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

Ming Chen, Hailong Li, Howard Fan, Jonathan R Dillman, Hui Wang, Mekibib Altaye, Bin Zhang, Nehal A Parikh, Lili He, Ming Chen, Hailong Li, Howard Fan, Jonathan R Dillman, Hui Wang, Mekibib Altaye, Bin Zhang, Nehal A Parikh, Lili He

Abstract

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 (ConCeptCNN) 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.

Keywords: MRI; brain connectome; convolutional neural network; deep learning; medical image analysis.

Conflict of interest statement

CONFLICT OF INTEREST

The authors have no conflicts to disclose.

© 2022 American Association of Physicists in Medicine.

Figures

FIGURE 1
FIGURE 1
A vector-shaped filter to extract topological information from the brain connectome. For explanatory simplicity, the example displays single-size filter and pooling operation is omitted
FIGURE 2
FIGURE 2
An overview of the proposed Connectome–Inception convolutional neural network (ConCeptCNN) model for early prediction of neurodevelopmental disorders using brain connectome
FIGURE 3
FIGURE 3
Top discriminative brain regions associated with cognitive deficit in very preterm infants identified by the Connectome–Inception convolutional neural network (ConCeptCNN) model
FIGURE 4
FIGURE 4
Top discriminative brain regions associated with attention deficit hyperactivity disorder (ADHD) in adolescents identified by the Connectome–Inception convolutional neural network (ConCeptCNN) model

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