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Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants
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Abstract
Sex differences in the human brain are of interest for many reasons: for example, there are sex differences in the observed prevalence of psychiatric disorders and in some psychological traits that brain differences might help to explain. We report the largest single-sample study of structural and functional sex differences in the human brain (2750 female, 2466 male participants; mean age 61.7 years, range 44-77 years). Males had higher raw volumes, raw surface areas, and white matter fractional anisotropy; females had higher raw cortical thickness and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume, total surface area, mean cortical thickness, or height. There was generally greater male variance across the raw structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.
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“Sex-related differences in brain structure and function have been consistently reported across large-scale neuroimaging studies, involving both global and regional measures such as total brain volume, cortical thickness, white matter microstructure, and functional connectivity [29,30,31,32].”
From Benchmarking Multimodal Deep Fusion Strategies for Heterogeneous Neuroimaging and Cognitive Data Using a Controlled Sex Classification Task (2026) · cites this paper as [31]
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“As sex differences exist in brain morphology,33”
From Sex Differences in the Association of Global Amyloid and Regional Tau Deposition Measured by Positron Emission Tomography in Clinically Normal Older Adults (2019) · cites this paper as [33]
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“Memory impairment is similarly found among patients with Cushing disease and Cushing syndrome (694).”
From Congenital Adrenal Hyperplasia—Current Insights in Pathophysiology, Diagnostics, and Management (2022) · cites this paper as [694]
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Items 201–300 of 854
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ResultMeasures of Brain Connectivity and Cognition by Sex in US Children
Tomasi, D. G., & Volkow, N. D. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.0157 · Cited by 27
Complexity of Sex Differences and Their Impact on Alzheimer’s Disease
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Oltra, J., Habich, A., Schwarz, C. G., Nedelská, Z., Przybelski, S. A., Inguanzo, A., Diaz‐Galvan, P., Lowe, V. J., Oppedal, K., González, M. C., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13571 · Cited by 25
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BackgroundDo Sex and Gender Have Separate Identities?
Quintana, G. R., & Pfaus, J. G. (2024) · Archives of Sexual Behavior · doi:10.1007/s10508-024-02933-2 · Cited by 30
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“2011), followed by reanalysis of those 105 regions of interest, eliminated most putative sexual dimorphisms in brain volumes (Ritchie et al. 2018).”
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Opposite effects of positive and negative symptoms on resting-state brain networks in schizophrenia
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“1 Hz); and (7) elimination of 6 rigid body motion correction parameters and the signal from the white matter and a ventricular region of interest using linear regression.”
Higher performance for women than men in MRI-based Alzheimer’s disease detection
Klingenberg, M., Stark, D., Eitel, F., Budding, C., Habes, M., Ritter, K., & Initiative, A. D. N. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01225-6 · Cited by 26
“These differences cover overall brain size, cortical thickness, and grey matter volume in specific brain areas, with men generally having a greater variance in these structural measures [51, 52].”
BackgroundSex Differences in Alcohol Use: Is It All About Hormones?
Maddern, X. J., Ursich, L. T., Bailey, G., Pearl, A. J., Anversa, R. G., Lawrence, A. J., & Walker, L. C. (2024) · Endocrinology · doi:10.1210/endocr/bqae088 · Cited by 23
Association Between Visceral Fat and Brain Cortical Thickness in the Elderly: A Neuroimaging Study
Cho, J., Seo, S., Kim, W., Kim, C., & Noh, Y. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.694629 · Cited by 23
“, cortical thickness and volume) (Ritchie et al., 2018), sex-stratified analyses were additionally conducted.”
