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Wilhelm RA, Lacey MF, Masters SL, Breeden CJ, Mann E, MacDonald HV, Gable PA, White EJ, Stewart JL. Greater weekly physical activity linked to left resting frontal alpha asymmetry in women: A study on gender differences in highly active young adults. PSYCHOLOGY OF SPORT AND EXERCISE 2024; 74:102679. [PMID: 38797225 DOI: 10.1016/j.psychsport.2024.102679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/29/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024]
Abstract
Physical activity, beneficial for physical and psychological health, may facilitate affective mechanisms of positive emotion and approach-motivation. Greater resting frontal alpha asymmetry (FAA), an index of greater relative left than right frontal cortical activity, is a neural correlate of affective mechanisms possibly associated with active lifestyles. This study sought to amplify limited literature on the relationship between physical (in)activity, FAA, and gender differences. College students (n = 70) self-reported physical activity (Total PA) and sedentary activity (Total Sitting) via the International Physical Activity Questionnaire-Short Form (IPAQ-SF), followed by a resting electroencephalography session to record FAA. A Total PA × gender interaction (β = 0.462, t = 3.163, p = 0.002) identified a positive relationship between Total PA and FAA in women (β = 0.434, t = 2.221, p = 0.030) and a negative relationship for men (β = -0.338, t = -2.300, p = 0.025). Total Sitting was positively linked to FAA (β = 0.288, t = 2.228, p = 0.029; no gender effect). Results suggest affective mechanisms reflected by FAA (e.g., positive emotion, approach-motivation) are associated with physical activity for women, indicating a possible mechanism of the psychological benefits linked with physically active lifestyles. A positive relationship between sedentary behavior and greater left FAA may also reflect motivated mechanisms of behavior that aid in minimizing energy expenditure, particularly within the context of our highly active sample.
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Affiliation(s)
- Ricardo A Wilhelm
- Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA; Department of Psychology, University of Alabama, Tuscaloosa, AL, USA.
| | - Micayla F Lacey
- Department of Psychology, University of Alabama, Tuscaloosa, AL, USA; Department of Behavioral & Social Sciences, Wilkes University, Wilkes-Barre, PA, USA.
| | - Stephanie L Masters
- Department of Psychology, University of Alabama, Tuscaloosa, AL, USA; Department of Psychology & Counseling, Hood College, Frederick, MD, USA
| | - Christopher J Breeden
- Department of Psychology, University of Alabama, Tuscaloosa, AL, USA; Department of Psychology, Wingate University, Wingate, NC, USA
| | - Eric Mann
- Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA
| | | | - Philip A Gable
- Department of Psychology, University of Alabama, Tuscaloosa, AL, USA; Department of Psychological & Brain Sciences, University of Delaware, Newark, DE, USA
| | - Evan J White
- Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA; Oxley School of Community Medicine, University of Tulsa, Tulsa, OK, USA
| | - Jennifer L Stewart
- Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA; Oxley School of Community Medicine, University of Tulsa, Tulsa, OK, USA
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Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [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: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
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3
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Zhang X, Liu W, Xu F, He W, Song Y, Li G, Zhang Y, Dai G, Xiao Q, Meng Q, Zeng X, Bai S, Zhong R. Neural signals-based respiratory motion tracking: a proof-of-concept study. Phys Med Biol 2023; 68:195015. [PMID: 37683675 DOI: 10.1088/1361-6560/acf819] [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: 03/30/2023] [Accepted: 09/08/2023] [Indexed: 09/10/2023]
Abstract
Objective.Respiratory motion tracking techniques can provide optimal treatment accuracy for thoracoabdominal radiotherapy and robotic surgery. However, conventional imaging-based respiratory motion tracking techniques are time-lagged owing to the system latency of medical linear accelerators and surgical robots. This study aims to investigate the precursor time of respiratory-related neural signals and analyze the potential of neural signals-based respiratory motion tracking.Approach.The neural signals and respiratory motion from eighteen healthy volunteers were acquired simultaneously using a 256-channel scalp electroencephalography (EEG) system. The neural signals were preprocessed using the MNE python package to extract respiratory-related EEG neural signals. Cross-correlation analysis was performed to assess the precursor time and cross-correlation coefficient between respiratory-related EEG neural signals and respiratory motion.Main results.Respiratory-related neural signals that precede the emergence of respiratory motion are detectable via non-invasive EEG. On average, the precursor time of respiratory-related EEG neural signals was 0.68 s. The representative cross-correlation coefficients between EEG neural signals and respiratory motion of the eighteen healthy subjects varied from 0.22 to 0.87.Significance.Our findings suggest that neural signals have the potential to compensate for the system latency of medical linear accelerators and surgical robots. This indicates that neural signals-based respiratory motion tracking is a potential promising solution to respiratory motion and could be useful in thoracoabdominal radiotherapy and robotic surgery.
