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BACKGROUND: Puberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce. METHODS: We investigated if pre- vs. post-menarche status can be classified using machine learning on cortical and subcortical structural magnetic resonance imaging (MRI) data from strictly age-matched adolescent females from the Adolescent Brain Cognitive Development (ABCD) cohort. For comparison of the identified menarche-related patterns to age-related patterns of neurodevelopment, we trained a brain age prediction model on data from the Philadelphia Neurodevelopmental Cohort and applied it to the same ABCD data, yielding differences between predicted and chronological age referred to as brain age gaps. We tested the sensitivity of both these frameworks to measures of pubertal maturation, specifically age at menarche and puberty status. RESULTS: The machine learning model achieved moderate but statistically significant accuracy in the menarche classification task, yielding for each subject a class probability ranging from 0 (pre-) to 1 (post- menarche). Comparison to brain age predictions revealed shared and distinct patterns of neurodevelopment captured by both approaches. Continuous menarche class probabilities were positively associated with brain age gaps, but only the menarche class probabilities-not the brain age gaps-were associated with age at menarche. CONCLUSIONS: This study demonstrates the use of a machine learning model to classify menarche status from structural MRI data while accounting for age-related neurodevelopment. Given its sensitivity towards measures of puberty timing, our work suggests that menarche class probabilities may be developed toward an objective brain-based marker of pubertal development.

Puberty is a period of substantial changes in the life of youths, and these include profound brain changes. Most studies have investigated age related changes in brain development, recent work however suggests that looking at brain development through the lens of pubertal development can provide additional insights beyond age effects. We here analyzed brain imaging data from a group of same-aged adolescent girls from the Adolescent Brain Cognitive Development study. Our goal was to investigate if we could determine from brain images whether a girl had started her menstrual period (menarche) or not, and we used machine learning to classify between them. This machine learning model does not just return a "yes/no" decision, but also returns a number between 0 and 1 indicating a probability to be pre- (0) or post- (1) menarche. To rule out that our approach only maps age-related development, we selected a strictly age-matched sample of girls and compared our classification model to a brain age model trained on independent individuals. Our model classified between pre- and post-menarche with moderate accuracy. The obtained class probability was partly related to age-related brain development, but only the probability was significantly associated with pubertal timing (age at menarche). In summary, our study uses a machine learning model to estimate whether a girl has reached menarche based on her brain structure. This approach offers new insights into the connection between puberty and brain development and might serve as an objective way to assess pubertal timing from imaging data.

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OBJECTIVE: Noninvasive neural decoding enables predicting motor output from neural activities without physically damaging the human body. A recent study demonstrated the applicability of functional near-infrared spectroscopy (fNIRS) to decode muscle force production from hemodynamic signals measured in the male brain. However, given the sex differences in cerebral blood flow and muscle physiology, whether the fNIRS approach can also be applied to the female brain remains elusive. Therefore, this study aimed to evaluate whether fNIRS can be used to identify the optimal cortical region and hemodynamic predictor to decode muscle force output in females. RESULTS: Statistical group analysis for eight healthy female adults showed that the cortical region for wrist control was topologically dorsal to that for finger control over the primary sensorimotor cortex. This cortical area was maximally activated while the wrist flexor muscles were contracted to hold a load on the subject's palm, as was the case for males. However, the dynamics of oxyhemoglobin concentration measured from the most activated cortical area differed between females and males. The signal intensity during 100% maximal voluntary contraction and the signal increase rate at 50% maximal voluntary contraction was lower and faster in females. Eight predictors were used to characterize hemodynamic signals' amplitude and temporal variation in the female cortex. Unlike the case for males, only the trajectory predictors for the amplitude of oxyhemoglobin concentration change were strongly correlated with the strengths of force produced by the wrist flexor muscles, showing a linear relationship. These results suggest gender-specific hemodynamics must be considered for decoding low-level motor control with fNIRS in females.

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Many brain functions that underlie behavior, cognition, and emotions vary with age, as does susceptibility to neuropsychological disorders. The expression of specific genes that are involved in these functions, such as the genes encoding for oxytocin, its receptors, and apolipoprotein D, varies with age across different brain regions. The cannabinoid 1 receptor (CB1 R) is one of the most widely spread G-protein coupled receptors in the central nervous system and is increasingly recognized for its important contribution to various brain functions. Although changes in CB1 R expression with age have been reported in the male mouse brain, they have not been well investigated in the female brain. Here, we used fluorescence in situ hybridization to target CB1 R mRNA in the whole brains of female C57BL/6J mice aged 4, 6, 12, 52 (12 months) and 86 weeks (20 months), and quantified CB1 R-positive cells in 36 brain regions across the whole brain. The results showed that CB1 R-positive cells number changed with age. Specifically, CB1 R expression increased with age in some subregions of the cortex, decreased with age in the lateral septal area, and reached its lowest level at 52 weeks in the thalamus, hypothalamus, and hindbrain subregions. Cluster analysis revealed that some brain regions shared similar temporal characteristics in CB1 R-positive cell number across the lifespan. Our results provide evidence that investigation of the neural basis of age-related characteristics of female brain functions is not only warranted but required.