MethodSex Differences in Lifespan Trajectories and Variability of Human Sulcal and Gyral Morphology
Díaz‐Caneja, C. M., Alloza, C., Gordaliza, P. M., Fernández‐Pena, A., de Hoyos, L., Santonja, J., Buimer, E. E. L., van Haren, N. E. M., Cahn, W., Arango, C., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab145 · Cited by 24
Self-citationLin, J., Kang, X., Xiong, Y., Zhang, D., Zong, R., Yu, X., Pan, L., & Lou, X. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118550 · Cited by 23
Huang, S., Faul, L., Sevinc, G., Mwilambwe-Tshilobo, L., Setton, R., Lockrow, A. W., Ebner, N. C., Turner, G. R., Spreng, R. N., & Brigard, F. D. (2021) · Psychology and Aging · doi:10.1037/pag0000633 · Cited by 25
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Sex differences in resting state functional connectivity across the first two years of life
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“showing greater connectivity in the DMN in adolescent girls [42] and adult women [101] [102]”
BackgroundTesting a convolutional neural network‐based hippocampal segmentation method in a stroke population
Zavaliangos‐Petropulu, A., Tubi, M. A., Haddad, E., Zhu, A. H., Braskie, M. N., Jahanshad, N., Thompson, P. M., & Liew, S. (2020) · Human Brain Mapping · doi:10.1002/hbm.25210 · Cited by 23
“…cortical and subcortical structures, is an atlas-based approach and commonly used to study hippocampal volume in cognitively healthy populations (Ritchie et al., 2018; Nobis et al., 2019) as well as in people with neurodevelopmental, psychiatric, and neurodegenerative conditions (Schmaal et…”
BackgroundMethodAdult lifespan trajectories of neuromagnetic signals and interrelations with cortical thickness
Stier, C., Braun, C., & Focke, N. K. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120275 · Cited by 25
An international mega-analysis of psychedelic drug effects on brain circuit function
Girn, M., Doss, M. K., Roseman, L., Preller, K. H., Palhano-Fontes, F., Pasquini, L., Barrett, F. S., Mallaroni, P., Mason, N. L., Timmermann, C., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04287-9 · Cited by 29
Sex effects on cortical morphological networks in healthy young adults
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“The significantly lower mean brain weight in females compared with males was expected and consistent with what is already known about sex differences in brain weight [70].”
Seidler, R. D., Stern, C., Basner, M., Stahn, A. C., Wuyts, F. L., & zu Eulenburg, P. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2022.876789 · Cited by 24
“Recent large-scale and meta-analytic studies reveal structural and functional brain differences between men and women, including brain gray and white matter volumes and structures critically involved in spatial navigation such as the hippocampus, caudate nucleus, and precuneus (Ritchie et al., 2018).”
BackgroundTomoto, T., Tarumi, T., & Zhang, R. (2023) · Journal of Hypertension · doi:10.1097/hjh.0000000000003404 · Cited by 22
Different effects of cardiometabolic syndrome on brain age in relation to gender and ethnicity
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“When microstructural metrics were compared to SCQ total score a similar pattern to diagnosis was observed (Fig 9).”
BackgroundResultUsing Organoids to Model Sex Differences in the Human Brain
Pavlinek, A., Adhya, D., Tsompanidis, A., Warrier, V., Baron‐Cohen, S., Allison, C. L., Warrier, V., Tsompanidis, A., Adhya, D., Holt, R., et al. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100343 · Cited by 22
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“, females show higher raw CT than males (Ritchie et al. 2018).”
BackgroundSex differences in gene expression in the human fetal brainPreprint
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“Sex differences have been reported in the regional volumes (Ruigrok et al. 2014; Ritchie et al. 2018) connectivity (Ingalhalikar et al. 2014) and chemistry (Nishizawa et al. 1997; Laakso et al. 2002) of the adolescent and adult human brain.”
BackgroundColmenares, A. M., Prytherch, B., Thomas, M. L., & Burzynska, A. Z. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00045 · Cited by 24
“So far, cross-sectional DTI studies have reported greater FA in men ( Kochunov et al., 2012; Lebel et al., 2012; Ritchie et al., 2018) or no sex differences across the adult lifespan ( Kennedy & Raz, 2009).”
BackgroundNishikawa, Y., Watanabe, K., Holobar, A., Kitamura, R., Maeda, N., & Hyngstrom, A. S. (2024) · European Journal of Applied Physiology · doi:10.1007/s00421-024-05420-7 · Cited by 21
“In adulthood, males exhibited higher fractional anisotropy (FA) and lower mean diffusivity than females in many regions (Westerhausen et al. 2003; Hsu et al. 2008; Abe et al. 2010; Ritchie et al. 2018).”
