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Zhang C, Beste C, Prochazkova L, Wang K, Speer SPH, Smidts A, Boksem MAS, Hommel B. Resting-state BOLD signal variability is associated with individual differences in metacontrol. Sci Rep 2022; 12:18425. [PMID: 36319653 PMCID: PMC9626555 DOI: 10.1038/s41598-022-21703-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022] Open
Abstract
Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.
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Affiliation(s)
- Chenyan Zhang
- grid.5132.50000 0001 2312 1970Cognitive Psychology Unit, Leiden Institute for Brain and Cognition, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Christian Beste
- grid.4488.00000 0001 2111 7257Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany ,grid.4488.00000 0001 2111 7257University Neuropsychology Center, Faculty of Medicine, TU Dresden, Dresden, Germany ,grid.410585.d0000 0001 0495 1805School of Psychology, Shandong Normal University, Jinan, China
| | - Luisa Prochazkova
- grid.5132.50000 0001 2312 1970Cognitive Psychology Unit, Leiden Institute for Brain and Cognition, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Kangcheng Wang
- grid.410585.d0000 0001 0495 1805School of Psychology, Shandong Normal University, Jinan, China
| | - Sebastian P. H. Speer
- grid.419918.c0000 0001 2171 8263Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands ,grid.16750.350000 0001 2097 5006Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ale Smidts
- grid.6906.90000000092621349Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Maarten A. S. Boksem
- grid.6906.90000000092621349Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Bernhard Hommel
- grid.5132.50000 0001 2312 1970Cognitive Psychology Unit, Leiden Institute for Brain and Cognition, Institute of Psychology, Leiden University, Leiden, The Netherlands ,grid.4488.00000 0001 2111 7257Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany ,grid.410585.d0000 0001 0495 1805School of Psychology, Shandong Normal University, Jinan, China
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2
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Ding L, Pang G. Identification of Brain Regions with Enhanced Functional Connectivity with the Cerebellum Region in Children with Attention Deficit Hyperactivity Disorder: A Resting-State fMRI Study. Int J Gen Med 2021; 14:2109-2115. [PMID: 34079352 PMCID: PMC8166311 DOI: 10.2147/ijgm.s303339] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 04/29/2021] [Indexed: 12/24/2022] Open
Abstract
Background To explore the brain regions with higher functional connectivity with the cerebellum at resting state and the brain functions related to cognitive function in children with attention-deficit hyperactivity disorder (ADHD). Methods Thirty children with ADHD and 33 typically developing children (TDC) were examined using resting-state functional magnetic resonance imaging (fMRI) scans. Seed-based functional connectivity (FC) analysis was performed. Results Four brain areas with higher FC values were identified in ADHD children. These four areas were the left middle frontal gyrus, right middle frontal gyrus, right superior temporal gyrus and left parahippocampal gyrus (P < 0.05). The results of the CPT show that the number of omission errors was significantly higher in the children with ADHD than in the TD group (5.13±5.12 vs 2.18±2.36, P = 0.000). The commission number in the ADHD group was also significantly higher than that of the TD group (4.03±6.56 vs 2.00±2.85, P = 0.002). However, no statistically significant difference was observed in the correct reaction time between the two groups (641.54±146.79 ms vs 584.81±145.82 ms, P = 0.835). Conclusion The dysfunction of cerebellar functional connectivity in specific brain regions may be one of the pathological and physiological causes of cognitive impairment of ADHD.
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Affiliation(s)
- Li Ding
- Department of Pediatrics, Changzhou Children's Hospital of Nantong University, Changzhou, 213003, People's Republic of China
| | - Gaofeng Pang
- Department of Pediatrics, The Third Affiliated Hospital of Soochow University, Changzhou, 213003, People's Republic of China
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3
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Weng Y, Liu X, Hu H, Huang H, Zheng S, Chen Q, Song J, Cao B, Wang J, Wang S, Huang R. Open eyes and closed eyes elicit different temporal properties of brain functional networks. Neuroimage 2020; 222:117230. [PMID: 32771616 DOI: 10.1016/j.neuroimage.2020.117230] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 07/24/2020] [Accepted: 07/31/2020] [Indexed: 12/16/2022] Open
Abstract
The eyes are our windows to the brain. There are differences in brain activity between people who have their eyes closed (EC) and eyes open (EO). Previous studies focused on differences in brain functional properties between these eyes conditions based on an assumption that brain activity is a static phenomenon. However, the dynamic nature of the brain activity in different eyes conditions is still unclear. In this study, we collected resting-state fMRI data from 21 healthy subjects in the EC and EO conditions. Using a sliding time window approach and a k-means clustering algorithm, we calculated the temporal properties of dynamic functional connectivity (dFC) states in the eyes conditions. We also used graph theory to estimate the dynamic topological properties of functional networks in the two conditions. We detected two dFC states, a hyper-connected State 1 and a hypo-connected State 2. We showed the following results: (i) subjects in the EC condition stayed longer in the hyper-connected State 1 than those in the EO; (ii) subjects in the EO condition stayed longer in the hypo-connected State 2 than those in the EC; and (iii) the dFC state transformed into the other state more frequently during EC than during EO. We also found the variance of the characteristic path length was higher during EC than during EO in the hyper-connected State 1. These results indicate that brain activity may be more active and unstable during EC than during EO. Our findings may provide insights into the dynamic nature of the resting-state brain and could be a useful reference for future rs-fMRI studies.
