Enabling Advanced Multi-Modal Neuroimaging Analysis within a Trusted Research Environment
Hotchkiss, L.; Squires, E.; Gallacher, J.; Morris, C.; Newbury, M.; Lyons, R.; Thompson, S.
Show abstract
IntroductionGlobally, 55 million individuals have dementia, with an increasing annual incident of 10 million. Enabling development of new multi-modal models can improve the current diagnostic pathways and potentially contribute to early diagnosis and treatment of dementia. Here, we report how multi-modal resources is achieved within the successful Trusted Research Environment (TRE) providing access to 60+ cohort datasets for dementia research, the Dementias Platform UK (DPUK). ObjectivesWe aimed to identify the challenges of the storage, distribution and analysis of neuroimaging data and how we could implement a comprehensive infrastructure to deal with these. The problems we specifically aimed to address were how to: anonymise scans, store large amounts of data, standardise datasets to a common format, extract metadata, provision the data, and allow for analysis. MethodsWhile, data within majority of existing research platforms are focused on a single aspect, DPUK data provides an enriched view of disease dynamic for dementia cohorts by providing access to linkable brain imaging and genomic data at the individual-level. We document various stages and capacities required for multi-modal neuroimaging analysis for dementia and conclude that achieving research ready assets to enable neuroimaging analysis for dementia from existing resources requires an engineered process to facilitate multiple aspects of curation, provisioning and large scale analysis. ResultsWe developed an ingest pipeline for neuroimaging data to meet the requirements set out in the objectives. This involved standardising all datasets to the Brain Imaging Data Structure, defacing scans and anonymising data, using MinIO for data storage and extracting metadata from header information for data discovery and provisioning. ConclusionThe neuroimaging ingest pipeline developed has allowed for the distribution of imaging datasets within DPUK which has facilitated multi-modal research on anonymised and standardised data. Our pipelines create research-ready datasets in a simplified way, reducing the time and effort of getting these datasets ready for data sharing and making the process easier for the data owners.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Classification of Hyper-scale Multimodal Imaging Datasets 94%
- Implementation and prospective real-time evaluation of a generalized system for in-clinic deployment and validation of machine learning models in radiology 93%
- Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images 89%
Similar papers in this journal
Similar papers in this journal
- Age-informed, attention-based weakly supervised learning for neuropathological image assessment 92%
- Quantifying Numerical and Spatial Reliability of Amygdala and Hippocampal Subdivisions in FreeSurfer 92%
- Early Detection of Alzheimer’s Disease with Low-Cost Neuropsychological Tests: A Novel Predict-Diagnose Approach using Recurrent Neural Networks 90%
Similar papers in this journal
- Automated quality control of T1-weighted brain MRI scans for clinical research: methods comparison and design of a quality prediction classifier 95%
- BrainQCNet: a Deep Learning attention-based model for the automated detection of artifacts in brain structural MRI scans. 95%
- A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.