Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants.

Lili He,Nehal A Parikh,Jonathan R Dillman,Hailong Li,Mekibib Altaye,Ming Chen,Jinghua Wang

doi:10.3389/fnins.2021.753033

Abstract

The prevalence of disabled survivors of prematurity has increased dramatically in the past 3 decades. These survivors, especially, very preterm infants (VPIs), born ≤ 32 weeks gestational age, are at high risk for neurodevelopmental impairments. Early and clinically effective personalized prediction of outcomes, which forms the basis for early treatment decisions, is urgently needed during the peak neuroplasticity window—the first couple of years after birth—for at-risk infants, when intervention is likely to be most effective. Advances in MRI enable the noninvasive visualization of infants' brains through acquired multimodal images, which are more informative than unimodal MRI data by providing complementary/supplementary depicting of brain tissue characteristics and pathology. Thus, analyzing quantitative multimodal MRI features affords unique opportunities to study early postnatal brain development and neurodevelopmental outcome prediction in VPIs. In this study, we investigated the predictive power of multimodal MRI data, including T2-weighted anatomical MRI, diffusion tensor imaging, resting-state functional MRI, and clinical data for the prediction of neurodevelopmental deficits. We hypothesize that integrating multimodal MRI and clinical data improves the prediction over using each individual data modality. Employing the aforementioned multimodal data, we proposed novel end-to-end deep multimodal models to predict neurodevelopmental (i.e., cognitive, language, and motor) deficits independently at 2 years corrected age. We found that the proposed models can predict cognitive, language, and motor deficits at 2 years corrected age with an accuracy of 88.4, 87.2, and 86.7%, respectively, significantly better than using individual data modalities. This current study can be considered as proof-of-concept. A larger study with external validation is important to validate our approach to further assess its clinical utility and overall generalizability.

Highlights

With the continuing high incidence of preterm births (Martin et al, 2019) and improving survival rates (Blencowe et al, 2012) in the United States, the prevalence of disabled survivors of prematurity has increased dramatically
The main contributions of our work are highlighted as follows: (1) We proposed end-to-end deep multimodal learning models that incorporate features from multimodal MRI and clinical data; (2) We demonstrated that the application of deep multimodal learning to analyze highdimensional objectively-quantified anatomical and connectome features may detect brain structural and functional abnormalities and tissue pathology that are not readily visible to the naked eye, thereby facilitating risk stratification; (3) We unwrapped and identified discriminative MRI and clinical features used by the proposed models to make predictions
We have presented how imaging and clinical data were acquired and preprocessed, as well as how multimodal MRI features were quantified in subsections (Subjects and MRI Acquisition, Clinical Features and Neurodevelopmental Assessments, diffuse white matter abnormality (DWMA) Quantification, Structural Connectome Quantification, Functional Connectome Quantification, and Data Augmentation and Balancing)

Summary

Introduction

With the continuing high incidence of preterm births (about 380,000 in 2018) (Martin et al, 2019) and improving survival rates (exceeding 90%) (Blencowe et al, 2012) in the United States, the prevalence of disabled survivors of prematurity has increased dramatically These survivors, especially, very preterm infants (VPIs), born ≤ 32 weeks gestational age (GA), are at high risk for cognitive deficits and other neurodevelopmental disorders, thereby increasing their risk for poor educational, health, and social outcomes (Jarjour, 2015). Research supports the findings that brain imaging features are modulated by genetic (Thompson et al, 2001), non-genetic biological (Hackman and Farah, 2009), and environmental (May, 2011) influences, and show high variability among subjects Such variability can potentially provide valuable information for personalized prognosis based on the characteristics of individual patients (Valizadeh et al, 2018). We may gain a better understanding of how an individual brain’s organizational changes influence cognitive, language, and motor functions

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Conclusion