Leveraging research into sex differences and steroid hormones to improve brain health
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Dopaminergic modulation and dosage effects on brain state dynamics and working memory component processes in Parkinson’s disease
Parkinson’s disease (PD) is primarily diagnosed through its characteristic motor deficits, yet it also encompasses progressive cognitive impairments that profoundly affect quality of life. While dopaminergic medications are routinely prescribed to manage motor symptoms in PD, their influence extends to cognitive functions as well. Here we investigate how dopaminergic medication influences aberrant brain circuit dynamics associated with encoding, maintenance and retrieval working memory (WM) task-phases processes. PD participants, both on and off dopaminergic medication, and healthy controls, performed a Sternberg WM task during fMRI scanning. We employ a Bayesian state-space computational model to delineate brain state dynamics related to different task phases. Importantly, a within-subject design allows us to examine individual differences in the effects of dopaminergic medication on brain circuit dynamics and task performance. We find that dopaminergic medication alters connectivity within prefrontal-basal ganglia-thalamic circuits, with changes correlating with enhanced task performance. Dopaminergic medication also restores engagement of task-phase-specific brain states, enhancing task performance. Critically, we identify an “inverted-U-shaped” relationship between medication dosage, brain state dynamics, and task performance. Our study provides valuable insights into the dynamic neural mechanisms underlying individual differences in dopamine treatment response in PD, paving the way for more personalized therapeutic strategies.
PGRMC2 is a pressure-volume regulator critical for myocardial responses to stress in mice
Progesterone receptors are classified into nuclear and membrane-bound receptor families. Previous unbiased proteomic studies indicate a potential association between cardiac diseases and the progesterone receptor membrane-bound component-2 (PGRMC2); however, the role of PGRMC2 in the heart remains unknown. In this study, we use a heart-specific knockout (KO) mouse model (MyH6•Pgrmc2flox/flox) in which the Pgrmc2 gene was selectively deleted in cardiomyocytes. Here we show that PGRMC2 serves as a mediator of steroid hormones for rapid calcium signaling in cardiomyocytes to maintain cardiac contraction, sufficient stroke volume, and adequate cardiac output by regulating the cardiac pressure-volume relationship. The KO hearts from male and female mice exhibit an impairment in pressure-volume relationship. Under hypoxic conditions, this pressure-volume dysregulation progresses to congestive left and right ventricular failure in the KO hearts. Overall, we propose that PGRMC2 is a cardiac pressure-volume regulator to maintain normal cardiac physiology, especially during hypoxic stress.
The impact of biological sex on diseases of the urinary tract
Biological sex, being female or male, broadly influences diverse immune phenotypes, including immune responses to diseases at mucosal surfaces. Sex hormones, sex chromosomes, sexual dimorphism, and gender differences all contribute to how an organism will respond to diseases of the urinary tract, such as bladder infection or cancer. Although the incidence of urinary tract infection is strongly sex biased, rates of infection change over a lifetime in women and men, suggesting that accompanying changes in the levels of sex hormones may play a role in the response to infection. Bladder cancer is also sex biased in that 75% of newly diagnosed patients are men. Bladder cancer development is shaped by contributions from both sex hormones and sex chromosomes, demonstrating that the influence of sex on disease can be complex. With a better understanding of how sex influences disease and immunity, we can envision sex-specific therapies to better treat diseases of the urinary tract and potentially diseases of other mucosal tissues.
Functional brain network dynamics mediate the relationship between female reproductive aging and interpersonal adversity
Premature reproductive aging is linked to heightened stress sensitivity and psychological maladjustment across the life course. However, the brain dynamics underlying this relationship are poorly understood. Here, to address this issue, we analyzed multimodal data from female participants in the Adolescent Brain and Cognitive Development (longitudinal, N = 441; aged 9–12 years) and Human Connectome-Aging (cross-sectional, N = 130; aged 36–60 years) studies. Age-specific intrinsic functional brain network dynamics mediated the link between reproductive aging and perceptions of greater interpersonal adversity. The adolescent profile overlapped areas of greater glutamatergic and dopaminergic receptor density, and the middle-aged profile was concentrated in visual, attentional and default mode networks. The two profiles showed opposite relationships with patterns of functional neural network variability and cortical atrophy observed in psychosis versus major depressive disorder. Our findings underscore the divergent patterns of brain aging linked to reproductive maturation versus senescence, which may explain developmentally specific vulnerabilities to distinct disorders.
Self-reports map the landscape of task states derived from brain imaging
Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.
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