusted volume in a single ROI. The statistical significance of sex differences in age-dependent trajectories of magnetic susceptibilities and adjusted volumes were determined using one-way ANCOVA. Multiple regression analysis was used to evaluate the ability to estimate brain age using a combination of the mean susceptibilities and adjusted volumes in multiple DGM nuclei. RESULTS Mean susceptibility and total iron content increased linearly, quadratically, or exponentially with age in all six DGM nuclei. Negative linear correlation was observed between adjusted volume and age in the head of the caudate nucleus (CN; R2 = 0.196, p < 0.001). Quadratic relationships were found between adjusted volume and age in the putamen (PUT; R2 = 0.335, p < 0.001), globus pallidus (GP; R2 = 0.062, p = 0.001), and dentate nucleus (DN; R2 = 0.077, p < 0.001). Males had higher mean magnetic susceptibility than females in the PUT (p = 0.001), red nucleus (RN; p = 0.002), and substantia nigra (SN; p < 0.001). Adjusted volumes of the CN (p < 0.001), PUT (p = 0.030), GP (p = 0.007), SN (p = 0.021), and DN (p < 0.001) were higher in females than those in males throughout the entire age range (10-70 years old). The total iron content of females was higher than that of males in the CN (p < 0.001), but lower than that of males in the PUT (p = 0.014) and RN (p = 0.043) throughout the entire age range (10-70 years old). Multiple regression analyses revealed that the combination of the mean susceptibility value of the PUT, and the volumes of the CN and PUT had the strongest associations with brain age (R2 = 0.586). CONCLUSIONS QSM can be used to simultaneously investigate age- and sex- dependent changes in magnetic susceptibility and volume of DGM nuclei, thus enabling a comprehensive understanding of the developmental trajectories of iron accumulation and volume in DGM nuclei during brain development and aging.
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Affiliation(s)
- Gaiying Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062
| | - Rui Tong
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062
| | - Miao Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062
| | - Kelly M Gillen
- Department of Radiology, Weill Medical College of Cornell University, 407 East 61st St., New York, New York, United States 10065
| | - Wenqing Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China 200030
| | - Yasong Du
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China 200030
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, 407 East 61st St., New York, New York, United States 10065
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062; Institute of Brain and Education Innovation, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062.
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20
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Wang Q, Aljassar M, Bhagwat N, Zeighami Y, Evans AC, Dagher A, Pike GB, Sadikot AF, Poline JB. Reproducibility of cerebellar involvement as quantified by consensus structural MRI biomarkers in advanced essential tremor. Sci Rep 2023; 13:581. [PMID: 36631461 PMCID: PMC9834264 DOI: 10.1038/s41598-022-25306-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/28/2022] [Indexed: 01/13/2023] Open
Abstract
Essential tremor (ET) is the most prevalent movement disorder with poorly understood etiology. Some neuroimaging studies report cerebellar involvement whereas others do not. This discrepancy may stem from underpowered studies, differences in statistical modeling or variation in magnetic resonance imaging (MRI) acquisition and processing. To resolve this, we investigated the cerebellar structural differences using a local advanced ET dataset augmented by matched controls from PPMI and ADNI. We tested the hypothesis of cerebellar involvement using three neuroimaging biomarkers: VBM, gray/white matter volumetry and lobular volumetry. Furthermore, we assessed the impacts of statistical models and segmentation pipelines on results. Results indicate that the detected cerebellar structural changes vary with methodology. Significant reduction of right cerebellar gray matter and increase of the left cerebellar white matter were the only two biomarkers consistently identified by multiple methods. Results also show substantial volumetric overestimation from SUIT-based segmentation-partially explaining previous literature discrepancies. This study suggests that current estimation of cerebellar involvement in ET may be overemphasized in MRI studies and highlights the importance of methods sensitivity analysis on results interpretation. ET datasets with large sample size and replication studies are required to improve our understanding of regional specificity of cerebellum involvement in ET. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 21 March 2022. The protocol, as accepted by the journal, can be found at: https://doi.org/10.6084/m9.figshare.19697776 .
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Affiliation(s)
- Qing Wang
- grid.14709.3b0000 0004 1936 8649Neuro Data Science - ORIGAMI Laboratory, McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine and Health Sciences, McGill University, Montreal, QC Canada
| | - Meshal Aljassar
- grid.14709.3b0000 0004 1936 8649Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre (BIC), The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine and Health Sciences, McGill University, Montreal, QC Canada
| | - Nikhil Bhagwat
- grid.14709.3b0000 0004 1936 8649Neuro Data Science - ORIGAMI Laboratory, McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine and Health Sciences, McGill University, Montreal, QC Canada
| | - Yashar Zeighami
- grid.14709.3b0000 0004 1936 8649Ludmer Centre for Neuroinformatics and Mental Health, McConnell Brain Imaging Centre (BIC), The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine and Health Sciences, McGill University, Montreal, QC Canada
| | - Alan C. Evans
- grid.14709.3b0000 0004 1936 8649Ludmer Centre for Neuroinformatics and Mental Health, McConnell Brain Imaging Centre (BIC), The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine and Health Sciences, McGill University, Montreal, QC Canada
| | - Alain Dagher
- grid.14709.3b0000 0004 1936 8649Ludmer Centre for Neuroinformatics and Mental Health, McConnell Brain Imaging Centre (BIC), The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine and Health Sciences, McGill University, Montreal, QC Canada
| | - G. Bruce Pike
- grid.22072.350000 0004 1936 7697Department of Radiology, Cumming School of Medicine, Hotchkiss Brain Institute (HBI), University of Calgary, Calgary, QC Canada
| | - Abbas F. Sadikot
- grid.14709.3b0000 0004 1936 8649Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre (BIC), The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine and Health Sciences, McGill University, Montreal, QC Canada
| | - Jean-Baptiste Poline
- Neuro Data Science - ORIGAMI Laboratory, McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada.
