Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks

Chen, Ming; Li, Hailong; Wang, Jinghua; Yuan, Weihong; Altaye, Mekbib; Parikh, Nehal A.; He, Lili

doi:10.3389/fnins.2020.00858

Cited by 21 publications

(25 citation statements)

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“…Neurodevelopmental deficits can be understood as dysconnectivity syndromes, therefore the quantifications of the abnormal structural and functional network using graph theory may enable neurodevelopmental prognosis. In VPIs, we have previously established correlations of later neurodevelopmental outcomes with at term obtained functional connectivity features derived from rs-fMRI (Gozdas et al, 2018); and structural connectivity features derived from DTI (Chen et al, 2020). In this work, our results showed both structural and functional connectivity features obtained at termequivalent age are predictive of abnormal cognitive, language, and motor outcomes at 2 years corrected age.…”

Section: Most Discriminative Feature Identificationsupporting

confidence: 54%

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

Chen

et al. 2021

Front. Neurosci.

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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.

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Section: Most Discriminative Feature Identificationsupporting

confidence: 54%

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

Chen

et al. 2021

Front. Neurosci.

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“…Brain connectome has demonstrated promising predictive power in many brain disorder studies. 3,4,42 The spatial and topological information is discriminative for prediction. In this study, the structural brain connectome was constructed using an infant brain atlas,and the atlas is developed based on an anatomical parcellation of brain regions.…”

Section: Discussionmentioning

confidence: 99%

“…The rapid development of magnetic resonance imaging (MRI) techniques has facilitated quantitative mapping of the connections within and between brain regions, that is, brain connectome 1,2 . The brain connectome has been considered as a key to understanding the exact etiologies behind many neurological disorders, ranging from cognitive deficits in preterm infants to Alzheimer's disease in the elderly 3–8 . Mathematically, a connectome is a graph, representing the brain connectivity (described as a set of edges) between pairs of brain regions of interest (ROI) (described as a set of nodes).…”

Section: Introductionmentioning

confidence: 99%

“…1,2 The brain connectome has been considered as a key to understanding the exact etiologies behind many neurological disorders, ranging from cognitive deficits in preterm infants to Alzheimer's disease in the elderly. [3][4][5][6][7][8] Mathematically, a connectome is a graph,representing the brain connectivity (described as a set of edges) between pairs of brain regions of interest (ROI) (described as a set of nodes). The connectome can be encoded as an adjacency matrix, in which each entry represents the connections between a pair of ROIs.…”

Section: Introductionmentioning

confidence: 99%

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ConCeptCNN: A novel multi‐filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome

Chen

Fan

et al. 2022

Medical Physics

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Background: Deep convolutional neural network (CNN) and its derivatives have recently shown great promise in the prediction of brain disorders using brain connectome data. Existing deep CNN methods using single global row and column convolutional filters have limited ability to extract discriminative information from brain connectome for prediction tasks. Purpose: This paper presents a novel deep Connectome-Inception CNN (Con-CeptCNN) model,which is developed based on multiple convolutional filters.The proposed model is used to extract topological features from brain connectome data for neurological disorders classification and analysis. Methods: The ConCeptCNN uses multiple vector-shaped filters extract topological information from the brain connectome at different levels for complementary feature embeddings of brain connectome. The proposed model is validated using two datasets: the Neuro Bureau ADHD-200 dataset and the Cincinnati Early Prediction Study (CINEPS) dataset. Results: In a cross-validation experiment, the ConCeptCNN achieved a prediction accuracy of 78.7% for the detection of attention deficit hyperactivity disorder (ADHD) in adolescents and an accuracy of 81.6% for the prediction of cognitive deficits at 2 years corrected age in very preterm infants. In addition to the classification tasks, the ConCeptCNN identified several brain regions that are discriminative to neurodevelopmental disorders. Conclusions: We compared the ConCeptCNN with several peer CNN methods. The results demonstrated that proposed model improves overall classification performance of neurodevelopmental disorders prediction tasks.

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“…Therefore, being agnostic to cumulative errors is undeniably substantial for making predictions that are interpretable in the clinical world. Recent studies aimed to solve this issue by leveraging deep learning (DL) models such as convolutional neural network (CNN) which is inherently trained in an end-to-end fashion (36). While effective when compared to traditional machine learning methods, still DL methods do not generalize well to non-Euclidean data types (e.g., graphs).…”

Section: Functional Brain Graphmentioning

confidence: 99%

Graph Neural Networks in Network Neuroscience

Bessadok¹,

Mahjoub²,

Rekik³

2021

Preprint

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Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-Euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration.

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Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks

Cited by 21 publications

References 50 publications

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

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

ConCeptCNN: A novel multi‐filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome

Graph Neural Networks in Network Neuroscience

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