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Gao Y, Zhu Z, Fang F, Zhang Y, Meng M. EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion. J Affect Disord 2024; 361:356-366. [PMID: 38885847 DOI: 10.1016/j.jad.2024.06.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 05/27/2024] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods.
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Affiliation(s)
- Yunyuan Gao
- College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Zehao Zhu
- College of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, USA
| | - Ming Meng
- College of Automation, Hangzhou Dianzi University, Hangzhou, China.
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2
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Chen Y, Liu S, Hao Y, Zhao Q, Ren J, Piao Y, Wang L, Yang Y, Jin C, Wang H, Zhou X, Gao JH, Zhang X, Wei Z. Higher emotional synchronization is modulated by relationship quality in romantic relationships and not in close friendships. Neuroimage 2024; 297:120733. [PMID: 39033788 DOI: 10.1016/j.neuroimage.2024.120733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 07/23/2024] Open
Abstract
Emotions are fundamental to social interaction and deeply intertwined with interpersonal dynamics, especially in romantic relationships. Although the neural basis of interaction processes in romance has been widely explored, the underlying emotions and the connection between relationship quality and neural synchronization remain less understood. Our study employed EEG hyperscanning during a non-interactive video-watching paradigm to compare the emotional coordination between romantic couples and close friends. Couples showed significantly greater behavioral and prefrontal alpha synchronization than friends. Notably, couples with low relationship quality required heightened neural synchronization to maintain robust behavioral synchronization. Further support vector machine analysis underscores the crucial role of prefrontal activity in differentiating couples from friends. In summary, our research addresses gaps concerning how intrinsic emotions linked to relationship quality influence neural and behavioral synchronization by investigating a natural non-interactive context, thereby advancing our understanding of the neural mechanisms underlying emotional coordination in romantic relationships.
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Affiliation(s)
- Yijun Chen
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Shen Liu
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Yaru Hao
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Qian Zhao
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Jiecheng Ren
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Yi Piao
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Liuyun Wang
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Yunping Yang
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Chenggong Jin
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Hangwei Wang
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Xuezhi Zhou
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230027, China
| | - Xiaochu Zhang
- Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, 230027, China; Department of Psychology, School of Humanities & Social Science, University of Science & Technology of China, Hefei, Anhui 230026, China; Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science & Technology of China, Hefei, 230031, China; Institute of Health and Medicine, Hefei Comprehensive Science Center, Hefei, 230071, China.
| | - Zhengde Wei
- Department of Psychology, School of Humanities & Social Science, University of Science & Technology of China, Hefei, Anhui 230026, China; Key Laboratory of Brain-Machine Intelligence for Information Behavior- Ministry of Education, Shanghai International Studies University, Shanghai, 201620, China.
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Haslacher D, Cavallo A, Reber P, Kattein A, Thiele M, Nasr K, Hashemi K, Sokoliuk R, Thut G, Soekadar SR. Working memory enhancement using real-time phase-tuned transcranial alternating current stimulation. Brain Stimul 2024:S1935-861X(24)00122-0. [PMID: 39029737 DOI: 10.1016/j.brs.2024.07.007] [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: 02/02/2024] [Revised: 07/02/2024] [Accepted: 07/12/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Prior work has shown that transcranial alternating current stimulation (tACS) of parietooccipital alpha oscillations (8 - 14 Hz) can modulate working memory (WM) performance as a function of the phase lag to endogenous oscillations. However, leveraging this effect using real-time phase-tuned tACS has not been feasible so far due to stimulation artifacts preventing continuous phase tracking. OBJECTIVES/HYPOTHESIS We aimed to develop a system that tracks and adapts the phase lag between tACS and ongoing parietooccipital alpha oscillations in real-time. We hypothesized that such real-time phase-tuned tACS enhances working memory performance, depending on the phase lag. METHODS We developed real-time phase-tuned closed-loop amplitude-modulated tACS (CLAM-tACS) targeting parietooccipital alpha oscillations. CLAM-tACS was applied at six different phase lags relative to ongoing alpha oscillations while participants (N = 21) performed a working memory task. To exclude that behavioral effects of CLAM-tACS were mediated by other factors such as sensory co-stimulation, a second group of participants (N = 25) received equivalent stimulation of the forehead. RESULTS WM accuracy improved in a phase lag dependent manner (p = 0.0350) in the group receiving parietooccipital stimulation, with the strongest enhancement observed at 330° phase lag between tACS and ongoing alpha oscillations (p = 0.00273, d = 0.976). Moreover, across participants, modulation of frontoparietal alpha oscillations correlated both in amplitude (p = 0.0248) and phase (p = 0.0270) with the modulation of WM accuracy. No such effects were observed in the control group receiving frontal stimulation. CONCLUSIONS Our results demonstrate the feasibility and efficacy of real-time phase-tuned CLAM-tACS in modulating both brain activity and behavior, thereby paving the way for further investigation into brain-behavior relationships and the exploration of innovative therapeutic applications.
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Affiliation(s)
- David Haslacher
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Alessia Cavallo
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Neurology and Experimental Neurology, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Philipp Reber
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Psychology, University of California, Berkeley, California, USA
| | - Anna Kattein
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Moritz Thiele
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Khaled Nasr
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kimia Hashemi
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Rodika Sokoliuk
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gregor Thut
- School of Psychology & Neuroscience, University of Glasgow, Glasgow, UK
| | - Surjo R Soekadar
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany.
