• Integrating multi-omics datasets and multimodal imaging features toward correct diagnosis and treatment of neurocognitive disorders
  • Ramin Ardalani,1 Ehsan Sharif-Paghaleh,2 Zahra Salehi,3,*
    1. Preclinical Core Facility, Immunological Disorders Imaging Group, Tehran University of Medical Sciences, Tehran, Iran
    2. Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
    3. Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran


  • Introduction: Neurocognitive disorders (NCDs) are heterogeneous group of diseases accompanied by cognitive decline. With the prevalence of nearly 50 million patients worldwide, NCDs rises exponentially with the increasing ageing population. Hence, early diagnosis which provides care at the earliest possible stage will improve the diseases outcome. Due to exploring the interactions across multiple types of biological features, multi-omics studies have the ability to provide a holistic view of causal and functional mechanisms associated with complex diseases. Moreover, utilizing multimodal imaging modalities in clinical and preclinical settings yields the complementary and complete visualization of diseases and their stages. Here, we present a viewpoint on how the diagnosis and treatment of neurocognitive disorders could be improved by multi-omics and multi-modal imaging data integration.
  • Methods: We searched relevant studies through PubMed/Medline and Google Scholar.
  • Results: The use of multi-omics and multimodal imaging modalities might have the potential to be the gold standard approach in the modern clinical practice to a correct disease diagnosis.
  • Conclusion: Novel data analysis methods are required to integrate multimodal molecular-omics and neuroimaging data acquired from multiple experiments in different conditions and to determine the relevance of the results to human disease.
  • Keywords: Neurocognitive disorders, multi-omics, multimodal imaging, data integration