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Affiliation(s)
- Xiangbin Zhang
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Wenjie Liu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, People's Republic of China
| | - Feng Xu
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Weizhong He
- Magstim Electrical Geodesics, Inc, Plymouth, MA, United States of America
| | - Yingpeng Song
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Guangjun Li
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Yingjie Zhang
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Guyu Dai
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Qing Xiao
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Qianqian Meng
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Xianhu Zeng
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Sen Bai
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Renming Zhong
- Radiotherapy Physics and Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China
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Mayeli A, Al Zoubi O, White EJ, Chappelle S, Kuplicki R, Morton A, Bruce J, Smith R, Feinstein JS, Bodurka J, Paulus MP, Khalsa SS. Parieto-occipital ERP indicators of gut mechanosensation in humans. Nat Commun 2023; 14:3398. [PMID: 37311748 PMCID: PMC10264354 DOI: 10.1038/s41467-023-39058-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 05/24/2023] [Indexed: 06/15/2023] Open
Abstract
Understanding the neural processes governing the human gut-brain connection has been challenging due to the inaccessibility of the body's interior. Here, we investigated neural responses to gastrointestinal sensation using a minimally invasive mechanosensory probe by quantifying brain, stomach, and perceptual responses following the ingestion of a vibrating capsule. Participants successfully perceived capsule stimulation under two vibration conditions (normal and enhanced), as evidenced by above chance accuracy scores. Perceptual accuracy improved significantly during the enhanced relative to normal stimulation, which was associated with faster stimulation detection and reduced reaction time variability. Capsule stimulation induced late neural responses in parieto-occipital electrodes near the midline. Moreover, these 'gastric evoked potentials' showed intensity-dependent increases in amplitude and were significantly correlated with perceptual accuracy. Our results replicated in a separate experiment, and abdominal X-ray imaging localized most capsule stimulations to the gastroduodenal segments. Combined with our prior observation that a Bayesian model is capable of estimating computational parameters of gut-brain mechanosensation, these findings highlight a unique form of enterically-focused sensory monitoring within the human brain, with implications for understanding gut feelings and gut-brain interactions in healthy and clinical populations.
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Affiliation(s)
- Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Harvard Medical School/McLean Hospital, Boston, MA, USA
| | - Evan J White
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | | | | | - Alexa Morton
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Jaimee Bruce
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | | | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Stephenson School of Biomedical Engineering, University of Oklahoma, Tulsa, OK, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, Tulsa, OK, USA.
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA.
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Levitt J, Yang Z, Williams SD, Lütschg Espinosa SE, Garcia-Casal A, Lewis LD. EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI. Neuroimage 2023; 273:120092. [PMID: 37028736 PMCID: PMC10202030 DOI: 10.1016/j.neuroimage.2023.120092] [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: 10/31/2022] [Revised: 03/02/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023] Open
Abstract
Simultaneous EEG-fMRI is a powerful multimodal technique for imaging the brain, but its use in neurofeedback experiments has been limited by EEG noise caused by the MRI environment. Neurofeedback studies typically require analysis of EEG in real time, but EEG acquired inside the scanner is heavily contaminated with ballistocardiogram (BCG) artifact, a high-amplitude artifact locked to the cardiac cycle. Although techniques for removing BCG artifacts do exist, they are either not suited to real-time, low-latency applications, such as neurofeedback, or have limited efficacy. We propose and validate a new open-source artifact removal software called EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software), which adapts and advances existing artifact removal techniques for low-latency experiments. We first used simulations to validate LLAMAS in data with known ground truth. We found that LLAMAS performed better than the best publicly-available real-time BCG removal technique, optimal basis sets (OBS), in terms of its ability to recover EEG waveforms, power spectra, and slow wave phase. To determine whether LLAMAS would be effective in practice, we then used it to conduct real-time EEG-fMRI recordings in healthy adults, using a steady state visual evoked potential (SSVEP) task. We found that LLAMAS was able to recover the SSVEP in real time, and recovered the power spectra collected outside the scanner better than OBS. We also measured the latency of LLAMAS during live recordings, and found that it introduced a lag of less than 50 ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. A limitation of the method is its use of a reference layer, a piece of EEG equipment which is not commercially available, but can be assembled in-house. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.
