Geschlechterunterschiede im Gehirn und deren Erkennung

Ich hatte schon Artikel, die sich mit der auch sehr frühen Erkennung von Geschlechterunterschieden direkt am Gehirn, etwa durchs Scans, beschäftigen:

Hier einige weitere interessante Studien in die gleiche Richtung:

1. Brain Differences Between Men and Women: Evidence From Deep Learning

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.

2. Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity

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.

3. Beyond a Binary Classification of Sex: An Examination of Brain Sex Differentiation, Psychopathology, and Genotype

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.

4. Patterns in the human brain mosaic discriminate males from females

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; 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).

5 Machine learning of brain gray matter differentiates sex in a large forensic sample

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.

6. Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

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).

Studie: Geschlechterunterschiede in der Persönlichkeit korrelieren mit ökologischen Stress in der jeweiligen Gesellschaft

Tim Kaiser, der schon Autor der gestrigen Studie war, hat eine weitere interessante Studie veröffentlicht:

Sex differences in personality were found to be larger in more developed and more genderequal societies. However, the studies that report this effect either have methodological shortcomings or do not take into account possible underlying effects of ecological variables.
Here, a large, multinational (N = 867,782) dataset of personality profiles was used to examine sex differences in Big Five facet scores for 50 countries. Gender differences were related to estimates of ecological stress as well as socio-cultural variables. Using a regularized partial-correlation approach, the unique associations of those correlates with sex
differences were isolated.
Sex differences were large (median Mahalanobis’ D = 1.97) and varied substantially across countries (range 1.49 to 2.48). Global sex differences are larger in more developed countries with higher food availability, less pathogen prevalence, higher gender equality and an individualistic culture. However, after controlling for confounds, only historic pathogen prevalence, food availability and cultural individualism remained. Sex differences in personality are uniquely correlated to ecological stress. Previously reported correlations between greater sex differences and outcomes of gender equality could be due to confounding by influences of ecological stress.

Quelle: Nature and evoked culture: sex differences in personality are uniquely correlated with ecological stress.

Hier etwas zu den Daten: Eine Übersicht über die Ausprägung der Geschlechterunterschiede:

Geschlechterunterschiede weltweit

Wie man sieht liegt Deutschland eher im Mittelfeld, Russland weißt einen der größten Geschlechterunterschiede aus, Pakistan den kleinsten. Es wäre interessant noch zu sehen, in welche Richtung die Unterschiede kleiner oder größer geworden sind. Interessant natürlich auch, dass Länder wie Norwegen und Dänemark oder Schweden oder die Niederlande keineswegs sehr weit unten stehen, sie bewegen sich in der Mitte bzw im oberen Mittelfeld

Vermutlich sind die Russen extrem männlich und die Russinnen extrem weiblich in der Persönlichkeit.

Sind aber die pakistanischen Männer eher weiblicher oder die pakistanischen Frauen eher männlicher oder bewegen sich beide auf die Mitte zu? Welche Eigenschaften haben sich angeglichen?

Weiter aus der Studie:

Geschlechterunterschiede in der Persönlichkeit und Variablen

Und die Besprechung dazu aus der Studie:

Sex differences in personality were larger in more individualistic countries with higher food availability and lower pathogen prevalence. Effect sizes of the unregularized correlations were medium to large. After regularization, the unique correlations still represent medium to slightly above-average effects when following empirically derived guidelines for effect size interpretation in individual differences research (Gignac & Szodorai, 2016). Gender equality and cultural individualism were positively correlated with larger sex differences, thus replicating existing findings. However, the specific correlation between gender equality and sex differences was reduced to zero after regularization. This indicates that previously reported findings reporting this effect could be attributed to confounding variables and may be better explained by ecological influences. In this study, sex differences in personality showed the strongest unique associations with ecological variables, namely historic pathogen prevalence and historic food consumption. A possible explanation for this   could be that ecological influences on the development of human cultures unfold over one or more generations. For example, changes in pathogen prevalence precede shifts in gender equality by up to three decades (Varnum & Grossmann, 2017). Still, longitudinal studies are needed to validate this speculation.

Demnach wäre also die Verfügbarkeit von Essen und das Nichtvorhandensein von Krankheitserregern  ein guter Indikator für größere Geschlechterunterschiede. Was ja durchaus mit der Theorie, dass man unter schwereren Bedingungen eher praktisch handelt, in guten aber seine Geschlechterunterschiede ausleben kann.

