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McCann A, Xu E, Yen FY, Joseph N, Fang Q. Creating anatomically-derived, standardized, customizable, and three-dimensional printable head caps for functional neuroimaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.30.610386. [PMID: 39257741 PMCID: PMC11383710 DOI: 10.1101/2024.08.30.610386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Significance Consistent and accurate probe placement is a crucial step towards enhancing the reproducibility of longitudinal and group-based functional neuroimaging studies. While the selection of headgear is central to these efforts, there does not currently exist a standardized design that can accommodate diverse probe configurations and experimental procedures. Aim We aim to provide the community with an open-source software pipeline for conveniently creating low-cost, 3-D printable neuroimaging head caps with anatomically significant landmarks integrated into the structure of the cap. Approach We utilize our advanced 3-D head mesh generation toolbox and 10-20 head landmark calculations to quickly convert a subject's anatomical scan or an atlas into a 3-D printable head cap model. The 3-D modeling environment of the open-source Blender platform permits advanced mesh processing features to customize the cap. The design process is streamlined into a Blender add-on named "NeuroCaptain". Results Using the intuitive user interface, we create various head cap models using brain atlases, and share those with the community. The resulting mesh-based head cap designs are readily 3-D printable using off-the-shelf printers and filaments while accurately preserving the head topology and landmarks. Conclusions The methods developed in this work result in a widely accessible tool for community members to design, customize and fabricate caps that incorporate anatomically derived landmarks. This not only permits personalized head cap designs to achieve improved accuracy, but also offers an open platform for the community to propose standardizable head caps to facilitate multi-centered data collection and sharing.
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Affiliation(s)
- Ashlyn McCann
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Edward Xu
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Fan-Yu Yen
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Noah Joseph
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
- Northeastern University, Department of EECS, 360 Huntington Avenue, Boston, USA, 02115
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Long K, Zhang X, Wang N, Lei H. Event-related prefrontal activations during online video game playing are modulated by game mechanics, physiological arousal and the amount of daily playing. Behav Brain Res 2024; 469:115038. [PMID: 38705282 DOI: 10.1016/j.bbr.2024.115038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/09/2024] [Accepted: 04/30/2024] [Indexed: 05/07/2024]
Abstract
There is a trend to study human brain functions in ecological contexts and in relation to human factors. In this study, functional near-infrared spectroscopy (fNIRS) was used to record real-time prefrontal activities in 42 male university student habitual video game players when they played a round of multiplayer online battle arena game, League of Legends. A content-based event coding approach was used to analyze regional activations in relation to event type, physiological arousal indexed by heart rate (HR) change, and individual characteristics of the player. Game events Slay and Slain were found to be associated with similar HR and prefrontal responses before the event onset, but differential responses after the event onset. Ventrolateral prefrontal cortex (VLPFC) activation preceding the Slay onset correlated positively with HR change, whereas activations in dorsolateral prefrontal cortex (DLPFC) and rostral frontal pole area (FPAr) preceding the Slain onset were predicted by self-reported hours of weekly playing (HoWP). Together, these results provide empirical evidence to support the notion that event-related regional prefrontal activations during online video game playing are shaped by game mechanics, in-game dynamics of physiological arousal and individual characteristics the players.
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Affiliation(s)
- Kehong Long
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei, PR China; University of Chinese Academy of Sciences, Beijing, PR China
| | - Xuzhe Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei, PR China; University of Chinese Academy of Sciences, Beijing, PR China
| | - Ningxin Wang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei, PR China; Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Hao Lei
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei, PR China; Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, PR China; University of Chinese Academy of Sciences, Beijing, PR China.
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Zhang Y, Wu P, Xie S, Hou Y, Wu H, Shi H. The neural mechanism of communication between graduate students and advisers in different adviser-advisee relationships. Sci Rep 2024; 14:11741. [PMID: 38778035 PMCID: PMC11111769 DOI: 10.1038/s41598-024-58308-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 03/27/2024] [Indexed: 05/25/2024] Open
Abstract
Communication is crucial in constructing the relationship between students and advisers, ultimately bridging interpersonal interactions. Only a few studies however explore the communication between postgraduate students and advisers. To fill the gaps in the empirical researches, this study uses functional near-infrared spectroscopy (FNIRS) techniques to explore the neurophysiology differences in brain activation of postgraduates with different adviser-advise relationships during simulated communication with their advisers. Results showed significant differences in the activation of the prefrontal cortex between high-quality and the low-quality students during simulating and when communicating with advisers, specifically in the Broca's areas, the frontal pole, and the orbitofrontal and dorsolateral prefrontal cortices. This further elucidated the complex cognitive process of communication between graduate students and advisers.
