1
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Quintana GR, Pfaus JG. Do Sex and Gender Have Separate Identities? ARCHIVES OF SEXUAL BEHAVIOR 2024:10.1007/s10508-024-02933-2. [PMID: 39105983 DOI: 10.1007/s10508-024-02933-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 06/07/2024] [Accepted: 06/09/2024] [Indexed: 08/07/2024]
Abstract
The largely binary nature of biological sex and its conflation with the socially constructed concept of gender has created much strife in the last few years. The notion of gender identity and its differences and similarities with sex have fostered much scientific and legal confusion and disagreement. Settling the debate can have significant repercussions for science, medicine, legislation, and people's lives. The present review addresses this debate though different levels of analysis (i.e., genetic, anatomical, physiological, behavioral, and sociocultural), and their implications and interactions. We propose a rationale where both perspectives coexist, where diversity is the default, establishing a delimitation to the conflation between sex and gender, while acknowledging their interaction. Whereas sex in humans and other mammals is a biological reality that is largely binary and based on genes, chromosomes, anatomy, and physiology, gender is a sociocultural construct that is often, but not always, concordant with a person' sex, and can span a multitude of expressions.
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Affiliation(s)
- Gonzalo R Quintana
- Departamento de Psicología y Filosofía, Facultad de Ciencias Sociales, Universidad de Tarapacá, Arica, Arica y Parinacota, Chile
| | - James G Pfaus
- Department of Psychology and Life Sciences, Charles University, Prague, 18200, Czech Republic.
- Center for Sexual Health and Intervention, Czech National Institute of Mental Health, Klecany, Czech Republic.
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2
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Khayretdinova M, Zakharov I, Pshonkovskaya P, Adamovich T, Kiryasov A, Zhdanov A, Shovkun A. Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model. Neuroimage 2024; 285:120495. [PMID: 38092156 DOI: 10.1016/j.neuroimage.2023.120495] [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: 07/13/2023] [Revised: 11/29/2023] [Accepted: 12/10/2023] [Indexed: 12/17/2023] Open
Abstract
This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG study, affirming that gender prediction can be attained with noteworthy accuracy. The best performing model achieved an accuracy of 85% and an ROC AUC of 89%, surpassing all prior benchmarks set using EEG data and rivaling the top-tier results derived from fMRI studies. A comparative analysis of LightGBM and Deep Convolutional Neural Network (DCNN) models revealed DCNN's superior performance, attributed to its ability to learn complex spatial-temporal patterns in the EEG data and handle large volumes of data effectively. Despite this, interpretability remained a challenge for the DCNN model. The LightGBM interpretability analysis revealed that the most important EEG features for accurate sex prediction were related to left fronto-central and parietal EEG connectivity. We also showed the role of both low (delta and theta) and high (beta and gamma) activity in the accurate sex prediction. These results, however, have to be approached with caution, because it was obtained from a dataset comprised largely of participants with various mental health conditions, which limits the generalizability of the results and necessitates further validation in future studies. . Overall, the study illuminates the potential of interpretable machine learning for sex prediction, alongside highlighting the importance of considering individual differences in prediction sex from brain activity.
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3
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Joo Y, Namgung E, Jeong H, Kang I, Kim J, Oh S, Lyoo IK, Yoon S, Hwang J. Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms. Sci Rep 2023; 13:22388. [PMID: 38104173 PMCID: PMC10725434 DOI: 10.1038/s41598-023-49514-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer's disease groups by identifying variances in brain age gaps between them, highlighting the algorithm's potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.
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Affiliation(s)
- Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Eun Namgung
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Hyeonseok Jeong
- Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Ilhyang Kang
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Jinsol Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Sohyun Oh
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea.
| | - Jaeuk Hwang
- Department of Psychiatry, Soonchunhyang University College of Medicine, Seoul, South Korea.
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4
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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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5
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Machine learning classification of chronic traumatic brain injury using diffusion tensor imaging and NODDI: A replication and extension study. NEUROIMAGE: REPORTS 2023; 3. [PMID: 37169013 PMCID: PMC10168530 DOI: 10.1016/j.ynirp.2023.100157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Individuals with acute and chronic traumatic brain injury (TBI) are associated with unique white matter (WM) structural abnormalities, including fractional anisotropy (FA) differences. Our research group previously used FA as a feature in a linear support vector machine (SVM) pattern classifier, observing high classification between individuals with and without acute TBI (i.e., an area under the curve [AUC] value of 75.50%). However, it is not known whether FA could similarly classify between individuals with and without history of chronic TBI. Here, we attempted to replicate our previous work with a new sample, investigating whether FA could similarly classify between incarcerated men with (n = 80) and without (n = 80) self-reported history of chronic TBI. Additionally, given limitations associated with FA, including underestimation of FA values in WM tracts containing crossing fibers, we extended upon our previous study by incorporating neurite orientation dispersion and density imaging (NODDI) metrics, including orientation dispersion (ODI) and isotropic volume (Viso). A linear SVM based classification approach, similar to our previous study, was incorporated here to classify between individuals with and without self-reported chronic TBI using FA and NODDI metrics as separate features. Overall classification rates were similar when incorporating FA and NODDI ODI metrics as features (AUC: 82.50%). Additionally, NODDI-based metrics provided the highest sensitivity (ODI: 85.00%) and specificity (Viso: 82.50%) rates. The current study serves as a replication and extension of our previous study, observing that multiple diffusion MRI metrics can reliably classify between individuals with and without self-reported history of chronic TBI.