Aus einer Besprechung zu dieser Studie:

This basic pattern of findings was replicated in another recent large-scale survey of narrow personality traits conducted on nearly a million people across 50 countries. Using different personality tests, and averaging across all countries, Tim Kaiser found a D = 2.16, which is very similar to the effect size found in the other study on English-speaking countries. While there was cross-cultural variation in the effect, there was a general trend for more developed, individualistic countries with higher food availability, less pathogen prevalence, and higher gender equality to show the largest sex differences in global personality [6].

In particular, Scandinavian countries consistently showed larger-than-average sex differences in global personality, together with the US, Canada, Australia, the UK, and other Northern and Eastern European Countries. The countries with the smallest sex differences in global personality included several Southeast Asian countries. To be sure, there wasn’t a perfect correlation between more developed, gender-egalitarian countries and sex differences (e.g., Russia displayed the largest sex difference with D = 2.48). But even Pakistan– the country with the smallest sex differences in global personality in the world according to this study– had a D = 1.49. This means that even when you look around the world for the country with the smallest sex difference in global personality, the classification accuracy of that country is still 77%!

Es sind also selbst in den Ländern mit den niedrigsten Geschlechterunterschieden noch riesige Geschlechterunterschiede vorhanden.

Das asiatische Länder, in denen Männer einen niedrigeren Testosteronspiegel haben und Frauen relativ hohes pränatales Testosteron hingegen niedrigere Unterschiede zeigen ist interessant.

Mich würde auch interessieren, wie Russland da reinpasst: Krankheitserreger spielen vielleicht angesichts der Kälte eine geringere Rolle, aber Russland hat ja eigentlich genug Armut hinter sich.

Frauen wollen Pädagogik studieren, Männer etwas technisches

Ein Artikel behandelt die Studienwahl der Geschlechter:

Die Generation Z bestätigt nicht nur Klischees: Viele wollen im Job organisieren und suchen mehr Sicherheit im Berufsleben. Doch darauf sind die Unis nicht vorbereitet.

Die Interessen von vielen Jugendlichen decken sich mit traditionellen Geschlechterrollen. Das legen Daten nahe, die auf dem Studium-Interessenstest (SIT) von ZEIT ONLINE basieren. In den vergangenen fünf Jahren haben 500.000 Studieninteressierte daran teilgenommen, jetzt hat die Hochschule für Musik, Theater und Medien Hannover (HMTMH) eine repräsentative Stichprobe von 20.000 Profilen ausgewertet. Die typischen Domänen für Frauen und Männer bestehen weiterhin. Während sich 10,8 Prozent der Testteilnehmerinnen vorstellen können, später als Pädagogin mit Menschen zu arbeiten, interessieren sich nur 4,7 Prozent der jungen Männer für ein Studium in diesem Bereich. Gleichzeitig möchten mehr als sechsmal so viele Männer (16,8 Prozent) wie Frauen (2,7 Prozent) in einem technisch-forschenden Beruf arbeiten.

Das ist zum einen eine relativ große Datenlage, zum anderen bestätigt es die klassischen Geschlechterinteressen.

Es sei „erstaunlich, wie sehr die jungen Menschen Genderklischees reproduzieren“, sagt Kommunikationswissenschaftler Helmut Scherer, der die Daten mit seiner wissenschaftlichen Mitarbeiterin Sophie Bruns ausgewertet hat. „Teilweise steigt die Diskrepanz zwischen Männern und Frauen in bestimmten Feldern sogar noch“, sagt Bruns. So ist zwar über die Geschlechter hinweg das Interesse an technischen Tätigkeiten gestiegen – bei Männern allerdings stärker als bei Frauen. Nur bei den managementorientierten Aufgaben bewegt sich das Interesse von Männern und Frauen aufeinander zu: Bei den Männern stagnierte es, bei den Frauen nahm es leicht zu.

Also auch hier nicht etwa eine Reduzierung, sondern sogar ein Anstieg. Das passt wenig zu den feministischen Theorien, aber sehr gut dazu, dass sich mit steigender Freiheit die biologischen Unterschiede in den Interessen durchsetzen.

