Women develop addiction and drug-related health consequences after fewer years of drug use than men; this accelerated time course, or telescoping effect, has been observed clinically for multiple drugs, including opioids. Preclinical studies indicate that this is a biologically based phenomenon; however, these studies have focused exclusively on cocaine, and none have considered health effects.
In this study, we used a rat (Sprague Dawley) model to determine sex differences in the time course for the development of an opioid addiction–like phenotype, as defined by the development of physical dependence (withdrawal-induced weight loss) and an increase in motivation for fentanyl (under a progressive-ratio schedule). Effects were determined following either 10 days (optimized, experiment 1) or 3 days (threshold, experiment 2) of extended-access fentanyl self-administration (24 hours/day, fixed ratio 1, 2- to 5-minute trials/hour) or following short-access fentanyl self-administration (subthreshold, experiment 3; fixed ratio 1, up to 40 infusions/day). Opioid-related adverse health effects were also determined (experiment 4).
Motivation for fentanyl was similarly increased in males and females following 10 days of extended-access self-administration (experiment 1), was transiently increased in females, but not males, following 3 days of extended-access self-administration (experiment 2) and was not increased in either sex following short-access self-administration (experiment 3). Females developed fentanyl-associated adverse health effects more readily than males (experiment 4), with particularly robust differences during extended-access self-administration and withdrawal.
As with findings in humans, female rats developed opioid addiction–like features and adverse health consequences more readily than male rats. These data provide support for a biologically based telescoping effect in females for opioids, particularly for opioid-related adverse health consequences.
Short mindfulness-based interventions have gained traction in research due to their positive impact on well-being, cognition, and clinical symptoms across various settings. However, these short-term trainings are viewed as preliminary steps within a more extensive transformative path, presumably leading to long-lasting trait changes. Despite this, little is still known about the brain correlates of these meditation traits.
To address this gap, we investigated the neural correlates of meditation expertise in long-term Buddhist practitioners, comparing the large-scale brain functional connectivity of 28 expert meditators with 47 matched novices. Our hypothesis posited that meditation expertise would be associated with specific and enduring patterns of functional connectivity present during both meditative (open monitoring/open presence and loving-kindness and compassion meditations) and nonmeditative resting states, as measured by connectivity gradients.
Applying a support vector classifier to states not included in training, we successfully decoded expertise as a trait, demonstrating its non–state-dependent nature. The signature of expertise was further characterized by an increased integration of large-scale brain networks, including the dorsal and ventral attention, limbic, frontoparietal, and somatomotor networks. The latter correlated with a higher ability to create psychological distance from thoughts and emotions.
Such heightened integration of bodily maps with affective and attentional networks in meditation experts could point toward a signature of the embodied cognition cultivated in these contemplative practices.
Aberrant functional connectivity is a hallmark of schizophrenia. The precise nature and mechanism of dysconnectivity in schizophrenia remains unclear, but evidence suggests that dysconnectivity is different in wake versus sleep. Microstate analysis uses electroencephalography (EEG) to investigate large-scale patterns of coordinated brain activity by clustering EEG data into a small set of recurring spatial patterns, or microstates. We hypothesized that this technique would allow us to probe connectivity between brain networks at a fine temporal resolution and uncover previously unknown sleep-specific dysconnectivity.
We studied microstates during sleep in patients with schizophrenia by analyzing high-density EEG sleep data from 114 patients with schizophrenia and 79 control participants. We used a polarity-insensitive k-means analysis to extract a set of 6 microstate topographies.
These 6 states included 4 widely reported canonical microstates. In patients and control participants, falling asleep was characterized by a shift from microstates A, B, and C to microstates D, E, and F. Microstate F was decreased in patients during wake, and microstate E was decreased in patients during sleep. The complexity of microstate transitions was greater in patients than control participants during wake, but this reversed during sleep.
Our findings reveal behavioral state–dependent patterns of cortical dysconnectivity in schizophrenia. Furthermore, these findings are largely unrelated to previous sleep-related EEG markers of schizophrenia such as decreased sleep spindles. Therefore, these findings are driven by previously undescribed sleep-related pathology in schizophrenia.