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6
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Weber KA, Teplin ZM, Wager TD, Law CSW, Prabhakar NK, Ashar YK, Gilam G, Banerjee S, Delp SL, Glover GH, Hastie TJ, Mackey S. Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction. Front Neurol 2022; 13:960760. [PMID: 36601297 PMCID: PMC9806266 DOI: 10.3389/fneur.2022.960760] [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: 06/03/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
Muscle weakness is common in many neurological, neuromuscular, and musculoskeletal conditions. Muscle size only partially explains muscle strength as adaptions within the nervous system also contribute to strength. Brain-based biomarkers of neuromuscular function could provide diagnostic, prognostic, and predictive value in treating these disorders. Therefore, we sought to characterize and quantify the brain's contribution to strength by developing multimodal MRI pipelines to predict grip strength. However, the prediction of strength was not straightforward, and we present a case of sex being a clear confound in brain decoding analyses. While each MRI modality-structural MRI (i.e., gray matter morphometry), diffusion MRI (i.e., white matter fractional anisotropy), resting state functional MRI (i.e., functional connectivity), and task-evoked functional MRI (i.e., left or right hand motor task activation)-and a multimodal prediction pipeline demonstrated significant predictive power for strength (R 2 = 0.108-0.536, p ≤ 0.001), after correcting for sex, the predictive power was substantially reduced (R 2 = -0.038-0.075). Next, we flipped the analysis and demonstrated that each MRI modality and a multimodal prediction pipeline could significantly predict sex (accuracy = 68.0%-93.3%, AUC = 0.780-0.982, p < 0.001). However, correcting the brain features for strength reduced the accuracy for predicting sex (accuracy = 57.3%-69.3%, AUC = 0.615-0.780). Here we demonstrate the effects of sex-correlated confounds in brain-based predictive models across multiple brain MRI modalities for both regression and classification models. We discuss implications of confounds in predictive modeling and the development of brain-based MRI biomarkers, as well as possible strategies to overcome these barriers.
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Affiliation(s)
- Kenneth A. Weber
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States,*Correspondence: Kenneth A. Weber II
| | - Zachary M. Teplin
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Tor D. Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Christine S. W. Law
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Nitin K. Prabhakar
- Division of Physical Medicine and Rehabilitation, Department of Orthopaedic Surgery, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Yoni K. Ashar
- Department of Psychiatry, Weill Cornell Medicine, New York, NY, United States
| | - Gadi Gilam
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States,The Institute of Biomedical and Oral Research, Faculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | | | - Scott L. Delp
- Department of Bioengineering and Mechanical Engineering, Stanford University, Palo Alto, CA, United States
| | - Gary H. Glover
- Radiological Sciences Laboratory, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Trevor J. Hastie
- Department of Statistics, Stanford University, Palo Alto, CA, United States
| | - Sean Mackey
- Systems Neuroscience and Pain Lab, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States
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7
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Ujma PP, Dresler M, Simor P, Fabó D, Ulbert I, Erőss L, Bódizs R. The sleep EEG envelope is a novel, neuronal firing-based human biomarker. Sci Rep 2022; 12:18836. [PMID: 36336717 PMCID: PMC9637727 DOI: 10.1038/s41598-022-22255-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 10/12/2022] [Indexed: 11/08/2022] Open
Abstract
Sleep EEG reflects voltage differences relative to a reference, while its spectrum reflects its composition of various frequencies. In contrast, the envelope of the sleep EEG reflects the instantaneous amplitude of oscillations, while its spectrum reflects the rhythmicity of the occurrence of these oscillations. The sleep EEG spectrum is known to relate to demographic, psychological and clinical characteristics, but the envelope spectrum has been rarely studied. In study 1, we demonstrate in human invasive data from cortex-penetrating microelectrodes and subdural grids that the sleep EEG envelope spectrum reflects neuronal firing. In study 2, we demonstrate that the scalp EEG envelope spectrum is stable within individuals. A multivariate learning algorithm could predict age (r = 0.6) and sex (r = 0.5) from the EEG envelope spectrum. With age, oscillations shifted from a 4-5 s rhythm to faster rhythms. Our results demonstrate that the sleep envelope spectrum is a promising biomarker of demographic and disease-related phenotypes.
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Affiliation(s)
- Péter P Ujma
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.
- National Institute of Clinical Neuroscience, Budapest, Hungary.
| | - Martin Dresler
- Radboud University Medical Center, Donders Institute, Nijmegen, The Netherlands
| | - Péter Simor
- Institute of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary
- UR2NF, Neuropsychology and Functional Neuroimaging Research Unit at CRCN - Center for Research in Cognition and Neurosciences and UNI - ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Dániel Fabó
- National Institute of Clinical Neuroscience, Budapest, Hungary
| | - István Ulbert
- Department of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Research Centre for Natural Sciences, Institute for Cognitive Neuroscience and Psychology, Budapest, Hungary
| | - Loránd Erőss
- National Institute of Clinical Neuroscience, Budapest, Hungary
| | - Róbert Bódizs
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
- National Institute of Clinical Neuroscience, Budapest, Hungary
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8
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Allen CH, Aharoni E, Gullapalli AR, Edwards BG, Harenski CL, Harenski KA, Kiehl KA. Hemodynamic activity in the limbic system predicts reoffending in women. Neuroimage Clin 2022; 36:103238. [PMID: 36451349 PMCID: PMC9668656 DOI: 10.1016/j.nicl.2022.103238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/02/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
Previous research (Aharoni et al., 2013, 2014) found that hemodynamic activity in the dorsal anterior cingulate cortex (dACC) during error monitoring predicted non-violent felony rearrest in men released from prison. This article reports an extension of the Aharoni et al. (2013, 2014) model in a sample of women released from state prison (n = 248). Replicating aspects of prior work, error monitoring activity in the dACC, as well as psychopathy scores and age at release, predicted non-violent felony rearrest in women. Sex differences in the directionality of dACC activity were observed-high error monitoring activity predicted rearrest in women, whereas prior work found low error monitoring activity predicted rearrest in men. As in prior analyses, the ability of the dACC to predict rearrest outcomes declines with more generalized outcomes (i.e., general felony). Implications for future research and clinical and forensic risk assessment are discussed.
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Affiliation(s)
- Corey H. Allen
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - Eyal Aharoni
- Department of Psychology, Georgia State University, P.O. Box 5010, Atlanta, GA 30302-5010, USA
| | | | - Bethany G. Edwards
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA,Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Carla L. Harenski
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - Keith A. Harenski
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA
| | - Kent A. Kiehl
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106-4188, USA,Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA,Corresponding author at: Department of Psychology, University of New Mexico, Albuquerque, NM 87131, USA.
