A novel fMRI group data analysis method based on data-driven reference extracting from group subjects

Shi, Yuhu; Zeng, Weiming; Wang, Nizhuan; Chen, Dongtailang

doi:10.1016/j.cmpb.2015.09.002

Cited by 31 publications

(16 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The BFNs extraction has been formulated as a source separation problem, based on the functional integration property of the brain (McKeown et al, 1998 ; Du and Fan, 2013 ; Shi et al, 2015a ). This source separation problem can usually be divided into blind source separation (BSS) and semi-blind source separation (SBSS), depending on whether the prior is given or not.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…The fifth group analysis approach, makes a post-hoc analysis of the single-subject ICAs, to combine the components into groups by spatial correlation (Schöpf et al, 2010 ; Wang et al, 2012 ), self-organized clustering (Esposito et al, 2005 ), or retrospective matching of the components (Langers, 2010 ). Additionally, by incorporating the intragroup sources as a priori of ICA model, called ICA-R (ICA with references; Lu and Rajapakse, 2006 ; Shi et al, 2015a ), it is expected to obtain the more accurate subject-specific brain sources. For example, a novel group information guided ICA model (GIG-ICA) with the spatial reference of the intragroup sources generated by TCGICA (Calhoun et al, 2001 ) was able to extract more accurate subject-specific brain sources than the traditional ICA (Du and Fan, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets

et al. 2017

Self Cite

View full text Add to dashboard Cite

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.

show abstract

Section: Theory and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…This phenomenon might be partly due to the different spatial distribution of the individual DMN, which is likely affected by diseases, and to the change in the spatial distribution results from the decreased and increased regional FC of the whole brain to the original healthy DMN, which might mutually offset. Therefore, though the single-subject ICA method is better to reflect current individual FC of the DMN (Anderson et al 2011, Shi et al 2015, Esposito et al 2005, this method might not be able to detect the influence of impaired regions of DMN and to diminish the effect of compensatory regions on the DMN (Damoiseaux et al 2012, Vemuri et al 2012, Seidler et al 2010. Instead, our method, using a DMN template from young and normal subjects to extract FC of the DMN for all subjects, is likely to solve this problem and truly demonstrate a much better clinical correlation than the single-subject ICA method.…”

Section: Discussionmentioning

confidence: 99%

“…From the literature review, this problem is mainly handled by clustering single-subject ICA results instead of performing group ICA, such as self-organizing clustering method, bagged clustering method, and group ICA with intrinsic reference, all of which avoid the required assumption of common functional networks across all subjects in group ICA. These methods reflect the individual functional network better and the resultant ICA components are more correlated with individual components than those from traditional group ICA (Anderson et al 2011, Shi et al 2015, Esposito et al 2005. Computing intra-network FC based on these methods may reflect individual network function, but the resultant RSNs are likely composed of partly different brain regions in different subjects and even more different in diseased subjects because the commonness of ICA components across subjects is not included in these new ICA methods.…”

Section: Introductionmentioning

confidence: 99%

The effect of network template from normal subjects in the detection of network impairment

Chun-Chao¹,

Shang-Hua²,

Ching-Po³

2016

Acta Neurobiologiae Experimentalis

View full text Add to dashboard Cite

This study aimed to provide a simple way to approach group differences by independent component analysis when researching functional connectivity changes of resting-state network in brain disorders. We used baseline resting state functional magnetic resonance imaging from the Alzheimer's disease neuroimaging initiative dataset and performed independent component analysis based on different kinds of subject selection, by including two downloaded templates and single-subject independent component analysis method. All conditions were used to calculate the functional connectivity of the default mode network, and to test group differences and evaluate correlation with cognitive measurements and hippocampal volume. The default mode network functional connectivity results most fitting clinical evaluations were from templates based on young healthy subjects and the worst results were from heterogeneous or more severe disease groups or single-subject independent component analysis method. Using independent component analysis network maps derived from normal young subjects to extract all individual functional connectivities provides significant correlations with clinical evaluations.

show abstract

“…Functional magnetic resonance imaging (fMRI) technology is one of the most significant approaches to obtain the data of neuroinformatics. It has been widely used in human behavior experiment and pathology because of its noninvasive, repeatability and other advantages [2][3][4][5][6][7][8]. FMRI can be used to obtain high-resolution threedimensional images of the brain through the BOLD (blood oxygen level dependent) effect, which can dynamically reflect changes in brain activity signals.…”

Section: Introductionmentioning

confidence: 99%

Resting State fMRI Data Classification Method Based on K-means Algorithm Optimized by Rough Set

Zeng

Shi

et al. 2017

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

With the development of brain science, a variety of new methods and techniques continue to emerge. Functional magnetic resonance imaging (fMRI) has become one of the important ways to study the brain functional connection and of brain functional connectivity detection because of its noninvasive and repeatability. However, there are still some issues in the fMRI researches such as the amounts of data and the interference noise in the data. Therefore, how to effectively reduce the fMRI data dimension and extract data features has become one of the core content of study. In this paper, a K-means algorithm based on rough set optimization is proposed to solve these problems. Firstly, the concept of important attributes is put forward according to the characteristics of Rough Set, and the attribute importance is calculated by observing the change of attribute positive domain. Then, the best attributes reduction is selected by the attribute importance, so that these important attributes are the best attributes reduction. Finally, the K-means algorithm is used to classify the important attributes. The experiments of two datasets are designed to evaluate the proposed algorithm, and the experimental results show that the K-means algorithm based on rough set optimization has more classification accuracy than the original Kmeans algorithm.

show abstract

A novel fMRI group data analysis method based on data-driven reference extracting from group subjects

Cited by 31 publications

References 41 publications

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets

The effect of network template from normal subjects in the detection of network impairment

Resting State fMRI Data Classification Method Based on K-means Algorithm Optimized by Rough Set

Contact Info

Product

Resources

About