Beyond "sex prediction": Estimating and interpreting multivariate sex differences and similarities in the brain

Statistical indices of masculinity-femininity: A theoretical and practical framework

Statistical indices of masculinity-femininity (M-F) summarize multivariate profiles of sex-related traits as positions on a single continuum of individual differences, from masculine to feminine. This approach goes back to the early days of sex differences research; however, a systematic discussion of alternative M-F indices (including their meaning, their mutual relations, and their psychometric properties) has been lacking. In this paper I present an integrative theoretical framework for the statistical assessment of masculinity-femininity, and provide practical guidance to researchers who wish to apply these methods to their data. I describe four basic types of M-F indices: sex-directionality, sex-typicality, sex-probability, and sex-centrality. I examine their similarities and differences in detail, and consider alternative ways of computing them. Next, I discuss the impact of measurement error on the validity of these indices, and outline some potential remedies. Finally, I illustrate the concepts presented in the paper with a selection of real-world datasets on body morphology, brain morphology, and personality. An R function is available to easily calculate multiple M-F indices from empirical data (with or without correction for measurement error) and draw summary plots of their individual and joint distributions.

Do Sex and Gender Have Separate Identities?

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.

Beyond network connectivity: A classification approach to brain age prediction with resting-state fMRI

The brain is a complex, dynamic organ that shows differences in the same subject at various periods. Understanding how brain activity changes across age as a function of the brain networks has been greatly abetted by fMRI. Canonical analysis consists of determining how alterations in connectivity patterns (CPs) of certain regions are affected. An alternative approach is taken here by not considering connectivity but rather features computed from recordings at the regions of interest (ROIs). Using machine learning (ML) we assess how neural signals are altered by and prospectively predictive of age and sex via a methodology that is novel in drawing upon pairwise classification across six decades of subjects' chronological ages. ML is used to answer the equally important questions of what properties of the computed features are most predictive as well as which brain networks are most affected by aging. It was found that there is decreased differentiation among the neural signals of older subjects that are separated in age by the same number of years as younger subjects. Furthermore, the burstiness of the signals change at different rates between males and females. The findings provide insight into brain aging via an ROI-based analysis, the consideration of several feature groups, and a novel classification-based ML pipeline. There is also a contribution to understanding the effects of data aggregated from different recording centers on the conclusions of fMRI studies.

Researchers' sex/gender identity influences how sex/gender question is investigated in neuroscience: an example from an OHBM meeting

Gender inequality and diversity in STEM is a challenging field of research. Although the relation between the sex/gender of the researcher and the scientific research practices has been previously examined, less interest has been demonstrated towards the relation between sex/gender of the researcher and the way sex/gender as a variable is explored. Here, we examine, from a neurofeminist perspective, both questions: whether sex/gender identity is related to the examination of sex/gender as a variable and whether different approaches towards examining sex/gender are being used in different topics of study within neuroscience. Using the database of submitted posters to the Organization of Human Brain Mapping 2022 annual conference, we identified abstracts examining a sex/gender-related research question. Among these target abstracts, we identified four analytical categories, varying in their degree of content-related complexity: (1) sex/gender as a covariate, (2) sex/gender as a binary variable for the study of sex/gender differences, (3) sex/gender with additional biological information, and (4) sex/gender with additional social information. Statistical comparisons between sex/gender of researcher and the target abstract showed that the proportion of abstracts from Non-binary or Other first authors compared to both Women and Men was lower for all submitted abstracts than for the target abstracts; that more researchers with sex/gender-identity other than man implemented analytical category of sex/gender with additional social information; and, for instance, that research involving cognitive, affective, and behavioural neuroscience more frequently fit into the sex/gender with additional social information-category. Word cloud analysis confirmed the validity of the four exploratorily identified analytical categories. We conclude by discussing how raising awareness about contemporary neurofeminist approaches, including perspectives from the global south, is critical to neuroscientific and societal progress.

