Given the wide success of convolutional neural networks (CNNs) applied to natural images, researchers have begun to apply them to neuroimaging data. To date, however, exploration of novel CNN architectures tailored to neuroimaging data has been limited. Several recent works fail to leverage the 3D structure of the brain, instead treating the brain as a set of independent 2D slices. Approaches that do utilize 3D convolutions rely on architectures developed for object recognition tasks in natural 2D images. Such architectures make assumptions about the input that may not hold for neuroimaging. For example, existing architectures assume that patterns in the brain exhibit translation invariance. However, a pattern in the brain may have different meaning depending on where in the brain it is located. There is a need to explore novel architectures that are tailored to brain images. We present two simple modifications to existing CNN architectures based on brain image structure. Applied to the task of brain age prediction, our network achieves a mean absolute error (MAE) of 1.4 years and trains 30 faster than a CNN baseline that achieves a MAE of 1.6 years. Our results suggest that lessons learned from developing models on natural images may not directly transfer to neuroimaging tasks. Instead, there remains a large space of unexplored questions regarding model development in this area, whose answers may differ from conventional wisdom.
Few works exist that explicitly attempt to adapt CNNs to brain image tasks like age prediction or disease prediction. @cite_1 and @cite_0 propose novel architectures to predict mild cognitive impairment and age, respectively, from functional connectivity networks extracted from functional MRI. They model the network as a graph and apply convolutions across the edges. Neither consider applying CNNs directly to brain scans. @cite_3 introduce a method involving a new pooling operation to predict HIV and ADHD from directly from functional imaging. However, their method requires an ensemble of CNNs and a separate algorithm to mix the ensemble, which increases computational complexity during training. Many of the works that focus on learning from brain data do not discuss architectural modifications. As described above, those that do either focus on learning from functional connectivity networks rather than actual images, or require computationally expensive models.
@cite_0 @cite_1 @cite_3
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Using predictive models to identify patterns that can act as biomarkers for different neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the problem of Autism Spectrum Disorder (ASD) classification. While non-invasive imaging measurements, such as the rest state fMRI, are typically used in this problem, it can be beneficial to incorporate a wide variety of non-imaging features, including personal and socio-cultural traits, into predictive modeling. We propose to employ a graph-based approach for combining both types of feature, where a contextual graph encodes the traits of a larger population while the brain activity patterns are defined as a multivariate function at the nodes of the graph. Since the underlying graph dictates the performance of the resulting predictive models, we explore the use of different graph construction strategies. Furthermore, we develop a bootstrapped version of graph convolutional neural networks (G-CNNs) that utilizes an ensemble of weakly trained G-CNNs to avoid overfitting and also reduce the sensitivity of the models on the choice of graph construction. We demonstrate its effectiveness on the Autism Brain Imaging Data Exchange (ABIDE) dataset and show that the proposed approach outperforms state-of-the-art approaches for this problem. Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals’ brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55 of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain’s major functional networks. With the advance in neuraimaging technology, such as functional Magnetic Resonance Imaging (fMRI), more brain image data become available, and mining the brain images emerges as a hot area in data mining communities. In contrast to ordinary images extensively studied in computer vision communities, brain images can be parcellated into different Regions of Interest (ROIs) due to the underlying functional and structural organization of human brain. Distinct diseases are highly relevant to a specific set of ROIs. In this paper, in order to leverage regional patterns to predict diseases and identify relevant ROIs, we present a novel Ensemble Approach on Regionalized Neural Networks (EARNN). EARNN is of a two-stage learning architecture. Its first stage consists of training a set of Regionalized Neural Network models, each of which learns from a brain region by using a proposed stochastic pooling operation. Its second stage adopts an effective EM algorithm to ensemble a mixture of Regionalized Neural Networks and iteratively estimate the mixing coefficients. We test our model on two real fMRI datasets. Experiments demonstrate that by effectively exploiting regional patterns and structural information, EARNN achieves the superior performance in comparison to previous approaches. In addition, according to our domain knowledge, EARNN successfully identifies relevant ROIs of diseases.