Siehe dazu auch:

Die Studie selbst scheint noch nicht veröffentlicht zu sein, ich habe sie jedenfalls noch nicht gefunden. Wenn jemand sie schon gesehen hat, dann gerne einen Link in den Kommentaren posten.

Geschlechterunterschiede in der Persönlichkeit

Eine Studie untersucht anhand eines großen Datensatzes (n=320,128) Unterschiede in der Persönlichkeit zwischen den Geschlechtern:


We studied the sex gap in 30 facet traits (IPIP-NEO) in a large US sample (N = 320,128).

Women scored higher (d > 0.50) in Anxiety, Vulnerability, Openness to Emotions, Altruism, and Sympathy.

Men only scored higher (d > 0.20) in Excitement-seeking and Openness to Intellect.

The present study reports on the scope and size of sex differences in 30 personality facet traits, using one of the largest US samples to date (N = 320,128). The study was one of the first to utilize the open access version of the Five-Factor Model of personality (IPIP-NEO-120) in the large public. Overall, across age-groups 19–69 years old, women scored notably higher than men in Agreeableness (d = 0.58) and Neuroticism (d = 0.40). Specifically, women scored d > 0.50 in facet traits Anxiety, Vulnerability, Openness to Emotions, Altruism, and Sympathy, while men only scored slightly higher (d > 0.20) than women in facet traits Excitement-seeking and Openness to Intellect. Sex gaps in the five trait domains were fairly constant across all age-groups, with the exception for age-group 19–29 years old. The discussion centers on how to interpret effects sizes in sex differences in personality traits, and tentative consequences.

Quelle: Sex differences in 30 facets of the five factor model of personality in the large public (N = 320,128) (abstract/ Volltext Sci-hub)

Die einzelnen Werte werden hier dargestellt:


Unterschiede Persönlichkeit Mann Frau

Unterschiede Persönlichkeit Mann Frau

Wie man hier sieht sind viele der Eigenschaften konstant unterschiedlich über alle Alterstufen, einige verändern sich auch stark mit dem Alter, die Geschlechter kommen zumindest etwas zusammen.

Die Werte im Einzelnen:

Hier sieht man, dass von leichten bis mittleren Unterschieden alles dabei ist. Hohe werte sind zB erreicht bei „Verletzbarkeit“ „Ängstlichkeit“ „Moralität“ „Altruismus“ „Ehrlichkeit“ und „Sympathie“

Aus der Besprechung:

The current study showed that almost 50% of the specific FFM personality trait facets showed above small effects, and almost 25% above medium effects in sex differences. The most notable difference was seen in the trait domains Neuroticism and Agreeableness. Some specific facets, such as Anxiety (N1) and Sympathy (A6), reported mean effects of over d ~ 0.50 (Table 1). Interestingly, Neuroticism was, unlike Agreeableness, not uniformly different between sexes across the age-spans, with the largest gap found in the late teens, narrowing and stabilizing first at around 45 years of age.

According to a broad evolutionary perspective, this trend seems to coincide with female sexual fertility. In this phase of life, females tend to be more vulnerable than males, in regard to the heightened male sexual aggression (Archer, 2004), while simultaneously investing in pregnancy and caring for infants (Wood & Eagly, 2002). Having women more disposed to anxiety (and empathy), while men are more disposed to assertiveness, may have been an optimal strategy for the propagation of the human species. Certainly, part of the sex gap could also be explained by cultural factors, such as young men not admitting to questionnaire-items assessing neuroticism. However, this explanation may not be supported by other-reports and behavioral observation (Vianello et al., 2013).
Differences in the other trait domains in the FFM were smaller (Openness, Extraversion, and Conscientiousness), and tended to be driven by single specific facets, such as Openness to Emotions (d = 0.64), Conscientiousness Achievement (d = 0.25), and Extraversion Activity (d = 0.24). Overall, these sex differences in the present US sample (Table 1) aligned well with the now almost 20-year old landmark findings in the original FFM NEO-PI-R model (Costa Jr et al., 2001). Comparing the sex gap in facet traits in US adults in our present study with Costa Jr et al. (2001) showed no reversed effects, while a few (e.g., Friendliness, Gregariousness, Trust, and Self-efficacy) had dropped to trivial levels. +

However, even more traits showed increased sex gaps, which may be implicated by the thesis that the more progressive a society becomes, the greater the sex differences in personality (Schmitt et al., 2008; Stoet & Geary, 2018).