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21
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Abstract
There is now a significant body of literature concerning sex/gender differences in the human brain. This chapter will critically review and synthesise key findings from several studies that have investigated sex/gender differences in structural and functional lateralisation and connectivity. We argue that while small, relative sex/gender differences reliably exist in lateralisation and connectivity, there is considerable overlap between the sexes. Some inconsistencies exist, however, and this is likely due to considerable variability in the methodologies, tasks, measures, and sample compositions between studies. Moreover, research to date is limited in its consideration of sex/gender-related factors, such as sex hormones and gender roles, that can explain inter-and inter-individual differences in brain and behaviour better than sex/gender alone. We conclude that conceptualising the brain as 'sexually dimorphic' is incorrect, and the terms 'male brain' and 'female brain' should be avoided in the neuroscientific literature. However, this does not necessarily mean that sex/gender differences in the brain are trivial. Future research involving sex/gender should adopt a biopsychosocial approach whenever possible, to ensure that non-binary psychological, biological, and environmental/social factors related to sex/gender, and their interactions, are routinely accounted for.
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Affiliation(s)
- Sophie Hodgetts
- School of Psychology, University of Sunderland, Sunderland, UK
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22
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Sun J, Tu Z, Meng D, Gong Y, Zhang M, Xu J. Interpretation for Individual Brain Age Prediction Based on Gray Matter Volume. Brain Sci 2022; 12:1517. [PMID: 36358443 PMCID: PMC9688302 DOI: 10.3390/brainsci12111517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/14/2023] Open
Abstract
The relationship between age and the central nervous system (CNS) in humans has been a classical issue that has aroused extensive attention. Especially for individuals, it is of far greater importance to clarify the mechanisms between CNS and age. The primary goal of existing methods is to use MR images to derive high-accuracy predictions for age or degenerative diseases. However, the associated mechanisms between the images and the age have rarely been investigated. In this paper, we address the correlation between gray matter volume (GMV) and age, both in terms of gray matter themselves and their interaction network, using interpretable machine learning models for individuals. Our goal is not only to predict age accurately but more importantly, to explore the relationship between GMV and age. In addition to targeting each individual, we also investigate the dynamic properties of gray matter and their interaction network with individual age. The results show that the mean absolute error (MAE) of age prediction is 7.95 years. More notably, specific locations of gray matter and their interactions play different roles in age, and these roles change dynamically with age. The proposed method is a data-driven approach, which provides a new way to study aging mechanisms and even to diagnose degenerative brain diseases.
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Affiliation(s)
- Jiancheng Sun
- School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China
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23
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Dhamala E, Ooi LQR, Chen J, Kong R, Anderson KM, Chin R, Yeo BTT, Holmes AJ. Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development. Neuroimage 2022; 260:119485. [PMID: 35843514 PMCID: PMC9425854 DOI: 10.1016/j.neuroimage.2022.119485] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/08/2022] [Accepted: 07/13/2022] [Indexed: 01/03/2023] Open
Abstract
Individual differences in brain anatomy can be used to predict variations in cognitive ability. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n = 1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n = 1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranial volume from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, United States; Kavli Institute for Neuroscience, Yale University, New Haven, United States.
| | - Leon Qi Rong Ooi
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Kevin M Anderson
- Department of Psychology, Yale University, New Haven, United States
| | - Rowena Chin
- Department of Psychology, Yale University, New Haven, United States
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, United States; Kavli Institute for Neuroscience, Yale University, New Haven, United States; Department of Psychiatry, Yale University, New Haven, United States; Wu Tsai Institute, Yale University, New Haven, United States.
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24
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Williams T, Tur C, Eshaghi A, Doshi A, Chan D, Binks S, Wellington H, Heslegrave A, Zetterberg H, Chataway J. Serum neurofilament light and MRI predictors of cognitive decline in patients with secondary progressive multiple sclerosis: Analysis from the MS-STAT randomised controlled trial. Mult Scler 2022; 28:1913-1926. [PMID: 35946107 PMCID: PMC9493411 DOI: 10.1177/13524585221114441] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Cognitive impairment affects 50%-75% of people with secondary progressive multiple sclerosis (PwSPMS). Improving our ability to predict cognitive decline may facilitate earlier intervention. OBJECTIVE The main aim of this study was to assess the relationship between longitudinal changes in cognition and baseline serum neurofilament light chain (sNfL) in PwSPMS. In a multi-modal analysis, MRI variables were additionally included to determine if sNfL has predictive utility beyond that already established through MRI. METHODS Participants from the MS-STAT trial underwent a detailed neuropsychological test battery at baseline, 12 and 24 months. Linear mixed models were used to assess the relationships between cognition, sNfL, T2 lesion volume (T2LV) and normalised regional brain volumes. RESULTS Median age and Expanded Disability Status Score (EDSS) were 51 and 6.0. Each doubling of baseline sNfL was associated with a 0.010 [0.003-0.017] point per month faster decline in WASI Full Scale IQ Z-score (p = 0.008), independent of T2LV and normalised regional volumes. In contrast, lower baseline volume of the transverse temporal gyrus was associated with poorer current cognitive performance (0.362 [0.026-0.698] point reduction per mL, p = 0.035), but not change in cognition. The results were supported by secondary analyses on individual cognitive components. CONCLUSION Elevated sNfL is associated with faster cognitive decline, independent of T2LV and regional normalised volumes.