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9
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Spets DS, Slotnick SD. Sex is predicted by spatial memory multivariate activation patterns. Learn Mem 2022; 29:297-301. [PMID: 36206398 PMCID: PMC9488029 DOI: 10.1101/lm.053608.122] [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: 05/02/2022] [Accepted: 06/25/2022] [Indexed: 11/24/2022]
Abstract
Whether sex differences exist in the brain at the macroscopic level, as measured with magnetic resonance imaging (MRI), is a topic of debate. The present spatial long-term memory functional MRI (fMRI) study predicted sex based on event-related patterns of brain activity. Within spatial memory regions of interest, patterns of activity associated with females and males were used to predict the sex of each member of left-out female-male pairs at above-chance accuracy. The current results provide evidence for sex differences in the brain processes underlying spatial long-term memory. This is the first time that sex has been predicted using event-related fMRI activation patterns. The present findings contribute to a growing body of evidence that there are functional and anatomic sex differences in the brain and, more broadly, question the widespread practice of collapsing across sex in the field of cognitive neuroscience.
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Affiliation(s)
- Dylan S Spets
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Scott D Slotnick
- Department of Psychology and Neuroscience, Boston College, Chestnut Hill, Massachusetts 02467, USA
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10
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Malik N, Bzdok D. From YouTube to the brain: Transfer learning can improve brain-imaging predictions with deep learning. Neural Netw 2022; 153:325-338. [PMID: 35777174 DOI: 10.1016/j.neunet.2022.06.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 04/20/2022] [Accepted: 06/09/2022] [Indexed: 12/01/2022]
Abstract
Deep learning has recently achieved best-in-class performance in several fields, including biomedical domains such as X-ray images. Yet, data scarcity poses a strict limit on training successful deep learning systems in many, if not most, biomedical applications, including those involving brain images. In this study, we translate state-of-the-art transfer learning techniques for single-subject prediction of simpler (sex and age) and more complex phenotypes (number of people in household, household income, fluid intelligence and smoking behavior). We fine-tuned 2D and 3D ResNet-18 convolutional neural networks for target phenotype predictions from brain images of ∼40,000 UK Biobank participants, after pretraining on YouTube videos from the Kinetics dataset and natural images from the ImageNet dataset. Transfer learning was effective on several phenotypes, especially sex and age classification. Additionally, transfer learning in particular outperformed deep learning models trained from scratch especially on smaller sample sizes. The out-of-sample performance using transfer learning from previously learned knowledge based on real-world images and videos could unlock the potential in many areas of imaging neuroscience where deep learning solutions are currently infeasible.
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Affiliation(s)
- Nahiyan Malik
- School of Computer Science, McGill University, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada.
| | - Danilo Bzdok
- School of Computer Science, McGill University, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, QC, Canada.
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11
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Brain Sex in Transgender Women Is Shifted towards Gender Identity. J Clin Med 2022; 11:jcm11061582. [PMID: 35329908 PMCID: PMC8955456 DOI: 10.3390/jcm11061582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/07/2022] [Accepted: 03/09/2022] [Indexed: 01/09/2023] Open
Abstract
Transgender people report discomfort with their birth sex and a strong identification with the opposite sex. The current study was designed to shed further light on the question of whether the brains of transgender people resemble their birth sex or their gender identity. For this purpose, we analyzed a sample of 24 cisgender men, 24 cisgender women, and 24 transgender women before gender-affirming hormone therapy. We employed a recently developed multivariate classifier that yields a continuous probabilistic (rather than a binary) estimate for brains to be male or female. The brains of transgender women ranged between cisgender men and cisgender women (albeit still closer to cisgender men), and the differences to both cisgender men and to cisgender women were significant (p = 0.016 and p < 0.001, respectively). These findings add support to the notion that the underlying brain anatomy in transgender people is shifted away from their biological sex towards their gender identity.
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12
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Bell C, Tesli N, Gurholt TP, Rokicki J, Hjell G, Fischer-Vieler T, Melle I, Agartz I, Andreassen OA, Rasmussen K, Johansen R, Friestad C, Haukvik UK. Associations between amygdala nuclei volumes, psychosis, psychopathy, and violent offending. Psychiatry Res Neuroimaging 2022; 319:111416. [PMID: 34847406 DOI: 10.1016/j.pscychresns.2021.111416] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 09/30/2021] [Accepted: 11/07/2021] [Indexed: 12/13/2022]
Abstract
The amygdala is involved in fear perception and aggression regulation, and smaller volumes have been associated with psychotic and non-psychotic violence. We explored the relationship between amygdala nuclei volumes in violent offenders with and without psychosis, and the association to psychopathy traits. 3T MRI scans (n = 204, males, 18-66 years) were obtained from psychotic violent offenders (PSY-V, n = 29), non-psychotic violent offenders (NPV, n = 19), non-violent psychosis patients (PSY-NV, n = 67), and healthy controls (HC, n = 89). Total amygdala and 9 amygdala nuclei volumes were obtained with FreeSurfer. Psychopathy traits were measured with the Psychopathy Checklist-revised (PCL-R). Multivariate analyses explored diagnostic differences in amygdala nuclei volumes and associations to psychosis, violence, and psychopathy traits. PSY-V had a smaller basal nucleus, anterior amygdaloid area, and cortical amygdalar transition area (CATA), whereas PSY-NV had a smaller CATA than HC. Volumes in NPV did not differ from HC, and there were no associations between PCL-R total or factor scores and any of the nuclei or whole amygdala volumes. The lower volumes of amygdala nuclei involved in fear modulation, stress responses, and social interpretation may point towards some mechanisms of relevance to violence in psychosis, but the results warrant replication in larger subject samples.
