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Kristanto D, Burkhardt M, Thiel C, Debener S, Gießing C, Hildebrandt A. The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis. Neurosci Biobehav Rev 2024; 165:105846. [PMID: 39117132 DOI: 10.1016/j.neubiorev.2024.105846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/04/2024] [Accepted: 08/04/2024] [Indexed: 08/10/2024]
Abstract
The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.
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Affiliation(s)
- Daniel Kristanto
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany.
| | - Micha Burkhardt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany
| | - Christiane Thiel
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany; Cluster of Excellence "Hearing4All", Carl von Ossietzky Universität Oldenburg, Germany
| | - Stefan Debener
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany; Cluster of Excellence "Hearing4All", Carl von Ossietzky Universität Oldenburg, Germany
| | - Carsten Gießing
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany.
| | - Andrea Hildebrandt
- Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg 26129, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Germany; Cluster of Excellence "Hearing4All", Carl von Ossietzky Universität Oldenburg, Germany.
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Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, Teshnehlab M, Gorriz JM, Khozeimeh F, Zhang YD, Nahavandi S, Acharya UR. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 146:105554. [DOI: 10.1016/j.compbiomed.2022.105554] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 12/21/2022]
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Wang F, Xu Q, Fu R. Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4883. [PMID: 31717422 PMCID: PMC6891316 DOI: 10.3390/s19224883] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 10/29/2019] [Accepted: 11/04/2019] [Indexed: 11/17/2022]
Abstract
Rapid and accurate detection of driver fatigue is of great significance to improve traffic safety. In the present work, we propose the man-machine response mode (MRM) to relieve driver fatigue caused by long-term driving. In this paper, the characteristics of the complex brain network, which can effectively reflect brain activity information, were used to detect the change of driving fatigue over time. Combined with the traditional eye movement characteristics and a subjective questionnaire (SQ), the changes in driving fatigue characteristics were comprehensively analyzed. The results show that driving fatigue can be effectively delayed using the MRM. Additionally, the response equipment is low in cost and practical, so it will be practical to use in actual driving situations in the future.
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Affiliation(s)
- Fuwang Wang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China;
| | - Qing Xu
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China;
| | - Rongrong Fu
- College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
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Wang F, Zhang X, Fu R, Sun G. EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical network. RSC Adv 2018; 8:29745-29755. [PMID: 35547294 PMCID: PMC9085270 DOI: 10.1039/c8ra04846k] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 08/06/2018] [Indexed: 12/20/2022] Open
Abstract
This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment. First, the θ, β and 36–44 Hz rhythm from the EEG signals of drivers were extracted using wavelet packet decomposition (WPD). The correlation between EEG channels was calculated using a Pearson correlation coefficient and subsequently, the brain networks were built. Furthermore, the clustering coefficient (C) and global efficiency (G) of the complex brain networks were calculated to analyze the functional differences in the brains of drivers over time. Combined with the relative power spectrum ratio (β/θ) of EEG signals and the mean value from questionnaires, the correlation of data characteristics between brain networks and subjective and objective data was analyzed. The results show that changes in the fatigue state of drivers can be effectively detected by calculating the data characteristics of brain networks in a real driving environment. This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment.![]()
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Affiliation(s)
- Fuwang Wang
- School of Mechanic Engineering, Northeast Electric Power University Jilin 132012 China +86-432-64807382
| | - Xiaolei Zhang
- School of Mechanic Engineering, Northeast Electric Power University Jilin 132012 China +86-432-64807382
| | - Rongrong Fu
- College of Electrical Engineering, Yanshan University Qinhuangdao 066004 China
| | - Guangbin Sun
- Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences Beijing 100094 China
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Wang F, Zhang X, Fu R, Sun G. Study of the Home-Auxiliary Robot Based on BCI. SENSORS (BASEL, SWITZERLAND) 2018; 18:E1779. [PMID: 29865175 PMCID: PMC6021918 DOI: 10.3390/s18061779] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2018] [Revised: 05/20/2018] [Accepted: 05/29/2018] [Indexed: 01/13/2023]
Abstract
A home-auxiliary robot platform is developed in the current study which could assist patients with physical disabilities and older persons with mobility impairments. The robot, mainly controlled by brain computer interface (BCI) technology, can not only perform actions in a person's field of vision, but also work outside the field of vision. The wavelet decomposition (WD) is used in this study to extract the δ (0~4 Hz) and θ (4~8 Hz) sub-bands of subjects' electroencephalogram (EEG) signals. The correlation between pairs of 14 EEG channels is determined with synchronization likelihood (SL), and the brain network structure is generated. Then, the motion characteristics are analyzed using the brain network parameters clustering coefficient (C) and global efficiency (G). Meanwhile, the eye movement characteristics in the F3 and F4 channels are identified. Finally, the motion characteristics identified by brain networks and eye movement characteristics can be used to control the home-auxiliary robot platform. The experimental result shows that the accuracy rate of left and right motion recognition using this method is more than 93%. Additionally, the similarity between that autonomous return path and the real path of the home-auxiliary robot reaches up to 0.89.
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Affiliation(s)
- Fuwang Wang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China.
| | - Xiaolei Zhang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China.
| | - Rongrong Fu
- College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
| | - Guangbin Sun
- Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China.
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Wang F, Wang H, Fu R. Real-Time ECG-Based Detection of Fatigue Driving Using Sample Entropy. ENTROPY (BASEL, SWITZERLAND) 2018; 20:E196. [PMID: 33265287 PMCID: PMC7512712 DOI: 10.3390/e20030196] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 03/03/2018] [Accepted: 03/13/2018] [Indexed: 11/25/2022]
Abstract
In present work, the heart rate variability (HRV) characteristics, calculated by sample entropy (SampEn), were used to analyze the driving fatigue state at successive driving stages. Combined with the relative power spectrum ratio β/(θ + α), subjective questionnaire, and brain network parameters of electroencephalogram (EEG) signals, the relationships between the different characteristics for driving fatigue were discussed. Thus, it can conclude that the HRV characteristics (RR SampEn and R peaks SampEn), as well as the relative power spectrum ratio β/(θ + α) of the channels (C3, C4, P3, P4), the subjective questionnaire, and the brain network parameters, can effectively detect driving fatigue at various driving stages. In addition, the method for collecting ECG signals from the palm part does not need patch electrodes, is convenient, and will be practical to use in actual driving situations in the future.
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Affiliation(s)
- Fuwang Wang
- School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China
| | - Hong Wang
- School of Mechanic Engineering, Northeastern University, Shenyang 110819, China
| | - Rongrong Fu
- College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
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Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics. J Int Neuropsychol Soc 2016; 22:240-9. [PMID: 26888620 DOI: 10.1017/s1355617715000867] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVES One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. METHODS Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. RESULTS The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity--connections among high degree "rich club" nodes, "feeder" connections to these rich club nodes, and "local" connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. CONCLUSIONS Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA.
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Magalhães R, Marques P, Soares J, Alves V, Sousa N. The Impact of Normalization and Segmentation on Resting-State Brain Networks. Brain Connect 2015; 5:166-76. [DOI: 10.1089/brain.2014.0292] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center, Braga, Portugal
- Department of Informatics, University of Minho, Braga, Portugal
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center, Braga, Portugal
| | - José Soares
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center, Braga, Portugal
| | - Victor Alves
- Department of Informatics, University of Minho, Braga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center, Braga, Portugal
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