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Adham A, Le BT, Bonnal J, Bessaguet H, Ojardias E, Giraux P, Auzou P. Neural basis of lower-limb visual feedback therapy: an EEG study in healthy subjects. J Neuroeng Rehabil 2024; 21:114. [PMID: 38978051 PMCID: PMC11229246 DOI: 10.1186/s12984-024-01408-8] [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: 02/15/2024] [Accepted: 06/20/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Video-feedback observational therapy (VOT) is an intensive rehabilitation technique based on movement repetition and visualization that has shown benefits for motor rehabilitation of the upper and lower limbs. Despite an increase in recent literature on the neurophysiological effects of VOT in the upper limb, there is little knowledge about the cortical effects of visual feedback therapies when applied to the lower limbs. The aim of our study was to better understand the neurophysiological effects of VOT. Thus, we identified and compared the EEG biomarkers of healthy subjects undergoing lower limb VOT during three tasks: passive observation, observation and motor imagery, observation and motor execution. METHODS We recruited 38 healthy volunteers and monitored their EEG activity while they performed a right ankle dorsiflexion task in the VOT. Three graded motor tasks associated with action observation were tested: action observation alone (O), motor imagery with action observation (OI), and motor execution synchronized with action observation (OM). The alpha and beta event-related desynchronization (ERD) and event-related synchronization (or beta rebound, ERS) rhythms were used as biomarkers of cortical activation and compared between conditions with a permutation test. Changes in connectivity during the task were computed with phase locking value (PLV). RESULTS During the task, in the alpha band, the ERD was comparable between O and OI activities across the precentral, central and parietal electrodes. OM involved the same regions but had greater ERD over the central electrodes. In the beta band, there was a gradation of ERD intensity in O, OI and OM over central electrodes. After the task, the ERS changes were weak during the O task but were strong during the OI and OM (Cz) tasks, with no differences between OI and OM. CONCLUSION Alpha band ERD results demonstrated the recruitment of mirror neurons during lower limb VOT due to visual feedback. Beta band ERD reflects strong recruitment of the sensorimotor cortex evoked by motor imagery and action execution. These results also emphasize the need for an active motor task, either motor imagery or motor execution task during VOT, to elicit a post-task ERS, which is absent during passive observation. Trial Registration NCT05743647.
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Affiliation(s)
- Ahmed Adham
- Department of Physical Rehabilitation, CHU of St Etienne, Saint-Étienne, France.
- Laboratory Trajectoires, INSERM 1028, CNRS 5229, University of Lyon-St-Etienne, Saint-Étienne, France.
- Univ. Grenoble Alpes, CEA, LETI, Clinatec, Grenoble, France.
| | - Ba Thien Le
- Department of Neurology, CHU of Orleans, Orleans, France
| | - Julien Bonnal
- Department of Neurology, CHU of Orleans, Orleans, France
| | - Hugo Bessaguet
- Department of Physical Rehabilitation, CHU of St Etienne, Saint-Étienne, France
- Jean Monnet University, Lyon 1, Université Savoie Mont-Blanc, "Laboratoire Inter-Universitaire de Biologie de La Motricité", 42023, Saint-Étienne, France
| | - Etienne Ojardias
- Department of Physical Rehabilitation, CHU of St Etienne, Saint-Étienne, France
- Jean Monnet University, Lyon 1, Université Savoie Mont-Blanc, "Laboratoire Inter-Universitaire de Biologie de La Motricité", 42023, Saint-Étienne, France
| | - Pascal Giraux
- Department of Physical Rehabilitation, CHU of St Etienne, Saint-Étienne, France
- Laboratory Trajectoires, INSERM 1028, CNRS 5229, University of Lyon-St-Etienne, Saint-Étienne, France
| | - Pascal Auzou
- Department of Neurology, CHU of Orleans, Orleans, France
- "Laboratoire Interdisciplinaire d'innovation et de Recherche en Santé d'Orléans", LI2RSO, University of Orleans, Orleans, France
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5
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Tong W, Yue W, Chen F, Shi W, Zhang L, Wan J. MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection. SENSORS (BASEL, SWITZERLAND) 2024; 24:4234. [PMID: 39001013 PMCID: PMC11244239 DOI: 10.3390/s24134234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/12/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.
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Affiliation(s)
- Wei Tong
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Weiqi Yue
- School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Fangni Chen
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Wei Shi
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Lei Zhang
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
| | - Jian Wan
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China; (W.T.); (W.S.); (L.Z.); (J.W.)
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Harlow TJ, Marquez SM, Bressler S, Read HL. Individualized Closed-Loop Acoustic Stimulation Suggests an Alpha Phase Dependence of Sound Evoked and Induced Brain Activity Measured with EEG Recordings. eNeuro 2024; 11:ENEURO.0511-23.2024. [PMID: 38834300 PMCID: PMC11181104 DOI: 10.1523/eneuro.0511-23.2024] [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: 12/06/2023] [Revised: 04/25/2024] [Accepted: 05/20/2024] [Indexed: 06/06/2024] Open
Abstract
Following repetitive visual stimulation, post hoc phase analysis finds that visually evoked response magnitudes vary with the cortical alpha oscillation phase that temporally coincides with sensory stimulus. This approach has not successfully revealed an alpha phase dependence for auditory evoked or induced responses. Here, we test the feasibility of tracking alpha with scalp electroencephalogram (EEG) recordings and play sounds phase-locked to individualized alpha phases in real-time using a novel end-point corrected Hilbert transform (ecHT) algorithm implemented on a research device. Based on prior work, we hypothesize that sound-evoked and induced responses vary with the alpha phase at sound onset and the alpha phase that coincides with the early sound-evoked response potential (ERP) measured with EEG. Thus, we use each subject's individualized alpha frequency (IAF) and individual auditory ERP latency to define target trough and peak alpha phases that allow an early component of the auditory ERP to align to the estimated poststimulus peak and trough phases, respectively. With this closed-loop and individualized approach, we find opposing alpha phase-dependent effects on the auditory ERP and alpha oscillations that follow stimulus onset. Trough and peak phase-locked sounds result in distinct evoked and induced post-stimulus alpha level and frequency modulations. Though additional studies are needed to localize the sources underlying these phase-dependent effects, these results suggest a general principle for alpha phase-dependence of sensory processing that includes the auditory system. Moreover, this study demonstrates the feasibility of using individualized neurophysiological indices to deliver automated, closed-loop, phase-locked auditory stimulation.