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Affiliation(s)
- Yihe Weng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Xiaojin Liu
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Huiqing Hu
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Huiyuan Huang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Senning Zheng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Qinyuan Chen
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Jie Song
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Bolin Cao
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Junjing Wang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Shuai Wang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Ruiwang Huang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China.
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Opening or closing eyes at rest modulates the functional connectivity of V1 with default and salience networks. Sci Rep 2020; 10:9137. [PMID: 32499585 PMCID: PMC7272628 DOI: 10.1038/s41598-020-66100-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/14/2020] [Indexed: 01/07/2023] Open
Abstract
Current evidence suggests that volitional opening or closing of the eyes modulates brain activity and connectivity. However, how the eye state influences the functional connectivity of the primary visual cortex has been poorly investigated. Using the same scanner, fMRI data from two groups of participants similar in age, sex and educational level were acquired. One group (n = 105) performed a resting state with eyes closed, and the other group (n = 63) performed a resting state with eyes open. Seed-based voxel-wise functional connectivity whole-brain analyses were performed to study differences in the connectivity of the primary visual cortex. This region showed higher connectivity with the default mode and sensorimotor networks in the eyes closed group, but higher connectivity with the salience network in the eyes open group. All these findings were replicated using an open source shared dataset. These results suggest that opening or closing the eyes may set brain functional connectivity in an interoceptive or exteroceptive state.
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5
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Jia XZ, Sun JW, Ji GJ, Liao W, Lv YT, Wang J, Wang Z, Zhang H, Liu DQ, Zang YF. Percent amplitude of fluctuation: A simple measure for resting-state fMRI signal at single voxel level. PLoS One 2020; 15:e0227021. [PMID: 31914167 PMCID: PMC6948733 DOI: 10.1371/journal.pone.0227021] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Accepted: 12/09/2019] [Indexed: 01/25/2023] Open
Abstract
The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel's ALFF by the global mean ALFF, named mALFF. Although fractional ALFF (fALFF), a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. The current study borrowed the percent signal change in task fMRI studies and proposed percent amplitude of fluctuation (PerAF) for RS-fMRI. We firstly applied PerAF and mPerAF (i.e., divided by global mean PerAF) to eyes open (EO) vs. eyes closed (EC) RS-fMRI data. PerAF and mPerAF yielded prominently difference between EO and EC, being well consistent with previous studies. We secondly performed test-retest reliability analysis and found that (PerAF ≈ mPerAF ≈ mALFF) > (fALFF ≈ mfALFF). Head motion regression (Friston-24) increased the reliability of PerAF, but decreased all other metrics (e.g. mPerAF, mALFF, fALFF, and mfALFF). The above results suggest that mPerAF is a valid, more reliable, more straightforward, and hence a promising metric for voxel-level RS-fMRI studies. Future study could use both PerAF and mPerAF metrics. For prompting future application of PerAF, we implemented PerAF in a new version of REST package named RESTplus.
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Affiliation(s)
- Xi-Ze Jia
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Jia-Wei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, Heilongjiang, China
| | - Gong-Jun Ji
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
- Department of Medical Psychology, Chaohu Clinical Medical College, Anhui Medical University, Hefei, China
| | - Wei Liao
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Ya-Ting Lv
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Jue Wang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Ze Wang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Han Zhang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Dong-Qiang Liu
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang, China
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6
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Functional network connectivity changes in children with attention‐deficit hyperactivity disorder: A resting‐state fMRI study. Int J Dev Neurosci 2019; 78:1-6. [DOI: 10.1016/j.ijdevneu.2019.07.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/18/2019] [Accepted: 07/08/2019] [Indexed: 10/26/2022] Open
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7
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Wei J, Chen T, Li C, Liu G, Qiu J, Wei D. Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective. Front Hum Neurosci 2018; 12:422. [PMID: 30405376 PMCID: PMC6200849 DOI: 10.3389/fnhum.2018.00422] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 09/28/2018] [Indexed: 01/30/2023] Open
Abstract
Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states. However, it remains largely unclear whether spontaneous brain activity is effectively related to EO and EC resting states. The amplitude, local functional concordance, inter-hemisphere functional synchronization, and network centrality of spontaneous brain activity were measured by the fraction amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC), respectively. Using the public Eyes-open/Eyes-closed dataset, we employed the support vector machine (SVM) and bootstrap technique to establish linking models for the fALFF, ReHo, VMHC and DC dimensions. The classification accuracies of linking models are 0.72 (0.59, 0.82), 0.88 (0.79, 0.97), 0.82 (0.74, 0.91) and 0.70 (0.62, 0.79), respectively. Specifically, we observed that brain activity in the EO condition is significantly greater in attentional system areas, including the fusiform gyrus, occipital and parietal cortex, but significantly lower in sensorimotor system areas, including the precentral/postcentral gyrus, paracentral lobule (PCL) and temporal cortex compared to the EC condition from the four dimensions. The results consistently indicated that spontaneous brain activity is effectively related to EO and EC resting states, and the two resting states are of opposite brain activity in sensorimotor and occipital regions. It may provide new insight into the neural substrate of the resting state and help computational neuroscientists or neuropsychologists to choose an appropriate resting state condition to investigate various mental disorders from the resting state functional magnetic resonance imaging (fMRI) technique.