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Background: The underlying factors of the male predominance in Autism Spectrum Disorders (ASD) are largely unknown, although a female advantage in social communication has been pointed out as a potential factor. Recently, attention has been given to ASD as a sensory processing disorder, focusing on the audio-visual temporal processing paramount for the development of communication. In ASD, a deviant audio-visual processing has been noted, resulting in difficulties interpreting multisensory information. Typically Developed (TD) females have shown an enhanced language processing in unisensory situations compared to multisensory situations. We aim to find out whether such an advantage also can be seen in girls within the ASD population, and if so, is it related to social communication skills? Method: Forty children (IQ > 85), 20 females (mean age = 13.90 years, SD = 2.34) and 20 males (mean age = 12.15 years, SD = 2.83) triaged for an ASD assessment were recruited from a child and youth psychiatric clinic in Sweden. Using The Social Responsiveness Scale (SRS) we looked at associations with child performance on the Integrated Visual and Auditory Continuous Performance Test (IVA-2). Results: An auditory advantage in the female group was associated with less rated problems in social communications in unisensory processing whereas in multisensory processing an auditory dominance was associated with more rated problems in Social Awareness. In the male group, a visual dominance was associated with more rated problems in Social Rigidity. Conclusion: A female unisensory processing advantage in ASD could very well be explaining the male domination in ASD. However, the social difficulties related to multisensory processing indicate that ASD females might be struggling as hard as males in more complex settings. Implications on the assessment procedure are discussed.

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The paper reviews the relations between sex and brain in light of the binary conceptualization of these relations and the challenges posed to it by the 'mosaic' hypothesis. Recent formulations of the binary framework range from arguing that the typical male brain is different from the typical female brain to claiming that brains are typically male or female because brain structure can be used to predict the sex category (female/male) of the brain's owner. These formulations are challenged by evidence that sex effects on the brain may be opposite under different conditions, that human brains are comprised of mosaics of female-typical and male-typical features, and that sex category explains only a small part of the variability in human brain structure. These findings led to a new, non-binary, framework, according to which mosaic brains reside in a multi-dimensional space that cannot meaningfully be reduced to a male-female continuum or to a binary variable. This framework may also apply to sex-related variables and has implications for research.

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Acoustic communication signals are typically generated to influence the behavior of conspecific receivers. In songbirds, for instance, such cues are routinely used by males to influence the behavior of females and rival males. There is remarkable diversity in vocalizations across songbird species, and the mechanisms of vocal production have been studied extensively, yet there has been comparatively little emphasis on how the receiver perceives those signals and uses that information to direct subsequent actions. Here, we emphasize the receiver as an active participant in the communication process. The roles of sender and receiver can alternate between individuals, resulting in an emergent feedback loop that governs the behavior of both. We describe three lines of research that are beginning to reveal the neural mechanisms that underlie the reciprocal exchange of information in communication. These lines of research focus on the perception of the repertoire of songbird vocalizations, evaluation of vocalizations in mate choice, and the coordination of duet singing.

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Findings of average differences between females and males in the structure of specific brain regions are often interpreted as indicating that the typical male brain is different from the typical female brain. An alternative interpretation is that the brain types typical of females are also typical of males, and sex differences exist only in the frequency of rare brain types. Here we contrasted the two hypotheses by analyzing the structure of 2176 human brains using three analytical approaches. An anomaly detection analysis showed that brains from females are almost as likely to be classified as "normal male brains," as brains from males are, and vice versa. Unsupervised clustering algorithms revealed that common brain "types" are similarly common in females and in males and that a male and a female are almost as likely to have the same brain "type" as two females or two males are. Large sex differences were found only in the frequency of some rare brain "types." Last, supervised clustering algorithms revealed that the brain "type(s)" typical of one sex category in one sample could be typical of the other sex category in another sample. The present findings demonstrate that even when similarity and difference are defined mathematically, ignoring biological or functional relevance, sex category (i.e., whether one is female or male), is not a major predictor of the variability of human brain structure. Rather, the brain types typical of females are also typical of males, and vice versa, and large sex differences are found only in the prevalence of some rare brain types. We discuss the implications of these findings to studies of the structure and function of the human brain.