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Affiliation(s)
- Yan Zhang
- School of Education, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
- Research Center for Innovative Education and Critical Thinking, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Peipei Wu
- School of Education, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
| | - Simiao Xie
- School of Education, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
- Mental Health Education Center, Jinan University, Guangzhou, 510631, Guangdong, China
| | - Yan Hou
- School of Education, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
- Mental Health Education Center, Hubei University for Nationalities, Enshi, 450004, Hubei, China
| | - Huifen Wu
- School of Education, Hubei Engineering University, Xiaogan, 432100, Hubei, China.
| | - Hui Shi
- Department of Clinical Psychology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China.
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Wang Q, Li Y, Su H, Zhong N, Xu Q, Li X. Deep neural network to differentiate internet gaming disorder from healthy controls during stop-signal task: a multichannel near-infrared spectroscopy study. BIOMED ENG-BIOMED TE 2023; 68:457-468. [PMID: 37099486 DOI: 10.1515/bmt-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 03/27/2023] [Indexed: 04/27/2023]
Abstract
Internet Gaming Disorder (IGD), as one of worldwide mental health issues, leads to negative effects on physical and mental health and has attracted public attention. Most studies on IGD are based on screening scales and subjective judgments of doctors, without objective quantitative assessment. However, public understanding of internet gaming disorder lacks objectivity. Therefore, the researches on internet gaming disorder still have many limitations. In this paper, a stop-signal task (SST) was designed to assess inhibitory control in patients with IGD based on prefrontal functional near-infrared spectroscopy (fNIRS). According to the scale, the subjects were divided into health and gaming disorder. A total of 40 subjects (24 internet gaming disorders; 16 healthy controls) signals were used for deep learning-based classification. The seven algorithms used for classification and comparison were deep learning algorithms (DL) and machine learning algorithms (ML), with four and three algorithms in each category, respectively. After applying hold-out method, the performance of the model was verified by accuracy. DL models outperformed traditional ML algorithms. Furthermore, the classification accuracy of the two-dimensional convolution neural network (2D-CNN) was 87.5% among all models. This was the highest accuracy out of all models that were tested. The 2D-CNN was able to outperform the other models due to its ability to learn complex patterns in data. This makes it well-suited for image classification tasks. The findings suggested that a 2D-CNN model is an effective approach for predicting internet gaming disorder. The results show that this is a reliable method with high accuracy to identify patients with IGD and demonstrate that the use of fNIRS to facilitate the development of IGD diagnosis has great potential.
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Affiliation(s)
- Qiwen Wang
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yongkang Li
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Hang Su
- Shanghai Mental health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Na Zhong
- Shanghai Mental health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qi Xu
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiaoou Li
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Fu Y, Wang F, Li Y, Gong A, Qian Q, Su L, Zhao L. Real-time recognition of different imagined actions on the same side of a single limb based on the fNIRS correlation coefficient. BIOMED ENG-BIOMED TE 2022; 67:173-183. [PMID: 35420003 DOI: 10.1515/bmt-2021-0422] [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: 12/22/2021] [Accepted: 03/25/2022] [Indexed: 11/15/2022]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a type of functional brain imaging. Brain-computer interfaces (BCIs) based on fNIRS have recently been implemented. Most existing fNIRS-BCI studies have involved off-line analyses, but few studies used online performance testing. Furthermore, existing online fNIRS-BCI experimental paradigms have not yet carried out studies using different imagined movements of the same side of a single limb. In the present study, a real-time fNIRS-BCI system was constructed to identify two imagined movements of the same side of a single limb (right forearm and right hand). Ten healthy subjects were recruited and fNIRS signal was collected and real-time analyzed with two imagined movements (leftward movement involving the right forearm and right-hand clenching). In addition to the mean and slope features of fNIRS signals, the correlation coefficient between fNIRS signals induced by different imagined actions was extracted. A support vector machine (SVM) was used to classify the imagined actions. The average accuracy of real-time classification of the two imagined movements was 72.25 ± 0.004%. The findings suggest that different imagined movements on the same side of a single limb can be recognized real-time based on fNIRS, which may help to further guide the practical application of online fNIRS-BCIs.
