31 research outputs found
Prediction of severity and treatment outcome for ASD from fMRI
Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome.
Early diagnosis and precise treatment are essential for ASD patients. Although
researchers have built many analytical models, there has been limited progress
in accurate predictive models for early diagnosis. In this project, we aim to
build an accurate model to predict treatment outcome and ASD severity from
early stage functional magnetic resonance imaging (fMRI) scans. The difficulty
in building large databases of patients who have received specific treatments
and the high dimensionality of medical image analysis problems are challenges
in this work. We propose a generic and accurate two-level approach for
high-dimensional regression problems in medical image analysis. First, we
perform region-level feature selection using a predefined brain parcellation.
Based on the assumption that voxels within one region in the brain have similar
values, for each region we use the bootstrapped mean of voxels within it as a
feature. In this way, the dimension of data is reduced from number of voxels to
number of regions. Then we detect predictive regions by various feature
selection methods. Second, we extract voxels within selected regions, and
perform voxel-level feature selection. To use this model in both linear and
non-linear cases with limited training examples, we apply two-level elastic net
regression and random forest (RF) models respectively. To validate accuracy and
robustness of this approach, we perform experiments on both task-fMRI and
resting state fMRI datasets. Furthermore, we visualize the influence of each
region, and show that the results match well with other findings
A Facial Affect Analysis System for Autism Spectrum Disorder
In this paper, we introduce an end-to-end machine learning-based system for
classifying autism spectrum disorder (ASD) using facial attributes such as
expressions, action units, arousal, and valence. Our system classifies ASD
using representations of different facial attributes from convolutional neural
networks, which are trained on images in the wild. Our experimental results
show that different facial attributes used in our system are statistically
significant and improve sensitivity, specificity, and F1 score of ASD
classification by a large margin. In particular, the addition of different
facial attributes improves the performance of ASD classification by about 7%
which achieves a F1 score of 76%.Comment: 5 pages (including 1 page for reference), 3 figure
Prediction of treatment outcome for autism from structure of the brain based on sure independence screening
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and
behavioral treatment interventions have shown promise for young children with
ASD. However, there is limited progress in understanding the effect of each
type of treatment. In this project, we aim to detect structural changes in the
brain after treatment and select structural features associated with treatment
outcomes. The difficulty in building large databases of patients who have
received specific treatments and the high dimensionality of medical image
analysis problems are the challenges in this work. To select predictive
features and build accurate models, we use the sure independence screening
(SIS) method. SIS is a theoretically and empirically validated method for
ultra-high dimensional general linear models, and it achieves both predictive
accuracy and correct feature selection by iterative feature selection. Compared
with step-wise feature selection methods, SIS removes multiple features in each
iteration and is computationally efficient. Compared with other linear models
such as elastic-net regression, support vector regression (SVR) and partial
least squares regression (PSLR), SIS achieves higher accuracy. We validated the
superior performance of SIS in various experiments: First, we extract brain
structural features from FreeSurfer, including cortical thickness, surface
area, mean curvature and cortical volume. Next, we predict different measures
of treatment outcomes based on structural features. We show that SIS achieves
the highest correlation between prediction and measurements in all tasks.
Furthermore, we report regions selected by SIS as biomarkers for ASD
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical
to help explain ASD and predict or monitor treatment outcomes. Toward this end,
deep learning classifiers have recently been used for identifying ASD from
functional magnetic resonance imaging (fMRI) with higher accuracy than
traditional learning strategies. However, a key challenge with deep learning
models is understanding just what image features the network is using, which
can in turn be used to define the biomarkers. Current methods extract
biomarkers, i.e., important features, by looking at how the prediction changes
if "ignoring" one feature at a time. In this work, we go beyond looking at only
individual features by using Shapley value explanation (SVE) from cooperative
game theory. Cooperative game theory is advantageous here because it directly
considers the interaction between features and can be applied to any machine
learning method, making it a novel, more accurate way of determining
instance-wise biomarker importance from deep learning models. A barrier to
using SVE is its computational complexity: given features. We
explicitly reduce the complexity of SVE computation by two approaches based on
the underlying graph structure of the input data: 1) only consider the
centralized coalition of each feature; 2) a hierarchical pipeline which first
clusters features into small communities, then applies SVE in each community.