BackgroundAge but no sex effects on subareas of the amygdala
Kurth, F., Cherbuin, N., & Eileen Luders, A. (2018) · Human Brain Mapping · doi:10.1002/hbm.24481 · Cited by 20
Yang, G., Božek, J., Han, M., & Gao, J. (2020) · Human Brain Mapping · doi:10.1002/hbm.24960 · Cited by 20
The Cambridge Handbook of the International Psychology of Women
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Tract Specificity of Age Effects on Diffusion Tensor Imaging Measures of White Matter Health
Matijevic, S., & Ryan, L. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.628865 · Cited by 22
A Plug-In Graph Neural Network to Boost Temporal Sensitivity in fMRI Analysis
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“…in the default mode network (temporoparietal junction) are assumed to be involved in aspects of social cognition such as face recognition and emotion processing, and regions in the control network (prefrontal) are assumed to be involved in high-level cognition including decision making [45], [46].”
BackgroundGender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity
Luo, Z., Hou, C., Wang, L., & Hu, D. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00029 · Cited by 19
“, 2018), we particularly compared our findings with those using more than 1,000 samples (Chekroud et al., 2016; Gur and Gur, 2016; Anderson et al., 2018; Ritchie et al., 2018), and we found considerable accordance.”
BackgroundResultInfluentialAngelopoulou, G., Meier, E. L., Kasselimis, D. S., Pan, Y., Tsolakopoulos, D., Velonakis, G., Karavasilis, E., Kelekis, N., Goutsos, D., Potagas, C. M., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01424 · Cited by 20
APOE, TOMM40, and sex interactions on neural network connectivity
Li, T., Pappas, C. A., Le, S. T., Wang, Q., Klinedinst, B. S., Larsen, B. A., Pollpeter, A., Lee, L. Y., Lutz, M. W., Gottschalk, W. K., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.09.020 · Cited by 19
Kim, G., Joo, Y. Y., Ahn, G., Wang, H., Moon, S., Kim, H., Ahn, W., & Cha, J. (2022) · Human Brain Mapping · doi:10.1002/hbm.25888 · Cited by 18
Nazlee, N., Waiter, G. D., & Sandu, A. (2022) · Human Brain Mapping · doi:10.1002/hbm.26076 · Cited by 16
“subtle and it is increasingly recognized that there is substantial overlap in the general population (Ritchie et al., 2018).”
BackgroundPeng, D., Zheng, W., Liu, L., Jiang, W.-B., Li, Z., Lu, Y., & Lu, B. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad085a · Cited by 19
“…study of structural and functional sex differences in the adult human brain from 5216 UK Biobank participants (2750 females and 2466 males) provides a clear characterization of neurobiological sex differences in adult brains and encourages further exploration of their causes and consequences [22].”
Sex Differences in Intelligence
Halpern, D. F., & Wai, J. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108770422.015 · Cited by 19
Pearse, R. V., & Young‐Pearse, T. L. (2019) · Brain Research · doi:10.1016/j.brainres.2019.146352 · Cited by 22
“A recent highly-powered study using 5,216 living human subjects (2,750 females and 2,466 males ranging in age from 44–77) from the UK biobank analyzed brain imaging data, all generated from a single MRI scanner to minimize technical variability (Ritchie et al., 2018).”
BackgroundEbbesen, C. L., Bobrov, E., Rao, R. P., & Brecht, M. (2019) · Nature Communications · doi:10.1038/s41467-019-12511-z · Cited by 17
Liu, G., Liu, C., Qiu, A., & Initiative, T. A. D. N. (2021) · Human Brain Mapping · doi:10.1002/hbm.25414 · Cited by 19
“Gender and education were considered as covariates since the education level was a risk factor for AD (Qiu, Backman, Winblad, Aguero-Torres, & Fratiglioni, 2001) and the brain size of male and female was different (Ritchie et al., 2018).”
Sex/Gender Differences in Brain Lateralisation and Connectivity
Hodgetts, S., & Hausmann, M. (2022) · Current topics in behavioral neurosciences · doi:10.1007/7854_2022_303 · Cited by 17
Mori, K., & Haruno, M. (2022) · Human Brain Mapping · doi:10.1002/hbm.25822 · Cited by 16
Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI
Mahmood, U., Fu, Z., Ghosh, S., Calhoun, V. D., & Plis, S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119737 · Cited by 20
“For OASIS results 6 b we see that females show high connectivity scores in default-mode network (DMN) compare to males and low sensori-motor network (SMN) connectivity compare to males, this has been verified by existing studies (Filippi et al., 2013; Kim et al., 2021; Mak et al., 2016; Ritchie et al., 2018).”
