Brain imaging data are increasingly made publicly accessible, and volumetric imaging measures derived from population-based cohorts may serve as normative data for individual patient diagnostic assessment. Yet, these normative cohorts are usually not a perfect reflection of a patient's base population, nor are imaging parameters such as field strength or scanner type similar. In this proof of principle study, we assessed differences between reference curves of subcortical structure volumes of normal controls derived from two population-based studies and a case-control study. We assessed the impact of any differences on individual assessment of brain structure volumes. Percentile curves were fitted on the three healthy cohorts. Next, percentile values for these subcortical structures for individual patients from these three cohorts, 91 mild cognitive impairment and 95 Alzheimer's disease cases and patients from the Alzheimer Center, were calculated, based on the distributions of each of the three cohorts. Overall, we found that the subcortical volume normative data from these cohorts are highly interchangeable, suggesting more flexibility in clinical implementation.
Genome-wide and brain-wide association studies
Genome-wide association studies (GWAS) have identified dozens of genetic variants related to brain structure. These studies have focused on a few aggregate measures since GWAS are computationally demanding. However, the brain is complex and can be described using millions of measures. We recently developed methods to perform such genome-wide and brain-wide association studies and applied this in several cohort studies. We would like to replicate our findings in the UK Biobank by jointly meta-analyzing the results. It fits well with the purpose of the UK Biobank in two ways:
1) It build upon this major resource by providing a range of novel neuroimaging biomarkers, which will be made available to other researchers.
2) It will hopefully identify novel genetic variants that are important for brain structure and diseases of the brain (e.g., Alzheimer's disease, schizophrenia) We will analyze brain images to calculate million of measures that describe the structure of the brain. Next, we will perform genome-wide screens of millions of genetic variants to identify ones that affect brain structure. The full cohort of individuals with both brain imaging and genetic data available.
|Lead investigator:||Dr Hieab Adams|
|Lead institution:||Erasmus MC|
1 related Return
|Return ID||App ID||Description||Archive Date|
|3342||23509||GenNet framework: interpretable neural networks for phenotype prediction||15 Apr 2021|