Monte Carlo approximation can be used for large permutation sets. We first
validate our methods on the MNIST dataset and compare to human perception.
Next, to insure plausibility of our biomarker results, we train a Random Forest
(RF) to classify ASD/control subjects from fMRI and compare SVE results to
standard RF-based feature importance. Finally, we show initial results on
ranked fMRI biomarkers using SVE on a deep learning classifier for the
ASD/control dataset.Comment: 12 pages, 7 figures, accpeted as a full paper in IPMI 201
Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
Significant progress has been made using fMRI to characterize the brain
changes that occur in ASD, a complex neuro-developmental disorder. However, due
to the high dimensionality and low signal-to-noise ratio of fMRI, embedding
informative and robust brain regional fMRI representations for both graph-level
classification and region-level functional difference detection tasks between
ASD and healthy control (HC) groups is difficult. Here, we model the whole
brain fMRI as a graph, which preserves geometrical and temporal information and
use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data.
We investigate the potential of including mutual information (MI) loss
(Infomax), which is an unsupervised term encouraging large MI of each nodal
representation and its corresponding graph-level summarized representation to
learn a better graph embedding. Specifically, this work developed a pipeline
including a GNN encoder, a classifier and a discriminator, which forces the
encoded nodal representations to both benefit classification and reveal the
common nodal patterns in a graph. We simultaneously optimize graph-level
classification loss and Infomax. We demonstrated that Infomax graph embedding
improves classification performance as a regularization term. Furthermore, we
found separable nodal representations of ASD and HC groups in prefrontal
cortex, cingulate cortex, visual regions, and other social, emotional and
execution related brain regions. In contrast with GNN with classification loss
only, the proposed pipeline can facilitate training more robust ASD
classification models. Moreover, the separable nodal representations can detect
the functional differences between the two groups and contribute to revealing
new ASD biomarkers
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder.
Finding the biomarkers associated with ASD is extremely helpful to understand
the underlying roots of the disorder and can lead to earlier diagnosis and more
targeted treatment. Although Deep Neural Networks (DNNs) have been applied in
functional magnetic resonance imaging (fMRI) to identify ASD, understanding the
data-driven computational decision making procedure has not been previously
explored. Therefore, in this work, we address the problem of interpreting
reliable biomarkers associated with identifying ASD; specifically, we propose a
2-stage method that classifies ASD and control subjects using fMRI images and
interprets the saliency features activated by the classifier. First, we trained
an accurate DNN classifier. Then, for detecting the biomarkers, different from
the DNN visualization works in computer vision, we take advantage of the
anatomical structure of brain fMRI and develop a frequency-normalized sampling
method to corrupt images. Furthermore, in the ASD vs. control subjects
classification scenario, we provide a new approach to detect and characterize
important brain features into three categories. The biomarkers we found by the
proposed method are robust and consistent with previous findings in the
literature. We also validate the detected biomarkers by neurological function
decoding and comparing with the DNN activation maps.Comment: 8 pagers, accepted by MICCAI 201
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
Deep learning models have shown their advantage in many different tasks,
including neuroimage analysis. However, to effectively train a high-quality
deep learning model, the aggregation of a significant amount of patient
information is required. The time and cost for acquisition and annotation in
assembling, for example, large fMRI datasets make it difficult to acquire large
numbers at a single site. However, due to the need to protect the privacy of
patient data, it is hard to assemble a central database from multiple
institutions. Federated learning allows for population-level models to be
trained without centralizing entities' data by transmitting the global model to
local entities, training the model locally, and then averaging the gradients or
weights in the global model. However, some studies suggest that private
information can be recovered from the model gradients or weights. In this work,
we address the problem of multi-site fMRI classification with a
privacy-preserving strategy. To solve the problem, we propose a federated