Wang, X., Huang, C., Tsai, S., Lin, C., & Cai, Q. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1331574 · Cited by 19
Systematic review on the characterization of chronic traumatic encephalopathy by MRI and MRS
Ruprecht, R., Scheurer, E., & Lenz, C. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26162 · Cited by 19
Evolutionary approaches to understanding crime: explaining the gender gap in offending
Durrant, R. (2018) · Psychology Crime and Law · doi:10.1080/1068316x.2018.1558224 · Cited by 20
Qi, R., Luo, Y., Zhang, L., Weng, Y., Surento, W., Jahanshad, N., Xu, Q., Yin, Y., Li, L., Cao, Z., et al. (2020) · Neurobiology of Stress · doi:10.1016/j.ynstr.2020.100227 · Cited by 16
Delvecchio, G., Maggioni, E., Pigoni, A., Crespo‐Facorro, B., Nenadić, I., Benedetti, F., Christian Gaser, B., Sauer, H., Roiz‐Santiáñez, R., Poletti, S., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.622054 · Cited by 18
“Overall, these results aligned with the evidence reported by previous literature showing selective GM volume deficits in female subjects in healthy populations (Ritchie et al., 2018).”
BackgroundResultInfluentialPredicting fluid intelligence in adolescence from structural MRI with deep learning methods
Saha, S., Pagnozzi, A. M., Bradford, D., & Fripp, J. (2021) · Intelligence · doi:10.1016/j.intell.2021.101568 · Cited by 20
Uchida, Y., Nishimaki, K., Soldan, A., Pettigrew, C. A., Ho, S. G., Moghekar, A., Wang, M., Miller, M. I., Albert, M., & Oishi, K. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70544 · Cited by 17
Genetic control of variability in subcortical and intracranial volumes
Córdova‐Palomera, A., van der Meer, D., Kaufmann, T., Bettella, F., Wang, Y., Alnæs, D., Doan, N. T., Agartz, I., Bertolino, A., Buitelaar, J. K., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0664-1 · Cited by 17
“An additional validation stage was implemented in recognition that, despite the conservativeness of the previous approach, variables like sex and batch might still confound the variance test results [14, 15].”
MethodRunning the Female Power Grid Across Lifespan Through Brain Estrogen Signaling
Ingraham, H. A., Herber, C. B., & Krause, W. C. (2021) · Annual Review of Physiology · doi:10.1146/annurev-physiol-061121-035914 · Cited by 18
Saha, P., & Sarkar, D. (2022) · Child Psychiatry & Human Development · doi:10.1007/s10578-022-01432-6 · Cited by 17
“Ritchie et al. [44] had observed dense functional brain connectivity for male participants than females grossly within the somatomotor, frontoparietal, and visual networks.”
Sex differences in neural processing of speech in neonates
Alexopoulos, J., Giordano, V., Doering, S., Seidl, R., Benavides‐Varela, S., Russwurm, M., Greenwood, S., Berger, A., & Bartha, L. (2022) · Cortex · doi:10.1016/j.cortex.2022.09.007 · Cited by 22
“This finding may point to a greater variation in brain maturation within the group of boys and is in accordance with studies that report greater male than female variability in regional brain structure across the whole lifespan (Forde et al., 2020; Ritchie et al., 2018; Wierenga et al., 2022).”
