Ich hatte schon Artikel, die sich mit der auch sehr frühen Erkennung von Geschlechterunterschieden direkt am Gehirn, etwa durchs Scans, beschäftigen:
- Geschlechtsunterschiede in der funktionellen Konnektivität während der Entwicklung des fetalen Gehirns
- Vorhersage des Geschlechts anhand der Gehirnwellen
- Geschlechterunterschiede im Gehirn sind bereits im Alter von einem Monat vorhanden
Hier einige weitere interessante Studien in die gleiche Richtung:
Do men and women have different brains? Previous neuroimage studies sought to answer this question based on morphological difference between specific brain regions, reporting unfortunately conflicting results. In the present study, we aim to use a deep learning technique to address this challenge based on a large open-access, diffusion MRI database recorded from 1,065 young healthy subjects, including 490 men and 575 women healthy subjects. Different from commonly used 2D Convolutional Neural Network (CNN), we proposed a 3D CNN method with a newly designed structure including three hidden layers in cascade with a linear layer and a terminal Softmax layer. The proposed 3D CNN was applied to the maps of factional anisotropy (FA) in the whole-brain as well as specific brain regions. The entropy measure was applied to the lowest-level image features extracted from the first hidden layer to examine the difference of brain structure complexity between men and women. The obtained results compared with the results from using the Support Vector Machine (SVM) and Tract-Based Spatial Statistics (TBSS). The proposed 3D CNN yielded a better classification result (93.3%) than the SVM (78.2%) on the whole-brain FA images, indicating gender-related differences likely exist in the whole-brain range. Moreover, high classification accuracies are also shown in several specific brain regions including the left precuneus, the left postcentral gyrus, the left cingulate gyrus, the right orbital gyrus of frontal lobe, and the left occipital thalamus in the gray matter, and middle cerebellum peduncle, genu of corpus callosum, the right anterior corona radiata, the right superior corona radiata and the left anterior limb of internal capsule in the while matter. This study provides a new insight into the structure difference between men and women, which highlights the importance of considering sex as a biological variable in brain research.
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.
Sex differences in the brain are traditionally treated as binary. We present new evidence that a continuous measure of sex differentiation of the brain can explain sex differences in psychopathology. The degree of sex-differentiated brain features (ie, features that are more common in one sex) may predispose individuals toward sex-biased psychopathology and may also be influenced by the genome. We hypothesized that individuals with a female-biased differentiation score would have greater female-biased psychopathology (internalizing symptoms, such as anxiety and depression), whereas individuals with a male-biased differentiation score would have greater male-biased psychopathology (externalizing symptoms, such as disruptive behaviors).
Using the Philadelphia Neurodevelopmental Cohort database acquired from database of Genotypes and Phenotypes, we calculated the sex differentiation measure, a continuous data-driven calculation of each individual’s degree of sex-differentiating features extracted from multimodal brain imaging data (magnetic resonance imaging [MRI] /diffusion MRI) from the imaged participants (n = 866, 407 female and 459 male).
In male individuals, higher differentiation scores were correlated with higher levels of externalizing symptoms (r = 0.119, p = .016). The differentiation measure reached genome-wide association study significance (p < 5∗10−8) in male individuals with single nucleotide polymorphisms Chromsome5:rs111161632:RASGEF1C and Chromosome19:rs75918199:GEMIN7, and in female individuals with Chromosome2:rs78372132:PARD3B and Chromosome15:rs73442006:HCN4.
The sex differentiation measure provides an initial topography of quantifying male and female brain features. This demonstration that the sex of the human brain can be conceptualized on a continuum has implications for both the presentation of psychopathology and the relation of the brain with genetic variants that may be associated with brain differentiation.
In their PNAS article, Joel et al. (1) demonstrate extensive overlap between the distributions of females and males for many brain characteristics, measured across multiple neuroimaging modalities and datasets. They pose two requirements for categorizing brains into distinct male/female classes: (i) gender differences should appear as dimorphic form differences between male and female brains, and (ii) there should be internal consistency in the degree of “maleness–femaleness” of different elements within a single brain. Based on these criteria, the authors convincingly establish that there is little evidence for this strict sexually dimorphic view of human brains, counter to the popular lay conception of a “male” and “female” brain. This finding has broad implications not only for the ontology of gender, but also for the statistical treatment of sex in morphometric analyses.
Critically, however, the conclusion that human brains cannot be categorized into two distinct classes depends largely on the level of analysis. Although the set of properties that distinguish one category from another is rich and flexible, there is rarely a diagnostic form (e.g., what singular physical characteristic reliably distinguishes cats from dogs?) and there is often substantial within-category variability (e.g., breeds of dogs) (2). The failure of the brain to meet these two requirements does not mean that “human brains cannot be categorized into two distinct classes: male brain/female brain.” In fact, an individual’s biological sex can be classified with extremely high accuracy by considering the brain mosaic as a whole.
To demonstrate this, we acquired T1-weighted structural MRI scans for 1,566 individuals, aged 19–35 y (57.7% female), from the freely available Brain Genomics Superstruct Project (3). Cortical thickness and subcortical volume estimates were calculated using the FreeSurfer automatic segmentation algorithm (v5.3; surfer.nmr.mgh.harvard.edu/fswiki). First, 400 subjects were retained as a held-out validation set. Next, penalized logistic regression [elastic net (4, 5)] was used to predict the sex of each individual based on their mosaic, or pattern, of morphometric brain data. Within the training set (n = 1,166), a regression model was built using three repeats of 10-fold cross-validation. The model was then used, without modification, to predict the sex of each individual in the held-out sample. Classification accuracy was extremely high [accuracy: 93%, 95% confidence interval (CI) 89.5–94.9%, P < 10−16] and remained significant if head-size-related measurements were excluded [92% (CI 88.9–94.5%), P < 10−16] or regressed out [70% (CI 65.0–74.2%), P < 10−6]. To borrow the framing of Joel et al. (1), the human brain may be a mosaic, but it is one with predictable patterns.
Despite the absence of dimorphic differences and lack of internal consistency observed by Joel et al. (1), multivariate analyses of whole-brain patterns in brain morphometry can reliably discriminate sex. These two results are not mutually inconsistent. We wholly agree that a strict dichotomy between male/female brains does not exist, but this does not diminish or negate the importance of considering statistical differences between the sexes (e.g., including sex as a covariate in morphometric analyses).
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.
Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).