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Affiliation(s)
- Thomas Williams
- Queen Square Multiple Sclerosis Centre,
Department of Neuroinflammation, UCL Queen Square Institute of Neurology,
University College London, Russell Square House, 10-12 Russell Square,
London WC1B 5EH, UK
- Queen Square Multiple Sclerosis Centre,
Department of Neuroinflammation, UCL Queen Square Institute of Neurology,
University College London, London, UK
| | - Carmen Tur
- Queen Square Multiple Sclerosis Centre,
Department of Neuroinflammation, UCL Queen Square Institute of Neurology,
University College London, London, UK/Multiple Sclerosis Centre of Catalonia
(Cemcat), Vall d’Hebron Institute of Research, Vall d’Hebron Barcelona
Hospital Campus, Barcelona, Spain
| | - Arman Eshaghi
- Queen Square Multiple Sclerosis Centre,
Department of Neuroinflammation, UCL Queen Square Institute of Neurology,
University College London, London, UK
| | - Anisha Doshi
- Queen Square Multiple Sclerosis Centre,
Department of Neuroinflammation, UCL Queen Square Institute of Neurology,
University College London, London, UK
| | - Dennis Chan
- UCL Institute of Cognitive Neuroscience,
University College London, London, UK
| | - Sophie Binks
- Department of Neurology, Nuffield Department of
Clinical Neurosciences, Oxford, UK
| | - Henny Wellington
- UK Dementia Research Institute, University
College London, London, UK
| | - Amanda Heslegrave
- UK Dementia Research Institute, University
College London, London, UK
| | - Henrik Zetterberg
- UK Dementia Research Institute, University
College London, London, UK/ Department of Psychiatry and Neurochemistry,
Institute of Neuroscience and Physiology, The Sahlgrenska Academy,
University of Gothenburg, Mölndal, Sweden/Clinical Neurochemistry
Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden/Department of
Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London,
UK/Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong
Kong, China
| | - Jeremy Chataway
- Queen Square Multiple Sclerosis Centre,
Department of Neuroinflammation, UCL Queen Square Institute of Neurology,
University College London, London, UK/National Institute for Health
Research, University College London Hospitals, Biomedical Research Centre,
London, UK/Medical Research Council Clinical Trials Unit, Institute of
Clinical Trials and Methodology, University College London, London, UK
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25
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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: 11] [Impact Index Per Article: 5.5] [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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26
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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: 8] [Impact Index Per Article: 4.0] [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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27
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Antal B, McMahon LP, Sultan SF, Lithen A, Wexler DJ, Dickerson B, Ratai EM, Mujica-Parodi LR. Type 2 diabetes mellitus accelerates brain aging and cognitive decline: Complementary findings from UK Biobank and meta-analyses. eLife 2022; 11:73138. [PMID: 35608247 PMCID: PMC9132576 DOI: 10.7554/elife.73138] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 04/26/2022] [Indexed: 01/17/2023] Open
Abstract
Background Type 2 diabetes mellitus (T2DM) is known to be associated with neurobiological and cognitive deficits; however, their extent, overlap with aging effects, and the effectiveness of existing treatments in the context of the brain are currently unknown. Methods We characterized neurocognitive effects independently associated with T2DM and age in a large cohort of human subjects from the UK Biobank with cross-sectional neuroimaging and cognitive data. We then proceeded to evaluate the extent of overlap between the effects related to T2DM and age by applying correlation measures to the separately characterized neurocognitive changes. Our findings were complemented by meta-analyses of published reports with cognitive or neuroimaging measures for T2DM and healthy controls (HCs). We also evaluated in a cohort of T2DM-diagnosed individuals using UK Biobank how disease chronicity and metformin treatment interact with the identified neurocognitive effects. Results The UK Biobank dataset included cognitive and neuroimaging data (N = 20,314), including 1012 T2DM and 19,302 HCs, aged between 50 and 80 years. Duration of T2DM ranged from 0 to 31 years (mean 8.5 ± 6.1 years); 498 were treated with metformin alone, while 352 were unmedicated. Our meta-analysis evaluated 34 cognitive studies (N = 22,231) and 60 neuroimaging studies: 30 of T2DM (N = 866) and 30 of aging (N = 1088). Compared to age, sex, education, and hypertension-matched HC, T2DM was associated with marked cognitive deficits, particularly in executive functioning and processing speed. Likewise, we found that the diagnosis of T2DM was significantly associated with gray matter atrophy, primarily within the ventral striatum, cerebellum, and putamen, with reorganization of brain activity (decreased in the caudate and premotor cortex and increased in the subgenual area, orbitofrontal cortex, brainstem, and posterior cingulate cortex). The structural and functional changes associated with T2DM show marked overlap with the effects correlating with age but appear earlier, with disease duration linked to more severe neurodegeneration. Metformin treatment status was not associated with improved neurocognitive outcomes. Conclusions The neurocognitive impact of T2DM suggests marked acceleration of normal brain aging. T2DM gray matter atrophy occurred approximately 26% ± 14% faster than seen with normal aging; disease duration was associated with increased neurodegeneration. Mechanistically, our results suggest a neurometabolic component to brain aging. Clinically, neuroimaging-based biomarkers may provide a valuable adjunctive measure of T2DM progression and treatment efficacy based on neurological effects. Funding The research described in this article was funded by the W. M. Keck Foundation (to LRMP), the White House Brain Research Through Advancing Innovative Technologies (BRAIN) Initiative (NSFNCS-FR 1926781 to LRMP), and the Baszucki Brain Research Fund (to LRMP). None of the funding sources played any role in the design of the experiments, data collection, analysis, interpretation of the results, the decision to publish, or any aspect relevant to the study. DJW reports serving on data monitoring committees for Novo Nordisk. None of the authors received funding or in-kind support from pharmaceutical and/or other companies to write this article.