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Mitochondria play an essential role in the generation of steroid hormones including the female sex hormones. These hormones are, in turn, able to modulate mitochondrial activities. Mitochondria possess crucial roles in cell maintenance, survival and well-being, because they are the main source of energy as well as of reactive oxygen species (ROS) within the cell. The impairment of these important organelles is one of the central features of aging. In women's health, estrogen plays an important role during adulthood not only in the estrous cycle, but also in the brain via neuroprotective, neurotrophic and antioxidant modes of action. The hypestrogenic state in the peri- as well as in the prolonged postmenopause might increase the vulnerability of elderly women to brain degeneration and age-related pathologies. However, the underlying mechanisms that affect these processes are not well elucidated. Understanding the relationship between estrogen and mitochondria might therefore provide better insights into the female aging process. Thus, in this review, we first describe mitochondrial dysfunction in the aging brain. Second, we discuss the estrogen-dependent actions on the mitochondrial activity, including recent evidence of the estrogen-brain-derived neurotrophic factor and estrogen-sirtuin 3 (SIRT3) pathways, as well as their potential implications during female aging.

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The perimenopause is an aging transition unique to the female that leads to reproductive senescence which can be characterized by multiple neurological symptoms. To better understand potential underlying mechanisms of neurological symptoms of perimenopause, the present study determined genomic, biochemical, brain metabolic, and electrophysiological transformations that occur during this transition using a rat model recapitulating fundamental characteristics of the human perimenopause. Gene expression analyses indicated two distinct aging programs: chronological and endocrine. A critical period emerged during the endocrine transition from regular to irregular cycling characterized by decline in bioenergetic gene expression, confirmed by deficits in fluorodeoxyglucose-positron emission tomography (FDG-PET) brain metabolism, mitochondrial function, and long-term potentiation. Bioinformatic analysis predicted insulin/insulin-like growth factor 1 and adenosine monophosphate-activated protein kinase/peroxisome proliferator-activated receptor gamma coactivator 1 alpha (AMPK/PGC1α) signaling pathways as upstream regulators. Onset of acyclicity was accompanied by a rise in genes required for fatty acid metabolism, inflammation, and mitochondrial function. Subsequent chronological aging resulted in decline of genes required for mitochondrial function and ß-amyloid degradation. Emergence of glucose hypometabolism and impaired synaptic function in brain provide plausible mechanisms of neurological symptoms of perimenopause and may be predictive of later-life vulnerability to hypometabolic conditions such as Alzheimer's.

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One unexpected property of selective serotonin reuptake inhibitors is their ability, at doses well below those that effect 5-HT systems, to raise brain concentrations of neuroactive steroids such as the progesterone metabolite allopregnanolone. In women, rapid withdrawal from allopregnanolone when progesterone secretion drops sharply in the late luteal phase precipitates menstrual cycle-linked disorders such as premenstrual syndrome and catamenial epilepsy. Short-term, low-dose fluoxetine during the late luteal phase has the potential to prevent the development of such disorders, by raising brain allopregnanolone concentration. In female rats, withdrawal from allopregnanolone, as ovarian progesterone secretion falls rapidly in the late diestrus phase (similar to late luteal phase in women), induces upregulation of extrasynaptic GABAA receptors on GABAergic neurons in brain regions involved in mediating anxiety-like behaviors. The functional consequence of this receptor plasticity is disinhibition of principal neurons, hyperexcitable neuronal circuitry and increased behavioral responsiveness to anxiogenic stress. These withdrawal responses were prevented by short-term treatment with fluoxetine during the late diestrus phase, which raised brain allopregnanolone concentration, so blunting the rapid physiological fall. The steroid-stimulating properties of fluoxetine offer untapped opportunities for developing new treatments for menstrual cycle-linked disorders in women, which are precipitated by abrupt falls in brain concentration of allopregnanolone.

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The underlying assumption in popular and scientific publications on sex differences in the brain is that human brains can take one of two forms "male" or "female," and that the differences between these two forms underlie differences between men and women in personality, cognition, emotion, and behavior. Documented sex differences in brain structure are typically taken to support this dimorphic view of the brain. However, neuroanatomical data reveal that sex interacts with other factors in utero and throughout life to determine the structure of the brain, and that because these interactions are complex, the result is a multi-morphic, rather than a dimorphic, brain. More specifically, here I argue that human brains are composed of an ever-changing heterogeneous mosaic of "male" and "female" brain characteristics (rather than being all "male" or all "female") that cannot be aligned on a continuum between a "male brain" and a "female brain." I further suggest that sex differences in the direction of change in the brain mosaic following specific environmental events lead to sex differences in neuropsychiatric disorders.

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Alpha-fetoprotein (AFP) is a well-known diagnostic biomarker used in medicine to detect fetal developmental anomalies such as neural tube defects or Down's syndrome, or to follow up the development of tumors such as hepatocellular carcinomas. However, and despite the fact that the protein was discovered almost half a century ago, little was known about its physiological function. The study of Afp knock-out mice uncovered a surprising function of AFP: it is essential for female fertility and for expression of normal female behaviors, and this action is mediated through its estrogen binding capacity. AFP sequestrates estrogens and by so doing protects the female developing brain from deleterious (defeminizing/masculinizing) effects of these hormones.