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Affiliation(s)
- Christina Bell
- Department of Psychiatry, Oslo University Hospital, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Natalia Tesli
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tiril P Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Jaroslav Rokicki
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Gabriela Hjell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, Østfold Hospital Trust, Graalum, Norway
| | - Thomas Fischer-Vieler
- Department of Psychiatry, Oslo University Hospital, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Adult Psychiatry, Institute of Clinical Medicine, University of Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kirsten Rasmussen
- St.Olavs Hospital, Forensic Research Unit, Brøset, Norway; Norwegian University of Science and Technology (NTNU), Department of Psychology, and Department of Mental Health, Norway
| | - Ragnhild Johansen
- St.Olavs Hospital, Forensic Research Unit, Brøset, Norway; Norwegian University of Science and Technology (NTNU), Department of Psychology, and Department of Mental Health, Norway
| | - Christine Friestad
- Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Norway; University College of Norwegian Correctional Service, Oslo, Norway
| | - Unn K Haukvik
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Adult Psychiatry, Institute of Clinical Medicine, University of Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Norway
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13
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AI in Forensic Medicine for the Practicing Doctor. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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14
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15
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Eitel F, Albrecht JP, Weygandt M, Paul F, Ritter K. Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data. Sci Rep 2021; 11:24447. [PMID: 34961762 PMCID: PMC8712523 DOI: 10.1038/s41598-021-03785-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/22/2021] [Indexed: 11/09/2022] Open
Abstract
Convolutional neural networks (CNNs)-as a type of deep learning-have been specifically designed for highly heterogeneous data, such as natural images. Neuroimaging data, however, is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. To harness spatial homogeneity of neuroimaging data, we suggest here a new CNN architecture that combines the idea of hierarchical abstraction in CNNs with a prior on the spatial homogeneity of neuroimaging data. Whereas early layers are trained globally using standard convolutional layers, we introduce patch individual filters (PIF) for higher, more abstract layers. By learning filters in individual latent space patches without sharing weights, PIF layers can learn abstract features faster and specific to regions. We thoroughly evaluated PIF layers for three different tasks and data sets, namely sex classification on UK Biobank data, Alzheimer's disease detection on ADNI data and multiple sclerosis detection on private hospital data, and compared it with two baseline models, a standard CNN and a patch-based CNN. We obtained two main results: First, CNNs using PIF layers converge consistently faster, measured in run time in seconds and number of iterations than both baseline models. Second, both the standard CNN and the PIF model outperformed the patch-based CNN in terms of balanced accuracy and receiver operating characteristic area under the curve (ROC AUC) with a maximal balanced accuracy (ROC AUC) of 94.21% (99.10%) for the sex classification task (PIF model), and 81.24% and 80.48% (88.89% and 87.35%) respectively for the Alzheimer's disease and multiple sclerosis detection tasks (standard CNN model). In conclusion, we demonstrated that CNNs using PIF layers result in faster convergence while obtaining the same predictive performance as a standard CNN. To the best of our knowledge, this is the first study that introduces a prior in form of an inductive bias to harness spatial homogeneity of neuroimaging data.
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Affiliation(s)
- Fabian Eitel
- Department of Psychiatry and Neurosciences | CCM, Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), 10117, Berlin, Germany
- Humboldt-Universität zu Berlin, 10117, Berlin, Germany
- Bernstein Center for Computational Neuroscience, 10117, Berlin, Germany
| | - Jan Philipp Albrecht
- Department of Psychiatry and Neurosciences | CCM, Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), 10117, Berlin, Germany
- Freie Universität Berlin, 14195, Berlin, Germany
| | - Martin Weygandt
- Department of Neurology, NeuroCure Clinical Research Center, Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), 10117, Berlin, Germany
| | - Friedemann Paul
- Department of Neurology, NeuroCure Clinical Research Center, Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), 10117, Berlin, Germany
- Einstein Center for Neurosciences Berlin, 10117, Berlin, Germany
| | - Kerstin Ritter
- Department of Psychiatry and Neurosciences | CCM, Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), 10117, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, 10117, Berlin, Germany.
- Einstein Center for Neurosciences Berlin, 10117, Berlin, Germany.
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16
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Friedrich P, Patil KR, Mochalski LN, Li X, Camilleri JA, Kröll JP, Wiersch L, Eickhoff SB, Weis S. Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality. Brain Struct Funct 2021; 227:425-440. [PMID: 34882263 PMCID: PMC8844166 DOI: 10.1007/s00429-021-02418-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 10/18/2021] [Indexed: 11/09/2022]
Abstract
Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.
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Affiliation(s)
- Patrick Friedrich
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa N Mochalski
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Xuan Li
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Julia A Camilleri
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Jean-Philippe Kröll
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Lisa Wiersch
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, 52428, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
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17
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Joel D. Beyond sex differences and a male-female continuum: Mosaic brains in a multidimensional space. HANDBOOK OF CLINICAL NEUROLOGY 2021; 175:13-24. [PMID: 33008521 DOI: 10.1016/b978-0-444-64123-6.00002-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
In the last two decades, the 60 years old view that in utero exposure to testosterone irreversibly masculinizes the brain of males away from a default female form has been replaced by a complex scenario according to which sex affects the brains of both females and males via multiple mechanisms, which are susceptible to internal and external factors. These observations led to the "mosaic" hypothesis-the expectation that the degree of "maleness"/"femaleness" of different features within a single brain would not be internally consistent. Following a short review of the animal studies providing the basis of the mosaic hypothesis, I describe three studies conducted in humans that assessed internal consistency in regional volume, cortical thickness, and connectivity as revealed by magnetic resonance imaging (MRI); in neuronal numbers in the postmortem hypothalamus; and in changes in regional volume and cortical thickness (assessed with MRI) following exposure to extreme real-life stress. The conclusion from these studies, that human brains are largely composed of unique mosaics of female-typical and male-typical features, was supported by recent findings that the brain "types" typical of women are also typical of men, and vice versa. Lastly, I discuss criticism of the mosaic hypothesis and suggest replacing the framework of a male-female continuum with thinking about mosaic brains residing in a multidimensional space.
