Beyond “Sex Prediction”: Estimating and Interpreting Multivariate Sex Differences and Similarities in the Brain

Sanchis-Segura, Carla; Aguirre, Naiara; Cruz-Gómez, Álvaro Javier; Félix, Sonia; Forn, Cristina

doi:10.21203/rs.3.rs-741734/v2

Cited by 3 publications

(3 citation statements)

References 54 publications

(114 reference statements)

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“…In other words, males with low and females with high TIV were not more likely to be misclassified, indicating that this model was not biased by TIV. However, in line with previous studies [39,43,49,50], we did observe decreased classification accuracies for both the AM and the ATM test samples. This decrease in accuracy might result from the featurewise confound removal of TIV likely removing relatively large amounts of (TIVrelated) variance from the features, which in turn reduced the amount of information available to the model to accurately learn sex classification.…”

Section: Reducing Bias By Featurewise Tiv Controlsupporting

confidence: 93%

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

Lisa

Hamdan

Hoffstaedter

et al. 2022

Preprint

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Brain size differs substantially between human males and females. This difference in total intracranial volume (TIV) can cause bias when employing machine-learning approaches for the investigation of sex differences in brain morphology. TIV-biased models will likely not capture actual 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. Here, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for brain size either through featurewise confound removal or by matching training samples for TIV. Our results provide evidence that non-TIV-biased models can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modelling to avoid bias in automated decision making.

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Section: Reducing Bias By Featurewise Tiv Controlsupporting

confidence: 93%

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

Lisa

Hamdan

Hoffstaedter

et al. 2022

Preprint

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“…However, this approach can limit our understanding of individual variation within the sexes [ 5 , 131 ]. Furthermore, these findings should not be interpreted as evidence for large sex differences in brain structure since classification and estimation are very different concepts [ 132 ]. Accordingly, identifying specific brain areas that exhibit sex-biased volumes, estimating the magnitude of those average differences, and investigating inter-regional and inter-individual deviations from these averages requires the approach used in many studies discussed here (including those by Williams and colleagues [ 2 , 3 ]), namely direct analysis of sex differences in regional brain anatomy using large sMRI datasets.…”

Section: Main Textmentioning

confidence: 99%

Sex differences in the human brain: a roadmap for more careful analysis and interpretation of a biological reality

et al. 2022

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The presence, magnitude, and significance of sex differences in the human brain are hotly debated topics in the scientific community and popular media. This debate is largely fueled by studies containing strong, opposing conclusions: either little to no evidence exists for sex differences in human neuroanatomy, or there are small-to-moderate differences in the size of certain brain regions that are highly reproducible across cohorts (even after controlling for sex differences in average brain size). Our Commentary uses the specific comparison between two recent large-scale studies that adopt these opposing views—namely the review by Eliot and colleagues (2021) and the direct analysis of ~ 40k brains by Williams and colleagues (2021)—in an effort to clarify this controversy and provide a framework for conducting this research. First, we review observations that motivate research on sex differences in human neuroanatomy, including potential causes (evolutionary, genetic, and environmental) and effects (epidemiological and clinical evidence for sex-biased brain disorders). We also summarize methodological and empirical support for using structural MRI to investigate such patterns. Next, we outline how researchers focused on sex differences can better specify their study design (e.g., how sex was defined, if and how brain size was adjusted for) and results (by e.g., distinguishing sexual dimorphisms from sex differences). We then compare the different approaches available for studying sex differences across a large number of individuals: direct analysis, meta-analysis, and review. We stress that reviews do not account for methodological differences across studies, and that this variation explains many of the apparent inconsistencies reported throughout recent reviews (including the work by Eliot and colleagues). For instance, we show that amygdala volume is consistently reported as male-biased in studies with sufficient sample sizes and appropriate methods for brain size correction. In fact, comparing the results from multiple large direct analyses highlights small, highly reproducible sex differences in the volume of many brain regions (controlling for brain size). Finally, we describe best practices for the presentation and interpretation of these findings. Care in interpretation is important for all domains of science, but especially so for research on sex differences in the human brain, given the existence of broad societal gender-biases and a history of biological data being used justify sexist ideas. As such, we urge researchers to discuss their results from simultaneously scientific and anti-sexist viewpoints.

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“…When differences were demonstrated, we followed up with post hoc comparisons using Mann-Whitney tests. We reported effect sizes of those comparisons by using the Hodges-Lehmann difference between pairs of medians (MndD = Mdn Group1-Mdn Group2), their 95% confidence intervals (95% CI), and the exact p-values of those differences (Guarque-Chabrera et al, 2022b;Sanchis-Segura et al, 2022). Then, we corrected the significance level by adjusting for multiple comparisons using the False Discovery Rate method (Benjamini and Hochberg, 1995).…”

Section: Discussionmentioning

confidence: 99%

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

Rodríguez-Borillo

Roselló-Jiménez

Guarque-Chabrera

et al. 2023

Front. Behav. Neurosci.

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

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Beyond “Sex Prediction”: Estimating and Interpreting Multivariate Sex Differences and Similarities in the Brain

Cited by 3 publications

References 54 publications

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

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

Sex differences in the human brain: a roadmap for more careful analysis and interpretation of a biological reality

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

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