A Novel Brain Networks Enhancement Model (BNEM) for BOLD fMRI Data Analysis With Highly Spatial Reproducibility

Wang, Nizhuan; Zeng, Weiming; Chen, Dongtailang; Yin, Jun; Chen, Lei

doi:10.1109/jbhi.2015.2439685

Cited by 13 publications

(10 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One important issue in cognitive neuroscience concerns the relationship between brain’s functional plasticity and individually extensive career training or long-term work experience. In recent years, many studies have demonstrated that blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is a powerful modality to help reveal the neural correlates of cognitive processes in different conditions, such as undergoing a cognitive task, resting-states ( Calhoun et al, 2008 ; Wang et al, 2012 , 2013 , 2016b ; Liu et al, 2013 ; Ren et al, 2014 ; Jing et al, 2015 ; Shi et al, 2015a ; Tang et al, 2015 ; Wang N. et al, 2015 ) or mental disorders, including psychological subhealth ( Shi et al, 2015b ), autism spectrum disorder ( Ambrosino et al, 2014 ), dementia ( Rytty et al, 2013 ), and schizophrenia ( Du et al, 2015 ). Recently, it has also been shown that the resting-state functional connectivity in specific regions is modulated by individual behaviors ( Hampson et al, 2006 ), extensive learning ( Albert et al, 2009 ; Tung et al, 2013 ), and experiences ( Jeong et al, 2006 ; Orr et al, 2014 ; Wang L. et al, 2015 ; Shen et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Most subjects had strong SCM values in visual task-related areas such as the occipital, fusiform, calcarine, and lingual gyri. Unlike many brain network separation models under the assumption of statistical independence or sparse distribution (Wang et al, 2012(Wang et al, , 2013(Wang et al, , 2016b(Wang et al, , 2015Yaesoubi et al, 2017;Yao et al, 2013;Shi et al, 2017;Shi and Zeng, 2018), the SCM paid more attention to the spectrum energy contrast of each voxel.…”

Section: Results and Analysismentioning

confidence: 99%

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

Yu¹,

Cai

Li³

et al. 2021

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…At the subject-specific level, compared to the intrinsic BFNs from different subjects, the intrinsic BFNs originated from the different sessions of the same subject should be more correlated. Moreover, the intragroup-specific intrinsic BFNs from different sessions should be also highly correlated with each other due to the high reproducibility of the intrinsic BFNs (Shehzad et al, 2009 ; Zuo et al, 2010 ; Wang et al, 2016b ).…”

Section: Results and Analysismentioning

confidence: 99%

“…Correlation analysis was performed to quantify such consistency in various cases. Firstly, mean and std values of correlation coefficients among the intragroup-specific BFNs from different sessions of the resting-state datasets under the same condition (e.g., session 1 and session 2 of resting-state datasets), were calculated and presented in Figure 9A , demonstrating a high mean correlation of 0.8464 and thus implying high across-sessions reproducibility of the BFNs in resting-state datasets (Wang et al, 2016b ). Then, the same correlation analysis were performed across the test-retest task datasets of Tasks 1, 2, and 3 from the same subjects, with results presented in Figure 9B , showing also a high mean correlation of 0.8314 and thus implying across-tasks similarity of the intrinsic functional connectivity architecture (Finn et al, 2015 ).…”

Section: Results and Analysismentioning

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

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

A Novel Brain Networks Enhancement Model (BNEM) for BOLD fMRI Data Analysis With Highly Spatial Reproducibility

Cited by 13 publications

References 55 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

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

Contact Info

Product

Resources

About