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Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Identification of subject-specific brain functional networks of interest is of great importance in fMRI based brain network analysis. In this study, a novel method is proposed to identify subject-specific brain functional networks using a graph theory based semi-supervised learning technique by incorporating not only prior information of the network to be identified as similarly used in seed region...
In this study, we propose a semi-supervised clustering method for parcellating the hippocampus into functionally homogeneous subregions based on resting state fMRI data. Particularly, the semi-supervised clustering is implemented as a graph partition problem by modeling each voxel as one node of the graph and connecting each pair of voxels with an edge weighted by a similarity measure between their...
Recent findings highlighted the non-stationarity of brain functional connectivity (FC) during resting-state functional magnetic resonance imaging (fMRI), encouraging the development of methods allowing to explore brain network dynamics. This appears particularly relevant when dealing with brain diseases involving dynamic neuronal processes, like epilepsy. In this study, we introduce a new method to...
It is widely believed that working memory process involves large-scale functional interactions among multiple brain networks. However, network-level functional interactions across large-scale brain networks in working memory have been rarely explored yet in the literature. In this paper, we propose a novel framework for modeling network-level functional interactions in working memory based on our...
Recent studies have proposed the theory of functional network-level neural cell assemblies and their hierarchical organization architecture. In this study, we first proposed a novel Bayesian binary connectivity change point model to be applied on the binary spiking time series recorded from multiple neurons in the mouse hippocampus during three different emotional events, to find stable temporal segments...
In the human brain mapping field, virtually most existing fMRI activation detection methods, such as the general linear model (GLM), have assumed that the fMRI signal magnitude should follow the alternations of baseline and task periods. However, our extensive observation shows that different brain regions or networks exhibit quite dissimilar temporal activation patterns. Inspired by this observation,...
Multi-voxel pattern analysis is an approach to investigating brain activity measured by functional Magnetic Resonance Imaging (fMRI) in response to given stimuli. The signal acquired using fMRI is spatiotemporal, and can be used to predict the stimuli causing brain activation. Existing prediction methods suffer from the ‘curse of dimensionality’ by embedding all time points of the experiment in feature...
In this work, the feasibility of classifying amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer's disease, was investigated using fMRI activation patterns in the medial temporal lobes (MTL). The activation volume or relative activation extent in each of fourteen subregions of the MTL, when subjects were performing memory tasks, served as features for radial basis function networks...
Denoising is an important preprocessing step to remove the signal noise with minimum effect on informative part. Wavelet transform is usually used for denoising through some criteria such as Minimum Description Length (MDL) which provides a suitable thresholding value for denoising. In this paper, the wavelet denoising via MDL is optimized in terms of wavelet function, decomposition level and noise...
To minimize slice excitation leakage to adjacent slices, interleaved slice acquisition is nowadays performed regularly in fMRI scanners. In interleaved slice acquisition, the number of slices skipped between two consecutive slice acquisitions is often referred to as the ‘interleave parameter’ the loss of this parameter can be catastrophic for the analysis of fMRI data. In this article we present a...
Multi-contrast magnetic resonance images are not only compressible but also share the same inter-spatial structure as they are scanned from the same anatomical cross section. In addition, the wavelet coefficients of a MR image naturally yield an intra-quadtree structure and has been used in compressed imaging. In this paper, we propose a new method to reconstruct multi-contrast MR images by exploiting...
A principal component analysis (PCA) based dictionary initialization approach accompanied by a computationally efficient dictionary learning algorithm for statistical analysis of functional magnetic resonance imaging (fMRI) is proposed. It replaces a singular value decomposition (SVD) computation with an approximate solution to obtain a local minima for a given initial dictionary. The K-SVD has been...
Despite almost 50 years of research on the use of microbub-bles as ultrasound contrast agents (UCAs), the promise of high resolution dynamic perfusion imaging has not been fulfilled. This is due to the fact that the echogenicity enhancements from small clusters of bubbles in microvessels remain difficult to detect in the presence strong tissue echogenicity. A well-known pulse inversion (PI) method...
Optic nerve head (ONH) segmentation problem has been of interest for automated glaucoma assessment. Although various segmentation methods have been proposed in the recent past, it is difficult to evaluate and compare the performance of individual methods due to a lack of a benchmark dataset. The problem of segmentation involves segmentation of optic disk and cup region within ONH region. Available...
The development of brain Magentic Resonance Imaging (MRI) is driving increasing demand for quantitative measurements. Quantitative MRI (qMRI) templates of relaxation times and proton density can be of particular interest for dedicated clinical applications such as characterizing brain tissue abnormalities, as well as general research purposes. In this paper, we have developed 3D qMRI statistical templates...
Worldwide, it is believed that there are between 1000 to 2000 skin conditions of which 20% are difficult to diagnose. An intelligent computer-aided diagnosing system not only helps patients with no or little access to health services but also can benefit typical general practitioners, who have received minimal dermatology training. We have built a challenging dataset containing 2309 images from 44...
To reduce the radiation dose delivered to patients, a number of novel computed tomography (CT) reconstruction algorithms have been proposed to recover images from the sparsely sampled datasets or the datasets from low dose exposure. However, the performance of these algorithms has not been quantitatively evaluated with realistic CT datasets in an easily reproducible fashion. Here, we present four...
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