Cocaine Destroys Gray Matter Brain Cells and Accelerates Brain Aging

Chronic Cocaine Use and Parkinson's Disease: An Interpretative Model

Over the years, the growing "epidemic" spread of cocaine use represents a crucial public health and social problem worldwide. According to the 2023 World Drug Report, 0.4% of the world's population aged 15 to 64 report using cocaine; this number corresponds to approximately 24.6 million cocaine users worldwide and approximately 1 million subjects with cocaine use disorder (CUD). While we specifically know the short-term side effects induced by cocaine, unfortunately, we currently do not have exhaustive information about the medium/long-term side effects of the substance on the body. The scientific literature progressively highlights that the chronic use of cocaine is related to an increase in cardio- and cerebrovascular risk and probably to a greater incidence of psychomotor symptoms and neurodegenerative processes. Several studies have highlighted an increased risk of antipsychotic-induced extrapyramidal symptoms (EPSs) in patients with psychotic spectrum disorders comorbid with psychostimulant abuse. EPSs include movement dysfunction such as dystonia, akathisia, tardive dyskinesia, and characteristic symptoms of Parkinsonism such as rigidity, bradykinesia, and tremor. In the present paper, we propose a model of interpretation of the neurobiological mechanisms underlying the hypothesized increased vulnerability in chronic cocaine abusers to neurodegenerative disorders with psychomotor symptoms. Specifically, we supposed that the chronic administration of cocaine produces significant neurobiological changes, causing a complex dysregulation of various neurotransmitter systems, mainly affecting subcortical structures and the dopaminergic pathways. We believe that a better understanding of these cellular and molecular mechanisms involved in cocaine-induced neuropsychotoxicity may have helpful clinical implications and provide targets for therapeutic intervention.

Investigating cocaine- and abstinence-induced effects on astrocyte gene expression in the nucleus accumbens

In recent years, astrocytes have been increasingly implicated in cellular mechanisms of substance use disorders (SUD). Astrocytes are structurally altered following exposure to drugs of abuse; specifically, astrocytes within the nucleus accumbens (NAc) exhibit significantly decreased surface area, volume, and synaptic colocalization after operant self-administration of cocaine and extinction or protracted abstinence (45 days). However, the mechanisms that elicit these morphological modifications are unknown. The current study aims to elucidate the molecular modifications that lead to observed astrocyte structural changes in rats across cocaine abstinence using astrocyte-specific RiboTag and RNA-seq, as an unbiased, comprehensive approach to identify genes whose transcription or translation change within NAc astrocytes following cocaine self-administration and extended abstinence. Using this method, our data reveal cellular processes including cholesterol biosynthesis that are altered specifically by cocaine self-administration and abstinence, suggesting that astrocyte involvement in these processes is changed in cocaine-abstinent rats. Overall, the results of this study provide insight into astrocyte functional adaptations that occur due to cocaine exposure or during cocaine withdrawal, which may pinpoint further mechanisms that contribute to cocaine-seeking behavior.

Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features

Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.

Differences in brain aging between sexes in Parkinson's disease

Parkinson's disease (PD) is linked to faster brain aging. Male sex is associated with higher prevalence, severe symptoms, and a faster progression rate in PD. There remains a significant gap in understanding the function of sex in the process of brain aging in PD. The structural T1-weighted MRI-driven brain-predicted age difference (i.e., Brain-PAD: the actual age subtracted from the brain-predicted age) was computed in a group of 373 people with PD (mean age ± SD: 61.37 ± 9.81, age range: 33-85, 34% female) from the Parkinson's Progression Marker Initiative database using a robust brain-age estimation framework that was trained on 949 healthy subjects. Linear regression models were used to investigate the association between Brain-PAD and clinical variables in PD, stratified by sex. Males with Parkinson's disease (PD-M) exhibited a significantly higher mean Brain-PAD than their female counterparts (PD-F) (t(256) = 2.50, p = 0.012). In the propensity score-matched PD-M group (PD-M*), Brain-PAD was found to be associated with a decline in general cognition, a worse degree of sleep behavior disorder, reduced visuospatial acuity, and caudate atrophy. Conversely, no significant links were observed between these factors and Brain-PAD in the PD-F group. Having 'older' looking brains in PD-M than PD-F supports the idea that sex plays a vital function in PD, such that the PD mechanism may be different in males and females. This study has the potential to broaden our understanding of dissimilarities in brain aging between sexes in the context of PD.

Improving brain age prediction with anatomical feature attention-enhanced 3D-CNN

Currently, significant progress has been made in predicting brain age from structural Magnetic Resonance Imaging (sMRI) data using deep learning techniques. However, despite the valuable structural information they contain, the traditional engineering features known as anatomical features have been largely overlooked in this context. To address this issue, we propose an attention-based network design that integrates anatomical and deep convolutional features, leveraging an anatomical feature attention (AFA) module to effectively capture salient anatomical features. In addition, we introduce a fully convolutional network, which simplifies the extraction of deep convolutional features and overcomes the high computational memory requirements associated with deep learning. Our approach outperforms several widely-used models on eight publicly available datasets (n = 2501), with a mean absolute error (MAE) of 2.20 years in predicting brain age. Comparisons with deep learning models lacking the AFA module demonstrate that our fusion model effectively improves overall performance. These findings provide a promising approach for combining anatomical and deep convolutional features from sMRI data to predict brain age, with potential applications in clinical diagnosis and treatment, particularly for populations with age-related cognitive decline or neurological disorders.