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Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on

Date April 29 2014-May 2 2014

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  • Welcome message

    Publication Year: 2014 , Page(s): 1
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  • Program at a glance

    Publication Year: 2014 , Page(s): 1 - 2
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  • General information

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  • Organizing committee

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  • Keynote speaker: Advancing precision medicine through biomedical imaging

    Publication Year: 2014 , Page(s): 1 - 19
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  • Engineering in Medicine and Biology Society

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  • Program in chronological order

    Publication Year: 2014 , Page(s): 1 - 37
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  • Program in chronological order

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  • Author index

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  • [Copyright notice]

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  • Semi-supervised learning of brain functional networks

    Publication Year: 2014 , Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3124 KB) |  | HTML iconHTML  

    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 based correlation analysis (SCA) but also background information, which leads to robust performance for fMRI data with low signal noise ratio (SNR). Comparison experiments on both simulated and real fMRI data demonstrate that our method is more robust and accurate for identification of known brain functional networks than SCA, blind independent component analysis (ICA), and clustering based methods including Ncut and Kmeans. View full abstract»

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  • Functional parcellation of the hippocampus by clustering resting state fMRI signals

    Publication Year: 2014 , Page(s): 5 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (865 KB) |  | HTML iconHTML  

    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 functional signals. A geometric parcellation result of the hippocampus is adopted as prior information and a spatial consistent constraint is adopted as a regularization term to achieve spatially contiguous clustering. The graph partition problem is solved using an efficient algorithm similar to the well-known weighted kernel k-means algorithm. Our method has been validated based on resting state fMRI data of 28 subjects for the hippocampus parcellation with three subregions. The experiment results have demonstrated that the proposed method could parcellate the hippocampus into its head, body and tail parts. The distinctive functional and structural connectivity patterns of these subregions, derived from resting state fMRI and dMRI data respectively, have further demonstrated the validity of the parcellation results. View full abstract»

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  • Epileptic network activity revealed by dynamic functional connectivity in simultaneous EEG-fMRI

    Publication Year: 2014 , Page(s): 9 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2677 KB) |  | HTML iconHTML  

    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 pinpoint connectivity changes related to epileptic activity by integrating EEG and dynamic FC information. To our knowledge, no previous work has attempted to integrate dFC with the epileptic activity from EEG. The detailed results obtained from the analysis of two patients successfully detected specific patterns of connections/disconnections related to the epileptic activity and highlighted the potential of a dynamic analysis for a better understanding of network organisation in epilepsy. View full abstract»

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  • Discovering network-level functional interactions from working memory fMRI data

    Publication Year: 2014 , Page(s): 13 - 16
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (841 KB) |  | HTML iconHTML  

    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 publicly released 358 DICCCOL landmarks. First, 14 DICCCOLs are detected as group-wise activated ROIs via GLM and compose the ‘basic network’ of working memory. Second, the time-frequency functional interaction patterns of each pair of activated DICCCOL and other DICCCOLs are calculated using cross-wavelet transform. Third, the common functional interaction patterns and corresponding brain networks are learned via effective online dictionary learning and sparse coding methods. Experimental results showed that multiple brain networks are involved in working memory processes. More importantly, each brain network interacts with the ‘basic network’ via a specific functionally meaningful time-frequency interaction pattern. View full abstract»

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  • Detecting cell assembly interaction patterns via Bayesian based change-point detection and graph inference model

    Publication Year: 2014 , Page(s): 17 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (718 KB) |  | HTML iconHTML  

    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 of neural activity. We then applied a Bayesian graph inference algorithm on the segmentation results to find multiple functional interaction patterns underlying each experience. The resulting interaction patterns were analyzed by multi-view co-training method to identify the common sub-network structure of cell assemblies which are strongly connected i.e. "neural cliques". By analyzing the resulting sub-networks from three memory-producing events, it is found that there exist certain common neurons participating in the functional interactions across different events, lending strong support evidence to the hypothesis of hierarchical organization architecture of neuronal assemblies. View full abstract»

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  • Generalized fMRI activation detection via Bayesian magnitude change point model

    Publication Year: 2014 , Page(s): 21 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (725 KB) |  | HTML iconHTML  

