A Comparative, Developmental, and Clinical Perspective of Neurobehavioral Sexual Dimorphisms
Bibliographic record
Abstract
Women and men differ in a wide variety of behavioral traits and in their vulnerability to developing certain mental disorders. This review endeavors to explore how recent preclinical and clinical research findings have enhanced our understanding of the factors that underlie these disparities. We start with a brief overview of some of the important genetic, molecular, and hormonal determinants that contribute to the process of sexual differentiation. We then discuss the importance of animal models in studying the mechanisms responsible for sex differences in neuropsychiatric disorders (e.g., drug dependence) - with a special emphasis on experimental models based on the neurodevelopmental and "three hits" hypotheses. Next, we describe the most common brain phenotypes observed in vivo with magnetic resonance imaging. We discuss the challenges in interpreting these phenotypes vis-à-vis the underlying neurobiology and revisit the known sex differences in brain structure from birth, through adolescence, and into adulthood. This is followed by a presentation of pertinent clinical and epidemiological data that point to important sex differences in the prevalence, course, and expression of psychopathologies such as schizophrenia, and mood disorders including major depression and posttraumatic stress disorder. Recent evidence implies that mood disorders and psychosis share some common genetic predispositions and neurobiological bases. Therefore, modern research is emphasizing dimensional representation of mental disorders and conceptualization of schizophrenia and major depression as a continuum of cognitive deficits and neurobiological abnormalities. Herein, we examine available evidence on cerebral sexual dimorphism to verify if sex differences vary quantitatively and/or qualitatively along the psychoses-depression continuum. Finally, sex differences in the prevalence of posttraumatic disorder and drug abuse have been described, and we consider the genomic and molecular data supporting these differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".