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Affiliation(s)
- Tylor J Harlow
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
- Brain-Computer Interface Core, University of Connecticut, Storrs, Connecticut 06269
- Institute of Brain and Cognitive Science (IBACS), University of Connecticut, Storrs, Connecticut 06269
| | - Samantha M Marquez
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
| | - Scott Bressler
- Elemind Technologies, Inc., Cambridge, Massachusetts 02139
| | - Heather L Read
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
- Brain-Computer Interface Core, University of Connecticut, Storrs, Connecticut 06269
- Institute of Brain and Cognitive Science (IBACS), University of Connecticut, Storrs, Connecticut 06269
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269
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Xiao S, Cunningham WJ, Kondabolu K, Lowet E, Moya MV, Mount RA, Ravasio C, Bortz E, Shaw D, Economo MN, Han X, Mertz J. Large-scale deep tissue voltage imaging with targeted-illumination confocal microscopy. Nat Methods 2024; 21:1094-1102. [PMID: 38840033 DOI: 10.1038/s41592-024-02275-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 04/09/2024] [Indexed: 06/07/2024]
Abstract
Voltage imaging with cellular specificity has been made possible by advances in genetically encoded voltage indicators. However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines the signal-to-noise ratio and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating while also maximizing signal detection efficiency. The resulting benefits in signal-to-noise ratio and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different genetically encoded voltage indicator classes.
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Affiliation(s)
- Sheng Xiao
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | | | | | - Eric Lowet
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands
| | - Maria V Moya
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Rebecca A Mount
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Cara Ravasio
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Emma Bortz
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Dana Shaw
- Graduate Program for Neuroscience, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
| | - Michael N Economo
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
| | - Xue Han
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
| | - Jerome Mertz
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Neurophotonics Center, Boston University, Boston, MA, USA
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8
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Nagy P, Tóth B, Winkler I, Boncz Á. The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks. Hum Brain Mapp 2024; 45:e26747. [PMID: 38825981 PMCID: PMC11144954 DOI: 10.1002/hbm.26747] [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: 07/04/2023] [Revised: 03/28/2024] [Accepted: 05/16/2024] [Indexed: 06/04/2024] Open
Abstract
Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.
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Affiliation(s)
- Péter Nagy
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
- Faculty of Electrical Engineering and Informatics, Department of Measurement and Information SystemsBudapest University of Technology and EconomicsBudapestHungary
| | - Brigitta Tóth
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - István Winkler
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - Ádám Boncz
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
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9
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Liu T, Wu Y, Ye A, Cao L, Cao Y. Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs. Front Hum Neurosci 2024; 18:1400077. [PMID: 38841120 PMCID: PMC11150693 DOI: 10.3389/fnhum.2024.1400077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/08/2024] [Indexed: 06/07/2024] Open
Abstract
Background Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. Methods In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA. Results The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA. Conclusion The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.
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Affiliation(s)
- Tianyu Liu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Yu Wu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - An Ye
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Lei Cao
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Yongnian Cao
- Tiktok Incorporation, San Jose, CA, United States
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10
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Hu L, Tan C, Xu J, Qiao R, Hu Y, Tian Y. Decoding emotion with phase-amplitude fusion features of EEG functional connectivity network. Neural Netw 2024; 172:106148. [PMID: 38309138 DOI: 10.1016/j.neunet.2024.106148] [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: 05/23/2023] [Revised: 12/20/2023] [Accepted: 01/23/2024] [Indexed: 02/05/2024]
Abstract
Decoding emotional neural representations from the electroencephalographic (EEG)-based functional connectivity network (FCN) is of great scientific importance for uncovering emotional cognition mechanisms and developing harmonious human-computer interactions. However, existing methods mainly rely on phase-based FCN measures (e.g., phase locking value [PLV]) to capture dynamic interactions between brain oscillations in emotional states, which fail to reflect the energy fluctuation of cortical oscillations over time. In this study, we initially examined the efficacy of amplitude-based functional networks (e.g., amplitude envelope correlation [AEC]) in representing emotional states. Subsequently, we proposed an efficient phase-amplitude fusion framework (PAF) to fuse PLV and AEC and used common spatial pattern (CSP) to extract fused spatial topological features from PAF for multi-class emotion recognition. We conducted extensive experiments on the DEAP and MAHNOB-HCI datasets. The results showed that: (1) AEC-derived discriminative spatial network topological features possess the ability to characterize emotional states, and the differential network patterns of AEC reflect dynamic interactions in brain regions associated with emotional cognition. (2) The proposed fusion features outperformed other state-of-the-art methods in terms of classification accuracy for both datasets. Moreover, the spatial filter learned from PAF is separable and interpretable, enabling a description of affective activation patterns from both phase and amplitude perspectives.
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Affiliation(s)
- Liangliang Hu
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; West China Institute of Children's Brain and Cognition, Chongqing University of Education, Chongqing 400065, China.
| | - Congming Tan
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Jiayang Xu
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Rui Qiao
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Yilin Hu
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Yin Tian
- College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China.
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11
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Ghezeljeh FK, Kazemi R, Rostami R, Zandbagleh A, Khomami S, Vandi FR, Hadipour AL. Female Cerebellum Seems Sociable; An iTBS Investigation. CEREBELLUM (LONDON, ENGLAND) 2024:10.1007/s12311-024-01686-x. [PMID: 38530595 DOI: 10.1007/s12311-024-01686-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/11/2024] [Indexed: 03/28/2024]
Abstract
The cerebellum has been shown to be engaged in tasks other than motor control, including cognitive and affective functions. Prior neuroimaging studies have documented the role of this area in social cognition and despite these findings, no studies have yet examined the causal relationship between the cerebellum and social cognition. This study aimed to investigate the role of the cerebellum in empathy and theory of mind (ToM) in a randomized, placebo-controlled, double-blind, parallel study. 32 healthy participants were assigned to either a sham or active group. For the active group, an intermittent theta-burst stimulation (iTBS) protocol at 100% of the motor threshold was applied to the cerebellum, while the control group received sham stimulation. An eyes-closed EEG session, the Empathy Quotient (EQ) test, and the Reading the Mind in the Eyes Test (RMET) were administered before and after the iTBS session. The results demonstrated differences in cognitive empathy, ToM, and a decrease in the activity of the default mode network (DMN) between the active and sham groups in females. Females also showed a decrease in the activity of the affective empathy network and connectivity in the DMN. We conclude that cognitive empathy and ToM are associated with cerebellar activity, and due to sex-related differences in the cortical organization of this area which is modulated by sex hormones, the stimulation of the cerebellum in males and females yields different results.