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Affiliation(s)
- Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Fan Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Yu Li
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Anmin Gong
- School of Information Engineering, Chinese People's Armed Police Force Engineering University, Xian, China
| | - Qian Qian
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Lei Su
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China.,Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Lei Zhao
- Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.,Faculty of Science, Kunming University of Science and Technology, Kunming, China
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Cho TH, Nah Y, Park SH, Han S. Prefrontal cortical activation in Internet Gaming Disorder Scale high scorers during actual real-time internet gaming: A preliminary study using fNIRS. J Behav Addict 2022. [PMID: 35394923 PMCID: PMC9295239 DOI: 10.1556/2006.2022.00017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/10/2022] [Accepted: 03/19/2022] [Indexed: 12/21/2022] Open
Abstract
Background Observation of real-time neural characteristics during gameplay would provide distinct evidence for discriminating the currently controversial diagnosis of internet gaming disorder (IGD), and elucidate neural mechanisms that may be involved in addiction. We aimed to provide preliminary findings on possible neural features of IGD during real-time internet gaming using functional near-infrared spectroscopy (fNIRS). Methods Prefrontal cortical activations accompanying positive and negative in-game events were investigated. Positive events: (1) participant's champion slays or assists in slaying an opponent without being slain. (2) the opposing team's nexus is destroyed. Negative events: (1) participant's champion is slain without slaying or assisting in slaying any opponent. (2) the team's nexus is destroyed. Collected data were compared between the IGD group and control group, each with 15 participants. Results The IGD group scored significantly higher than the CTRL group on the craving scale. Following positive events, the IGD group displayed significantly stronger activation in the DLPFC. Following negative events, the IGD group displayed significantly weaker activation in the lateral OFC. Discussion and Conclusions Individuals scoring high on the IGD scale may crave for more internet gaming after encountering desired events during the game. Such observations are supported by the correlation between the craving scale and DLPFC activation. The IGD group may also show diminished punishment sensitivity to negative in-game experiences rendering them to continue playing the game. The present study provides preliminary evidence that IGD may demonstrate neural characteristics observed in other addictive disorders and suggests the use of fNIRS in behavioral addiction studies.
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Affiliation(s)
- Tae Hun Cho
- Department of Psychology, Yonsei University, Seoul, Korea
| | - Yoonjin Nah
- Department of Psychology, Yonsei University, Seoul, Korea
| | - Soo Hyun Park
- Department of Psychology, Yonsei University, Seoul, Korea
| | - Sanghoon Han
- Department of Psychology, Yonsei University, Seoul, Korea
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Yang C, Han X, Jin M, Xu J, Wang Y, Zhang Y, Xu C, Zhang Y, Jin E, Piao C. The Effect of Video Game-Based Interventions on Performance and Cognitive Function in Older Adults: Bayesian Network Meta-analysis. JMIR Serious Games 2021; 9:e27058. [PMID: 34967759 PMCID: PMC8759017 DOI: 10.2196/27058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 03/22/2021] [Accepted: 10/12/2021] [Indexed: 01/29/2023] Open
Abstract
Background The decline in performance of older people includes balance function, physical function, and fear of falling and depression. General cognitive function decline is described in terms of processing speed, working memory, attention, and executive functioning, and video game interventions may be effective. Objective This study evaluates the effect of video game interventions on performance and cognitive function in older participants in terms of 6 indicators: balance function, executive function, general cognitive function, physical function, processing speed, and fear of falling and depression. Methods Electronic databases were searched for studies from inception to June 30, 2020. Randomized controlled trials and case-controlled trials comparing video game interventions versus nonvideo game control in terms of performance and cognitive function outcomes were incorporated into a Bayesian network meta-analysis. All data were continuous variables. Results In total, 47 studies (3244 participants) were included. In pairwise meta-analysis, compared with nonvideo game control, video game interventions improved processing speed, general cognitive function, and depression scores. In the Bayesian network meta-analysis, interventions with video games improved balance function time (standardized mean difference [SMD] –3.34, 95% credible interval [CrI] –5.54 to –2.56), the cognitive function score (SMD 1.23, 95% CrI 0.82-1.86), processing speed time (SMD –0.29, 95% CrI –0.49 to –0.08), and processing speed number (SMD 0.72, 95% CrI 0.36-1.09), similar to the pairwise meta-analysis. Interventions with video games with strong visual senses and good interactivity ranked first, and these might be more beneficial for the elderly. Conclusions Our comprehensive Bayesian network meta-analysis provides evidence that video game interventions could be considered for the elderly for improving performance and cognitive function, especially general cognitive scores and processing speed. Games with better interactivity and visual stimulation have better curative effects. Based on the available evidence, we recommend video game interventions for the elderly. Trial Registration PROSPERO CRD42020197158; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=197158
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Affiliation(s)
- Chao Yang
- Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, China
| | - Xiaolei Han
- Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, China
| | - Mingxue Jin
- Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, China
| | - Jianhui Xu
- Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, China
| | - Yiren Wang
- Department of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
| | - Yajun Zhang
- Department of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
| | | | - Yingshi Zhang
- Department of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, China
| | - Enshi Jin
- Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, China.,Information Construction Department, Liaoning National Normal College, Shenyang, China
| | - Chengzhe Piao
- Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, China.,Information Construction Department, Liaoning National Normal College, Shenyang, China
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