From means to meaning in the study of sex/gender differences and similarities

The incorporation of sex and gender (S/G) related factors is commonly acknowledged as a necessary step to advance towards more personalized diagnoses and treatments for somatic, psychiatric, and neurological diseases. Until now, most attempts to integrate S/G-related factors have been reduced to identifying average differences between females and males in behavioral/ biological variables. The present commentary questions this traditional approach by highlighting three main sets of limitations: 1) Issues stemming from the use of classic parametric methods to compare means; 2) challenges related to the ability of means to accurately represent the data within groups and differences between groups; 3) mean comparisons impose a results' binarization and a binary theoretical framework that precludes advancing towards precision medicine. Alternative methods free of these limitations are also discussed. We hope these arguments will contribute to reflecting on how research on S/G factors is conducted and could be improved.

Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy

BACKGROUND

Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).

METHODS

Deep learning models were trained and tested on baseline clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in stroke patients who underwent EVT. Classical machine learning models (logistic regression and random forest classifiers) were constructed to compare their performance with the deep learning models. An external validation dataset was used to validate the models. The MR PREDICTS prognostic tool was tested on the external validation set, and its performance was compared with the deep learning and classical machine learning models.

RESULTS

A total of 975 patients (550 men; mean±SD age 67.5±15.1 years) were studied with 778 patients in the model development cohort and 197 in the external validation cohort. The deep learning model trained on baseline CT and clinical data, and the logistic regression model (clinical data alone) demonstrated the strongest discriminative abilities for 3-month functional outcome and were comparable (AUC 0.811 vs 0.817, Q=0.82). Both models exhibited superior prognostic performance than the other deep learning (CT head alone, CT head, and CT angiography) and MR PREDICTS models (all Q<0.05).

CONCLUSIONS

The discriminative performance of deep learning for predicting functional independence was comparable to logistic regression. Future studies should focus on whether incorporating procedural and post-procedural data significantly improves model performance.

Recommendations for a Better Understanding of Sex and Gender in the Neuroscience of Mental Health

There are prominent sex/gender differences in the prevalence, expression, and life span course of mental health and neurodiverse conditions. However, the underlying sex- and gender-related mechanisms and their interactions are still not fully understood. This lack of knowledge has harmful consequences for those with mental health problems. Therefore, we set up a cocreation session in a 1-week workshop with a multidisciplinary team of 25 researchers, clinicians, and policy makers to identify the main barriers in sex and gender research in the neuroscience of mental health. Based on this work, here we provide recommendations for methodologies, translational research, and stakeholder involvement. These include guidelines for recording, reporting, analysis beyond binary groups, and open science. Improved understanding of sex- and gender-related mechanisms in neuroscience may benefit public health because this is an important step toward precision medicine and may function as an archetype for studying diversity.

Univariate and multivariate sex differences and similarities in gray matter volume within essential language-processing areas

BACKGROUND

Sex differences in language-related abilities have been reported. It is generally assumed that these differences stem from a different organization of language in the brains of females and males. However, research in this area has been relatively scarce, methodologically heterogeneous and has yielded conflicting results.

METHODS

Univariate and multivariate sex differences and similarities in gray matter volume (GMVOL) within 18 essential language-processing brain areas were assessed in a sex-balanced sample (N = 588) of right-handed young adults. Univariate analyses involved location, spread, and shape comparisons of the females' and males' distributions and were conducted with several robust statistical methods able to quantify the size of sex differences and similarities in a complementary way. Multivariate sex differences and similarities were estimated by the same methods in the continuous scores provided by two distinct multivariate procedures (logistic regression and a multivariate analog of the Wilcoxon-Mann-Whitney test). Additional analyses were addressed to compare the outcomes of these two multivariate analytical strategies and described their structure (that is, the relative contribution of each brain area to the multivariate effects).