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Affiliation(s)
- Botond Antal
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States
| | - Liam P McMahon
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States
| | - Syed Fahad Sultan
- Department of Computer Science, Stony Brook University, Stony Brook, United States
| | - Andrew Lithen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States
| | - Deborah J Wexler
- Diabetes Center, Massachusetts General Hospital and Harvard Medical School, Boston, United States
| | - Bradford Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States.,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, United States
| | - Eva-Maria Ratai
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States
| | - Lilianne R Mujica-Parodi
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States.,Department of Neurology, Stony Brook University School of Medicine, Stony Brook, United States
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28
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Abstract
Sex and gender differences are seen in cognitive disturbances in a variety of neurological and psychiatry diseases. Men are more likely to have cognitive symptoms in schizophrenia whereas women are more likely to have more severe cognitive symptoms with major depressive disorder and Alzheimer's disease. Thus, it is important to understand sex and gender differences in underlying cognitive abilities with and without disease. Sex differences are noted in performance across various cognitive domains - with males typically outperforming females in spatial tasks and females typically outperforming males in verbal tasks. Furthermore, there are striking sex differences in brain networks that are activated during cognitive tasks and in learning strategies. Although rarely studied, there are also sex differences in the trajectory of cognitive aging. It is important to pay attention to these sex differences as they inform researchers of potential differences in resilience to age-related cognitive decline and underlying mechanisms for both healthy and pathological cognitive aging, depending on sex. We review literature on the progressive neurodegenerative disorder, Alzheimer's disease, as an example of pathological cognitive aging in which human females show greater lifetime risk, neuropathology, and cognitive impairment, compared to human males. Not surprisingly, the relationships between sex and cognition, cognitive aging, and Alzheimer's disease are nuanced and multifaceted. As such, this chapter will end with a discussion of lifestyle factors, like education and diet, as modifiable factors that can alter cognitive aging by sex. Understanding how cognition changes across age and contributing factors, like sex differences, will be essential to improving care for older adults.
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29
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Opfer R, Krüger J, Spies L, Kitzler HH, Schippling S, Buchert R. Single-subject analysis of regional brain volumetric measures can be strongly influenced by the method for head size adjustment. Neuroradiology 2022; 64:2001-2009. [PMID: 35462574 PMCID: PMC9474386 DOI: 10.1007/s00234-022-02961-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 04/14/2022] [Indexed: 12/03/2022]
Abstract
Purpose
Total intracranial volume (TIV) is often a nuisance covariate in MRI-based brain volumetry. This study compared two TIV adjustment methods with respect to their impact on z-scores in single subject analyses of regional brain volume estimates. Methods Brain parenchyma, hippocampus, thalamus, and TIV were segmented in a normal database comprising 5059 T1w images. Regional volume estimates were adjusted for TIV using the residual method or the proportion method. Age was taken into account by regression with both methods. TIV- and age-adjusted regional volumes were transformed to z-scores and then compared between the two adjustment methods. Their impact on the detection of thalamus atrophy was tested in 127 patients with multiple sclerosis. Results The residual method removed the association with TIV in all regions. The proportion method resulted in a switch of the direction without relevant change of the strength of the association. The reduction of physiological between-subject variability was larger with the residual method than with the proportion method. The difference between z-scores obtained with the residual method versus the proportion method was strongly correlated with TIV. It was larger than one z-score point in 5% of the subjects. The area under the ROC curve of the TIV- and age-adjusted thalamus volume for identification of multiple sclerosis patients was larger with the residual method than with the proportion method (0.84 versus 0.79). Conclusion The residual method should be preferred for TIV and age adjustments of T1w-MRI-based brain volume estimates in single subject analyses. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-022-02961-6.
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Affiliation(s)
| | | | | | - Hagen H Kitzler
- Institute of Diagnostic and Interventional Neuroradiology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Sven Schippling
- Center for Neuroscience Zurich (ZNZ), Federal Institute of Technology (ETH), Multimodal Imaging in Neuroimmunological Diseases (MINDS), University of Zurich, Zurich, Switzerland
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
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30
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Relationships between Personality Traits and Brain Gray Matter Are Different in Risky and Non-risky Drivers. Behav Neurol 2022; 2022:1775777. [PMID: 35422888 PMCID: PMC9005327 DOI: 10.1155/2022/1775777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 02/22/2022] [Accepted: 03/15/2022] [Indexed: 11/17/2022] Open
Abstract
Personality traits such as impulsivity or sensitivity to rewards and punishments have been associated with risky driving behavior, but it is still unclear how brain anatomy is related to these traits as a function of risky driving. In the present study, we explore the neuroanatomical basis of risky driving behavior and how the level of risk-taking influences the relationship between the traits of impulsivity and sensitivity to rewards and punishments and brain gray matter volume. One hundred forty-four participants with different risk-taking tendencies assessed by real-life driving situations underwent MRI. Personality traits were assessed with self-report measures. We observed that the total gray matter volume varied as a function of risky driving tendencies, with higher risk individuals showing lower gray matter volumes. Similar results were found for volumes of brain areas involved in the reward and cognitive control networks, such as the frontotemporal, parietal, limbic, and cerebellar cortices. We have also shown that sensitivity to reward and punishment and impulsivity are differentially related to gray matter volumes as a function of risky driving tendencies. Highly risky individuals show lower absolute correlations with gray matter volumes than less risk-prone individuals. Taken together, our results show that risky drivers differ in the brain structure of the areas involved in reward processing, cognitive control, and behavioral modulation, which may lead to dysfunctional decision-making and riskier driving behavior.
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31
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Basal ganglia correlates of wellbeing in early adolescence. Brain Res 2022; 1774:147710. [PMID: 34762929 DOI: 10.1016/j.brainres.2021.147710] [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: 10/09/2020] [Revised: 02/24/2021] [Accepted: 11/03/2021] [Indexed: 12/11/2022]
Abstract
It has been suggested that biological markers that define mental health are different to those that define mental illness. The basal ganglia changes dramatically over adolescence and has been linked to wellbeing and mental health disorders in young people. However, there remains a paucity of research on wellbeing and brain structure in early adolescence. This cross-sectional study examined relationships between grey matter volume (GMV) of basal ganglia regions (caudate, putamen, pallidum and nucleus accumbens) and self-reported wellbeing (COMPAS-W), in a sample of Australian adolescents aged 12 years (N = 49, M = 12.6, 46.9% female). Significant negative associations were found between left hemisphere caudate GMV and scores on 'total wellbeing', 'composure' and 'positivity'. The results of this study indicate that smaller caudate GMV at age 12 is linked to increased subjective wellbeing. While seemingly counter-intuitive, our finding is consistent with previous research of decreased GMV in the pons and increased COMPAS-W scores in adults. Our results suggest that protective neurobiological factors may be identifiable early in adolescence and be linked to specific types of wellbeing (such as positive affect and optimism). This has implications for interventions targeted at building resilience against mental health disorders in young people.