ResultWeller, S., Derntl, B., & Plewnia, C. (2023) · Biology of Sex Differences · doi:10.1186/s13293-023-00561-4 · Cited by 17
Morphologic alterations of the fear circuitry: the role of sex hormones and oral contraceptives
Brouillard, A., Davignon, L., Turcotte, A.-M., & Marin, M. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1228504 · Cited by 21
Liu, Y., Song, J., Xu, W., Hou, X., Li, J., Yu, J., Tan, L., Chi, S., & Initiative, A. A. D. N. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.633576 · Cited by 16
Malik, N., & Bzdok, D. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.06.014 · Cited by 19
Enhancement of indirect functional connections with shortest path length in the adult autistic brain
Guo, X., Simas, T., Lai, M., Lombardo, M. V., Chakrabarti, B., Ruigrok, A. N. V., Bullmore, E. T., Baron‐Cohen, S., Chen, H., & John Suckling, A. (2019) · Human Brain Mapping · doi:10.1002/hbm.24777 · Cited by 17
“While sex differences are well established for measures of restingstate functional connectivity in TD individuals (Biswal et al., 2010; Ritchie et al., 2018), these differences are currently poorly understood in autism partly due to the limited number of autistic females partici- pating in…”
BackgroundSelf-citationSex differences in the behavioral inhibition system and ventromedial prefrontal cortex connectivity
Jung, W. H., Lee, T. Y., Kim, M., Lee, J., Oh, S., Lho, S. K., Moon, S., & Kwon, J. S. (2021) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsab118 · Cited by 21
“For instance, a large sample RSfMRI study (2750 females vs 2466 males) showed that females had higher resting-state FC within the DMN than males (Ritchie et al., 2018).”
Processing speed predicts SuperAging years later
Tichá, Z., Georgi, H. S., Schmand, B. A., Heissler, R., & Kopeček, M. (2023) · BMC Psychology · doi:10.1186/s40359-023-01069-7 · Cited by 16
“It may be due to the greater cortical thickness in females compared to males [54].”
BackgroundPluck, G. C., Crespo‐Andrade, C., Parreño, P., Haro, K. I., Martínez, M. A., & Pontón, S. C. (2020) · Psychology & Neuroscience · doi:10.1037/pne0000195 · Cited by 18
Greater variability in chimpanzee ( Pan troglodytes ) brain structure among males
DeCasien, A. R., Sherwood, C. C., Schapiro, S. J., & Higham, J. P. (2020) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2019.2858 · Cited by 16
Tan, G. C.-Y., Chu, C., Lee, Y. T., Tan, C. C. K., Ashburner, J., Wood, N. W., & Frackowiak, R. S. J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117087 · Cited by 17
Khorashad, B. S., Khazai, B., Talaeı, A., Acar, F., Hudson, A. R., Borji, N., Saberi, H., Aminzadeh, B., & Mueller, S. C. (2020) · Journal of Neuroscience Research · doi:10.1002/jnr.24702 · Cited by 16
“…the sexes, large-scale studies and meta-analyses have tried to identify typical anatomical patterns for cisgender men (CM; assigned male at birth who identifies as a man) and cisgender women (CW; assigned female at birth who identifies as a woman; Ritchie et al., 2018; Ruigrok et al., 2014).”
BackgroundResultCahn, A. J., Little, G. E., Beaulieu, C., & Tétreault, P. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02181-9 · Cited by 15
Sleep Duration, Sleep Apnea, and Gray Matter Volume
Kim, R. E. Y., Abbott, R. D., Kim, S., THOMAS, R. J., Yun, C., Kim, H., Johnson, H. J., & Shin, C. (2021) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/0891988720988918 · Cited by 16
“Brain subregional differences may also be important.(28,41) Taking into account of all of the above mentioned, separate investigations for men and women seems warranted.”
BackgroundSex-related Differences in Stress Reactivity and Cingulum White Matter
Wheelock, M. D., Goodman, A. M., Harnett, N. G., Wood, K. H., Mrug, S., Granger, D. A., & Knight, D. C. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.02.014 · Cited by 16
“Although prior research has demonstrated sex differences in white matter microstructure (Inano et al., 2011; Kim et al., 2017; Menzler et al., 2011; Oh et al., 2007; Ritchie et al., 2018; van Hemmen et al., 2017), limited research has investigated whether white matter microstructure varies with contraceptive use (De B…”
BackgroundIdeological Bias in the Psychology of Sex and Gender
Giudice, M. D. (2023) · Springer eBooks · doi:10.1007/978-3-031-29148-7_28 · Cited by 18
Scofield, J. E., Johnson, J. D., Wood, P. K., & Geary, D. C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218891 · Cited by 16
Bruno, A., Bludau, S., Mohlberg, H., & Amunts, K. (2022) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2022.915877 · Cited by 19
“, 2020) to structural alterations, like gray matter volume and cortical thickness (Ruigrok et al., 2014; Ritchie et al., 2018).”