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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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18
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van Eijk L, Zhu D, Couvy-Duchesne B, Strike LT, Lee AJ, Hansell NK, Thompson PM, de Zubicaray GI, McMahon KL, Wright MJ, Zietsch BP. Are Sex Differences in Human Brain Structure Associated With Sex Differences in Behavior? Psychol Sci 2021; 32:1183-1197. [PMID: 34323639 DOI: 10.1177/0956797621996664] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
On average, men and women differ in brain structure and behavior, raising the possibility of a link between sex differences in brain and behavior. But women and men are also subject to different societal and cultural norms. We navigated this challenge by investigating variability of sex-differentiated brain structure within each sex. Using data from the Queensland Twin IMaging study (n = 1,040) and Human Connectome Project (n = 1,113), we obtained data-driven measures of individual differences along a male-female dimension for brain and behavior based on average sex differences in brain structure and behavior, respectively. We found a weak association between these brain and behavioral differences, driven by brain size. These brain and behavioral differences were moderately heritable. Our findings suggest that behavioral sex differences are, to some extent, related to sex differences in brain structure but that this is mainly driven by differences in brain size, and causality should be interpreted cautiously.
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Affiliation(s)
- Liza van Eijk
- Centre for Psychology and Evolution, School of Psychology, University of Queensland.,Queensland Brain Institute, University of Queensland.,Australian e-Health Research Centre, CSIRO, Herston, Australia.,Department of Psychology, James Cook University
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington
| | | | | | | | | | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California
| | - Greig I de Zubicaray
- Institute of Health and Biomedical Innovation, Queensland University of Technology
| | - Katie L McMahon
- Herston Imaging Research Facility and School of Clinical Sciences, Queensland University of Technology
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland.,Centre for Advanced Imaging, University of Queensland
| | - Brendan P Zietsch
- Centre for Psychology and Evolution, School of Psychology, University of Queensland
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19
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Brennan D, Wu T, Fan J. Morphometrical Brain Markers of Sex Difference. Cereb Cortex 2021; 31:3641-3649. [PMID: 33774662 DOI: 10.1093/cercor/bhab037] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 01/22/2021] [Accepted: 02/06/2021] [Indexed: 01/02/2023] Open
Abstract
Many major neuropsychiatric pathologies, some of which appear in adolescence, show differentiated prevalence, onset, and symptomatology across the biological sexes. Therefore, mapping differences in brain structure between males and females during this critical developmental period may provide information about the neural mechanisms underlying the dimorphism of these pathologies. Utilizing a large dataset collected through the Adolescent Brain Cognitive Development study, we investigated the differences of adolescent (9-10 years old) male and female brains (n = 8325) by using a linear Support-Vector Machine Classifier to predict sex based on morphometry and image intensity values of structural brain imaging data. The classifier correctly classified the sex of 86% individuals with the insula, the precentral and postcentral gyri, and the pericallosal sulcus as the most discernable features. These results demonstrate the existence of complex, yet robustly measurable morphometrical brain markers of sex difference.
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Affiliation(s)
- Daniel Brennan
- Department of Psychology, The Graduate Center, The City University of New York, New York, NY 10016, USA
| | - Tingting Wu
- Department of Psychology, Queens College, The City University of New York, Queens, NY 11367, USA
| | - Jin Fan
- Department of Psychology, Queens College, The City University of New York, Queens, NY 11367, USA
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20
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Kurth F, Gaser C, Luders E. Development of sex differences in the human brain. Cogn Neurosci 2021; 12:155-162. [PMID: 32902364 PMCID: PMC8510853 DOI: 10.1080/17588928.2020.1800617] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 07/08/2020] [Indexed: 01/24/2023]
Abstract
Sex differences in brain anatomy have been described from early childhood through late adulthood, but without any clear consensus among studies. Here, we applied a machine learning approach to estimate 'Brain Sex' using a continuous (rather than binary) classifier in 162 boys and 185 girls aged between 5 and 18 years. Changes in the estimated sex differences over time at different age groups were subsequently calculated using a sliding window approach. We hypothesized that males and females would differ in brain structure already during childhood, but that these differences will become even more pronounced with increasing age, particularly during adolescence. Overall, the classifier achieved a good performance, with an accuracy of 80.4% and an AUC of 0.897 across all age groups. Assessing changes in the estimated sex with age revealed a growing difference between the sexes with increasing age. That is, the very large effect size of d = 1.2 which was already evident during childhood increased even further from age 11 onward, and eventually reached an effect size of d = 1.6 at age 17. Altogether these findings suggest a systematic sex difference in brain structure already during childhood, and a subsequent increase of this difference during adolescence.
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Affiliation(s)
- Florian Kurth
- School of Psychology, University of Auckland, Auckland, New Zealand
| | - Christian Gaser
- Departments of Psychiatry and Neurology, Jena University Hospital, Jena, Germany
| | - Eileen Luders
- School of Psychology, University of Auckland, Auckland, New Zealand
- Laboratory of Neuro Imaging, School of Medicine, University of Southern California, Los Angeles, USA
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21
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Ball G, Kelly CE, Beare R, Seal ML. Individual variation underlying brain age estimates in typical development. Neuroimage 2021; 235:118036. [PMID: 33838267 DOI: 10.1016/j.neuroimage.2021.118036] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/19/2021] [Accepted: 03/26/2021] [Indexed: 12/14/2022] Open
Abstract
Typical brain development follows a protracted trajectory throughout childhood and adolescence. Deviations from typical growth trajectories have been implicated in neurodevelopmental and psychiatric disorders. Recently, the use of machine learning algorithms to model age as a function of structural or functional brain properties has been used to examine advanced or delayed brain maturation in healthy and clinical populations. Termed 'brain age', this approach often relies on complex, nonlinear models that can be difficult to interpret. In this study, we use model explanation methods to examine the cortical features that contribute to brain age modelling on an individual basis. In a large cohort of n = 768 typically-developing children (aged 3-21 years), we build models of brain development using three different machine learning approaches. We employ SHAP, a model-agnostic technique to identify sample-specific feature importance, to identify regional cortical metrics that explain errors in brain age prediction. We find that, on average, brain age prediction and the cortical features that explain model predictions are consistent across model types and reflect previously reported patterns of regions brain development. However, while several regions are found to contribute to brain age prediction error, we find little spatial correspondence between individual estimates of feature importance, even when matched for age, sex and brain age prediction error. We also find no association between brain age error and cognitive performance in this typically-developing sample. Overall, this study shows that, while brain age estimates based on cortical development are relatively robust and consistent across model types and preprocessing strategies, significant between-subject variation exists in the features that explain erroneous brain age predictions on an individual level.
