Effects of quercetin and derivatives on NAMPT/Sirtuin-1 metabolic pathway in neuronal cells: an approach to mitigate chemotherapy-induced cognitive impairment.

 The cognitive alterations observed in individuals undergoing cancer treatments have garnered more attention recently. Chemotherapy can reduce nicotinamide adenine dinucleotide (NAD+) levels by inhibiting nicotinamide phosphoribosyl transferase (NAMPT). This reduction can make cancer cells more susceptible to oxidative damage and death and may also affect non-cancerous cells, particularly the brain cells.

Circulating Blood-Based Proteins in Psychopathology and Cognition: A Mendelian Randomization Study.

Importance: Peripheral (blood-based) biomarkers for psychiatric illness could benefit diagnosis and treatment, but research to date has typically been low throughput, and traditional case-control studies are subject to potential confounds of treatment and other exposures. Large-scale 2-sample mendelian randomization (MR) can examine the potentially causal impact of circulating proteins on neuropsychiatric phenotypes without these confounds.

Objective: To identify circulating proteins associated with risk for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) as well as cognitive task performance (CTP).

Altered Functional Connectivity Dynamics Serving Cognitive Flexibility in Regular Cannabis Users.

Despite its widespread use and popularity, cannabis is known to impact higher order cognitive processes such as attention and executive function. However, far less is known about the impact of chronic cannabis use on cognitive flexibility, a component of executive function, and this is especially true for the underlying functional connectivity dynamics. To address this, we enrolled 25 chronic cannabis users and 30 demographically matched non-users who completed an interview probing current and past substance use, a urinalysis to confirm self-reported substance use and a task-switch cognitive paradigm during magnetoencephalography (MEG).

Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease.

This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro- and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India.

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts.

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort).