842 research outputs found
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Multi-view graph embedding has become a widely studied problem in the area of
graph learning. Most of the existing works on multi-view graph embedding aim to
find a shared common node embedding across all the views of the graph by
combining the different views in a specific way. Hub detection, as another
essential topic in graph mining has also drawn extensive attentions in recent
years, especially in the context of brain network analysis. Both the graph
embedding and hub detection relate to the node clustering structure of graphs.
The multi-view graph embedding usually implies the node clustering structure of
the graph based on the multiple views, while the hubs are the boundary-spanning
nodes across different node clusters in the graph and thus may potentially
influence the clustering structure of the graph. However, none of the existing
works in multi-view graph embedding considered the hubs when learning the
multi-view embeddings. In this paper, we propose to incorporate the hub
detection task into the multi-view graph embedding framework so that the two
tasks could benefit each other. Specifically, we propose an auto-weighted
framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain
network analysis. The MVGE-HD framework learns a unified graph embedding across
all the views while reducing the potential influence of the hubs on blurring
the boundaries between node clusters in the graph, thus leading to a clear and
discriminative node clustering structure for the graph. We apply MVGE-HD on two
real multi-view brain network datasets (i.e., HIV and Bipolar). The
experimental results demonstrate the superior performance of the proposed
framework in brain network analysis for clinical investigation and application
Functional connectivity alterations in epilepsy from resting-state functional MRI
The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.Scopu
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
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Fusion of fMRI and Non-Imaging Data for ADHD Classification
Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of different brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders affecting young children, yet its underlying mechanism is not completely understood and its diagnosis is mainly dependent on behavior analysis. This paper addresses the problem of classification of ADHD based on resting state fMRI and proposes a machine learning framework with integration of non-imaging data with imaging data to investigate functional connectivity alterations between ADHD and control subjects (not diagnosed with ADHD). Our aim is to apply computational techniques to (1) automatically classify a subject as ADHD or control, (2) identify differences in functional connectivity of these two groups and (3) evaluate the importance of fusing non-imaging with imaging data for classification. In the first stage of our framework, we determine the functional connectivity of brain regions by grouping brain activity using clustering algorithms. Next, we employ Elastic Net based feature selection to select the most discriminant features from the dense functional brain network and integrate non-imaging data. Finally, a Support Vector Machine classifier is trained to classify ADHD subjects vs. control. The proposed framework was evaluated on a public ADHD-200 dataset, and our results suggests that fusion of non-imaging data improves the performance of the framework. Classification results outperform the state-of-the-art on some subsets of the data
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