RESULTS

When not adjusted for total intracranial volume (TIV) variation, "large" univariate sex differences (males > females) were found in all 18 brain areas considered. In contrast, "small" differences (females > males) in just two of these brain areas were found when controlling for TIV. The two multivariate methods tested provided very similar results. Multivariate sex differences surpassed univariate differences, yielding "large" differences indicative of larger volumes in males when calculated from raw GMVOL estimates. Conversely, when calculated from TIV-adjusted GMVOL, multivariate differences were "medium" and indicative of larger volumes in females. Despite their distinct size and direction, multivariate sex differences in raw and TIV-adjusted GMVOL shared a similar structure and allowed us to identify the components of the SENT_CORE network which more likely contribute to the observed effects.

CONCLUSIONS

Our results confirm and extend previous findings about univariate sex differences in language-processing areas, offering unprecedented evidence at the multivariate level. We also observed that the size and direction of these differences vary quite substantially depending on whether they are estimated from raw or TIV-adjusted GMVOL measurements.

Accurate sex prediction of cisgender and transgender individuals without brain size bias

The increasing use of machine learning approaches on neuroimaging data comes with the important concern of confounding variables which might lead to biased predictions and in turn spurious conclusions about the relationship between the features and the target. A prominent example is the brain size difference between women and men. This difference in total intracranial volume (TIV) can cause bias when employing machine learning approaches for the investigation of sex differences in brain morphology. A TIV-biased model will not capture qualitative sex differences in brain organization but rather learn to classify an individual's sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. In this study, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for TIV either through featurewise confound removal or by matching the training samples for TIV. Our results provide strong evidence that models not biased by TIV can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modeling to avoid bias in automated decision making.

Neural correlates of cocaine-induced conditioned place preference in the posterior cerebellar cortex

Introduction

Addictive drugs are potent neuropharmacological agents capable of inducing long-lasting changes in learning and memory neurocircuitry. With repeated use, contexts and cues associated with consumption can acquire motivational and reinforcing properties of abused drugs, triggering drug craving and relapse. Neuroplasticity underlying drug-induced memories takes place in prefrontal-limbic-striatal networks. Recent evidence suggests that the cerebellum is also involved in the circuitry responsible for drug-induced conditioning. In rodents, preference for cocaine-associated olfactory cues has been shown to correlate with increased activity at the apical part of the granular cell layer in the posterior vermis (lobules VIII and IX). It is important to determine if the cerebellum's role in drug conditioning is a general phenomenon or is limited to a particular sensory modality.

Methods

The present study evaluated the role of the posterior cerebellum (lobules VIII and IX), together with the medial prefrontal cortex (mPFC), ventral tegmental area (VTA), and nucleus accumbens (NAc) using a cocaine-induced conditioned place preference procedure with tactile cues. Cocaine CPP was tested using ascending (3, 6, 12, and 24 mg/kg) doses of cocaine in mice.

Results

Compared to control groups (Unpaired and Saline animals), Paired mice were able to show a preference for the cues associated with cocaine. Increased activation (cFos expression) of the posterior cerebellum was found in cocaine CPP groups and showed a positive correlation with CPP levels. Such increases in cFos activity in the posterior cerebellum significantly correlated with cFos expression in the mPFC.

Discussion

Our data suggest that the dorsal region of the cerebellum could be an important part of the network that mediates cocaine-conditioned behavior.

Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction

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.

Breaking the binary: Gender versus sex analysis in human brain imaging

Despite decades of pursuit, human brain imaging has yet to uncover clear neural correlates of male-female behavioral differences. Given that such behavior does not always align with sex categories, we argue that neuroimaging research may find more success by partitioning subjects along nonbinary gender attributes in addition to sex. We review the handful of studies that have done this, several of which find as good or better association between brain measures and "gender" as they do with "sex." Recent advances in operationalizing "gender" as a multidimensional variable should facilitate such studies, along with discovery-based approaches that mine brain imaging data for gender-associated attributes, independent of sex.