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Affiliation(s)
| | - Reza Kazemi
- Faculty of Entrepreneurship, University of Tehran, Farshi Moghadam (16 St.), North Kargar Ave., Tehran, Iran.
| | - Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran
| | - Ahmad Zandbagleh
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Sanaz Khomami
- Department of Psychology, West Tehran Branch, Islamic Azad University, Tehran, Iran
| | | | - Abed L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
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12
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Easwaran K, Ramakrishnan K, Jeyabal SN. Classification of cognitive impairment using electroencephalography for clinical inspection. Proc Inst Mech Eng H 2024; 238:358-371. [PMID: 38366360 DOI: 10.1177/09544119241228912] [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] [Indexed: 02/18/2024]
Abstract
Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.
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Affiliation(s)
- Karuppathal Easwaran
- Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
| | - Kalpana Ramakrishnan
- Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
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13
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Momtaz S, Bidelman GM. Effects of Stimulus Rate and Periodicity on Auditory Cortical Entrainment to Continuous Sounds. eNeuro 2024; 11:ENEURO.0027-23.2024. [PMID: 38253583 PMCID: PMC10913036 DOI: 10.1523/eneuro.0027-23.2024] [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: 01/23/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
The neural mechanisms underlying the exogenous coding and neural entrainment to repetitive auditory stimuli have seen a recent surge of interest. However, few studies have characterized how parametric changes in stimulus presentation alter entrained responses. We examined the degree to which the brain entrains to repeated speech (i.e., /ba/) and nonspeech (i.e., click) sounds using phase-locking value (PLV) analysis applied to multichannel human electroencephalogram (EEG) data. Passive cortico-acoustic tracking was investigated in N = 24 normal young adults utilizing EEG source analyses that isolated neural activity stemming from both auditory temporal cortices. We parametrically manipulated the rate and periodicity of repetitive, continuous speech and click stimuli to investigate how speed and jitter in ongoing sound streams affect oscillatory entrainment. Neuronal synchronization to speech was enhanced at 4.5 Hz (the putative universal rate of speech) and showed a differential pattern to that of clicks, particularly at higher rates. PLV to speech decreased with increasing jitter but remained superior to clicks. Surprisingly, PLV entrainment to clicks was invariant to periodicity manipulations. Our findings provide evidence that the brain's neural entrainment to complex sounds is enhanced and more sensitized when processing speech-like stimuli, even at the syllable level, relative to nonspeech sounds. The fact that this specialization is apparent even under passive listening suggests a priority of the auditory system for synchronizing to behaviorally relevant signals.
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Affiliation(s)
- Sara Momtaz
- School of Communication Sciences & Disorders, University of Memphis, Memphis, Tennessee 38152
- Boys Town National Research Hospital, Boys Town, Nebraska 68131
| | - Gavin M Bidelman
- Department of Speech, Language and Hearing Sciences, Indiana University, Bloomington, Indiana 47408
- Program in Neuroscience, Indiana University, Bloomington, Indiana 47405
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14
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Li J, Kong X, Sun L, Chen X, Ouyang G, Li X, Chen S. Identification of autism spectrum disorder based on electroencephalography: A systematic review. Comput Biol Med 2024; 170:108075. [PMID: 38301514 DOI: 10.1016/j.compbiomed.2024.108075] [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: 09/30/2023] [Revised: 12/22/2023] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.
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Affiliation(s)
- Jing Li
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Xiaoli Kong
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Linlin Sun
- Neuroscience Research Institute, Peking University, Beijing, 100191, China; Key Laboratory for Neuroscience, Ministry of Education/National Health Commission of China, Beijing, 100191, China
| | - Xu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing, 100120, China; The Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100032, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Shengyong Chen
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
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15
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Kim YW, Kim S, Jin MJ, Im CH, Lee SH. The Importance of Low-frequency Alpha (8-10 Hz) Waves and Default Mode Network in Behavioral Inhibition. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2024; 22:53-66. [PMID: 38247412 PMCID: PMC10811390 DOI: 10.9758/cpn.22.1035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/13/2023] [Accepted: 02/17/2023] [Indexed: 01/23/2024]
Abstract
Objective : Alpha wave of electroencephalography (EEG) is known to be related to behavioral inhibition. Both the alpha wave and default mode network (DMN) are predominantly activated during resting-state. To study the mechanisms of the trait inhibition, this research investigating the relations among alpha wave, DMN and behavioral inhibition in resting-state. Methods : We explored the relationship among behavioral inhibition, resting-state alpha power, and DMN. Resting-state EEG, behavioral inhibition/behavioral activation scale (BIS/BAS), Barratt impulsivity scale, and no-go accuracy were assessed in 104 healthy individuals. Three groups (i.e., participants with low/middle/high band power) were formed based on the relative power of each total-alpha, low-alpha (LA), and high-alpha band. Source-reconstructed EEG and functional network measures of 25 DMN regions were calculated. Results : Significant differences and correlations were found based on LA band power alone. The high LA group had significantly greater BIS, clustering coefficient, efficiency, and strength, and significantly lower path length than low/middle LA group. BIS score showed a significant correlation with functional network measures of DMN. Conclusion : Our study revealed that LA power is related to behavioral inhibition and functional network measures of DMN of LA band appear to represent significant inhibitory function.
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Affiliation(s)
- Yong-Wook Kim
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Sungkean Kim
- Department of Human-Computer Interaction, Hanyang University, Ansan, Korea
| | - Min Jin Jin
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
- Institute of General Education, Kongju National University, Gongju, Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
- Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
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16
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Chen Y, Wang S, Yang L, Liu Y, Fu X, Wang Y, Zhang X, Wang S. Features of the speech processing network in post- and prelingually deaf cochlear implant users. Cereb Cortex 2024; 34:bhad417. [PMID: 38163443 DOI: 10.1093/cercor/bhad417] [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: 09/13/2023] [Revised: 10/13/2023] [Accepted: 10/14/2023] [Indexed: 01/03/2024] Open
Abstract
The onset of hearing loss can lead to altered brain structure and functions. However, hearing restoration may also result in distinct cortical reorganization. A differential pattern of functional remodeling was observed between post- and prelingual cochlear implant users, but it remains unclear how these speech processing networks are reorganized after cochlear implantation. To explore the impact of language acquisition and hearing restoration on speech perception in cochlear implant users, we conducted assessments of brain activation, functional connectivity, and graph theory-based analysis using functional near-infrared spectroscopy. We examined the effects of speech-in-noise stimuli on three groups: postlingual cochlear implant users (n = 12), prelingual cochlear implant users (n = 10), and age-matched individuals with hearing controls (HC) (n = 22). The activation of auditory-related areas in cochlear implant users showed a lower response compared with the HC group. Wernicke's area and Broca's area demonstrated differences network attributes in speech processing networks in post- and prelingual cochlear implant users. In addition, cochlear implant users maintain a high efficiency of the speech processing network to process speech information. Taken together, our results characterize the speech processing networks, in varying noise environments, in post- and prelingual cochlear implant users and provide new insights for theories of how implantation modes impact remodeling of the speech processing functional networks.