BackgroundMenken, M. S., Rivera, P. J. R., Isaiah, A., Ernst, T., Cloak, C. C., & Chang, L. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101247 · Cited by 15
Jiang, X., Zai, C. C. H., Kennedy, K. G., Zou, Y. Z., Nikolova, Y. S., Felsky, D., Young, L. T., MacIntosh, B. J., & Goldstein, B. I. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02607-y · Cited by 16
Aberizk, K., Addington, J., Bearden, C. E., Cadenhead, K. S., Cannon, T. D., Cornblatt, B. A., Keshavan, M. S., Mathalon, D. H., Perkins, D. O., Stone, W. S., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.11.027 · Cited by 15
Kirby, E., Andrushko, J., Rinat, S., D'Arcy, R. C. N., & Boyd, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-56453-z · Cited by 15
“Furthermore, in studies with large numbers of participants, regional differences between females and males have shown that males have greater cortical surface area, while females show a greater cortical thickness for many regions across the cortex, including motor and somatosensory regions 74,75 .”
BackgroundThe Prevalence of Mild Cognitive Impairment in a Convenience Sample of 202 Gulf War Veterans
Chao, L. L. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17197158 · Cited by 16
“Age [69,70], sex [71], and years of education [72,73] were included as covariates in analyses of hippocampal volume and cortical thickness because these variables have been shown to influence measures of brain volume and thickness.”
MethodLi, Y., Zhang, C., Ding, S., Li, J., Li, L., Kang, Y., Dong, X., Wan, Z., Luo, Y., Cheng, A. S. K., et al. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.11.054 · Cited by 15
Yerlikaya, D., Hünerli-Gündüz, D., Fide, E., Özbek, Y., Kıyı, İ., Öztura, İ., & Yener, G. (2022) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2022.08.002 · Cited by 15
Taebi, A., Kiesow, H., Vogeley, K. T., Schilbach, L., Bernhardt, B. C., & Bzdok, D. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa075 · Cited by 15
“This multigroup regression approach also capitalized on the fact that sex and age differences are among the by far largest sources of variability in MRI scans in general (Miller et al., 2016; Ritchie et al., 2018).”
MethodSex-dependent gut microbiota-brain-cognition associations: a multimodal MRI study
Zhang, S., Cai, H., Wang, C., Zhang, C., Zhao, W., Zhang, T., Zhang, B., Chen, J., Liu, S., Zhu, J., et al. (2023) · BMC Neurology · doi:10.1186/s12883-023-03217-3 · Cited by 15
“As to brain function, males show stronger functional connectivity in unimodal sensorimotor cortex, while females exhibit stronger functional connectivity in default mode network [15].”
BackgroundResultClassifying sex with volume-matched brain MRI
Ebel, M., Domín, M., Neumann, N., Schmidt, C. O., Lotze, M., & Stanke, M. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2023.100181 · Cited by 14
“Nevertheless, large cohort studies (Lotze et al., 2019; Ritchie et al., 2018; Williams et al., 2021) and meta-analyses (Ruigrok et al., 2014; Sacher et al., 2013) confirmed that there are sex differences in global and regional brain volumes, which were, depending on study characteristics, partly…”
Remembering the null hypothesis when searching for brain sex differences
Eliot, L. (2024) · Biology of Sex Differences · doi:10.1186/s13293-024-00585-4 · Cited by 14
“Most of these, such as the 6% greater gray matter-to-white matter ratio [20, 23] and the higher interhemispheric-to-intrahemispheric connectivity ratio [24] in women are fully attributable to brain size.”