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Affiliation(s)
- Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia; Department of Paediatrics, University of Melbourne, Australia.
| | - Claire E Kelly
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia; Victorian Infant Brain Studies (VIBeS), Murdoch Children's Research Institute, Australia
| | - Richard Beare
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia
| | - Marc L Seal
- Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, 3052 VIC, Australia; Department of Paediatrics, University of Melbourne, Australia
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22
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Ge R, Liu X, Long D, Frangou S, Vila-Rodriguez F. Sex effects on cortical morphological networks in healthy young adults. Neuroimage 2021; 233:117945. [PMID: 33711482 DOI: 10.1016/j.neuroimage.2021.117945] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/17/2021] [Accepted: 03/03/2021] [Indexed: 12/30/2022] Open
Abstract
Understanding sex-related differences across the human cerebral cortex is an important step in elucidating the basis of psychological, behavioural and clinical differences between the sexes. Prior structural neuroimaging studies primarily focused on regional sex differences using univariate analyses. Here we focus on sex differences in cortical morphological networks (CMNs) derived using multivariate modelling of regional cortical measures of volume and surface from high-quality structural MRI scans from healthy participants in the Human Connectome Project (HCP) (n = 1,063) and the Southwest University Longitudinal Imaging Multimodal (SLIM) study (n = 549). The functional relevance of the CMNs was inferred using the NeuroSynth decoding function. Sex differences were widespread but not uniform. In general, females had higher volume, thickness and cortical folding in networks that involve prefrontal (both ventral and dorsal regions including the anterior cingulate) and parietal regions while males had higher volume, thickness and cortical folding in networks that primarily include temporal and posterior cortical regions. CMN loading coefficients were used as input features to linear discriminant analyses that were performed separately in the HCP and SLIM; sex was predicted with a high degree of accuracy (81%-85%) across datasets. The availability of behavioral data in the HCP enabled us to show that male-biased surface-based CMNs were associated with externalizing behaviors. These results extend previous literature on regional sex-differences by identifying CMNs that can reliably predict sex, are relevant to the expression of psychopathology and provide the foundation for the future investigation of their functional significance in clinical populations.
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Affiliation(s)
- Ruiyang Ge
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, BC, Canada
| | - Xiang Liu
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, BC, Canada
| | - David Long
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, BC, Canada
| | - Sophia Frangou
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, BC, Canada; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, University of British Columbia, BC, Canada.
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23
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Abstract
Biological sex differences in brain function and structure are reliably associated with several cortico-subcortical brain regions. While sexual orientation (hetero- versus homosexuality) has been similarly linked to functional differences in several phylogenetically-old brain areas, the research on morphological brain phenotypes associated with sexual orientation is far from conclusive. We examined potential cerebral structural differences linked to sexual orientation in a group of 74 participants, including 37 men (21 homosexual) and 37 women (19 homosexual) using voxel-based morphometry (VBM). Gray matter volumes (GMV) were compared with respect to sexual orientation and biological sex across the entire sample using full factorial designs controlling for total intracranial volume, age, handedness, and education. We observed a significant effect of sexual orientation for the thalamus and precentral gyrus, with more GMV in heterosexual versus homosexual individuals, and for the putamen, with more GMV in homosexual + than heterosexual individuals. We found significant interactions between biological sex and sexual orientation, indicating that the significant effect for the putamen cluster was driven by homosexual women, whereas heterosexual women had increased precentral gyrus GMV. Heterosexual men exhibited more GMV in the thalamus than homosexual men. This study shows that sexual orientation is reflected in brain structure characteristics and that these differ between the sexes. The results emphasize the need to include or control for potential effects of participants' sexual orientation in neuroimaging studies. Furthermore, our findings provide important new insights into the brain morphology underlying sexual orientation and likely have important implications for understanding brain functions and behavior.
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24
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Abé C, Lebedev A, Zhang R, Jonsson L, Bergen SE, Ingvar M, Landén M, Rahman Q. Cross-sex shifts in two brain imaging phenotypes and their relation to polygenic scores for same-sex sexual behavior: A study of 18,645 individuals from the UK Biobank. Hum Brain Mapp 2021; 42:2292-2304. [PMID: 33635603 PMCID: PMC8046142 DOI: 10.1002/hbm.25370] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/07/2021] [Accepted: 02/04/2021] [Indexed: 12/13/2022] Open
Abstract
Genetic and hormonal factors have been suggested to influence human sexual orientation. Previous studied proposed brain differences related to sexual orientation and that these follow cross-sex shifted patterns. However, the neurobiological correlates of sexual orientation and how genetic factors relate to brain structural variation remains largely unexplored. Using the largest neuroimaging-genetics dataset available on same-sex sexual behavior (SSB) (n = 18,645), we employed a data-driven multivariate classification algorithm (PLS) on magnetic resonance imaging data from two imaging modalities to extract brain covariance patterns related to sex. Through analyses of latent variables, we tested for SSB-related cross-sex shifts in such patterns. Using genotype data, polygenic scores reflecting the genetic predisposition for SSB were computed and tested for associations with neuroimaging outcomes. Patterns important for classifying between males and females were less pronounced in non-heterosexuals. Predominantly in non-heterosexual females, multivariate brain patterns as represented by latent variables were shifted toward the opposite sex. Complementary univariate analyses revealed region specific SSB-related differences in both males and females. Polygenic scores for SSB were associated with volume of lateral occipital and temporo-occipital cortices. The present large-scale study demonstrates multivariate neuroanatomical correlates of SSB, and tentatively suggests that genetic factors related to SSB may contribute to structural variation in certain brain structures. These findings support a neurobiological basis to the differences in human sexuality.
