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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zhang Y, Qiu S, He H. Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion. J Neural Eng 2023; 20. [PMID: 36854181 DOI: 10.1088/1741-2552/acbfdf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 02/28/2023] [Indexed: 03/02/2023]
Abstract
Objective. A motor imagery-based brain-computer interface (MI-BCI) translates spontaneous movement intention from the brain to outside devices. Multimodal MI-BCI that uses multiple neural signals contains rich common and complementary information and is promising for enhancing the decoding accuracy of MI-BCI. However, the heterogeneity of different modalities makes the multimodal decoding task difficult. How to effectively utilize multimodal information remains to be further studied.Approach. In this study, a multimodal MI decoding neural network was proposed. Spatial feature alignment losses were designed to enhance the feature representations extracted from the heterogeneous data and guide the fusion of features from different modalities. An attention-based modality fusion module was built to align and fuse the features in the temporal dimension. To evaluate the proposed decoding method, a five-class MI electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) dataset were constructed.Main results and significance. The comparison experimental results showed that the proposed decoding method achieved higher decoding accuracy than the compared methods on both the self-collected dataset and a public dataset. The ablation results verified the effectiveness of each part of the proposed method. Feature distribution visualization results showed that the proposed losses enhance the feature representation of EEG and fNIRS modalities. The proposed method based on EEG and fNIRS modalities has significant potential for improving decoding performance of MI tasks.
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Affiliation(s)
- Yukun Zhang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Shuang Qiu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Huiguang He
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China.,Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
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Gao F, Hua L, He Y, Xu J, Li D, Zhang J, Yuan Z. Word Structure Tunes Electrophysiological and Hemodynamic Responses in the Frontal Cortex. Bioengineering (Basel) 2023; 10:bioengineering10030288. [PMID: 36978679 PMCID: PMC10044899 DOI: 10.3390/bioengineering10030288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 01/31/2023] [Accepted: 02/10/2023] [Indexed: 03/30/2023] Open
Abstract
To date, it is still unclear how word structure might impact lexical processing in the brain for languages with an impoverished system of grammatical morphology such as Chinese. In this study, concurrent electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) recordings were performed to inspect the temporal and spatial brain activities that are related to Chinese word structure (compound vs. derivation vs. non-morphological) effects. A masked priming paradigm was utilized on three lexical conditions (compound constitute priming, derivation constitute priming, and non-morphological priming) to tap Chinese native speakers' structural sensitivity to differing word structures. The compound vs. derivation structure effect was revealed by the behavioral data as well as the temporal and spatial brain activation patterns. In the masked priming task, Chinese derivations exhibited significantly enhanced brain activation in the frontal cortex and involved broader brain networks as compared with lexicalized compounds. The results were interpreted by the differing connection patterns between constitute morphemes within a given word structure from a spreading activation perspective. More importantly, we demonstrated that the Chinese word structure effect showed a distinct brain activation pattern from that of the dual-route mechanism in alphabetic languages. Therefore, this work paved a new avenue for comprehensively understanding the underlying cognitive neural mechanisms associated with Chinese derivations and coordinate compounds.
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Affiliation(s)
- Fei Gao
- Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR 999078, China
- Institute of Modern Languages and Linguistics, Fudan University, Shanghai 200433, China
| | - Lin Hua
- Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR 999078, China
- Faculty of Health Sciences, University of Macau, Macau SAR 999078, China
| | - Yuwen He
- Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR 999078, China
- Faculty of Health Sciences, University of Macau, Macau SAR 999078, China
| | - Jie Xu
- Faculty of Arts and Humanities, University of Macau, Macau SAR 999078, China
| | - Defeng Li
- Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR 999078, China
- Faculty of Arts and Humanities, University of Macau, Macau SAR 999078, China
| | - Juan Zhang
- Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR 999078, China
- Faculty of Education, University of Macau, Macau SAR 999078, China
| | - Zhen Yuan
- Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR 999078, China
- Faculty of Health Sciences, University of Macau, Macau SAR 999078, China
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Chen YH, Yang J, Wu H, Beier KT, Sawan M. Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction. Front Psychiatry 2023; 14:1085036. [PMID: 36911117 PMCID: PMC9995819 DOI: 10.3389/fpsyt.2023.1085036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/03/2023] [Indexed: 02/25/2023] Open
Abstract
Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.