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Affiliation(s)
- Joshua Levitt
- Department of Biomedical Engineering, Boston University, USA
| | - Zinong Yang
- Department of Biomedical Engineering, Boston University, USA; Graduate Program of Neuroscience, Boston University, USA
| | | | | | | | - Laura D Lewis
- Department of Biomedical Engineering, Boston University, USA; Institute for Medical Engineering and Sciences, Department of Electrical Engineering and Computer Science, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA.
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6
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Gallego-Rudolf J, Corsi-Cabrera M, Concha L, Ricardo-Garcell J, Pasaye-Alcaraz E. Preservation of EEG spectral power features during simultaneous EEG-fMRI. Front Neurosci 2022; 16:951321. [PMID: 36620439 PMCID: PMC9816433 DOI: 10.3389/fnins.2022.951321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Electroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches. Method Electroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods. Results The BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis. Discussion Current software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.
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Affiliation(s)
- Jonathan Gallego-Rudolf
- Unidad de Resonancia Magnética, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico
| | - María Corsi-Cabrera
- Laboratorio de Sueño, Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, Mexico,Unidad de Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Luis Concha
- Laboratorio de Conectividad Cerebral, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico
| | - Josefina Ricardo-Garcell
- Unidad de Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Erick Pasaye-Alcaraz
- Unidad de Resonancia Magnética, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico,*Correspondence: Erick Pasaye-Alcaraz,
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7
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Chuang CH, Chang KY, Huang CS, Jung TP. IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal. Neuroimage 2022; 263:119586. [PMID: 36031182 DOI: 10.1016/j.neuroimage.2022.119586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 07/27/2022] [Accepted: 08/22/2022] [Indexed: 10/31/2022] Open
Abstract
Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent the misinterpretation of neural signals and the underperformance of brain-computer interfaces. Based on the U-Net architecture, we developed a new artifact removal model, IC-U-Net, for removing pervasive EEG artifacts and reconstructing brain signals. IC-U-Net was trained using mixtures of brain and non-brain components decomposed by independent component analysis. It uses an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain activities and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noise) was demonstrated in a simulation study and four real-world EEG experiments. IC-U-Net can reconstruct a multi-channel EEG signal and is applicable to most artifact types, offering a promising end-to-end solution for automatically removing artifacts from EEG recordings. It also meets the increasing need to image natural brain dynamics in a mobile setting. The code and pre-trained IC-U-Net model are available at https://github.com/roseDwayane/AIEEG.
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Affiliation(s)
- Chun-Hsiang Chuang
- Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan; Institute of Information Systems and Applications, College of Electrical Engineering and Computer Science, National Tsing Hua University, Hsinchu, Taiwan; Department of Education and Learning Technology, National Tsing Hua University, Hsinchu, Taiwan.
| | - Kong-Yi Chang
- Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan; Institute of Information Systems and Applications, College of Electrical Engineering and Computer Science, National Tsing Hua University, Hsinchu, Taiwan; Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan
| | - Chih-Sheng Huang
- Department of Artificial Intelligence Research and Development, Elan Microelectronics Corporation, Hsinchu, Taiwan; College of Artificial Intelligence and Green Energy, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; College of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei, Taiwan
| | - Tzyy-Ping Jung
- Institute of Engineering in Medicine and Institute for Neural Computation, University of California San Diego, La Jolla, USA
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Zou G, Liu J, Zou Q, Gao JH. A-PASS: An automated pipeline to analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep. J Neural Eng 2022; 19. [PMID: 35878599 DOI: 10.1088/1741-2552/ac83f2] [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: 03/12/2022] [Accepted: 07/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Concurrent electroencephalography and functional magnetic resonance imaging (EEG-fMRI) signals can be used to uncover the nature of brain activities during sleep. However, analyzing simultaneously acquired EEG-fMRI data is extremely time consuming and experience dependent. Thus, we developed a pipeline, which we named A-PASS, to automatically analyze simultaneously acquired EEG-fMRI data for studying brain activities during sleep. APPROACH A deep learning model was trained on a sleep EEG-fMRI dataset from 45 subjects and used to perform sleep stage scoring. Various fMRI indices can be calculated with A-PASS to depict the neurophysiological characteristics across different sleep stages. We tested the performance of A-PASS on an independent sleep EEG-fMRI dataset from 28 subjects. Statistical maps regarding the main effect of sleep stages and differences between each pair of stages of fMRI indices were generated and compared using both A-PASS and manual processing methods. MAIN RESULTS The deep learning model implemented in A-PASS achieved both an accuracy and F1-score higher than 70% for sleep stage classification on EEG data acquired during fMRI scanning. The statistical maps generated from A-PASS largely resembled those produced from manually scored stages plus a combination of multiple software programs. SIGNIFICANCE A-PASS allowed efficient EEG-fMRI data processing without manual operation and could serve as a reliable and powerful tool for simultaneous EEG-fMRI studies on sleep.
