From Big Data to the Clinic: Methodological and Statistical Enhancements to Implement the UK Biobank Imaging Framework in a Memory Clinic

Résumé fourni par la source

The analysis tools and statistical methods used in large neuroimaging research studies differ from those applied in clinical contexts, making it unclear whether these techniques can be translated to a memory clinic setting. The Oxford Brain Health Clinic (OBHC) was established in 2020 to bridge this gap between research studies and memory clinics. We optimised the UK Biobank imaging framework for the memory clinic setting by integrating enhanced quality control (QC) processes (MRIQC, QUAD, and DSE decomposition) and supplementary dementia-informed analyses (lobar volumes, NBM volumes, WMH classification, PSMD, cortical diffusion MRI metrics, and tract volumes) into the analysis pipeline. We explored associations between resultant imaging-derived phenotypes (IDPs) and clinical phenotypes in the OBHC patient population (N = 213), applying hierarchical FDR correction to account for multiple testing. 14%-24% of scans were flagged by automated QC tools, but upon visual inspection, only 0%-2.4% of outputs were excluded. The pipeline successfully generated 5683 IDPs aligned with UK Biobank and 110 IDPs targeted towards dementia-related changes. We replicated established associations and found novel associations between brain metrics and age, cognition, and dementia-related diagnoses. The imaging protocol is feasible, acceptable, and yields high-quality data that is usable for both clinical and research purposes. We validated the use of this methodology in a real-world memory clinic population, which demonstrates the potential of this enhanced pipeline to bridge the gap between big data studies and clinical settings.

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