Surface Learning for Clinical Neuroimaging: regressing clinical phenotypes for cortical surface metrics
Creators: name: Williams, Logan
name: FAWAZ, Abdulah
name: Dahan, Simon
name: Robinson, Emma
name: O'Muircheartaigh, Jonathan
name: Marquand, Andre
name: Kia, Seyed Mostafa
Publisher: name: Zenodo
Year: 2023
Subjects: Neuroimaging
biomarkers
precision medicine
phenotype regression
surface modelling
MICCAI Challenges

description: Detecting biomarkers from neuroimaging data is highly challenging, since the degree of topographic variability of cortical shape and functional organisation across individuals, limits the accuracy with which brains may be compared through image registration (Glasser MF, Nature 2016; Kong R, Cerebral Cortex 2019) and obscures subtle signs of disease. There is a clear need for precision imaging tools of the brain which are robust to these factors. Recently, Fawaz et al. (2021) proposed a novel benchmark for prototyping tools for cortical analysis based on the regression of clinical phenotypes from the Developing Human Connectome Project (dHCP) - a unique open dataset of multimodal neonatal brain images, acquired from preterm and term neonates between 24 and 45 weeks postmenstrual age. Over this period of late gestation, the human brain undergoes rapid growth and maturation, characterised by highly dynamic and organised cellular and molecular processes. Disruptions to these processes, for example due to preterm birth, can have serious consequences resulting in significant increased risk of those neonates developing neuropsychiatric conditions including autism spectrum disorders, attention deficit hyperactivity disorder (ADHD), schizophrenia, bipolar affective disorder and major depressive disorder in later life. The goal of this challenge will therefore be to elicit submissions of novel methods for cortical phenotype regression, with emphasis on interpretable or explainable machine learning methods which deliver biomarkers predictive of risk for neurodevelopmental impairment. These will be benchmarked on the tasks of regression of gestational age at birth and classification of clinical outcome as measured by composite cognitive score of the Bayley-III exam. Standardised scores will be split into two classes: low (&lt;= 1 SD below the mean) and normal (&gt;=mean). The challenge will be run through Grand Challenge (https://slcn.grand-challenge.org/). The data set includes multimodal cortical imaging derived from the Developing Human Connectome Project including: cortical surface meshes and surface metric files (sulcal depth, curvature, cortical thickness, T1/T2 myelin maps, resting state networks, and cortical NODDI) acquired from 514 neonates scanned as part of the dHCP project, of which 95 are born preterm (&lt;37 weeks’ gestational age). All data will be made available through a https://gin.g-node.org/ repository, that will be made accessible to participants subject to their signing of the dHCP data access agreement. Terms of use are described here https://slcn.grand-challenge.org/DataAccess/. Submissions will be expected in the form of short reports which explain the method (with sufficient detail for reproduction) detail performance on the validation set, and provide some means for visual interpretation of the result. The winner will be the team that performs best on the IQ classification task with regression of GA used as a tie-breaker task. The participants will be asked to submit their code as a docker, and the results will be run a held-out test set by the challenge team. All submissions will be required to make an open-release of their code, and results on train, test and validation sets will be published on the Grand Challenge website. The best performing methods will be offered an opportunity to present at the Machine Learning for Clinical Neuroimaging (MLCN) workshop and may be shortlisted for a MELBA special issue. We will seek industry sponsorship for a prize for the best performing models.