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Han Zhang


napoleon1982@gmail.com

Journal articles

2011
H Zhang, Y J Zhang, L Duan, S Y Ma, C M Lu, C Z Zhu (2011)  Is resting-state functional connectivity revealed by functional near-infrared spectroscopy test-retest reliable?   Journal of Biomedical Optics 16: 6. June  
Abstract: Recently, resting-state functional near-infrared spectroscopy (rs-fNIRS) research has experienced tremendous progress. Resting-state functional connectivity (RSFC) has been adopted as a pivotal biomarker in rs-fNIRS studies. However, it is yet to be clear if the RSFC derived from rs-fNIRS is reliable. This concern impedes extensive utilization of rs-fNIRS.We systematically address the issue of reliability. Sixteen subjects participate in two rs-fNIRS sessions held one week apart. RSFC in sensorimotor system is calculated using the seed-correlation approach. Then, test-retest reliability is evaluated at three different scales (map-, cluster-, and channelwise) for individualand group-level RSFC derived from different types of fNIRS signals [oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT)]. The results show that, for HbO signals, individual-level RSFC generally has good-toexcellent map-/clusterwise reliability, while group-level RSFC has excellent reliability. For HbT signals, the results are similar. For HbR signals, the clusterwise reliability is comparable to that for HbO while the mapwise reliability is slightly lower (fair to good). Focusing on RSFC at a single channel, we report poor channelwise reliability for all three types of signals. We hereby propose that fNIRS-derived RSFC is a reliable biomarker if interpreted in mapand clusterwise manners. However, channelwise interpretation of individual RSFC should proceed with caution.
Notes:
2010
H Zhang, L Duan, Y J Zhang, C M Lu, H Liu, C Z Zhu (2010)  Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy   Neuroimage 55: 2. 607-615  
Abstract: Recent studies of resting-state functional near-infrared spectroscopy (fNIRS) have emerged as a hot topic and revealed that resting-state functional connectivity (RSFC) is an inherent characteristic of the resting brain. However, it is currently unclear if fNIRS-based RSFC is test-retest reliable. In this study, we utilized independent component analysis (ICA) as an effective RSFC detection tool to address the reliability question. Sixteen subjects participated in two rs-fNIRS recording sessions held 1 week (6.88±1.09 days) apart. Then, RSFC in the sensorimotor regions was extracted using ICA. Test-retest reliability was assessed for intra- and intersessions, at the individual- and group-level, and for different hemoglobin concentration signals. Our results clearly demonstrated that map-wise reliability was excellent at the group level (with Pearson r coefficients up to 0.88) and generally fair at the individual level. The cluster-wise reliability was better at the group level (having reproducibility indices of up to 0.97 for the size and up to 0.80 for the location of the detected RSFC) and was weaker but still fair at the individual level (0.56 and 0.46 for intra- and inter-session reliability, respectively). Cluster-wise intra-class correlation coefficients (ICCs) also exhibited fair-to-good reliability (with single-measure ICC up to 0.56), while channel-wise single-measure ICCs indicated lower reliability. We conclude that fNIRS-based, ICA-derived RSFC is an essential and reliable biomarker at the individual and group levels if interpreted in map- and cluster-wise manners. Our results also suggested that channel-wise individual-level RSFC results should be interpreted with caution if no optode co-registration procedure had been conducted and indicated that “cluster” should be treated as a minimal analytical unit in further RSFC studies using fNIRS.
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H Zhang, Y J Zhang, C M Lu, S Y Ma, Y F Zang, C Z Zhu (2010)  Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements   NeuroImage 51: 3. 1150-1161  
Abstract: As a promising non-invasive imaging technique, functional near infrared spectroscopy (fNIRS) has recently earned increasing attention in resting-state functional connectivity (RSFC) studies. Preliminary fNIRS-based RSFC studies adopted a seed correlation approach and yielded interesting results. However, the seed correlation approach has several inherent problems, such as neglecting of interactions among multiple regions and a dependence on seed region selection. Moreover, ineffectively reduced noise and artifacts in fNIRS measurements also negatively affect RSFC results. In this study, independent component analysis (ICA) was introduced to meet these challenges in RSFC detection based on resting-state fNIRS measurements. The results of ICA on data from the sensorimotor and the visual systems both showed functional system-specific RSFC maps. Results from comparison between ICA and the conventional seed correlation approach demonstrated, both qualitatively and quantitatively, the superior performance of ICA with higher sensitivity and specificity, especially in the case of higher noise level. The capability of ICA to separate noise and artifacts from resting-state fNIRS data was also demonstrated, and the extracted noise and artifacts were illustrated. Finally, some practical issues on performing ICA on resting-state fNIRS data were discussed.
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H Zhang, X N Zuo, S Y Ma, Y F Zang, M P Milham, C Z Zhu (2010)  Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis   NeuroImage 51: 4. 1414-1424  
Abstract: Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subjectorder- induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially those with large data sets.
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2008

Conference abstract

2010
2009
2008

Software copyright

2009
http //psychbrain bnu edu cn/home/chaozhezhu/ (2009) No. 2009SR057145 [Software copyright]  
Abstract:
Notes: This is a cooperation fruit from our research group, NIC group, with website: http://psychbrain.bnu.edu.cn/home/chaozhezhu/
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