BackgroundResultPain mechanisms in the transgender individual: a review
Anger, J. T., Case, L. K., Baranowski, A. P., Berger, A. S., Craft, R. M., Damitz, L., Gabriel, R. A., Harrison, T. E., Kaptein, K., Lee, S., et al. (2024) · Frontiers in Pain Research · doi:10.3389/fpain.2024.1241015 · Cited by 15
Why experimental variation in neuroimaging should be embraced
Kiar, G., Mumford, J. A., Xu, T., Vogelstein, J. T., Tristan Glatard, B., & Milham, M. P. (2024) · Nature Communications · doi:10.1038/s41467-024-53743-y · Cited by 18
The X chromosome’s influences on the human brain
Jiang, Z., Sullivan, P. F., Li, T., Zhao, B., Wang, X., Luo, T., Huang, S.-C., Guan, P. Y., Chen, J., Yang, Y., et al. (2025) · Science Advances · doi:10.1126/sciadv.adq5360 · Cited by 20
ComBatLS : A Location‐ and Scale‐Preserving Method for Multi‐Site Image Harmonization
Gardner, M., Shinohara, R. T., Bethlehem, R. A. I., Romero-García, R., Warrier, V., Dorfschmidt, L., Shanmugan, S., Thompson, P. M., Seidlitz, J., Alexander‐Bloch, A. F., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70197 · Cited by 20
“…most comparable when applied to cortical thickness features, whose variances are less impacted by sex than other neuroanatomical phenotypes 14–16 , 30 One motivation in developing ComBatLS was to prevent harmonization from inducing sex biases in normative scores by systematically inflating or…”
A scoping review of resting-state brain functional alterations in Type 2 diabetes
Chau, A. C. M., Smith, A. E., Hordacre, B. G., Kumar, S., Cheung, E. Y., & Mak, H. K. (2021) · Frontiers in Neuroendocrinology · doi:10.1016/j.yfrne.2021.100970 · Cited by 14
Piani, M. C., Maggioni, E., Delvecchio, G., Ferro, A., Gritti, D., Pozzoli, S. M., Fontana, E., Enrico, P., Cinnante, C. M., Triulzi, F. M., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.683912 · Cited by 14
“Although some studies reported the presence of structural differences between healthy males and females in this area (24, 25, 66, 67), the results are not univocal, possibly due to the different study characteristics.”
ResultZheng, K., Cai, S., Chua, H., Herschel, M., Zhang, M., & Ooi, B. C. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3554821.3554835 · Cited by 14
“These findings confirm that among the factors that are related to the aging of the human brain, demographics are of vital importance [6], including gender [53, 55], education [43, 61], etc.”
BackgroundBrown, A. A., Upton, S., Craig, S., & Froeliger, B. (2023) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2023.109766 · Cited by 14
Garner, M., Reith, W., & Krick, C. M. (2019) · Journal of Neuroimaging in Psychiatry and Neurology · doi:10.17756/jnpn.2019-027 · Cited by 15
Metadata
- Authors
- Ritchie, S. J., Cox, S. R., Shen, X., Lombardo, M. V., Reus, L. M., Alloza, C., Harris, M. A., Alderson, H. L., Hunter, S., Neilson, E., Liewald, D. C. M., Auyeung, B., Whalley, H. C., Lawrie, S. M., Gale, C. R., Bastin, M. E., McIntosh, A. M., & Deary, I. J.
- Affiliations
- University of Edinburgh (GB)
- University of Cambridge (GB)
- Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
- Queen Margaret Hospital (GB)
- NHS Lothian (GB)
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Corresponding author
- Ritchie, Stuart J
- ORCID
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-16
- Volume
- 28
- Issue
- 8
- Pages
- 2959-2975
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- PMC
- PMC6041980
- OpenAlex
- W2952574438
- Cited by
- 855
- References
- 125
- Citations by year
- 2026: 75 · 2025: 86 · 2024: 101 · 2023: 110 · 2022: 105 · 2021: 130 · 2020: 138 · 2019: 68 · 2018: 35
- FWCI
- 88.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cerebral Cortex (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Pure Amsterdam UMC (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- CCACE (MR/K026992/1)
- MRC (MR/M013111/1)
- Dementias Platform UK (MR/L015382/1)
- STRADL (104036/Z/14/Z)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Sex and Gender in Healthcare
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- sex differences, adult human brain, brain structure, brain function, brain volume, cortical thickness, white matter fractional anisotropy, functional connectome, default mode network, sensorimotor cortex, UK Biobank
- MeSH terms
- Adult, Aged, Brain, Brain Mapping, Female, United Kingdom, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Oxygen, Rest, Sex Characteristics, Biological Specimen Banks, Community Health Planning, Connectome, White Matter
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