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Affiliation(s)
- Jie Wei
- School of Electronic and Information Engineering, Southwest University, Chongqing, China.,School of Mathematics and Statistics, Southwest University, Chongqing, China.,Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
| | - Tong Chen
- School of Electronic and Information Engineering, Southwest University, Chongqing, China.,Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China.,Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Chuandong Li
- School of Electronic and Information Engineering, Southwest University, Chongqing, China.,Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
| | - Guangyuan Liu
- School of Electronic and Information Engineering, Southwest University, Chongqing, China.,Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
| | - Jiang Qiu
- Department of Psychology, Southwest University, Chongqing, China
| | - Dongtao Wei
- Department of Psychology, Southwest University, Chongqing, China
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8
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Li L, Huang G, Lin Q, Liu J, Zhang S, Zhang Z. Magnitude and Temporal Variability of Inter-stimulus EEG Modulate the Linear Relationship Between Laser-Evoked Potentials and Fast-Pain Perception. Front Neurosci 2018; 12:340. [PMID: 29904336 PMCID: PMC5991169 DOI: 10.3389/fnins.2018.00340] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 05/01/2018] [Indexed: 01/23/2023] Open
Abstract
The level of pain perception is correlated with the magnitude of pain-evoked brain responses, such as laser-evoked potentials (LEP), across trials. The positive LEP-pain relationship lays the foundation for pain prediction based on single-trial LEP, but cross-individual pain prediction does not have a good performance because the LEP-pain relationship exhibits substantial cross-individual difference. In this study, we aim to explain the cross-individual difference in the LEP-pain relationship using inter-stimulus EEG (isEEG) features. The isEEG features (root mean square as magnitude and mean square successive difference as temporal variability) were estimated from isEEG data (at full band and five frequency bands) recorded between painful stimuli. A linear model was fitted to investigate the relationship between pain ratings and LEP response for fast-pain trials on a trial-by-trial basis. Then the correlation between isEEG features and the parameters of LEP-pain model (slope and intercept) was evaluated. We found that the magnitude and temporal variability of isEEG could modulate the parameters of an individual's linear LEP-pain model for fast-pain trials. Based on this, we further developed a new individualized fast-pain prediction scheme, which only used training individuals with similar isEEG features as the test individual to train the fast-pain prediction model, and obtained improved accuracy in cross-individual fast-pain prediction. The findings could help elucidate the neural mechanism of cross-individual difference in pain experience and the proposed fast-pain prediction scheme could be potentially used as a practical and feasible pain prediction method in clinical practice.
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Affiliation(s)
- Linling Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Gan Huang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Qianqian Lin
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Jia Liu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Shengli Zhang
- Department of Communication Engineering, Shenzhen University, Shenzhen, China
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.,Experimental Center of Fundamental Teaching, Sun Yat-Sen University, Zhuhai, China
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9
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Shi Y, Zeng W, Tang X, Kong W, Yin J. An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis. Med Biol Eng Comput 2017; 56:683-694. [PMID: 28864838 DOI: 10.1007/s11517-017-1716-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Accepted: 08/17/2017] [Indexed: 11/26/2022]
Abstract
Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis. The experimental results of simulated and real data show that the activated regions and the time course detected by the improved CICA method are more accurate in some sense. Moreover, the GIC computed by the improved CICA method has a higher correlation with the corresponding independent component of each subject in the group, which means that the improved CICA method with the temporal a priori information extracted from the group can better reflect the commonality of the subjects. These results demonstrate that the improved CICA method has its own advantages in fMRI data analysis.
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Affiliation(s)
- Yuhu Shi
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
| | - Weiming Zeng
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
- Information Engineering College, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
| | - Xiaoyan Tang
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
| | - Wei Kong
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
| | - Jun Yin
- Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
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