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      Sex differences in the human brain: a roadmap for more careful analysis and interpretation of a biological reality

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          Abstract

          The presence, magnitude, and significance of sex differences in the human brain are hotly debated topics in the scientific community and popular media. This debate is largely fueled by studies containing strong, opposing conclusions: either little to no evidence exists for sex differences in human neuroanatomy, or there are small-to-moderate differences in the size of certain brain regions that are highly reproducible across cohorts (even after controlling for sex differences in average brain size). Our Commentary uses the specific comparison between two recent large-scale studies that adopt these opposing views—namely the review by Eliot and colleagues (2021) and the direct analysis of ~ 40k brains by Williams and colleagues (2021)—in an effort to clarify this controversy and provide a framework for conducting this research. First, we review observations that motivate research on sex differences in human neuroanatomy, including potential causes (evolutionary, genetic, and environmental) and effects (epidemiological and clinical evidence for sex-biased brain disorders). We also summarize methodological and empirical support for using structural MRI to investigate such patterns. Next, we outline how researchers focused on sex differences can better specify their study design (e.g., how sex was defined, if and how brain size was adjusted for) and results (by e.g., distinguishing sexual dimorphisms from sex differences). We then compare the different approaches available for studying sex differences across a large number of individuals: direct analysis, meta-analysis, and review. We stress that reviews do not account for methodological differences across studies, and that this variation explains many of the apparent inconsistencies reported throughout recent reviews (including the work by Eliot and colleagues). For instance, we show that amygdala volume is consistently reported as male-biased in studies with sufficient sample sizes and appropriate methods for brain size correction. In fact, comparing the results from multiple large direct analyses highlights small, highly reproducible sex differences in the volume of many brain regions (controlling for brain size). Finally, we describe best practices for the presentation and interpretation of these findings. Care in interpretation is important for all domains of science, but especially so for research on sex differences in the human brain, given the existence of broad societal gender-biases and a history of biological data being used justify sexist ideas. As such, we urge researchers to discuss their results from simultaneously scientific and anti-sexist viewpoints.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13293-022-00448-w.

          Highlights

          • Recent large-scale studies have reached different conclusions regarding the presence of sex differences in human neuroanatomy.

          • We show that these contradictory findings are explained by different methodological choices. While multiple large direct analyses highlight small, highly reproducible sex differences, reviews do not account for methodological heterogeneity across studies (e.g., statistical power/sample size, brain size-correction methods, segmentation, region selection, participant age). This explains many of the apparent inconsistencies reported in recent reviews.

          • We also summarize observations that motivate research on sex differences in human neuroanatomy (including potential causes and effects), review methodological and empirical support for using structural MRI to investigate such patterns, and outline best practices for analyzing and describing neuroanatomical sex differences.

          • Finally, we argue that broader historical and societal contexts make it important to reinforce the scientific method by adopting an actively "anti-sexist" viewpoint when conducting research on sex differences in the human brain.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13293-022-00448-w.

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          Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

          An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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            On aims and methods of Ethology

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              Large-scale automated synthesis of human functional neuroimaging data

              The explosive growth of the human neuroimaging literature has led to major advances in understanding of human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. Here we describe and validate an automated brain mapping framework that uses text mining, meta-analysis and machine learning techniques to generate a large database of mappings between neural and cognitive states. We demonstrate the capacity of our approach to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature, and support accurate ‘decoding’ of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results validate a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.
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                Author and article information

                Contributors
                decasienar@nih.gov
                Journal
                Biol Sex Differ
                Biol Sex Differ
                Biology of Sex Differences
                BioMed Central (London )
                2042-6410
                26 July 2022
                26 July 2022
                2022
                : 13
                : 43
                Affiliations
                GRID grid.416868.5, ISNI 0000 0004 0464 0574, Section On Developmental Neurogenomics, , National Institute of Mental Health, ; Bethesda, MD USA
                Author information
                http://orcid.org/0000-0002-6205-5408
                Article
                448
                10.1186/s13293-022-00448-w
                9327177
                35883159
                3f69eb06-6be9-441e-bdb7-726a5fbdfa0f
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 22 February 2022
                : 23 May 2022
                Funding
                Funded by: National Institute of Mental Health
                Award ID: 1ZIAMH002949
                Award Recipient :
                Funded by: National Institute of Child Health and Disease
                Award ID: R01HD100298
                Award Recipient :
                Categories
                Commentary
                Custom metadata
                © The Author(s) 2022

                Human biology
                sex differences,neuroanatomy,smri,direct analysis,meta-analysis,review,sex chromosomes,neurodevelopment,sexual selection,anti-sexism

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