Wie dort richtig angeführt sagt die Studie erst einmal nur aus, dass es Unterschiede gibt, nicht worauf sie beruhen. Auch interessant ist, dass man wohl feststellt, dass die Unterschiede eher größer werden.

vgl auch:

Vorhersage des Geschlechts anhand der Gehirnwellen

In der Zeitschrift „Nature“ wurde ein interessanter Artikel veröffentlicht, in dem es darum geht, ob man das Geschlecht einer Person an den Gehirnwellen erkennen kann. Dazu wurde eine AI auf entsprechende Muster angesetzt und ausgewertet, welche Unterschiede diese fand:

Aus dem Abstract:

We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar patterns, we explore if brain rhythms from either sex contain sex specific information. Here we show, in a ground truth scenario, that a deep neural net can predict sex from scalp electroencephalograms with an accuracy of >80% (p < 10−5), revealing that brain rhythms are sex specific. Further, we extracted sex-specific features from the deep net filter layers, showing that fast beta activity (20–25 Hz) and its spatial distribution is a main distinctive attribute. This demonstrates the ability of deep nets to detect features in spatiotemporal data unnoticed by visual assessment, and to assist in knowledge discovery. We anticipate that this approach may also be successfully applied to other specialties where spatiotemporal data is abundant, including neurology, cardiology and neuropsychology.

Quelle: Predicting sex from brain rhythms with deep learning

Vielleicht zunächst etwas zur Methode, um die es da geht, aus der Wikipedia:

Die Elektroenzephalografie (EEG, von altgriechisch ἐγκέφαλος enképhalos, deutsch ‚Gehirn‘, γράφειν gráphein, deutsch ‚schreiben‘) ist eine Methode der medizinischen Diagnostik und der neurologischen Forschung zur Messung der summierten elektrischen Aktivität des Gehirns durch Aufzeichnung der Spannungsschwankungen an der Kopfoberfläche. Das Elektroenzephalogramm (ebenfalls EEG abgekürzt) ist die grafische Darstellung dieser Schwankungen. Das EEG ist neben der Elektroneurografie (ENG) und der Elektromyografie (EMG) eine standardmäßige Untersuchungsmethode in der Neurologie.

Ursache dieser Potentialschwankungen sind physiologische Vorgänge einzelner Gehirnzellen, die durch ihre elektrischen Zustandsänderungen zur Informationsverarbeitung des Gehirns beitragen. Entsprechend ihrer spezifischen räumlichen Anordnung addieren sich die von einzelnen Neuronen erzeugten Potentiale auf, so dass sich über den gesamten Kopf verteilte Potentialänderungen messen lassen.

Zur klinischen Bewertung wird eine Aufzeichnung in mindestens zwölf Kanälen von verschiedenen Elektrodenkombinationen benötigt.

Die Ortsauflösung des üblichen EEGs liegt bei mehreren Zentimetern. Wenn eine höhere Ortsauflösung benötigt wird, so müssen die Elektroden nach neurochirurgischer Eröffnung des Schädels direkt auf die zu untersuchende Hirnrinde aufgelegt werden. Das ist jedoch nur in Sonderfällen z. B. vor epilepsiechirurgischen Eingriffen erforderlich. In diesem Falle spricht man von einem Elektrocorticogramm (ECoG; in deutscher Schreibung Elektrokortikogramm). Das ECoG ermöglicht eine räumliche Auflösung von unter 1 cm und bietet zusätzlich die Möglichkeit, durch selektive elektrische Reizung einer der Elektroden die Funktion der darunterliegenden Hirnrinde zu testen. Dies kann für den Neurochirurgen z. B. bei Eingriffen in der Nähe der Sprachregion von größter Wichtigkeit sein, um zu entscheiden, welche Teile er entfernen darf, ohne eine Funktionseinbuße fürchten zu müssen (vgl. Wachkraniotomie). Eine noch detailliertere Erfassung von Einzelzellaktivität ist nur im Tierexperiment möglich.

