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Wang W, Li H, Wang Y, Liu L, Qian Q. Changes in effective connectivity during the visual-motor integration tasks: a preliminary f-NIRS study. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2024; 20:4. [PMID: 38468270 DOI: 10.1186/s12993-024-00232-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 03/05/2024] [Indexed: 03/13/2024]
Abstract
BACKGROUND Visual-motor integration (VMI) is an essential skill in daily life. The present study aimed to use functional near-infrared spectroscopy (fNIRS) technology to explore the effective connectivity (EC) changes among brain regions during VMI activities of varying difficulty levels. METHODS A total of 17 healthy participants were recruited for the study. Continuous Performance Test (CPT), Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A), and Beery VMI test were used to evaluate attention performance, executive function, and VMI performance. Granger causality analysis was performed for the VMI task data to obtain the EC matrix for all participants. One-way ANOVA analysis was used to identify VMI load-dependent EC values among different task difficulty levels from brain network and channel perspectives, and partial correlation analysis was used to explore the relationship between VMI load-dependent EC values and behavioral performance. RESULTS We found that the EC values of dorsal attention network (DAN) → default mode network (DMN), DAN → ventral attention network (VAN), DAN → frontoparietal network (FPN), and DAN → somatomotor network (SMN) in the complex condition were higher than those in the simple and moderate conditions. Further channel analyses indicated that the EC values of the right superior parietal lobule (SPL) → right superior frontal gyrus (SFG), right middle occipital gyrus (MOG) → left SFG, and right MOG → right postcentral gyrus (PCG) in the complex condition were higher than those in the simple and moderate conditions. Subsequent partial correlation analysis revealed that the EC values from DAN to DMN, VAN, and SMN were positively correlated with executive function and VMI performance. Furthermore, the EC values of right MOG → left SFG and right MOG → right PCG were positively correlated with attention performance. CONCLUSIONS The DAN is actively involved during the VMI task and thus may play a critical role in VMI processes, in which two key brain regions (right SPL, right MOG) may contribute to the EC changes in response to increasing VMI load. Meanwhile, bilateral SFG and right PCG may also be closely related to the VMI performance.
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Affiliation(s)
- Wenchen Wang
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Haimei Li
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Yufeng Wang
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Lu Liu
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Qiujin Qian
- Peking University Sixth Hospital, Institute of Mental Health, Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
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Acuña K, Sapahia R, Jiménez IN, Antonietti M, Anzola I, Cruz M, García MT, Krishnan V, Leveille LA, Resch MD, Galor A, Habash R, DeBuc DC. Functional Near-Infrared Spectrometry as a Useful Diagnostic Tool for Understanding the Visual System: A Review. J Clin Med 2024; 13:282. [PMID: 38202288 PMCID: PMC10779649 DOI: 10.3390/jcm13010282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/24/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
This comprehensive review explores the role of Functional Near-Infrared Spectroscopy (fNIRS) in advancing our understanding of the visual system. Beginning with an introduction to fNIRS, we delve into its historical development, highlighting how this technology has evolved over time. The core of the review critically examines the advantages and disadvantages of fNIRS, offering a balanced view of its capabilities and limitations in research and clinical settings. We extend our discussion to the diverse applications of fNIRS beyond its traditional use, emphasizing its versatility across various fields. In the context of the visual system, this review provides an in-depth analysis of how fNIRS contributes to our understanding of eye function, including eye diseases. We discuss the intricacies of the visual cortex, how it responds to visual stimuli and the implications of these findings in both health and disease. A unique aspect of this review is the exploration of the intersection between fNIRS, virtual reality (VR), augmented reality (AR) and artificial intelligence (AI). We discuss how these cutting-edge technologies are synergizing with fNIRS to open new frontiers in visual system research. The review concludes with a forward-looking perspective, envisioning the future of fNIRS in a rapidly evolving technological landscape and its potential to revolutionize our approach to studying and understanding the visual system.
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Affiliation(s)
- Kelly Acuña
- School of Medicine, Georgetown University, Washington, DC 20007, USA;
| | - Rishav Sapahia
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Irene Newman Jiménez
- Department of Cognitive Science, Faculty of Arts & Science, McGill University, Montreal, QC H4A 3J1, Canada;
| | - Michael Antonietti
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Ignacio Anzola
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Marvin Cruz
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Michael T. García
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Varun Krishnan
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Lynn A. Leveille
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Miklós D. Resch
- Department of Ophthalmology, Semmelweis University, 1085 Budapest, Hungary;
| | - Anat Galor
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Ranya Habash
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
| | - Delia Cabrera DeBuc
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL 33136, USA; (R.S.); (M.A.); (M.T.G.); (V.K.); (L.A.L.); (A.G.)
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Lin J, Lu J, Shu Z, Yu N, Han J. An EEG-fNIRS neurovascular coupling analysis method to investigate cognitive-motor interference. Comput Biol Med 2023; 160:106968. [PMID: 37196454 DOI: 10.1016/j.compbiomed.2023.106968] [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: 12/26/2022] [Revised: 03/27/2023] [Accepted: 04/19/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND AND OBJECTIVE The simultaneous execution of a motor and cognitive dual task may lead to the deterioration of task performance in one or both tasks due to cognitive-motor interference (CMI). Neuroimaging techniques are promising ways to reveal the underlying neural mechanism of CMI. However, existing studies have only explored CMI from a single neuroimaging modality, which lack built-in validation and comparison of analysis results. This work is aimed to establish an effective analysis framework to comprehensively investigate the CMI by exploring the electrophysiological and hemodynamic activities as well as their neurovascular coupling. METHODS Experiments including an upper limb single motor task, single cognitive task, and cognitive-motor dual task were designed and performed with 16 healthy young participants. Bimodal signals of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded simultaneously during the experiments. A novel bimodal signal analysis framework was proposed to extract the task-related components for EEG and fNIRS signals respectively and analyze their correlation. Indicators including within-class similarity and between-class distance were utilized to validate the effectiveness of the proposed analysis framework compared to the canonical channel-averaged method. Statistical analysis was performed to investigate the difference in the behavior and neural correlates between the single and dual tasks. RESULTS Our results revealed that the extra cognitive interference caused divided attention in the dual task, which led to the decreased neurovascular coupling between fNIRS and EEG in all theta, alpha, and beta rhythms. The proposed framework was demonstrated to have a better ability in characterizing the neural patterns than the canonical channel-averaged method with significantly higher within-class similarity and between-class distance indicators. CONCLUSIONS This study proposed a method to investigate CMI by exploring the task-related electrophysiological and hemodynamic activities as well as their neurovascular coupling. Our concurrent EEG-fNIRS study provides new insight into the EEG-fNIRS correlation analysis and novel evidence for the mechanism of neurovascular coupling in the CMI.
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Affiliation(s)
- Jianeng Lin
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
| | - Jiewei Lu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
| | - Zhilin Shu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China
| | - Ningbo Yu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
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