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Affiliation(s)
- Christoph Abé
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Alexander Lebedev
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Ruyue Zhang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Lina Jonsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Pharmacology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Sarah E Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Martin Ingvar
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Landén
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Qazi Rahman
- Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
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25
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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: 142] [Impact Index Per Article: 47.3] [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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26
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Scarpazza C, Miolla A, Zampieri I, Melis G, Sartori G, Ferracuti S, Pietrini P. Translational Application of a Neuro-Scientific Multi-Modal Approach Into Forensic Psychiatric Evaluation: Why and How? Front Psychiatry 2021; 12:597918. [PMID: 33613339 PMCID: PMC7892615 DOI: 10.3389/fpsyt.2021.597918] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 01/14/2021] [Indexed: 01/01/2023] Open
Abstract
A prominent body of literature indicates that insanity evaluations, which are intended to provide influential expert reports for judges to reach a decision "beyond any reasonable doubt," suffer from a low inter-rater reliability. This paper reviews the limitations of the classical approach to insanity evaluation and the criticisms to the introduction of neuro-scientific approach in court. Here, we explain why in our opinion these criticisms, that seriously hamper the translational implementation of neuroscience into the forensic setting, do not survive scientific scrutiny. Moreover, we discuss how the neuro-scientific multimodal approach may improve the inter-rater reliability in insanity evaluation. Critically, neuroscience does not aim to introduce a brain-based concept of insanity. Indeed, criteria for responsibility and insanity are and should remain clinical. Rather, following the falsificationist approach and the convergence of evidence principle, the neuro-scientific multimodal approach is being proposed as a way to improve reliability of insanity evaluation and to mitigate the influence of cognitive biases on the formulation of insanity opinions, with the final aim to reduce errors and controversies.
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Affiliation(s)
- Cristina Scarpazza
- Department of General Psychology, University of Padova, Padova, Italy
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alessio Miolla
- Department of General Psychology, University of Padova, Padova, Italy
| | - Ilaria Zampieri
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Giulia Melis
- Department of General Psychology, University of Padova, Padova, Italy
| | - Giuseppe Sartori
- Department of General Psychology, University of Padova, Padova, Italy
| | - Stefano Ferracuti
- Department of Human Neurosciences, “Sapienza” University of Rome, Rome, Italy
| | - Pietro Pietrini
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca, Italy
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27
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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: 44] [Impact Index Per Article: 14.7] [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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28
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Artificial Intelligence in Forensic Medicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_220-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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29
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AI in Forensic Medicine for the Practicing Doctor. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_221-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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30
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Peña-Solórzano CA, Albrecht DW, Bassed RB, Burke MD, Dimmock MR. Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting. Forensic Sci Int 2020; 316:110538. [PMID: 33120319 PMCID: PMC7568766 DOI: 10.1016/j.forsciint.2020.110538] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/28/2020] [Accepted: 10/04/2020] [Indexed: 12/18/2022]
Abstract
Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensic radiology, the use of these algorithms presents significant challenges due to variability in organ position, structural changes from decomposition, inconsistent body placement in the scanner, and the presence of foreign bodies. Existing ML approaches in clinical imaging can likely be transferred to the forensic setting with careful consideration to account for the increased variability and temporal factors that affect the data used to train these algorithms. Additional steps are required to deal with these issues, by incorporating the possible variability into the training data through data augmentation, or by using atlases as a pre-processing step to account for death-related factors. A key application of ML would be then to highlight anatomical and gross pathological features of interest, or present information to help optimally determine the cause of death. In this review, we highlight results and limitations of applications in clinical medical imaging that use ML to determine key implications for their application in the forensic setting.
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Affiliation(s)
- Carlos A Peña-Solórzano
- Department of Medical Imaging and Radiation Sciences, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
| | - David W Albrecht
- Clayton School of Information Technology, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
| | - Richard B Bassed
- Victorian Institute of Forensic Medicine, 57-83 Kavanagh St., Southbank, Melbourne, VIC 3006, Australia; Department of Forensic Medicine, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
| | - Michael D Burke
- Victorian Institute of Forensic Medicine, 57-83 Kavanagh St., Southbank, Melbourne, VIC 3006, Australia; Department of Forensic Medicine, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
| | - Matthew R Dimmock
- Department of Medical Imaging and Radiation Sciences, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
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31
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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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32
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Duchesne A, Pletzer B, Pavlova MA, Lai MC, Einstein G. Editorial: Bridging Gaps Between Sex and Gender in Neurosciences. Front Neurosci 2020; 14:561. [PMID: 32595445 PMCID: PMC7301887 DOI: 10.3389/fnins.2020.00561] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 05/06/2020] [Indexed: 01/11/2023] Open
Affiliation(s)
- Annie Duchesne
- Department of Psychology, University of Northern British Columbia, Prince George, BC, Canada
| | - Belinda Pletzer
- Department of Psychology, University of Salzburg, Salzburg, Austria.,Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
| | - Marina A Pavlova
- Department of Psychiatry and Psychotherapy, Medical School and University Hospital, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Meng-Chuan Lai
- Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Azrieli Adult Neurodevelopmental Centre, and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry and Autism Research Unit, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.,Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.,Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Gillian Einstein
- Department of Psychology, University of Toronto, Toronto, ON, Canada.,Rotman Research Institute, Baycrest Hospital, Toronto, ON, Canada.,Department of Gender Studies, Linköping University, Linköping, Sweden.,Canadian Consortium on Neurodegeneration and Aging, Toronto, ON, Canada.,Wilfred and Joyce Posluns Chair in Women's Brain Health and Aging, Toronto, ON, Canada
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33
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Brivio E, Lopez JP, Chen A. Sex differences: Transcriptional signatures of stress exposure in male and female brains. GENES BRAIN AND BEHAVIOR 2020; 19:e12643. [PMID: 31989757 DOI: 10.1111/gbb.12643] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 01/13/2020] [Accepted: 01/22/2020] [Indexed: 12/19/2022]
Abstract
More than two-thirds of patients suffering from stress-related disorders are women but over two-thirds of suicide completers are men. These are just some examples of the many sex differences in the prevalence and manifestations of stress-related disorders, such as major depressive disorder, post-traumatic stress disorder, and anxiety disorders, which have been extensively documented in clinical research. Nonetheless, the molecular origins of this sex dimorphism are still quite obscure. In response to this lack of knowledge, the NIH recently advocated implementing sex as biological variable in the design of preclinical studies across disciplines. As a result, a newly emerging field within psychiatry is trying to elucidate the molecular causes underlying the clinically described sex dimorphism. Several studies in rodents and humans have already identified many stress-related genes that are regulated by acute and chronic stress in a sex-specific fashion. Furthermore, current transcriptomic studies have shown that pathways and networks in male and female individuals are not equally affected by stress exposure. In this review, we give an overview of transcriptional studies designed to understand how sex influences stress-specific transcriptomic changes in rodent models, as well as human psychiatric patients, highlighting the use of different methodological techniques. Understanding which mechanisms are more affected in males, and which in females, may lead to the identification of sex-specific mechanisms, their selective contribution to stress susceptibility, and their role in the development of stress-related psychiatric disorders.
