Iman Beheshti

Despite extensive evidence linking chronotype to behavioral and physiological outcomes, its structural neuroanatomical correlates, especially in healthy young adults, remain insufficiently characterized, and multimodal structural investigations integrating Voxel-based morphometry (VBM), cortical thickness (CT), and brain-age metrics are still limited. We examined whether chronotype—preference for early or late sleep–wake timing—is associated with structural brain variation in 136 healthy young adults (68 early chronotypes [EC], 68 late chronotypes [LC]) using high-resolution MRI. Early and late chronotypes were defined using the Morningness–Eveningness subscale of the Chronotype Questionnaire (ChQ-ME): early chronotype (EC) scores 11–21 and late chronotype (LC) scores 22–32. The VBM analyses were conducted to assess gray and white matter morphology, complemented by CT analyses and estimation of brain-predicted age difference (Brain-PAD) as an index of biological brain aging. Sensitivity analyses additionally modeled ChQ-ME as a continuous predictor. Primary voxel-wise VBM analyses did not identify between-group differences in gray or white matter morphology that survived family-wise error (FWE) correction ( p < 0.05). In pre-specified exploratory analyses (voxel-wise p < 0.001, uncorrected; cluster-level false discovery rate [FDR] correction q < 0.05), late chronotype (LC) participants showed an exploratory left cerebellar/occipital cluster with lower gray matter volume. Region-wise CT differences were nominal ( p < 0.05) and did not survive FDR correction across regions. No significant differences in Brain-PAD were observed. In healthy young adults, chronotype-related structural differences were not detectable under conservative voxel-wise FWE correction; however, pre-specified exploratory analyses suggested a regionally specific cerebellar gray matter pattern and nominal CT trends. These findings motivate larger and longitudinal studies with objective sleep–wake timing measures to clarify whether sleep timing is linked to early structural variation and to test its potential modifiability.

Cognitive decline in older adults, particularly during the preclinical stages of Alzheimer’s disease (AD), presents a critical opportunity for early detection and intervention. While T1-weighted MRI is widely used in AD research, its capacity to identify early vulnerability and monitor longitudinal progression remains incompletely characterized. We analyzed longitudinal T1-weighted MRI data from 224 cognitively unimpaired older adults followed for up to 12 years. Participants were stratified by clinical outcome into converters to mild cognitive impairment (HC-converters, n = 112) and stable controls (HC-stable, n = 112). Groups were matched at baseline for age (mean ~ 74–75 years), education (~ 16.4 years), and cognitive scores (MMSE ≈ 29; CDR-SB ≈ 0.04). Four MRI-derived biomarkers were examined: brain-predicted age difference (brain-PAD), mean cortical thickness, AD-cortical signature, and hippocampal volume. Brain-PAD showed the strongest baseline association with future conversion (β = 1.25, t = 3.52, p = 0.0009) and highest classification accuracy (AUC = 0.66; sensitivity = 62%, and specificity = 67%). Longitudinal mixed-effects models focusing on the group × time interaction revealed a significant positive slope in brain-PAD for converters (β = 0.0079, p = 0.003) and a non-significant trend in stable controls (β = 0.0047, p = 0.075), indicating incipient divergence in brain aging trajectories during the preclinical window. Hippocampal volume and AD-cortical signature declined similarly in both groups. The mean cortical thickness had limited discriminative or dynamic utility. These findings support brain-PAD, derived from routine T1-weighted MRI using machine learning, as a sensitive, performance-independent biomarker for early risk stratification and monitoring of cognitive aging trajectories.

Prostate cancer remains a major global health challenge, ranking as the second most common malignancy in men worldwide. Advances in diagnostic and therapeutic strategies have transformed its management, enhancing patient outcomes and quality of life. This review highlights recent breakthroughs in imaging, including multiparametric MRI and PSMA-PET, which have improved cancer detection and staging. Biomarker-based diagnostics, such as PHI and 4K Score, offer precise risk stratification, reducing unnecessary biopsies. Innovations in treatment, including robotic-assisted surgery, novel hormone therapies, immunotherapy, and PARP inhibitors, are redefining care for localized and advanced prostate cancer. Artificial intelligence (AI) and machine learning (ML) are emerging as powerful tools to optimize diagnostics, risk prediction, and treatment personalization. Additionally, advances in radiation therapy, such as IMRT and SBRT, provide targeted and effective options for high-risk patients. While these innovations have significantly improved survival and minimized overtreatment, challenges remain in optimizing therapy sequencing and addressing disparities in care. The integration of AI, theranostics, and gene-editing technologies holds immense promise for the future of prostate cancer management.

Introduction: Sleep patterns are critical for brain health and functioning, influencing mental health, learning, memory, and emotional regulation, particularly in young adults. However, the effects of chronotype, sleep quality, and daytime sleepiness on brain structure remain poorly understood. This study aimed to examine anatomical brain changes in individuals with early chronotype (EC) compared to those with late chronotype (LC). Early and late chronotypes, which refer to preferences for early or late sleep-wake times, represent distinct biological rhythms that may impact brain structure and function. Methods: We used anatomical magnetic resonance imaging (MRI) to identify morphological and macroscopic brain differences between 68 young, healthy EC individuals and 68 age– and sex-matched LC participants. Two widely used atrophy estimation pipelines, voxel-based morphometry (VBM) and cortical thickness analysis, were employed to assess regional brain structure differences between the two groups. Results: Whole-brain VBM analysis revealed decreased gray matter volume in LC individuals, specifically in the left cerebellum posterior lobe, left declive, and left cerebellum crus I. Additionally, linear regression models showed significant cortical thinning in the left caudal anterior cingulate, right caudal anterior cingulate, and right lateral occipital regions in the LC group compared to the EC group. However, there was no significant difference between the two groups in terms of white matter morphology or brain aging. Discussion: Our findings suggest that chronotype-related differences in sleep patterns are associated with significant structural alterations in gray matter volume and cortical thickness. These changes may reflect the impact of chronic circadian misalignment on neurobiological integrity, even in young, healthy individuals. Findings emphasize the importance of considering sleep timing as a critical factor in brain health and lay the groundwork for future longitudinal studies.

Understanding the risk and protective factors associated with Parkinson’s disease (PD) is crucial for improving outcomes for patients, individuals at risk, healthcare providers, and healthcare systems. Studying these factors not only enhances our knowledge of the disease but also aids in developing effective prevention, management, and treatment strategies. This paper reviews the key risk and protective factors associated with PD, with a particular focus on the biological mechanisms underlying these factors. Risk factors include genetic mutations, racial predispositions, and environmental exposures, all of which contribute to an increased likelihood of developing PD or accelerating its progression. Conversely, protective factors, such as regular physical exercise, adherence to a Mediterranean diet, and higher urate levels, have the potential to reduce inflammation and support mitochondrial function, thereby mitigating the risk of disease. However, identifying and validating these factors presents significant challenges. These challenges include the absence of reliable biomarkers, intricate interactions between genetic and environmental components, and clinical heterogeneity observed in patients with PD. These barriers complicate the establishment of clear causal relationships and hinder the development of targeted preventive strategies. To overcome these challenges, we propose several solutions and recommendations. Understanding the mechanisms underlying risk factors may inform future research aimed at developing standardized and more accurate biomarkers for PD, facilitating earlier diagnosis and improved monitoring of disease progression. Additionally, we offer actionable recommendations for PD prevention and management tailored to healthy individuals, patients diagnosed with PD, and healthcare systems. These strategies aim to improve clinical outcomes, enhance the quality of life, and optimize healthcare delivery for PD.