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32
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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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33
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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: 31] [Impact Index Per Article: 10.3] [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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Sung D, Park B, Kim B, Kim H, Jung KI, Lee SY, Kim BN, Park S, Park MH. Gray Matter Volume in the Developing Frontal Lobe and Its Relationship With Executive Function in Late Childhood and Adolescence: A Community-Based Study. Front Psychiatry 2021; 12:686174. [PMID: 34326786 PMCID: PMC8313766 DOI: 10.3389/fpsyt.2021.686174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Abstract
Background: During late childhood and adolescence, the frontal lobe undergoes critical developmental changes, affecting a wide range of executive functions significantly. Conversely, abnormality in the maturation of the frontal lobe during this period may result in a limited ability to effectively use various executive functions. However, at present, it is still unclear how the structural development of the frontal lobe is associated with different aspects of executive functions during this developmental period. To fill the gap in evidence, we aimed to elucidate gray matter volume (GMV) in the frontal lobe and its relationship with multiple aspects of executive functions in late childhood and adolescence. Methods: We recruited our participants aged between 6 and 17 years to assess GMV in the frontal lobe and its relationship with different domains of executive functions in late childhood and adolescence. We used the voxel-based morphometry-DARTEL procedure to measure GMVs in multiple frontal sub-regions and Stroop test and Advanced Test of Attention (ATA) to measure executive functions. We then conducted partial correlation analyses and performed multiple comparisons with different age and sex groups. Results: Overall, 123 participants took part in our study. We found that many regional GMVs in the frontal lobe were negatively correlated with ATA scores in participants in late childhood and positively correlated with ATA scores in participants in adolescence. Only a few correlations of the GMVs with Stroop test scores were significant in both age groups. Although most of our results did not survive false discovery rate (FDR) correction (i.e., FDR <0.2), considering their novelty, we discussed our results based on uncorrected p-values. Our findings indicate that the frontal sub-regions that were involved in attentional networks may significantly improve during late childhood and become stabilized later in adolescence. Moreover, our findings with the Stroop test may also present the possibility of the later maturation of higher-order executive functioning skills. Conclusion: Although our findings were based on uncorrected p-values, the novelty of our findings may provide better insights into elucidating the maturation of the frontal lobe and its relationship with the development of attention networks in late childhood and adolescence.
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Affiliation(s)
- Dajung Sung
- Department of Psychiatry, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea
| | - Bumhee Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
- Office of Biostatistics, Ajou Research Institute for Innovative Medicine, Ajou University Medical Center, Suwon, South Korea
| | - Bora Kim
- Department of Psychiatry, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea
| | - Hayeon Kim
- Department of Psychiatry, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea
| | - Kyu-In Jung
- Department of Psychiatry, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea
| | - Seung-Yup Lee
- Department of Psychiatry, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea
| | - Bung-Nyun Kim
- Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, South Korea
| | - Subin Park
- Department of Research Planning, National Center for Mental Health, Seoul, South Korea
| | - Min-Hyeon Park
- Department of Psychiatry, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea
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35
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Saguy T, Reifen-Tagar M, Joel D. The gender-binary cycle: the perpetual relations between a biological-essentialist view of gender, gender ideology, and gender-labelling and sorting. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200141. [PMID: 33612000 PMCID: PMC7934953 DOI: 10.1098/rstb.2020.0141] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 01/24/2023] Open
Abstract
Gender inequality is one of the most pressing issues of our time. A core factor that feeds gender inequality is people's gender ideology-a set of beliefs about the proper order of society in terms of the roles women and men should fill. We argue that gender ideology is shaped, in large parts, by the way people make sense of gender differences. Specifically, people often think of gender differences as expressions of a predetermined biology, and of men and women as different 'kinds'. We describe work suggesting that thinking of gender differences in this biological-essentialist way perpetuates a non-egalitarian gender ideology. We then review research that refutes the hypothesis that men and women are different 'kinds' in terms of brain function, hormone levels and personality characteristics. Next, we describe how the organization of the environment in a gender-binary manner, together with cognitive processes of categorization drive a biological-essentialist view of gender differences. We then describe the self-perpetuating relations, which we term the gender-binary cycle, between a biological-essentialist view of gender differences, a non-egalitarian gender ideology and a binary organization of the environment along gender lines. Finally, we consider means of intervention at different points in this cycle. This article is part of the theme issue 'The political brain: neurocognitive and computational mechanisms'.
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Affiliation(s)
- Tamar Saguy
- Baruch Ivcher School of Psychology, The Interdisciplinary Center (IDC), Herzliya, Israel
| | - Michal Reifen-Tagar
- Baruch Ivcher School of Psychology, The Interdisciplinary Center (IDC), Herzliya, Israel
| | - Daphna Joel
- School of Psychological Sciences, Tel-Aviv University, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
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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: 151] [Impact Index Per Article: 50.3] [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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37
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Joel D. Beyond the binary: Rethinking sex and the brain. Neurosci Biobehav Rev 2021; 122:165-175. [PMID: 33440198 DOI: 10.1016/j.neubiorev.2020.11.018] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 11/12/2020] [Accepted: 11/14/2020] [Indexed: 01/06/2023]
Abstract
The paper reviews the relations between sex and brain in light of the binary conceptualization of these relations and the challenges posed to it by the 'mosaic' hypothesis. Recent formulations of the binary framework range from arguing that the typical male brain is different from the typical female brain to claiming that brains are typically male or female because brain structure can be used to predict the sex category (female/male) of the brain's owner. These formulations are challenged by evidence that sex effects on the brain may be opposite under different conditions, that human brains are comprised of mosaics of female-typical and male-typical features, and that sex category explains only a small part of the variability in human brain structure. These findings led to a new, non-binary, framework, according to which mosaic brains reside in a multi-dimensional space that cannot meaningfully be reduced to a male-female continuum or to a binary variable. This framework may also apply to sex-related variables and has implications for research.