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Affiliation(s)
- Yun-Hsuan Chen
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Jie Yang
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
| | - Hemmings Wu
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kevin T Beier
- Department of Physiology and Biophysics, University of California, Irvine, Irvine, CA, United States.,Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, United States.,Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.,Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, United States.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.,Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China
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Porcaro C, Avanaki K, Arias-Carrion O, Mørup M. Editorial: Combined EEG in research and diagnostics: Novel perspectives and improvements. Front Neurosci 2023; 17:1152394. [PMID: 36875646 PMCID: PMC9978703 DOI: 10.3389/fnins.2023.1152394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy.,Institute of Cognitive Sciences and Technologies-National Research Council, Rome, Italy.,Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Kamran Avanaki
- University of Illinois at Chicago, Chicago, IL, United States
| | - Oscar Arias-Carrion
- Unidad de Trastornos del Movimiento y Sueño, Hospital General Dr. Manuel Gea González, Mexico City, Mexico
| | - Morten Mørup
- Technical University of Denmark, Lyngby, Denmark
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Abstract
In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.
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Pouliot P, Tran TPY, Birca V, Vannasing P, Tremblay J, Lassonde M, Nguyen DK. Hemodynamic changes during posterior epilepsies: an EEG-fNIRS study. Epilepsy Res 2014; 108:883-90. [PMID: 24755234 DOI: 10.1016/j.eplepsyres.2014.03.007] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2013] [Revised: 02/19/2014] [Accepted: 03/16/2014] [Indexed: 11/30/2022]
Abstract
Posterior epilepsies are mainly characterized clinically by visual symptoms. Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive imaging technique that has the potential to monitor hemodynamic changes during epileptic activity. Combined with electroencephalography (EEG), 9 patients with posterior epilepsies were recorded using EEG-fNIRS with large sampling (19 EEG electrodes and over 100 fNIRS channels). Spikes and seizures were carefully marked on EEG traces, and convolved with a standard hemodynamic response function for general linear model (GLM) analysis. GLM results for seizures (in 3 patients) and spikes (7 patients) were broadly sensitive to the epileptic focus in 7/9 patients, and specific in 5/9 patients with fNIRS deoxyhemoglobin responses lateralized to the correct lobe, and to plausible locations within the occipital or parietal lobes. This work provides evidence that EEG-fNIRS is a sensitive technique for monitoring posterior epileptic activity.
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Affiliation(s)
- Philippe Pouliot
- Département de génie électrique, École Polytechnique de Montréal, C.P. 6079, Succ. Centre-ville, Montréal, QC, Canada H3C3A7; Institut de cardiologie de Montréal, Centre de recherche, 5000 Rue Bélanger Est, Montréal, QC, Canada H1T1C8.
| | - Thi Phuoc Yen Tran
- Service de neurologie, Hôpital Notre-Dame du CHUM, 1560 Rue Sherbrooke Est, Montréal, QC, Canada H3L4M1
| | - Véronica Birca
- Service de neurologie, Hôpital Notre-Dame du CHUM, 1560 Rue Sherbrooke Est, Montréal, QC, Canada H3L4M1
| | - Phetsamone Vannasing
- Centre de recherche, Hôpital Sainte-Justine, 3175 Chemin de la côte-Sainte-Catherine, Montréal, QC, Canada H3T1C5
| | - Julie Tremblay
- Centre de recherche, Hôpital Sainte-Justine, 3175 Chemin de la côte-Sainte-Catherine, Montréal, QC, Canada H3T1C5
| | - Maryse Lassonde
- Centre de recherche, Hôpital Sainte-Justine, 3175 Chemin de la côte-Sainte-Catherine, Montréal, QC, Canada H3T1C5; Centre de recherche en neuropsychologie et cognition, Département de psychologie, Université de Montréal, Montréal, QC, Canada H3C3J7
| | - Dang Khoa Nguyen
- Service de neurologie, Hôpital Notre-Dame du CHUM, 1560 Rue Sherbrooke Est, Montréal, QC, Canada H3L4M1
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