Multi-pooling 3D Convolutional Neural Network for fMRI Classification of Visual Brain States

Neural decoding of visual object classification via functional magnetic resonance imaging (fMRI) data is challenging and is vital to understand underlying brain mechanisms. This paper proposed a multi-pooling 3D convolutional neural network (MP3DCNN) to improve fMRI classification accuracy. MP3DCNN is mainly composed of a three-layer 3DCNN, where the first and second layers of 3D convolutions each have a branch of pooling connection. The results showed that this model can improve the classification accuracy for categorical (face vs. object), face sub-categorical (male face vs. female face), and object sub-categorical (natural object vs. artificial object) classifications from 1.684% to 14.918% over the previous study in decoding brain mechanisms

Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks

Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have provided new insight into development of the human brain before birth, but these studies have predominately focused on brain functional connectivity (i.e. Fisher z-score), which requires manual processing steps for feature extraction from fMRI images. Deep learning approaches (i.e., Convolutional Neural Networks) have achieved remarkable success on learning directly from image data, yet have not been applied on fetal fMRI for understanding fetal neurodevelopment. Here, we bridge this gap by applying a novel application of deep 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI data. Specifically, we test a supervised CNN framework as a data-driven approach to isolate variation in fMRI signals that relate to younger v.s. older fetal age groups. Based on the learned CNN, we further perform sensitivity analysis to identify brain regions in which changes in BOLD signal are strongly associated with fetal brain age. The findings demonstrate that deep CNNs are a promising approach for identifying spontaneous functional patterns in fetal brain activity that discriminate age groups. Further, we discovered that regions that most strongly differentiate groups are largely bilateral, share similar distribution in older and younger age groups, and are areas of heightened metabolic activity in early human development.Comment: 9 page

A survey of deep learning models in medical therapeutic areas.

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Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

Machine Learning Methods for Depression Detection Using SMRI and RS-FMRI Images

Major Depression Disorder (MDD) is a common disease throughout the world that negatively influences people’s lives. Early diagnosis of MDD is beneficial, so detecting practical biomarkers would aid clinicians in the diagnosis of MDD. Having an automated method to find biomarkers for MDD is helpful even though it is difficult. The main aim of this research is to generate a method for detecting discriminative features for MDD diagnosis based on Magnetic Resonance Imaging (MRI) data. In this research, representational similarity analysis provides a framework to compare distributed patterns and obtain the similarity/dissimilarity of brain regions. Regions are obtained by either data-driven or model-driven methods such as cubes and atlases respectively. For structural MRI (sMRI) similarity of voxels of spatial cubes (data-driven) are explored. For resting-state fMRI (rs-fMRI) images, the similarity of the time series of both cubes (data-driven) and atlases (model-driven) are examined. Moreover, the similarity method of the inverse of Minimum Covariant Determinant is applied that excludes outliers from patterns and finds conditionally independent regions given the rest of regions. Next, a statistical test that is robust to outliers, identifies discriminative similarity features between two groups of MDDs and controls. Therefore, the key contribution is the way to get discriminative features that include obtaining similarity of voxel’s cubes/time series using the inverse of robust covariance along with the statistical test. The experimental results show that obtaining these features along with the Bernoulli Naïve Bayes classifier achieves superior performance compared with other methods. The performance of our method is verified by applying it to three imbalanced datasets. Moreover, the similarity-based methods are compared with deep learning and regional-based approaches for detecting MDD using either sMRI or rs-fMRI. Given that depression is famous to be a connectivity disorder problem, investigating the similarity of the brain’s regions is valuable to understand the behavior of the brain. The combinations of structural and functional brain similarities are explored to investigate the brain’s structural and functional properties together. Moreover, the combination of data-driven (cube) and model-driven (atlas) similarities of rs-fMRI are looked over to evaluate how they affect the performance of the classifier. Besides, discriminative similarities are visualized for both sMRI and rs-fMRI. Also, to measure the informativeness of a cube, the relationship of atlas regions with overlapping cubes and vise versa (cubes with overlapping regions) are explored and visualized. Furthermore, the relationship between brain structure and function has been probed through common similarities between structural and resting-state functional networks

New Statistical Methods for Evaluating Brain Functional Connectivity

The human brain functions through the coordination of a complex network of billions of neurons. This network, when defined by the functions it dictates, is known as functional brain connectivity. Associating brain networks with clinical symptoms and outcomes has great potential for shaping future work in neuroimaging and clinical practice. Resting-state functional magnetic resonance imaging (rfMRI) has commonly been used to establish the functional brain network; however, understanding the links to clinical characteristics is still an ongoing research question. Existing methods for analysis of functional brain networks, such as independent component analysis and canonical correlation analysis, have laid a good foundation for this research; yet most methods do not directly model the node-level association between connectivity and clinical characteristics, and thus provide limited ability for interpretation. To address those limitations, this dissertation research focuses on developing efficient methods that identify node-level associations to answer important research questions in brain imaging studies. In the first project, we propose a joint modeling framework for estimating functional connectivity networks from rfMRI time series data and evaluating the predictability of individual's brain connectivity patterns using their clinical characteristics. Our goal is to understand the link between clinical presentations of psychiatric disorders and functional brain connectivity at different region pairs. Our modeling framework consists of two components: estimation of individual functional connectivity networks and identifying associations with clinical characteristics. We propose a model fitting procedure for jointly estimating these components via the alternating direction method of multipliers (ADMM) algorithm. The key advantage of the proposed approach lies in its ability to directly identify the brain region pairs between which the functional connectivity is strongly associated with the clinical characteristics. Compared to existing methods, our framework has the flexibility to integrate machine learning methods to estimate the nonlinear predictive effects of clinical characteristics. Additionally, jointly modeling the precision matrix and the predictive model estimates provides a novel framework to accommodate the uncertainty in estimating functional connectivity. In the second project, we focus on a scalar-on-network regression problem which utilizes brain functional connectivity networks to predict a single clinical outcome of interest, where the regression coefficient is edge-dependent. To improve estimation efficiency, we develop a two stage boosting algorithm to estimate the sparse edge-dependent regression coefficients by leveraging the knowledge of brain functional organization. Simulations have shown the proposed method has higher power to detect the true signals while controlling the false discovery rate better than existing approaches. We apply the proposed method to analysis of rfMRI data in the Adolescent Brain Cognitive Development (ABCD) study and identify the important functional connectivity sub-networks that are associated with general cognitive ability. In the third project, we extend scalar-on-network regression via boosting in the second project by relaxing the homogeneity constraints within the prespecified functional connectivity networks. We adopt deep neural networks (DNN) to model the edge-dependent regression coefficients in light of the edge-level and node level features in the brain network, as well as the well-known brain functional organization. In addition, the proposed DNN-based scalar-on-network regression has the flexibility to incorporate the signal pattern from other imaging modalities into the model. We develop an efficient model fitting method based on ADMM. The proposed method is evaluated and compared with existing alternatives via simulations and analysis of rfMRI and task fMRI data in the ABCD study.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169684/1/emorrisl_1.pd