Poster No:
1215
Submission Type:
Abstract Submission
Authors:
Siao-Jhen Wu1, Chun-Ming Chen2, Jong-Tsun Huang3, Jeng-Ren Duann1,4
Institutions:
1Institute of Education, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan, 2Department of Medical Imaging, China Medical University Hospital, Taichung City, Taiwan, 3Graduate Institute of Biomedical Sciences, China Medical University, Taichung City, Taiwan, 4Institute for Neural Computation, University of California San Diego, La Jolla, CA
First Author:
Siao-Jhen Wu
Institute of Education, National Yang Ming Chiao Tung University
Hsinchu City, Taiwan
Co-Author(s):
Chun-Ming Chen
Department of Medical Imaging, China Medical University Hospital
Taichung City, Taiwan
Jong-Tsun Huang
Graduate Institute of Biomedical Sciences, China Medical University
Taichung City, Taiwan
Jeng-Ren Duann
Institute of Education, National Yang Ming Chiao Tung University|Institute for Neural Computation, University of California San Diego
Hsinchu City, Taiwan|La Jolla, CA
Introduction:
A significant challenge in estimating connectivity from time courses in conventional functional magnetic resonance imaging (fMRI) analysis lies in the dependence on statistical inference via the general linear model (GLM). This approach requires defining functional brain regions based on reference functions, which are generated by convolving task time courses with the canonical hemodynamic response function (HRF). In addition, conventional methods assume homogeneity in HRF and non-HRF signals across different brain regions and individuals. However, the variability of blood flow responses and the individual differences were not considered in such analyses. To address these issues, we developed an alternative method for analyzing fMRI data using independent component analysis (ICA) to decompose the fMRI time-series data into independent brain and non-brain independent components.
Methods:
We recruited 12 healthy volunteers who participated in a calligraphy appreciation task during fMRI scanning. Each trial comprised a 6-second viewing phase, during which participants watched a video of Chinese calligraphy displaying the same character with varying levels of aesthetic contrast, ranging from highly beautiful to unattractive, and vice versa. This was followed by a 2-second rating phase. In this study, we selected only the independent blood oxygenation level-dependent (BOLD) components of the primary visual cortex (V1), dorsal visual stream (DVS), default mode network (DMN), and left primary motor cortex (LM1) from the ICA decomposition for further analysis. The coherence, correlation coefficient, and cross-correlation between the selected component clusters were computed to represent the frequency-domain synchronicity, overall time-domain relationship, and the similarity between a time courses from different independent BOLD components. The coefficients of variation (CV) of these measures were also computed to rate the degree of variation across participants.
Results:
The CV for coherence was 0.40 for the V1-DMN component pair, 0.32 for the V1-DVS component pair, and 0.55 for the V1-LM1 component pair. The CV for correlation coefficient was 0.44 for the V1-DMN component pair, -5.41 for the V1-DVS component pair, and 0.47 for the V1-LM1 component pair. Lastly, the CV for cross-correlation was 0.32 for the V1-DMN component pair, 1.96 for the V1-DVS component pair, and 0.63 for the V1-LM1 component pair.
Conclusions:
These results highlighted the variability of dynamic connectivity across different pathways and demonstrated the feasibility of using ICA to analyze fMRI data. Among the measures examined, cross-correlation appeared to provide a more robust assessment of dynamic connectivity by accounting for temporal lags, thereby enhancing sensitivity. In contrast, coherence captured correlations within specific frequency bands as a Frequency-domain measure but might be strongly influenced by the low sampling rate of fMRI, which could lead to aliasing effects. This limitation compressed the variability and reduces the differentiability of the measures. Although coherence and cross-correlation show similar trends in some instances, discrepancies observed under certain conditions emphasize the need for careful interpretation. Notably, ICA decomposition, without relying on HRF-based reference function, successfully identified brain BOLD activities that were directly, indirectly, or even not correlated with the task performance. This approach enabled the analysis of dynamic connectivity without the influence of the high correlation introduced by the common reference function used in GLM analysis. Furthermore, the ICA-based approach provides valuable insights into brain connectivity while accounting for individual differences and regional variability in hemodynamic responses. This work was supported by the National Science and Technology Council, Taiwan (NSTC 113-2410-H-A49-051 and NSTC 110-2511-H-A49-012-MY3).
Higher Cognitive Functions:
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Data analysis
FUNCTIONAL MRI
Other - independent component analysis (ICA)
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Was this research conducted in the United States?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
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FMRLAB7
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