Die resultierenden Daten können von geübten Spezialisten auf auffällige Muster untersucht werden. Es gibt aber auch umfangreiche Software-Pakete zur automatischen Signalanalyse. Eine weitverbreitete Methode zur Analyse des EEGs ist die Fouriertransformation der Daten vom Zeitbereich (also der gewohnten Darstellung von Spannungsänderungen im Verlauf der Zeit) in den sogenannten Frequenzbereich. Die so gewonnene Darstellung erlaubt die schnelle Bestimmung von rhythmischer Aktivität.

Bei diesen Mustern treten also Unterschiede auf, die man mittels einer AI ermitteln kann. So etwas sieht dann wohl so aus:



Gehirnwellen Unterschiede Mann Frau

Gehirnwellen Unterschiede Mann Frau

Für einen Laien dürften da viele Bilder recht gleich aussehen, aber die Computer haben dann einige Unterschiede erkannt, die eine Zuordnung ermöglichen:

While not all details of the features used for classification by the deep net have been revealed, our data show that differences in brain rhythms between sexes are mainly in the beta frequency range (cf. Figs 3 and 4). Women are generally better at recognizing emotions and expressing themselves than men34, in part also reflected in differences in responses from the mu-rhythm as a presumed read-out of the mirror neuron system35, and modulations of beta activity during wakefulness have been associated with cognition and emotionally positive or negative tasks36. The discovery from the deep net that information in the beta-range differs between the sexes supports these observations. However, which particular spatiotemporal characteristics of the beta-rhythm differentiate remains enigmatic, and was not further explored.

Die Unterschiede lassen sich demnach auch mal wieder klassischen Geschlechterunterschieden im Schnitt zuordnen.

Es zeigt sich immer wieder, dass erhebliche Unterschiede zwischen den Geschlechtern im Gehirn feststellbar sind. Die Behauptung, dass sich dies nicht auch im Denken niedergeschlagen hat, erscheint damit auch immer absurder.

Ich finde die Anwendung von „Deep Learning“ in dem Bereich sehr interessant. Ich glaube damit werden in Zukunft noch viel mehr Unterschiede aufgezeigt werden, die ansonsten schwer zu bestimmen sind.

Siehe auch:



Die These der größeren männlichen Variabilität

Ein Text behandelt die Theorie, dass Männer eine größere Variabilität haben und daher beispielsweise in einigen Bereichen sowohl bei den sehr schlechten als auch den sehr guten eher anzutreffen sind:

Es wäre die Unterscheidung zwischen diesem Bild aus dem Googlemanifesto:

Damore Populationen Normalverteilungen

Damore Populationen Normalverteilungen

und diesem Bild:

variabilität Männer Frauen

variabilität Männer Frauen

Da würde man sehen, dass die eine Kurve etwas „breiter“ ist, dafür aber „flacher“ verläuft.

Entsprechendes wird zB auch bei der Intelligenz diskutiert.

In dem Artikel wird einiges an Studien dazu zitiert, die das nahelegen:

Das bringt sie zu folgendem Ergebnis:

  1. On average, male variability is greater than female variability on a variety of measures of cognitive ability, personality traits, and interests.  This means men are more likely to be found at both the low and high end of these distributions (see Halpern et al., 2007; Machin & Pekkarinen, 2008 and, especially, the supplementary materials; for an ungated summary click here).  This finding is consistent across decades.
  2. The gender difference in variability has reduced substantially over time within the United States and is variable across cultures. It is clearly responsive to social and cultural factors (see Hyde & Mertz, 2009; Wai et al., 2010); Educational programs can be effective.  It is also clear that there are cultural/societal influences, as the male:female variability ratios can vary considerably across cultures (e.g., Machin & Pekkarinen, 2008).
  3. While the gender difference in the male:female ratio for the upper tail of the distribution of math test scores (SAT, ACT) narrowed considerably in the United States in the 1980s, it appears to have remained steady since the early 1990s.  This can be seen visually in Figure 1 from Wai et al. (2010):
    • Therefore at the top end of any distribution of test scores where men have higher variability, we’d expect men to make up more than 50% of the upper end of the tail.  Thus, any company drawing from the top 5% is likely to find a pool that contains more males. As one goes further out into the tail (i.e. becomes even more selective) the gender tilt becomes larger.
  4. Further compounding the gender tilt: the women in this elite group generally have much better verbal skills than the men in that elite group (see Reilly, 2012).  This means that these women may be better employees than men who match them on quantitative skills, but because they have such superior verbal skills they have more choices available to them when selecting a profession.