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Affiliation(s)
- Guangyuan Zou
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
| | - Jiayi Liu
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
| | - Qihong Zou
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
| | - Jia-Hong Gao
- Peking University, 5 Yiheyuan Road, Haidian District, Beijing, China, Beijing, 100871, CHINA
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Al Zoubi O, Mayeli A, Misaki M, Tsuchiyagaito A, Zotev V, Refai H, Paulus M, Bodurka J. Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal. J Neural Eng 2022; 18:10.1088/1741-2552/ac4595. [PMID: 34937003 PMCID: PMC11008726 DOI: 10.1088/1741-2552/ac4595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 12/22/2021] [Indexed: 11/12/2022]
Abstract
Objective.Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60-120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear.Approach. In a cohort of healthy subjects (n= 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches.Main results.Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa.Significance.Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.
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Affiliation(s)
- Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States of America
- Harvard Medical School, Boston, United States of America
| | - Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States of America
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | | | - Hazem Refai
- Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States of America
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America
- Deceased
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Daniel Arzate-Mena J, Abela E, Olguín-Rodríguez PV, Ríos-Herrera W, Alcauter S, Schindler K, Wiest R, Müller MF, Rummel C. Stationary EEG pattern relates to large-scale resting state networks - An EEG-fMRI study connecting brain networks across time-scales. Neuroimage 2021; 246:118763. [PMID: 34863961 DOI: 10.1016/j.neuroimage.2021.118763] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 11/25/2022] Open
Abstract
Relating brain dynamics acting on time scales that differ by at least an order of magnitude is a fundamental issue in brain research. The same is true for the observation of stable dynamical structures in otherwise highly non-stationary signals. The present study addresses both problems by the analysis of simultaneous resting state EEG-fMRI recordings of 53 patients with epilepsy. Confirming previous findings, we observe a generic and temporally stable average correlation pattern in EEG recordings. We design a predictor for the General Linear Model describing fluctuations around the stationary EEG correlation pattern and detect resting state networks in fMRI data. The acquired statistical maps are contrasted to several surrogate tests and compared with maps derived by spatial Independent Component Analysis of the fMRI data. By means of the proposed EEG-predictor we observe core nodes of known fMRI resting state networks with high specificity in the default mode, the executive control and the salience network. Our results suggest that both, the stationary EEG pattern as well as resting state fMRI networks are different expressions of the same brain activity. This activity is interpreted as the dynamics on (or close to) a stable attractor in phase space that is necessary to maintain the brain in an efficient operational mode. We discuss that this interpretation is congruent with the theoretical framework of complex systems as well as with the brain's energy balance.
