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: 2022
Subjects: Neuroimaging
biomarkers
precision medicine
phenotype regression
surface modelling
MICCAI
Challenge

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. <br> 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. <br> 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 registration-free or<br> registration-robust 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 (seen as a correlate of prematurity) and prediction of clinical outcome, as measured by cognitive tests at 18 months: the Bayley-III (Johnson et al., 2014) and Q-CHAT (Allison et al. 2008). The challenge data set will include the same data sets and splits as used by Fawaz et al. (2021): these include cortical surface meshes and 4 cortical metric files (sulcal depth, cortical thickness, curvature and T1/T2 myelin maps) 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 grand challenge website that will be made accessible to participants subject to their signing of the dHCP data access agreement (available http://www.developingconnectome.org/data-release/second-data-release/open-access-dhcp-data-terms-of-useversion-4-0_2019-05-23/). <br> 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 participants will be asked to submit their code as a docker, and the results will be run on the held-out test set by the challenge team and reported as a mean square error. 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 MLCN website. We will seek industry sponsorship for a prize for the best performing models <br> Participants will also be offered the opportunity to submit a paper to the MLCN workshop, where the winning paper will be judged through peer review both on model performance and clinical explainability of result. The reviewer panel for each paper will include at least one reviewer with clinical neuroimaging expertise.The winners will be offered oral/poster presentations as part of the Machine Learning for Clinical Neuroimaging (MLCN) MICCAI workshop and the opportunity to submit their work to a special issue of the MELBA journal. MLCN already offers a best paper prize. <strong>References</strong> <em>The benchmark is based on:</em><br> Fawaz, A., et al. "Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental<br> Phenotype Prediction." (2021). https://europepmc.org/article/ppr/ppr427931<br> Dahan, Simon, et al. "Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis." (2021).<br> The following references reference the background:<br> Glasser, Matthew F., et al. "A multi-modal parcellation of human cerebral cortex." Nature 536.7615 (2016): 171-<br> 178.<br> Kong, Ru, et al. "Spatial topography of individual-specific cortical networks predicts human cognition, personality,<br> and emotion." Cerebral cortex 29.6 (2019): 2533-2551.<br> Johnson, Samantha, Tamanna Moore, and Neil Marlow. "Using the Bayley-III to assess neurodevelopmental delay:<br> which cut-off should be used?." Pediatric research 75.5 (2014): 670-674.<br> <em>The data collection</em><br> Allison, Carrie, et al. "The Q-CHAT (Quantitative CHecklist for Autism in Toddlers): a normally distributed<br> quantitative measure of autistic traits at 18–24 months of age: preliminary report." Journal of autism and<br> developmental disorders 38.8 (2008): 1414-1425. <em>These papers explain the data preprocessing:</em><br> Hughes, Emer J., et al. "A dedicated neonatal brain imaging system." Magnetic resonance in medicine 78.2 (2017):<br> 794-804.<br> Makropoulos, A., Robinson, E. C., Schuh, A., Wright, R., Fitzgibbon, S., Bozek, J., ... &amp; Rueckert, D. (2018). The<br> developing human connectome project: A minimal processing pipeline for neonatal cortical surface<br> reconstruction. Neuroimage, 173, 88-112.<br> CorderoGrande, Lucilio, et al. "Threedimensional motion corrected sensitivity encoding reconstruction for<br> multishot multislice MRI: application to neonatal brain imaging." Magnetic resonance in medicine 79.3 (2018):<br> 1365-1376.<br> Kuklisova-Murgasova, M., Quaghebeur, G., Rutherford, M.A., Hajnal, J.V., Schnabel, J.A., 2012. Reconstruction of<br> fetal brain mri with intensity matching and complete outlier removal. Medical image analysis 16, 1550– 1564. <br> <em>Examples of translational research conducted on dHCP cortical data (not an exhaustive list)</em><br> Dimitrova, Ralica, et al. "Preterm birth alters the development of cortical microstructure and morphology at termequivalent<br> age." NeuroImage 243 (2021): 118488.<br> Ball, Gareth, et al. "Cortical morphology at birth reflects spatiotemporal patterns of gene expression in the fetal<br> human brain." PLoS biology 18.11 (2020): e3000976.<br> Fenchel, Daphna, et al. "Development of microstructural and morphological cortical profiles in the neonatal brain."<br> Cerebral Cortex 30.11 (2020): 5767-5779.<br> Williams, Logan Zane John, et al. "Structural and functional asymmetry of the neonatal cerebral cortex." bioRxiv<br> (2021).