Application of machine learning in the discovery of multi-modal neuroimaging biomarkers for psychiatric disorders and altered brain development

Xin Niu

doi:10.17918/00000707

For a long history, the diagnosis of psychiatric disorders relies on behavioral and self-reported symptoms, lacking objective neural or imaging biomarkers. Recent advances in neuroimaging techniques open opportunities for objective diagnosis protocols in psychopathology. However, the formidable dimensionality of neuroimaging data and the complexity of their relation to psychiatric disorders pose great challenges to analytical methods. In this dissertation, we propose methods to improve both the prediction performance and consistency of biomarker detection of machine learning (ML) models utilizing multimodal neuroimaging data to assist in the diagnosis of psychiatric disorders. We also examine how altered brain development is related to psychiatric disorders and propose a multi-dimensional brain age prediction approach to achieve a more sensitive quantification of precocity and delay in brain development. We first investigate the reproducibility of feature selection of ML models with brain imaging data. Consistent selection of predictive neuroimaging features via ML plays an important role in improving our understanding of and ability to treat psychiatric disorders. We compute a reproducibility index (R-index) for each feature as the reciprocal of the coefficient of variation of absolute mean difference across a larger number of bootstrap samples. The R-index is then integrated into regularized classification models as penalty weight. Reproducible features with a larger R-index are assigned smaller penalty weights and thus are more likely to be selected by our proposed R-index regularized classification models. We expect the proposed models will result in better prediction accuracy and more consistent feature selection for both simulated and real brain imaging data. Results show that our proposed R-index models are effective in separating informative features from noise features. Additionally, the proposed models yield better prediction performance and coefficient estimation than the standard regularized classification models. Improvements achieved by the proposed models are essential to advance our understanding of the selected brain imaging features as well as their associations with psychiatric disorders. As psychiatric disorders frequently evolve during adolescence when drastic brain changes emerge, in the second project, we examine how brain development is related to psychiatric disorders and cognition. Brain age prediction using ML techniques has drawn great attention. The brain age gap, which is defined as the difference between the predicted age (brain age) and the chronological age, has been reported to associate with altered brain development in various psychiatric disorders and precocity or delay of cognition in a healthy population. We evaluate the performance of brain age prediction with 36 different combinations of imaging features and ML models including deep learning. Single and multimodal brain imaging data including MRI, DTI, and rs-fMRI from a large data set with 839 subjects are examined. Additionally, we explore the potential nonlinear relationship between the brain age gap and chronological age and propose a new approach to correct the systematic bias of the brain age gap. The result show that multi-modal brain imaging features improve prediction performance and that psychiatric disorders are vulnerable to altered brain development. Our study are also helpful to advance the practice of optimizing different analytic methodologies in brain age prediction. Lastly, we extend the brain age prediction approach outlined above to obtain a multi-dimensional brain age index. As different brain regions and sub-systems mature at different stages of the lifespan, a unidimensional brain age may not capture the diverged development trajectory of different brain subsystems. In contrast to unidimensional brain age prediction, our proposed multi-dimensional brain age index (MBAI) is helpful to quantify the brain development of multiple sub-systems that show staggered developmental pace and distinct patterns. In addition, brain imaging features clustered in one subgroup are more homogeneous. Prediction of brain age using a subset of homogeneous features helps to mitigate overfitting of the age prediction model and thus improves the estimates of the brain age gap. Furthermore, we investigate how the multi-dimensional brain age index is altered in psychiatric disorders using data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that the MBAI provides a flexible analysis of region-specific brain-age changes that are invisible to unidimensional brain-age prediction methods. Importantly, brain ages computed from region-specific feature clusters contain complementary information and demonstrate differential ability to classify disorder groups (e.g., specific phobia, depression, ADHD) from healthy controls. Compared to unidimensional brain-age indices, we show that the MBAI is sensitive to alterations in brain structures and captures distinct regional change patterns which may serve as biomarkers that may contribute to our understanding of healthy and pathological brain development and to the characterization, diagnosis, and, potentially, treatment of various disorders.

Application of machine learning in the discovery of multi-modal neuroimaging biomarkers for psychiatric disorders and altered brain development

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Application of machine learning in the discovery of multi-modal neuroimaging biomarkers for psychiatric disorders and altered brain development

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