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Affiliation(s)
- Younuo Chen
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing 100005, China
| | - Songjian Wang
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing 100005, China
| | - Liu Yang
- School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing 100069, China
| | - Yi Liu
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing 100005, China
| | - Xinxing Fu
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing 100005, China
| | - Yuan Wang
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing 100005, China
| | - Xu Zhang
- School of Biomedical Engineering, Capital Medical University, No. 10, Xitoutiao, YouAnMen, Fengtai District, Beijing 100069, China
| | - Shuo Wang
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing 100005, China
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17
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Tang Y, Gervais C, Moffitt R, Nareddula S, Zimmermann M, Nadew YY, Quinn CJ, Saldarriaga V, Edens P, Chubykin AA. Visual experience induces 4-8 Hz synchrony between V1 and higher-order visual areas. Cell Rep 2023; 42:113482. [PMID: 37999977 PMCID: PMC10790627 DOI: 10.1016/j.celrep.2023.113482] [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] [Revised: 09/20/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Visual perceptual experience induces persistent 4-8 Hz oscillations in the mouse primary visual cortex (V1), encoding visual familiarity. Recent studies suggest that higher-order visual areas (HVAs) are functionally specialized and segregated into information streams processing distinct visual features. However, whether visual memories are processed and stored within the distinct streams is not understood. We report here that V1 and lateromedial (LM), but not V1 and anterolateral, become more phase synchronized in 4-8 Hz after the entrainment of visual stimulus that maximally induces responses in LM. Directed information analysis reveals changes in the top-down functional connectivity between V1 and HVAs. Optogenetic inactivation of LM reduces post-stimulus oscillation peaks in V1 and impairs visual discrimination behavior. Our results demonstrate that 4-8 Hz familiarity-evoked oscillations are specific for the distinct visual features and are present in the corresponding HVAs, where they may be used for the inter-areal communication with V1 during memory-related behaviors.
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Affiliation(s)
- Yu Tang
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Catherine Gervais
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Rylann Moffitt
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Sanghamitra Nareddula
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Michael Zimmermann
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Yididiya Y Nadew
- Department of Computer Sciences, Iowa State University, Ames, IA 50011, USA
| | | | - Violeta Saldarriaga
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Paige Edens
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA
| | - Alexander A Chubykin
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue Autism Research Center, Purdue University, West Lafayette, IN 47907, USA.
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18
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Morand-Beaulieu S, Crowley MJ, Grantz H, Leckman JF, Sukhodolsky DG. Functional connectivity during tic suppression predicts reductions in vocal tics following behavior therapy in children with Tourette syndrome. Psychol Med 2023; 53:7857-7864. [PMID: 37485677 PMCID: PMC10755221 DOI: 10.1017/s0033291723001940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 06/06/2023] [Accepted: 06/23/2023] [Indexed: 07/25/2023]
Abstract
BACKGROUND Comprehensive Behavioral Intervention for Tics (CBIT) is recommended as a first-line treatment for Tourette syndrome in children and adults. While there is strong evidence proving its efficacy, the mechanisms of reduction in tic severity during CBIT are still poorly understood. In a recent study, our group identified a functional brain network involved in tic suppression in children with TS. We reasoned that voluntary tic suppression and CBIT may share some mechanisms and thus we wanted to assess whether functional connectivity during tic suppression was associated with CBIT outcome. METHODS Thirty-two children with TS, aged 8 to 13 years old, participated in a randomized controlled trial of CBIT v. a treatment-as-usual control condition. EEG was recorded during tic suppression in all participants at baseline and endpoint. We used a source-reconstructed EEG connectivity pipeline to assess functional connectivity during tic suppression. RESULTS Functional connectivity during tic suppression did not change from baseline to endpoint. However, baseline tic suppression-related functional connectivity specifically predicted the decrease in vocal tic severity from baseline to endpoint in the CBIT group. Supplementary analyses revealed that the functional connectivity between the right superior frontal gyrus and the right angular gyrus was mainly driving this effect. CONCLUSIONS This study revealed that functional connectivity during tic suppression at baseline predicted reduction in vocal tic severity. These results suggest probable overlap between the mechanisms of voluntary tic suppression and those of behavior therapy for tics.
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Affiliation(s)
- Simon Morand-Beaulieu
- Department of Psychology, McGill University, Montreal, QC, Canada
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | | | - Heidi Grantz
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - James F. Leckman
- Department of Psychology, McGill University, Montreal, QC, Canada
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19
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Rosanne O, Alves de Oliveira A, Falk TH. EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs. SENSORS (BASEL, SWITZERLAND) 2023; 23:9352. [PMID: 38067725 PMCID: PMC10708818 DOI: 10.3390/s23239352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023]
Abstract
Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.