    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, we develop a novel Bayesian magnitude change point model (BMCPM) that simultaneously considers the group-wise fMRI signals of corresponding cortical landmarks across a population of subjects and optimally determines the change boundaries. Then, these detected group-wise consistent magnitude change points are clustered into various patterns of temporal and spatial activations, which are named generalized activations here. The methods have been applied on a working memory task-based fMRI dataset and revealed complex and meaningful generalized brain activation patterns. View full abstract»

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  • The effect of temporal observation selection on the prediction of visual stimulus from block design functional MRI

    Publication Year: 2014 , Page(s): 25 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (748 KB) |  | HTML iconHTML  

    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 space. Although this problem can be alleviated by feature selection in spatial domain so that informative voxels are selected, feature selection in temporal domain has not been attempted. Henceforth, it is unclear which spatiotemporal combination of fMRI data gives the best prediction. In this study, we investigate the effect of using different combinations of fMRI time points on the prediction accuracy of visual stimuli, using support vector machine and random forest as classification methods. Using a publicly available fMRI dataset, we demonstrate that classification using multiple concatenated time points significantly outperforms a single time point based classification. Our results highlight the necessity of considering both temporal and spatial patterns to achieve better prediction of visual stimuli from fMRI data. View full abstract»

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  • Classification of amnestic mild cognitive impairment using fMRI

    Publication Year: 2014 , Page(s): 29 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (759 KB) |  | HTML iconHTML  

    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 (RBFN). The prediction performance was assessed among all combinations of subregions in different memory paradigms and contrasts. A high prediction accuracy of 93.75% (p=2.44×10−4) was achieved by as few as two subregions with one contrast. This result demonstrates the possibility of using compact fMRI activation patterns to aid in the diagnosis of aMCI. View full abstract»

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  • Optimization of MDL-based wavelet denoising for fMRI data analysis

    Publication Year: 2014 , Page(s): 33 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (939 KB) |  | HTML iconHTML  

    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 type for HRF estimation as well as activation detection in vision region of task-based fMRI data. Simulations show that the MDL-based denoising performance is independent from the noise type for both Refined- and Crude- MDLs. According to simulations, it is necessary to select a scaling function being the most similar to Hemodynamic Response Function (HRF) involved in the experimental fMRI data. Besides, R-MDL can lead to optimum denoising at lower decomposition level compared to C-MDL. Applying MDL-based denoising to fMRI data as a preprocessing step, a larger set of activated voxels for vision tasks has been obtained which appear to be more realistic in comparison to earlier works. View full abstract»

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  • Retrospective detection of interleaved slice acquisition parameters from fMRI data

    Publication Year: 2014 , Page(s): 37 - 40
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB) |  | HTML iconHTML  

    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 method to retrospectively detect the interleave parameter and the axis in which it is applied. Our method relies on the smoothness of the temporal-distance correlation function, which becomes disrupted along the axis on which interleaved slice acquisition is applied. We examined this method on simulated and real data in the presence of fMRI artifacts such as physiological noise, motion, etc. We also examined the reliability of this method in detecting different types of interleave parameters and demonstrated an accuracy of about 94% in more than 1000 real fMRI scans. View full abstract»

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  • Exploiting both intra-quadtree and inter-spatial structures for multi-contrast MRI

    Publication Year: 2014 , Page(s): 41 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (940 KB) |  | HTML iconHTML  

    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 their intra- and inter- structures simultaneously. Based on structured sparsity theory, it could further reduce the undersampled data for reconstruction or enhance the reconstruction quality. A new algorithm is proposed to efficiently solve this problem. Experiments demonstrate the superiority of the proposed algorithm over existing methods on multi-contrast MRI. View full abstract»

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  • Constrained maximum likelihood based efficient dictionary learning for fMRI analysis

    Publication Year: 2014 , Page(s): 45 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1506 KB) |  | HTML iconHTML  

    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 recently used to develop a data-driven sparse general linear model (GLM) framework for fMRI analysis solely based on the sparsity of signals. However, the K-SVD algorithm is computationally demanding and may require many iterations to converge. Replacing SVD with an approximate solution for the dictionary update combined with an optimal dictionary initialization, the desired results for a sparse GLM can be improved and achieved in few iterations. View full abstract»

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