Centre for Youth Mental Health - Research Publications

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    Separating Clinical and Subclinical Depression by Big Data Informed Structural Vulnerability Index and Its impact on Cognition: ENIGMA Dot Product
    Kochunov, P ; Ma, Y ; Hatch, KS ; Schmaal, L ; Jahanshad, N ; Thompson, PM ; Adhikari, BM ; Bruce, H ; Chiappelli, J ; Van der vaart, A ; Goldwaser, EL ; Sotiras, A ; Ma, T ; Chen, S ; Nichols, TE ; Hong, LE (WORLD SCIENTIFIC, 2021-12)
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    Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
    Petrov, D ; Gutman, BA ; Yu, S-HJ ; Alpert, K ; Zavaliangos-Petropulu, A ; Isaev, D ; Turner, JA ; van Erp, TGM ; Wang, L ; Schmaal, L ; Veltman, D ; Thompson, PM ; Wang, Q ; Shi, Y ; Suk, HI ; Suzuki, K (SPRINGER INTERNATIONAL PUBLISHING AG, 2017)
    As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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    Classification of major depressive disorder via multi-site weighted LASSO model
    Zhu, D ; Riedel, BC ; Jahanshad, N ; Groenewold, NA ; Stein, DJ ; Gotlib, IH ; Sacchet, MD ; Dima, D ; Cole, JH ; Fu, CHY ; Walter, H ; Veer, IM ; Frodl, T ; Schmaal, L ; Veltman, DJ ; Thompson, PM (Springer, 2017-01-01)
    Large-scale collaborative analysis of brain imaging data, in psychiatry and neurology, offers a new source of statistical power to discover features that boost accuracy in disease classification, differential diagnosis, and outcome prediction. However, due to data privacy regulations or limited accessibility to large datasets across the world, it is challenging to efficiently integrate distributed information. Here we propose a novel classification framework through multi-site weighted LASSO: each site performs an iterative weighted LASSO for feature selection separately. Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature. This new weight is used to guide the LASSO process at the next iteration. Only the features that help to improve the classification accuracy are preserved. In tests on data from five sites (299 patients with major depressive disorder (MDD) and 258 normal controls), our method boosted classification accuracy for MDD by 4.9% on average. This result shows the potential of the proposed new strategy as an effective and practical collaborative platform for machine learning on large scale distributed imaging and biobank data.