Study protocol of IMAGINE-HD: Imaging iron accumulation and neuroinflammation with 7T-MRI + CSF in Huntington's disease

How does the age of control individuals hinder the identification of target genes for Huntington's disease?

Several studies have compared the transcriptome across various brain regions in Huntington's disease (HD) gene-positive and neurologically normal individuals to identify potential differentially expressed genes (DEGs) that could be pharmaceutical or prognostic targets for HD. Despite adhering to technical recommendations for optimal RNA-Seq analysis, none of the genes identified as upregulated in these studies have yet demonstrated success as prognostic or therapeutic targets for HD. Earlier studies included samples from neurologically normal individuals older than the HD gene-positive group. Considering the gradual transcriptional changes induced by aging in the brain, we posited that utilizing samples from older controls could result in the misidentification of DEGs. To validate our hypothesis, we reanalyzed 146 samples from this study, accessible on the SRA database, and employed Propensity Score Matching (PSM) to create a "virtual" control group with a statistically comparable age distribution to the HD gene-positive group. Our study underscores the adverse impact of using neurologically normal individuals over 75 as controls in gene differential expression analysis, resulting in false positives and negatives. We conclusively demonstrate that using such old controls leads to the misidentification of DEGs, detrimentally affecting the discovery of potential pharmaceutical and prognostic markers. This underscores the pivotal role of considering the age of control samples in RNA-Seq analysis and emphasizes its inclusion in evaluating best practices for such investigations. Although our primary focus is HD, our findings suggest that judiciously selecting age-appropriate control samples can significantly improve best practices in differential expression analysis.

Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders

Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.