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Affiliation(s)
- Daphna Joel
- School of Psychological Sciences and Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel.
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38
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Mole JP, Fasano F, Evans J, Sims R, Kidd E, Aggleton JP, Metzler-Baddeley C. APOE-ε4-related differences in left thalamic microstructure in cognitively healthy adults. Sci Rep 2020; 10:19787. [PMID: 33188215 PMCID: PMC7666117 DOI: 10.1038/s41598-020-75992-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/15/2020] [Indexed: 01/05/2023] Open
Abstract
APOE-ε4 is a main genetic risk factor for developing late onset Alzheimer's disease (LOAD) and is thought to interact adversely with other risk factors on the brain. However, evidence regarding the impact of APOE-ε4 on grey matter structure in asymptomatic individuals remains mixed. Much attention has been devoted to characterising APOE-ε4-related changes in the hippocampus, but LOAD pathology is known to spread through the whole of the Papez circuit including the limbic thalamus. Here, we tested the impact of APOE-ε4 and two other risk factors, a family history of dementia and obesity, on grey matter macro- and microstructure across the whole brain in 165 asymptomatic individuals (38-71 years). Microstructural properties of apparent neurite density and dispersion, free water, myelin and cell metabolism were assessed with Neurite Orientation Density and Dispersion (NODDI) and quantitative magnetization transfer (qMT) imaging. APOE-ε4 carriers relative to non-carriers had a lower macromolecular proton fraction (MPF) in the left thalamus. No risk effects were present for cortical thickness, subcortical volume, or NODDI indices. Reduced thalamic MPF may reflect inflammation-related tissue swelling and/or myelin loss in APOE-ε4. Future prospective studies should investigate the sensitivity and specificity of qMT-based MPF as a non-invasive biomarker for LOAD risk.
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Affiliation(s)
- Jilu P Mole
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Fabrizio Fasano
- Siemens Healthcare, Henkestrasse 127, 91052, Erlangen, Germany
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Rebecca Sims
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Haydn Ellis Building, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Emma Kidd
- School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Redwood Building, King Edward VII Avenue,, Cardiff, CF10 3NB, UK
| | - John P Aggleton
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK
| | - Claudia Metzler-Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cathays, Cardiff, CF24 4HQ, UK.
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De Groote S, Goudman L, Van Schuerbeek P, Peeters R, Sunaert S, Linderoth B, De Andrés J, Rigoard P, De Jaeger M, Moens M. Effects of spinal cord stimulation on voxel-based brain morphometry in patients with failed back surgery syndrome. Clin Neurophysiol 2020; 131:2578-2587. [PMID: 32927213 DOI: 10.1016/j.clinph.2020.07.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/30/2020] [Accepted: 07/26/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Despite the clinical effectiveness of Spinal Cord Stimulation (SCS), potential structural brain modifications have not been explored. Our aim was to identify structural volumetric changes during subsensory SCS, in patients with Failed Back Surgery Syndrome (FBSS). METHODS In this cohort study, twenty-two FBSS patients underwent a magnetic resonance imaging protocol before SCS and 3 months after SCS. Clinical parameters were correlated with volumetric changes, calculated with voxel-based morphometry. RESULTS After 3 months, a significant volume decrease was found in the inferior frontal gyrus, precuneus, cerebellar posterior lobe and middle temporal gyrus. Significant increases were found in the inferior temporal gyrus, precentral gyrus and the middle frontal gyrus after SCS. Additionally, significant increases in volume of superior frontal and parietal white matter and a significant decrease in volume of white matter underlying the premotor/middle frontal gyrus were revealed after SCS. A significant correlation was highlighted between white matter volume underlying premotor/middle frontal gyrus and leg pain relief. CONCLUSIONS This study revealed for the first time that SCS is able to induce volumetric changes in gray and white matter, suggesting the reversibility of brain alterations after chronic pain treatment. SIGNIFICANCE Volumetric brain alterations are observable after 3 months of subsensory SCS in FBSS patients.
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Affiliation(s)
- Sander De Groote
- Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium
| | - Lisa Goudman
- Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium; STIMULUS consortium (reSearch and TeachIng neuroModULation Uz bruSsel), Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium.; Pain in Motion International Research Group, Belgium; Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium
| | - Peter Van Schuerbeek
- Department of Radiology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium
| | - Ronald Peeters
- Department of Radiology, Universitair Ziekenhuis Leuven, UZ Herestraat 49-bus 7003 54, 3000 Leuven, Belgium
| | - Stefan Sunaert
- Department of Radiology, Universitair Ziekenhuis Leuven, UZ Herestraat 49-bus 7003 54, 3000 Leuven, Belgium
| | - Bengt Linderoth
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jose De Andrés
- Surgical Specialties Department Valencia University Medical School, and Department of Anesthesiology Critical Care and Pain Management, General University Hospital, Valencia, Spain
| | - Philippe Rigoard
- Department of Neurosurgery, Poitiers University Hospital, Poitiers, France; Institut Pprime UPR 3346, CNRS, University of Poitiers, Poitiers, ISAE-ENSMA, France; PRISMATICS Lab (Predictive Research in Spine/Neuromodulation Management and Thoracic Innovation/Cardiac Surgery), Poitiers University Hospital, Poitiers, France
| | - Mats De Jaeger
- Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium
| | - Maarten Moens
- Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium; STIMULUS consortium (reSearch and TeachIng neuroModULation Uz bruSsel), Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium.; Department of Radiology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium; Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium.
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40
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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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41
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Williams CM, Peyre H, Toro R, Beggiato A, Ramus F. Adjusting for allometric scaling in ABIDE I challenges subcortical volume differences in autism spectrum disorder. Hum Brain Mapp 2020; 41:4610-4629. [PMID: 32729664 PMCID: PMC7555078 DOI: 10.1002/hbm.25145] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 06/29/2020] [Accepted: 07/07/2020] [Indexed: 12/17/2022] Open
Abstract
Inconsistencies across studies investigating subcortical correlates of autism spectrum disorder (ASD) may stem from small sample size, sample heterogeneity, and omitting or linearly adjusting for total brain volume (TBV). To properly adjust for TBV, brain allometry—the nonlinear scaling relationship between regional volumes and TBV—was considered when examining subcortical volumetric differences between typically developing (TD) and ASD individuals. Autism Brain Imaging Data Exchange I (ABIDE I; N = 654) data was analyzed with two methodological approaches: univariate linear mixed effects models and multivariate multiple group confirmatory factor analyses. Analyses were conducted on the entire sample and in subsamples based on age, sex, and full scale intelligence quotient (FSIQ). A similar ABIDE I study was replicated and the impact of different TBV adjustments on neuroanatomical group differences was investigated. No robust subcortical allometric or volumetric group differences were observed in the entire sample across methods. Exploratory analyses suggested that allometric scaling and volume group differences may exist in certain subgroups defined by age, sex, and/or FSIQ. The type of TBV adjustment influenced some reported volumetric and scaling group differences. This study supports the absence of robust volumetric differences between ASD and TD individuals in the investigated volumes when adjusting for brain allometry, expands the literature by finding no group difference in allometric scaling, and further suggests that differing TBV adjustments contribute to the variability of reported neuroanatomical differences in ASD.
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Affiliation(s)
- Camille Michèle Williams
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France
| | - Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes 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
- U1284, Center for Research and Interdisciplinarity (CRI), INSERM, Paris, France.,Unité Mixte de Recherche 3571, Human Genetics and Cognitive Functions, Centre National de la Recherche Scientifique, Institut Pasteur, Paris, France
| | - Anita Beggiato
- Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France.,Unité Mixte de Recherche 3571, Human Genetics and Cognitive Functions, Centre National de la Recherche Scientifique, Institut Pasteur, Paris, France
| | - Franck Ramus
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Etudes Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France
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42
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Azevedo T, Passamonti L, Lio P, Toschi N. A deep spatiotemporal graph learning architecture for brain connectivity analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1120-1123. [PMID: 33018183 DOI: 10.1109/embc44109.2020.9175360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In recent years, the conceptualisation of the brain as a "connectome" as summary measures derived from graph theory analyses, has become increasingly popular. Still, such approaches are inherently limited by the need to condense and simplify temporal fMRI dynamics and architecture into a purely spatial representation. We formulate a novel architecture based on Geometric Deep Learning which is specifically tailored to the one-step integration of spatial relationship between nodes and single-node temporal dynamics. We compare different spatiotemporal modelling mechanisms and demonstrate the effectiveness of our architecture in a binary prediction task based on a large homogeneous fMRI dataset made publicly available by the Human Connectome Project (HCP). As the idea of e.g. a dynamical network connectivity is beginning to make its way into the more mainstream toolset which neuroscientists commonly employ with neuroimaging data, our model can contribute to laying the groundwork for explicitly incorporating spatiotemporal information into every association and prediction problem in neuroscience.
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43
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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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44
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Peyre H, Mohanpuria N, Jednoróg K, Heim S, Grande M, van Ermingen-Marbach M, Altarelli I, Monzalvo K, Williams CM, Germanaud D, Toro R, Ramus F. Neuroanatomy of dyslexia: An allometric approach. Eur J Neurosci 2020; 52:3595-3609. [PMID: 31991019 DOI: 10.1111/ejn.14690] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 01/04/2020] [Accepted: 01/17/2020] [Indexed: 01/06/2023]
Abstract
Despite evidence for a difference in total brain volume between dyslexic and good readers, no previous neuroimaging study examined differences in allometric scaling (i.e. differences in the relationship between regional and total brain volumes) between dyslexic and good readers. The present study aims to fill this gap by testing differences in allometric scaling and regional brain volume differences in dyslexic and good readers. Object-based morphometry analysis was used to determine grey and white matter volumes of the four lobes, the cerebellum and limbic structures in 130 dyslexic and 106 good readers aged 8-14 years. Data were collected across three countries (France, Poland and Germany). Three methodological approaches were used as follows: principal component analysis (PCA), linear regression and multiple-group confirmatory factor analysis (MGCFA). Difference in total brain volume between good and dyslexic readers was Cohen's d = 0.39. We found no difference in allometric scaling, nor in regional brain volume between dyslexic and good readers. Results of our three methodological approaches (PCA, linear regression and MGCFA) were consistent. This study provides evidence for total brain volume differences between dyslexic and control children, but no evidence for differences in the volumes of the four lobes, the cerebellum or limbic structures, once allometry is taken into account. It also finds no evidence for a difference in allometric relationships between the groups. We highlight the methodological interest of the MGCFA approach to investigate such research issues.