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Affiliation(s)
- Olivier Rosanne
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada;
| | - Alcyr Alves de Oliveira
- Graduate Program in Psychology and Health, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil;
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC H5A 1K6, Canada;
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20
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Altukhov D, Kleeva D, Ossadtchi A. PSIICOS projection optimality for EEG and MEG based functional coupling detection. Neuroimage 2023; 280:120333. [PMID: 37619795 DOI: 10.1016/j.neuroimage.2023.120333] [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: 03/25/2023] [Revised: 07/12/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
Functional connectivity is crucial for cognitive processes in the healthy brain and serves as a marker for a range of neuropathological conditions. Non-invasive exploration of functional coupling using temporally resolved techniques such as MEG allows for a unique opportunity of exploring this fundamental brain mechanism. The indirect nature of MEG measurements complicates the estimation of functional coupling due to the volume conduction and spatial leakage effects. In the previous work (Ossadtchi et al., 2018), we introduced PSIICOS, a method that for the first time allowed us to suppress the volume conduction effect and yet retain information about functional networks whose nodes are coupled with close to zero or zero mutual phase lag. In this paper, we demonstrate analytically that the PSIICOS projection is optimal in achieving a controllable trade-off between suppressing mutual spatial leakage and retaining information about zero- or close to zero-phase coupled networks. We also derive an alternative solution using the regularization-based inverse of the mutual spatial leakage matrix and show its equivalence to the original PSIICOS. We then discuss how PSIICOS solution to the functional connectivity estimation problem can be incorporated into the conventional source estimation framework. Instead of sources, the unknowns are the elementary dyadic networks and their activation time series are formalized by the corresponding source-space cross-spectral coefficients. This view on connectivity estimation as a regression problem opens up new opportunities for formulating a set of principled estimators based on the rich intuition accumulated in the neuroimaging community.
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Affiliation(s)
- Dmitrii Altukhov
- AIRI, Artificial Intelligence Research Institute, Moscow, Russia
| | - Daria Kleeva
- Center for Bioelectric Interfaces, Higher School of Economics, Moscow, Russia
| | - Alexei Ossadtchi
- Center for Bioelectric Interfaces, Higher School of Economics, Moscow, Russia; AIRI, Artificial Intelligence Research Institute, Moscow, Russia; LLC "Life Improvement by Future Technologies Center", Moscow, Russia.
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21
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Uehara K, Yasuhara M, Koguchi J, Oku T, Shiotani S, Morise M, Furuya S. Brain network flexibility as a predictor of skilled musical performance. Cereb Cortex 2023; 33:10492-10503. [PMID: 37566918 DOI: 10.1093/cercor/bhad298] [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: 04/29/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
Interactions between the body and the environment are dynamically modulated by upcoming sensory information and motor execution. To adapt to this behavioral state-shift, brain activity must also be flexible and possess a large repertoire of brain networks so as to switch them flexibly. Recently, flexible internal brain communications, i.e. brain network flexibility, have come to be recognized as playing a vital role in integrating various sensorimotor information. Therefore, brain network flexibility is one of the key factors that define sensorimotor skill. However, little is known about how flexible communications within the brain characterize the interindividual variation of sensorimotor skill and trial-by-trial variability within individuals. To address this, we recruited skilled musical performers and used a novel approach that combined multichannel-scalp electroencephalography, behavioral measurements of musical performance, and mathematical approaches to extract brain network flexibility. We found that brain network flexibility immediately before initiating the musical performance predicted interindividual differences in the precision of tone timbre when required for feedback control, but not for feedforward control. Furthermore, brain network flexibility in broad cortical regions predicted skilled musical performance. Our results provide novel evidence that brain network flexibility plays an important role in building skilled sensorimotor performance.
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Affiliation(s)
- Kazumasa Uehara
- Neural Information Dynamics Laboratory, Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
| | - Masaki Yasuhara
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- Neural Engineering Laboratory, Department of Science of Technology Innovation, Nagaoka University of Technology, Nagaoka, Japan
| | - Junya Koguchi
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- Graduate School of Advanced Mathematical Sciences, Meiji University, Tokyo, Japan
| | | | | | - Masanori Morise
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- School of Interdisciplinary Mathematical Sciences, Meiji University, Tokyo, Japan
| | - Shinichi Furuya
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- NeuroPiano Institute, Kyoto 6008086, Japan
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22
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Chuang C, Hsu H. Pseudo-mutual gazing enhances interbrain synchrony during remote joint attention tasking. Brain Behav 2023; 13:e3181. [PMID: 37496332 PMCID: PMC10570487 DOI: 10.1002/brb3.3181] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 06/29/2023] [Accepted: 07/13/2023] [Indexed: 07/28/2023] Open
Abstract
INTRODUCTION Mutual gaze enables people to share attention and increase engagement during social interactions through intentional and implicit messages. Although previous studies have explored gaze behaviors and neural mechanisms underlying in-person eye contact, the growing prevalence of remote communication has raised questions about how to establish mutual gaze remotely and how the brains of interacting individuals synchronize. METHODS To address these questions, we conducted a study using eye trackers to create a pseudo-mutual gaze channel that mirrors the gazes of each interacting dyad on their respective remote screens. To demonstrate fluctuations in coupling across brains, we incorporated electroencephalographic hyperscanning techniques to simultaneously record the brain activity of interacting dyads engaged in a joint attention task in player-observer, collaborative, and competitive modes. RESULTS Our results indicated that mutual gaze could improve the efficiency of joint attention activities among remote partners. Moreover, by employing the phase locking value, we could estimate interbrain synchrony (IBS) and observe low-frequency couplings in the frontal and temporal regions that varied based on the interaction mode. While dyadic gender composition significantly affected gaze patterns, it did not impact the IBS. CONCLUSION These results provide insight into the neurological mechanisms underlying remote interaction through the pseudo-mutual gaze channel and have significant implications for developing effective online communication environments.
