Integrating high-throughput genetics and neuroimaging technologies promises greater information on neurobiological anomalies in neurodegenerative diseases

The integration of high-throughput genomics and neuroimaging technology has the promise of providing more information about neurobiological irregularities in neurodegenerative illnesses. Transcriptomics-derived connections provide insight into the molecular trajectory of neurodegeneration, prioritizing particular systems and networks while also considering other aspects, including neuropathology and cognition. Causal links between gene expression and brain morphology are unknown, however. If omics systems have a wide impact upstream, they can influence morphological changes identified by MRI. Gene expression is a signal indicating a process already underway in a diseased brain area if it is downstream of structural brain changes. More study on people in the early stages of disease may give insights into the temporal connection between anatomical and expression problems. One such thought is molecular stereotactic propagation. Changes in gene expression may travel across the brain, according to this notion, through tractography trails. They follow the successive pattern of afflicted regions and the temporal distribution of sensitive locations. In addition, cell motility genes are often overexpressed in vulnerable locations. On the other hand, the data on gene expression and its relevance to structural change propagation is still conflicting. The role of immunological processes and motility-related genes in neurodegeneration appears to be validated by expression data. There are no therapies available for neurodegenerative diseases. Symptom development and diagnosis is often delayed due to advanced MRI and clinical stages. Early diagnosis is crucial since therapy interventions should ideally aim at commencing pathogenic processes as soon as possible to avoid the onset of disease and restrict the course of disease. This requires dependable neurodegenerative biomarkers with diagnostic validity. Unfortunately, transcriptomics still has several important limitations. There is a paucity of high-quality expression data encompassing a large number of brain regions, expression data generally collected from healthy persons, comparisons with neuroimaging data from degenerative cohorts, and a lack of consistent approach for transcriptional imaging research. By overcoming this barrier, researchers can uncover prodromal stages of neurodegeneration and therapeutic molecular targets