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Affiliation(s)
- Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Supérieure, PSL University, Paris, France.,Neurodiderot, INSERM UMR 1141, Paris Diderot University, Paris, France.,Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France
| | - Neha Mohanpuria
- Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Supérieure, PSL University, Paris, France
| | - Katarzyna Jednoróg
- Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences (PAS), Warsaw, Poland
| | - Stefan Heim
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-1), Helmholtz-Gemeinschaft Deutscher Forschungszentren (HZ), Jülich, Germany
| | - Marion Grande
- Department of Neurology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Muna van Ermingen-Marbach
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany.,Department of Neurology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Irene Altarelli
- Laboratory for the Psychology of Child Development and Education, CNRS UMR 8240, Université de Paris, Paris, France
| | - Karla Monzalvo
- INSERM, UMR992, CEA, NeuroSpin Center, University Paris Saclay, Gif-sur-Yvette, France
| | - Camille Michèle Williams
- Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Supérieure, PSL University, Paris, France
| | - David Germanaud
- Neurodiderot, INSERM UMR 1141, Paris Diderot University, Paris, France.,INSERM, UMR992, CEA, NeuroSpin Center, University Paris Saclay, Gif-sur-Yvette, France.,Department of Pediatric Neurology and Metabolic Diseases, Robert Debré Hospital, APHP, Paris, France.,INSERM, CEA, UMR 1129, Sorbonne Paris Cité University (USPC), Paris, France
| | - Roberto Toro
- Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
| | - Franck Ramus
- Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Supérieure, PSL University, Paris, France
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45
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Peper JS, Burke SM, Wierenga LM. Sex differences and brain development during puberty and adolescence. HANDBOOK OF CLINICAL NEUROLOGY 2020; 175:25-54. [PMID: 33008529 DOI: 10.1016/b978-0-444-64123-6.00003-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Sex differences in behavior, and whether these behavioral differences are related to sex differences in brain development, has been a longstanding topic of debate. Presumably, sex differences can provide critically important leads for explaining the etiology of various illnesses that show (i) large sex differences in prevalence and (ii) have an origin before or during adolescence. The general aim of this chapter is to provide an overview of scientific studies on sex differences in normative brain and behavioral development across puberty and adolescence, including the (sex) hormone-driven transition phase of puberty. Moreover, we describe the literature on brain and behavioral development in gender dysphoria, a severe and persistent incongruence between the self-identified gender and the assigned sex at birth. From the literature it becomes clear there is evidence for a specific link between pubertal maturation and developmental changes in arousal, motivation, and emotion. However, this link is rather similar between boys and girls. Moreover, although there is substantial evidence for sex differences in mean brain structure, these have not always been linked to sex differences in behavior, cognition, or psychopathology. Furthermore, there is little evidence for sex differences in brain development and thus, studies so far have been unable to explain sex differences in cognition. Suggestions for future research and methodologic considerations are provided.
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Affiliation(s)
- Jiska S Peper
- Department of Psychology, Leiden University, Leiden, The Netherlands.
| | - Sarah M Burke
- Department of Psychology, Leiden University, Leiden, The Netherlands
| | - Lara M Wierenga
- Department of Psychology, Leiden University, Leiden, The Netherlands
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Sex differences in brain structure: a twin study on restricted and repetitive behaviors in twin pairs with and without autism. Mol Autism 2019; 11:1. [PMID: 31893022 PMCID: PMC6937723 DOI: 10.1186/s13229-019-0309-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 12/19/2019] [Indexed: 01/01/2023] Open
Abstract
Background Females with autism spectrum disorder have been reported to exhibit fewer and less severe restricted and repetitive behaviors and interests compared to males. This difference might indicate sex-specific alterations of brain networks involved in autism symptom domains, especially within cortico-striatal and sensory integration networks. This study used a well-controlled twin design to examine sex differences in brain anatomy in relation to repetitive behaviors. Methods In 75 twin pairs (n = 150, 62 females, 88 males) enriched for autism spectrum disorder (n = 32), and other neurodevelopmental disorders (n = 32), we explored the association of restricted and repetitive behaviors and interests—operationalized by the Autism Diagnostic Interview-Revised (C domain) and the Social Responsiveness Scale-2 (Restricted Interests and Repetitive Behavior subscale)—with cortical volume, surface area and thickness of neocortical, sub-cortical, and cerebellar networks. Results Co-twin control analyses revealed within-pair associations between RRBI symptoms and increased thickness of the right intraparietal sulcus and reduced volume of the right orbital gyrus in females only, even though the mean number of RRBIs did not differ between the sexes. In a sub-sample of ASD-discordant pairs, increased thickness in association with RRBIs was found exclusively in females in the orbitofrontal regions, superior frontal gyrus, and intraparietal sulcus, while in males RRBIs tended to be associated with increased volume of the bilateral pallidum. Limitations However, due to a small sample size and the small difference in RRBI symptoms within pairs, the results of this exploratory study need to be interpreted with caution. Conclusions Our findings suggest that structural alterations of fronto-parietal networks in association with RRBIs are found mostly in females, while striatal networks are more affected in males. These results endorse the importance of investigating sex differences in the neurobiology of autism symptoms, and indicate different etiological pathways underlying restricted and repetitive behaviors and interests in females and males.
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Neuroscience and Sex/Gender: Looking Back and Forward. J Neurosci 2019; 40:37-43. [PMID: 31488609 DOI: 10.1523/jneurosci.0750-19.2019] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 08/30/2019] [Accepted: 09/03/2019] [Indexed: 12/18/2022] Open
Abstract
Phoenix et al. (1959) reported that treating pregnant guinea pigs with testosterone had enduring effects on the sex-related behavior of their female offspring. Since then, similar enduring effects of early testosterone exposure have been found in other species, including humans, and for other behaviors that show average sex differences. In humans, the affected outcomes include gender identity, sexual orientation, and children's sex-typical play behavior. The evidence linking early testosterone exposure to sex-typed play is particularly robust, and sex-typed play is also influenced by many other factors, including socialization by parents and peers and self-socialization, based on cognitive understanding of gender. In addition to influencing behavior, testosterone and hormones produced from testosterone affect mammalian brain structure. Studies using human autopsy material have found some sex differences in the human brain similar to those seen in other species, and have reported that some brain sex differences correlate with sexual orientation or gender identity, although the causes of these brain/behavior relationships are unclear. Studies that have imaged the living human brain have found only a small number of sex differences, and these differences are generally small in magnitude. In addition, they have not been linked to robust psychological or behavioral sex differences. Future research might benefit from improved imaging technology, and attention to other brain characteristics. In addition, it might usefully explore how different types of factors, such as early testosterone exposure and parental socialization, work together in the developmental system that produces sex/gender differences in human brain and behavior.
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