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Affiliation(s)
- Chun‐Hsiang Chuang
- Research Center for Education and Mind Sciences, College of EducationNational Tsing Hua UniversityHsinchuTaiwan
- Institute of Information Systems and ApplicationsCollege of Electrical Engineering and Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan
| | - Hao‐Che Hsu
- Research Center for Education and Mind Sciences, College of EducationNational Tsing Hua UniversityHsinchuTaiwan
- Department of Computer ScienceNational Yang Ming Chiao Tung UniversityHsinchuTaiwan
- Department of Computer Science and EngineeringNational Taiwan Ocean UniversityKeelungTaiwan
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23
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Zandbagleh A, Mirzakuchaki S, Daliri MR, Sumich A, Anderson JD, Sanei S. Graph-based analysis of EEG for schizotypy classification applying flicker Ganzfeld stimulation. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:64. [PMID: 37735164 PMCID: PMC10514040 DOI: 10.1038/s41537-023-00395-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023]
Abstract
Ganzfeld conditions induce alterations in brain function and pseudo-hallucinatory experiences, particularly in people with high positive schizotypy. The current study uses graph-based parameters to investigate and classify brain networks under Ganzfeld conditions as a function of positive schizotypy. Participants from the general population (14 high schizotypy (HS), 29 low schizotypy (LS)) had an electroencephalography assessment during Ganzfeld conditions, with varying visual activation (8 frequencies of random light flicker) and soundscape-induced mood (neutral, serenity, and anxiety). Weighted functional networks were computed in six frequency sub-bands (delta, theta, alpha-low, alpha-high, beta, and gamma) as a function of light-flicker frequency and mood. The brain network was analyzed using graph theory parameters, including clustering coefficient (CC), strength, and global efficiency (GE). It was found that the LS groups had higher CC and strength than the HS groups, especially in bilateral temporal and frontotemporal brain regions. Moreover, some decreases in CC and strength measures were found in LS groups among occipital and parieto-occipital brain regions. LS groups also had significantly higher GE in all Ganzfeld conditions compared to the HS groups. The random under-sampling boosting (RUSBoost) algorithm achieved the best classification performance with an accuracy of 95.34%, specificity of 96.55%, and sensitivity of 92.85% during an anxiety-induction Ganzfeld condition. This is the first exploration of the relationship between brain functional state changes under Ganzfeld conditions in individuals who vary in positive schizotypy. The accuracy of graph-based parameters in classifying brain states as a function of schizotypy is shown, particularly for brain activity during anxiety induction, and should be investigated in psychosis.
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Affiliation(s)
- Ahmad Zandbagleh
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Sattar Mirzakuchaki
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
| | - Mohammad Reza Daliri
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Alexander Sumich
- Division of Psychology, Nottingham Trent University, Main Campus, Nottingham, UK
| | - John D Anderson
- Division of Psychology, Nottingham Trent University, Main Campus, Nottingham, UK
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, UK
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24
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Semenkov I, Fedosov N, Makarov I, Ossadtchi A. Real-time low latency estimation of brain rhythms with deep neural networks. J Neural Eng 2023; 20:056008. [PMID: 37683653 DOI: 10.1088/1741-2552/acf7f3] [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: 08/08/2023] [Accepted: 09/08/2023] [Indexed: 09/10/2023]
Abstract
Objective.Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increases the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm's phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits.Approach.Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was then trained to simultaneously filter and forecast EEG data. We compared it against the state-of-the-art techniques using synthetic and real data from 25 subjects.Main results.The temporal convolutional network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios>90% rhythm's envelope correlation with<10 ms effective delay and<20∘circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture.Significance.Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.
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Affiliation(s)
- Ilia Semenkov
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
- HSE University, Moscow 109028, Russia
| | - Nikita Fedosov
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
- HSE University, Moscow 109028, Russia
| | - Ilya Makarov
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
| | - Alexei Ossadtchi
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
- HSE University, Moscow 109028, Russia
- LLC 'Life Improvement by Future Technologies Center', Moscow, Russia
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25
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Ouyang G, Zhou C. Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time-Frequency Representation, and Phase Dynamics. Bioengineering (Basel) 2023; 10:1054. [PMID: 37760156 PMCID: PMC10525145 DOI: 10.3390/bioengineering10091054] [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: 08/15/2023] [Revised: 09/02/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
Characterizing the brain's dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain's response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time-frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.
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Affiliation(s)
- Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies, The Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
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26
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Qi X, Xu W, Li G. Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder. Brain Sci 2023; 13:1282. [PMID: 37759883 PMCID: PMC10526432 DOI: 10.3390/brainsci13091282] [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: 07/23/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.
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Affiliation(s)
- Xuchen Qi
- Department of Neurosurgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China;
- Department of Neurosurgery, Shaoxing People’s Hospital, Shaoxing 312000, China
| | - Wanxiu Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China;
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
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27
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URBAN KN, BONG H, ORELLANA J, KASS RE. Oscillating neural circuits: Phase, amplitude, and the complex normal distribution. CAN J STAT 2023; 51:824-851. [PMID: 38974813 PMCID: PMC11223177 DOI: 10.1002/cjs.11790] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/06/2023] [Indexed: 07/09/2024]
Abstract
Multiple oscillating time series are typically analyzed in the frequency domain, where coherence is usually said to represent the magnitude of the correlation between two signals at a particular frequency. The correlation being referenced is complex-valued and is similar to the real-valued Pearson correlation in some ways but not others. We discuss the dependence among oscillating series in the context of the multivariate complex normal distribution, which plays a role for vectors of complex random variables analogous to the usual multivariate normal distribution for vectors of real-valued random variables. We emphasize special cases that are valuable for the neural data we are interested in and provide new variations on existing results. We then introduce a complex latent variable model for narrowly band-pass-filtered signals at some frequency, and show that the resulting maximum likelihood estimate produces a latent coherence that is equivalent to the magnitude of the complex canonical correlation at the given frequency. We also derive an equivalence between partial coherence and the magnitude of complex partial correlation, at a given frequency. Our theoretical framework leads to interpretable results for an interesting multivariate dataset from the Allen Institute for Brain Science.
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Affiliation(s)
- Konrad N. URBAN
- Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Heejong BONG
- Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Josue ORELLANA
- Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Robert E. KASS
- Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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28
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Rolle CE, Ng GY, Nho YH, Barbosa DAN, Shivacharan RS, Gold JI, Bassett DS, Halpern CH, Buch V. Accumbens connectivity during deep-brain stimulation differentiates loss of control from physiologic behavioral states. Brain Stimul 2023; 16:1384-1391. [PMID: 37734587 PMCID: PMC10811591 DOI: 10.1016/j.brs.2023.09.010] [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: 04/13/2023] [Revised: 09/10/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Loss of control (LOC) eating, the subjective sense that one cannot control what or how much one eats, characterizes binge-eating behaviors pervasive in obesity and related eating disorders. Closed-loop deep-brain stimulation (DBS) for binge eating should predict LOC and trigger an appropriately timed intervention. OBJECTIVE/HYPOTHESIS This study aimed to identify a sensitive and specific biomarker to detect LOC onset for DBS. We hypothesized that changes in phase-locking value (PLV) predict the onset of LOC-associated cravings and distinguish them from potential confounding states. METHODS Using DBS data recorded from the nucleus accumbens (NAc) of two patients with binge eating disorder (BED) and severe obesity, we compared PLV between inter- and intra-hemispheric NAc subregions for three behavioral conditions: craving (associated with LOC eating), hunger (not associated with LOC), and sleep. RESULTS In both patients, PLV in the high gamma frequency band was significantly higher for craving compared to sleep and significantly higher for hunger compared to craving. Maximum likelihood classifiers achieved accuracies above 88% when differentiating between the three conditions. CONCLUSIONS High-frequency inter- and intra-hemispheric PLV in the NAc is a promising biomarker for closed-loop DBS that differentiates LOC-associated cravings from physiologic states such as hunger and sleep. Future trials should assess PLV as a LOC biomarker across a larger cohort and a wider patient population transdiagnostically.