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Affiliation(s)
- J Daniel Arzate-Mena
- Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos,Cuernavaca Morelos, Mexico
| | - Eugenio Abela
- Center for Neuropsychiatrics, Psychiatric Services Aargau AG, Windisch, Switzerland
| | | | - Wady Ríos-Herrera
- Facultad de Psicología Universidad Nacional Autónoma de México, Mexico City, Mexico; Centro de Ciencias de la Complejidad (C3), Universisdad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus F Müller
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos (UAEM), Cuernavaca, Morelos, Mexico; Centro de Ciencias de la Complejidad (C3), Universisdad Nacional Autónoma de México, Mexico City 04510, Mexico; Centro Internacional de Ciencias A. C., Cuernavaca, México
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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White EJ, Nacke M, Akeman E, Cannon MJ, Mayeli A, Touthang J, Zoubi OA, McDermott TJ, Kirlic N, Santiago J, Kuplicki R, Bodurka J, Paulus MP, Craske MG, Wolitzky-Taylor K, Abelson J, Martell C, Clausen A, Stewart JL, Aupperle RL. P300 amplitude during a monetary incentive delay task predicts future therapy completion in individuals with major depressive disorder. J Affect Disord 2021; 295:873-882. [PMID: 34706458 PMCID: PMC8554135 DOI: 10.1016/j.jad.2021.08.106] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 07/24/2021] [Accepted: 08/28/2021] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Treatment effectiveness for major depressive disorder (MDD) is often affected by client non-adherence, dropout, and non-response. Identification of client characteristics predicting successful treatment completion and/or response (i.e., symptom reduction) may be an important tool to increase intervention effectiveness. It is unclear whether neural attenuations in reward processing associated with MDD predict behavioral treatment outcome. METHODS This study aimed to determine whether blunted neural responses to reward at baseline differentiate MDD (n = 60; 41 with comorbid anxiety) and healthy control (HC; n = 40) groups; and predict MDD completion of and response to 7-10 sessions of behavior therapy. Participants completed a monetary incentive delay (MID) task. The N200, P300, contingent negative variation (CNV) event related potentials (ERPs) and behavioral responses (reaction time [RT], correct hits) were quantified and extracted for cross-sectional group analyses. ERPs and behavioral responses demonstrating group differences were then used to predict therapy completion and response within MDD. RESULTS MDD exhibited faster RT and smaller P300 amplitudes than HC across conditions. Within the MDD group, treatment completers (n = 37) exhibited larger P300 amplitudes than non-completers (n = 21). LIMITATIONS This study comprises secondary analyses of EEG data; thus task parameters are not optimized to examine feedback ERPs from the paradigm. We did not examine heterogenous presentations of MDD; however, severity and comorbidity did not influence findings. CONCLUSIONS Previous studies suggest that P300 is an index of motivational salience and stimulus resource allocation. In sum, individuals who deploy greater neural resources to task demands are more likely to persevere in behavioral therapy.
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Affiliation(s)
- Evan J White
- Laureate Institute for Brain Research, Tulsa, OK, United States.
| | - Mariah Nacke
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | | | | | - Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - James Touthang
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Psychiatry, Harvard Medical School/McLean Hospital, Boston MA, United States
| | - Timothy J McDermott
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Psychology, University of Tulsa, Tulsa, OK, United States
| | - Namik Kirlic
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | | | - Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States; Stephenson School of Biomedical Engineering, University of Oklahoma, Tulsa, OK, United States
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
| | - Michelle G Craske
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral science, University of California Los Angeles, Los Angeles, CA, United States
| | - Kate Wolitzky-Taylor
- Department of Psychiatry and Biobehavioral science, University of California Los Angeles, Los Angeles, CA, United States
| | - James Abelson
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Christopher Martell
- Department of Psychological and Brain Sciences, University of Massachusetts- Amherst, Amherst, MA United States
| | - Ashley Clausen
- Kansas City VA Medical Center, Kansas City, MO, United States; Department of Psychiatry and Behavioral Science, University of Kansas Medical Center, Kansas City, Kansas United States
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
| | - Robin L Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
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Kuplicki R, Touthang J, Al Zoubi O, Mayeli A, Misaki M, Aupperle RL, Teague TK, McKinney BA, Paulus MP, Bodurka J. Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies. Front Psychiatry 2021; 12:682495. [PMID: 34220587 PMCID: PMC8247461 DOI: 10.3389/fpsyt.2021.682495] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 05/19/2021] [Indexed: 01/16/2023] Open
Abstract
Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.
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Affiliation(s)
- Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - James Touthang
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - NeuroMAP-Investigators
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
| | - Robin L. Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States
| | - T. Kent Teague
- Department of Surgery, University of Oklahoma School of Community Medicine, Tulsa, OK, United States
- Department of Psychiatry, University of Oklahoma School of Community Medicine, Tulsa, OK, United States
- Department of Biochemistry and Microbiology, Oklahoma State University Center for Health Sciences, Tulsa, OK, United States
| | - Brett A. McKinney
- Department of Mathematics, University of Tulsa, Tulsa, OK, United States
- Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
| | | | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States
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