A Novel Sparse Dictionary Learning Separation (SDLS) Model With Adaptive Dictionary Mutual Incoherence Constraint for fMRI Data Analysis

Wang, Nizhuan; Zeng, Weiming; Chen, Dongtailang

doi:10.1109/tbme.2016.2533722

Cited by 20 publications

(12 citation statements)

References 46 publications

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“…Thus, in this study, the ATGP algorithm ( Ren and Chang, 2003 ; Chang et al, 2011 ) was applied to determine the initial cluster points, forming the ATGP-K-Means clustering algorithm. The effectiveness of ATGP in initialization was validated in our previous studies, overcoming the randomness of FastICA ( Hyvärinen, 1999 ; Yao et al, 2013 ) and improving the accuracy of the separation of brain sources in SDLS ( Wang et al, 2016a ).…”

Section: Methodsmentioning

confidence: 86%

Brain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer

et al. 2017

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The functional connectome derived from BOLD resting-state functional magnetic resonance imaging data represents meaningful functional organizations and a shift between distinct cognitive states. However, the body of knowledge on how the long-term career experience affects the brain’s functional plasticity is still very limited. In this study, we used a dynamic functional connectome characterization (DBFCC) model with the automatic target generation process K-Means clustering to explore the functional reorganization property of resting brain states, driven by long-term career experience. Taking sailors as an example, DBFCC generated seventeen reproducibly common atomic connectome patterns (ACP) and one reproducibly distinct ACP, i.e., ACP14. The common ACPs indicating the same functional topology of the resting brain state transitions were shared by two control groups, while the distinct ACP, which mainly represented functional plasticity and only existed in the sailors, showed close relationships with the long-term career experience of sailors. More specifically, the distinct ACP14 of the sailors was made up of four specific sub-networks, such as the auditory network, visual network, executive control network, and vestibular function-related network, which were most likely linked to sailing experience, i.e., continuously suffering auditory noise, maintaining balance, locating one’s position in three-dimensional space at sea, obeying orders, etc. Our results demonstrated DBFCC’s effectiveness in revealing the specifically functional alterations modulated by sailing experience and particularly provided the evidence that functional plasticity was beneficial in reorganizing brain’s functional topology, which could be driven by career experience.

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Section: Methodsmentioning

confidence: 86%

Brain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer

et al. 2017

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“…where A and B are two matrices with the same dimensions, respectively; cov(•) , var(•) , and vec(•) represent the calculation operation of covariance, variance, and vector transform, respectively ( Wang et al, 2016a ).…”

Section: Theory and Methodsmentioning

confidence: 99%

A Novel Spectrum Contrast Mapping Method for Functional Magnetic Resonance Imaging Data Analysis

Yu¹,

Cai

Li³

et al. 2021

Front. Hum. Neurosci.

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Many studies reported that spontaneous fluctuation of the blood oxygen level-dependent signal exists in multiple frequency components and changes over time. By assuming a reliable energy contrast between low- and high-frequency bands for each voxel, we developed a novel spectrum contrast mapping (SCM) method to decode brain activity at the voxel-wise level and further validated it in designed experiments. SCM consists of the following steps: first, the time course of each given voxel is subjected to fast Fourier transformation; the corresponding spectrum is divided into low- and high-frequency bands by given reference frequency points; then, the spectral energy ratio of the low- to high-frequency bands is calculated for each given voxel. Finally, the activity decoding map is formed by the aforementioned energy contrast values of each voxel. Our experimental results demonstrate that the SCM (1) was able to characterize the energy contrast of task-related brain regions; (2) could decode brain activity at rest, as validated by the eyes-closed and eyes-open resting-state experiments; (3) was verified with test-retest validation, indicating excellent reliability with most coefficients > 0.9 across the test sessions; and (4) could locate the aberrant energy contrast regions which might reveal the brain pathology of brain diseases, such as Parkinson’s disease. In summary, we demonstrated that the reliable energy contrast feature was a useful biomarker in characterizing brain states, and the corresponding SCM showed excellent brain activity-decoding performance at the individual and group levels, implying its potentially broad application in neuroscience, neuroimaging, and brain diseases.

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“…Data-based methods are data driven. For example, independent component analysis (ICA) [11, 12] assumes the independence of the brain patterns; Sparse representation analysis (SRA) [13, 14] assumes the spatial sparsity of brain patterns, but their performance is also limited by the fully data driven process, because sometimes a brain pattern can be further decomposed into more than one subpattern, which causes the difficulty of recognizing RSNs. While model-based methods manually select a representative signal as reference.…”

Section: Introductionmentioning

confidence: 99%

Classifying early and late mild cognitive impairment stages of Alzheimer’s disease by fusing default mode networks extracted with multiple seeds

2018

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BackgroundThe default mode network (DMN) in resting state has been increasingly used in disease diagnosis since it was found in 2001. Prior work has mainly focused on extracting a single DMN with various techniques. However, by using seeding-based analysis with more than one desirable seed, we can obtain multiple DMNs, which are likely to have complementary information, and thus are more promising for disease diagnosis. In the study, we used 18 early mild cognitive impairment (EMCI) participants and 18 late mild cognitive impairment (LMCI) participants of Alzheimer’s disease (AD). First, we used seeding-based analysis with four seeds to extract four DMNs for each subject. Then, we conducted fusion analysis for all different combinations of the four DMNs. Finally, we carried out nonlinear support vector machine classification based on the mixing coefficients from the fusion analysis.ResultsWe found that (1) the four DMNs corresponding to the four different seeds indeed capture different functional regions of each subject; (2) Maps of the four DMNs in the most different joint source from fusion analysis are centered at the regions of the corresponding seeds; (3) Classification results reveal the effectiveness of using multiple seeds to extract DMNs. When using a single seed, the regions of posterior cingulate cortex (PCC) extractions of EMCI and LMCI show the largest difference. For multiple-seed cases, the regions of PCC extraction and right lateral parietal cortex (RLP) extraction provide complementary information for each other in fusion, which improves the classification accuracy. Furthermore, the regions of left lateral parietal cortex (LLP) extraction and RLP extraction also have complementary effect in fusion. In summary, AD diagnosis can be improved by exploiting complementary information of DMNs extracted with multiple seeds.ConclusionsIn this study, we applied fusion analysis to the DMNs extracted by using different seeds for exploiting the complementary information hidden among the separately extracted DMNs, and the results supported our expectation that using the complementary information can improve classification accuracy.

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A Novel Sparse Dictionary Learning Separation (SDLS) Model With Adaptive Dictionary Mutual Incoherence Constraint for fMRI Data Analysis

Abstract: SDLS as an extension to current fMRI analysis methods was a promising model, which declared the advantage of sparsity.

Cited by 20 publications

References 46 publications

Brain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer

Brain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer

A Novel Spectrum Contrast Mapping Method for Functional Magnetic Resonance Imaging Data Analysis

Classifying early and late mild cognitive impairment stages of Alzheimer’s disease by fusing default mode networks extracted with multiple seeds

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