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Affiliation(s)
- Elena Brivio
- Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany.,International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Juan Pablo Lopez
- Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Alon Chen
- Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany.,Department of Neurobiology, Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science, Rehovot, Israel
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34
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Luo Z, Hou C, Wang L, Hu D. Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity. Front Hum Neurosci 2019; 13:29. [PMID: 30792634 PMCID: PMC6374327 DOI: 10.3389/fnhum.2019.00029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Accepted: 01/21/2019] [Indexed: 12/30/2022] Open
Abstract
Difference exists widely in cognition, behavior and psychopathology between males and females, while the underlying neurobiology is still unclear. As brain structure is the fundament of its function, getting insight into structural brain may help us to better understand the functional mechanism of gender difference. Previous structural studies of gender difference in Magnetic Resonance Imaging (MRI) usually focused on gray matter (GM) concentration and structural connectivity (SC), leaving cortical morphology not characterized properly. In this study a large dataset is used to explore whether cortical three-dimensional (3-D) morphology can offer enough discriminative morphological features to effectively identify gender. Data of all available healthy controls (N = 1113) from the Human Connectome Project (HCP) were utilized. We suggested a multivariate pattern analysis method called Hierarchical Sparse Representation Classifier (HSRC) and got an accuracy of 96.77% for gender identification. Permutation tests were used to testify the reliability of gender discrimination (p < 0.001). Cortical 3-D morphological features within the frontal lobe were found the most important contributors to gender difference of human brain morphology. Moreover, we investigated gender discriminative ability of cortical 3-D morphology in predefined Anatomical Automatic Labeling (AAL) and Resting-State Networks (RSN) templates, and found the superior frontal gyrus the most discriminative in AAL and the default mode network the most discriminative in RSN. Gender difference of surface-based morphology was also discussed. The frontal lobe, as well as the default mode network, was widely reported of gender difference in previous structural and functional MRI studies, which suggested that morphology indeed affect human brain function. Our study indicates that gender can be identified on individual level by using cortical 3-D morphology and offers a new approach for structural MRI research, as well as highlights the importance of gender balance in brain imaging studies.
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Affiliation(s)
- Zhiguo Luo
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, China
| | - Chenping Hou
- College of Science, National University of Defense Technology, Changsha, China
| | - Lubin Wang
- Cognitive and Mental Health Research Center, Beijing Institute of Basic Medical Science, Beijing, China
| | - Dewen Hu
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, China
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35
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Anderson NE, Harenski KA, Harenski CL, Koenigs MR, Decety J, Calhoun VD, Kiehl KA. Machine learning of brain gray matter differentiates sex in a large forensic sample. Hum Brain Mapp 2018; 40:1496-1506. [PMID: 30430711 DOI: 10.1002/hbm.24462] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 09/05/2018] [Accepted: 10/27/2018] [Indexed: 12/31/2022] Open
Abstract
Differences between males and females have been extensively documented in biological, psychological, and behavioral domains. Among these, sex differences in the rate and typology of antisocial behavior remains one of the most conspicuous and enduring patterns among humans. However, the nature and extent of sexual dimorphism in the brain among antisocial populations remains mostly unexplored. Here, we seek to understand sex differences in brain structure between incarcerated males and females in a large sample (n = 1,300) using machine learning. We apply source-based morphometry, a contemporary multivariate approach for quantifying gray matter measured with magnetic resonance imaging, and carry these parcellations forward using machine learning to classify sex. Models using components of brain gray matter volume and concentration were able to differentiate between males and females with greater than 93% generalizable accuracy. Highly differentiated components include orbitofrontal and frontopolar regions, proportionally larger in females, and anterior medial temporal regions proportionally larger in males. We also provide a complimentary analysis of a nonforensic healthy control sample and replicate our 93% sex discrimination. These findings demonstrate that the brains of males and females are highly distinguishable. Understanding sex differences in the brain has implications for elucidating variability in the incidence and progression of disease, psychopathology, and differences in psychological traits and behavior. The reliability of these differences confirms the importance of sex as a moderator of individual differences in brain structure and suggests future research should consider sex specific models.
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Affiliation(s)
- Nathaniel E Anderson
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Keith A Harenski
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | - Carla L Harenski
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico
| | | | | | - Vince D Calhoun
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.,University of New Mexico, Albuquerque, New Mexico
| | - Kent A Kiehl
- The Mind Research Network & Lovelace Biomedical and Environmental Research Institute, Albuquerque, New Mexico.,University of New Mexico, Albuquerque, New Mexico
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