learning approach, where a decentralized iterative optimization algorithm is
implemented and shared local model weights are altered by a randomization
mechanism. Considering the systemic differences of fMRI distributions from
different sites, we further propose two domain adaptation methods in this
federated learning formulation. We investigate various practical aspects of
federated model optimization and compare federated learning with alternative
training strategies. Overall, our results demonstrate that it is promising to
utilize multi-site data without data sharing to boost neuroimage analysis
performance and find reliable disease-related biomarkers. Our proposed pipeline
can be generalized to other privacy-sensitive medical data analysis problems.Comment: 12 pagers, 11 figures, published at Medical Image Analysi
Graph Neural Network for Interpreting Task-fMRI Biomarkers
Finding the biomarkers associated with ASD is helpful for understanding the
underlying roots of the disorder and can lead to earlier diagnosis and more
targeted treatment. A promising approach to identify biomarkers is using Graph
Neural Networks (GNNs), which can be used to analyze graph structured data,
i.e. brain networks constructed by fMRI. One way to interpret important
features is through looking at how the classification probability changes if
the features are occluded or replaced. The major limitation of this approach is
that replacing values may change the distribution of the data and lead to
serious errors. Therefore, we develop a 2-stage pipeline to eliminate the need
to replace features for reliable biomarker interpretation. Specifically, we
propose an inductive GNN to embed the graphs containing different properties of
task-fMRI for identifying ASD and then discover the brain regions/sub-graphs
used as evidence for the GNN classifier. We first show GNN can achieve high
accuracy in identifying ASD. Next, we calculate the feature importance scores
using GNN and compare the interpretation ability with Random Forest. Finally,
we run with different atlases and parameters, proving the robustness of the
proposed method. The detected biomarkers reveal their association with social
behaviors. We also show the potential of discovering new informative
biomarkers. Our pipeline can be generalized to other graph feature importance
interpretation problems
Sparsely Grouped Input Variables for Neural Networks
In genomic analysis, biomarker discovery, image recognition, and other
systems involving machine learning, input variables can often be organized into
different groups by their source or semantic category. Eliminating some groups
of variables can expedite the process of data acquisition and avoid
over-fitting. Researchers have used the group lasso to ensure group sparsity in
linear models and have extended it to create compact neural networks in
meta-learning. Different from previous studies, we use multi-layer non-linear
neural networks to find sparse groups for input variables. We propose a new
loss function to regularize parameters for grouped input variables, design a
new optimization algorithm for this loss function, and test these methods in
three real-world settings. We achieve group sparsity for three datasets,
maintaining satisfying results while excluding one nucleotide position from an
RNA splicing experiment, excluding 89.9% of stimuli from an eye-tracking
experiment, and excluding 60% of image rows from an experiment on the MNIST
dataset
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
Understanding how certain brain regions relate to a specific neurological
disorder has been an important area of neuroimaging research. A promising
approach to identify the salient regions is using Graph Neural Networks (GNNs),
which can be used to analyze graph structured data, e.g. brain networks
constructed by functional magnetic resonance imaging (fMRI). We propose an
interpretable GNN framework with a novel salient region selection mechanism to
determine neurological brain biomarkers associated with disorders.
Specifically, we design novel regularized pooling layers that highlight salient
regions of interests (ROIs) so that we can infer which ROIs are important to
identify a certain disease based on the node pooling scores calculated by the
pooling layers. Our proposed framework, Pooling Regularized-GNN (PR-GNN),
encourages reasonable ROI-selection and provides flexibility to preserve either
individual- or group-level patterns. We apply the PR-GNN framework on a
Biopoint Autism Spectral Disorder (ASD) fMRI dataset. We investigate different
choices of the hyperparameters and show that PR-GNN outperforms baseline
methods in terms of classification accuracy. The salient ROI detection results
show high correspondence with the previous neuroimaging-derived biomarkers for
ASD.Comment: 11 pages, 4 figure