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Affiliation(s)
- Camarin E Rolle
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Pennsylvania Hospital, Spruce Building 3rd Floor, 801 Spruce Street, Philadelphia, PA 19107, USA; Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, 3900 Woodland Ave, Philadelphia, PA, USA; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Grace Y Ng
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Pennsylvania Hospital, Spruce Building 3rd Floor, 801 Spruce Street, Philadelphia, PA 19107, USA; Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, 3900 Woodland Ave, Philadelphia, PA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St, Boston, MA 02114, USA
| | - Young-Hoon Nho
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Pennsylvania Hospital, Spruce Building 3rd Floor, 801 Spruce Street, Philadelphia, PA 19107, USA; Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, 3900 Woodland Ave, Philadelphia, PA, USA
| | - Daniel A N Barbosa
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Pennsylvania Hospital, Spruce Building 3rd Floor, 801 Spruce Street, Philadelphia, PA 19107, USA; Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, 3900 Woodland Ave, Philadelphia, PA, USA
| | - Rajat S Shivacharan
- Department of Neurosurgery, Stanford University School of Medicine, 453 Quarry Road Office 245C, Stanford, CA 94304, USA
| | - Joshua I Gold
- Department of Neuroscience, University of Pennsylvania, 3700 Hamilton Walk, Richards D407, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Departments of Bioengineering, Physics and Astronomy, Electrical and Systems Engineering, Neurology, and Psychiatry, University of Pennsylvania, 210 S. 33rd St, Skirkanich Hall 240, Philadelphia, PA 19104, USA; Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Pennsylvania Hospital, Spruce Building 3rd Floor, 801 Spruce Street, Philadelphia, PA 19107, USA; Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, 3900 Woodland Ave, Philadelphia, PA, USA
| | - Vivek Buch
- Department of Neurosurgery, Stanford University School of Medicine, 453 Quarry Road Office 245C, Stanford, CA 94304, USA.
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29
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Andrzejak RG, Espinoso A, García-Portugués E, Pewsey A, Epifanio J, Leguia MG, Schindler K. High expectations on phase locking: Better quantifying the concentration of circular data. CHAOS (WOODBURY, N.Y.) 2023; 33:091106. [PMID: 37756609 DOI: 10.1063/5.0166468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023]
Abstract
The degree to which unimodal circular data are concentrated around the mean direction can be quantified using the mean resultant length, a measure known under many alternative names, such as the phase locking value or the Kuramoto order parameter. For maximal concentration, achieved when all of the data take the same value, the mean resultant length attains its upper bound of one. However, for a random sample drawn from the circular uniform distribution, the expected value of the mean resultant length achieves its lower bound of zero only as the sample size tends to infinity. Moreover, as the expected value of the mean resultant length depends on the sample size, bias is induced when comparing the mean resultant lengths of samples of different sizes. In order to ameliorate this problem, here, we introduce a re-normalized version of the mean resultant length. Regardless of the sample size, the re-normalized measure has an expected value that is essentially zero for a random sample from the circular uniform distribution, takes intermediate values for partially concentrated unimodal data, and attains its upper bound of one for maximal concentration. The re-normalized measure retains the simplicity of the original mean resultant length and is, therefore, easy to implement and compute. We illustrate the relevance and effectiveness of the proposed re-normalized measure for mathematical models and electroencephalographic recordings of an epileptic seizure.
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Affiliation(s)
- Ralph G Andrzejak
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
| | - Anaïs Espinoso
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
- Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Carrer Baldiri Reixac 10-12, 08028 Barcelona, Catalonia, Spain
| | - Eduardo García-Portugués
- Department of Statistics, Universidad Carlos III de Madrid, Av. de la Universidad 30, 28911 Leganés, Madrid, Spain
| | - Arthur Pewsey
- Mathematics Department, Escuela Politécnica, Universidad de Extremadura, Av. de la Universidad s/n, 10003 Cáceres, Spain
| | - Jacopo Epifanio
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
| | - Marc G Leguia
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
| | - Kaspar Schindler
- Sleep Wake Epilepsy Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
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30
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Yu Y, Bezerianos A, Cichocki A, Li J. Latent Space Coding Capsule Network for Mental Workload Classification. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3417-3427. [PMID: 37607136 DOI: 10.1109/tnsre.2023.3307481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Mental workload can be monitored in real time, which helps us improve work efficiency by maintaining an appropriate workload level. Based on previous studies, we have known that features, such as band power and brain connectivity, can be utilized to classify the levels of mental workload. As band power and brain connectivity represent different but complementary information related to mental workload, it is helpful to integrate them together for workload classification. Although deep learning models have been utilized for workload classification based on EEG, the classification performance is not satisfactory. This is because the current models cannot well tackle variances in the features extracted from non-stationary EEG. In order to address this problem, we, in this study, proposed a novel deep learning model, called latent space coding capsule network (LSCCN). The features of band power and brain connectivity were fused and then modelled in a latent space. The subsequent convolutional and capsule modules were used for workload classification. The proposed LSCCN was compared to the state-of-the-art methods. The results demonstrated that the proposed LSCCN was superior to the compared methods. LSCCN achieved a higher testing accuracy with a relatively smaller standard deviation, indicating a more reliable classification across participants. In addition, we explored the distribution of the features and found that top discriminative features were localized in the frontal, parietal, and occipital regions. This study not only provides a novel deep learning model but also informs further studies in workload classification and promotes practical usage of workload monitoring.
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31
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Lowet E, Sheehan DJ, Chialva U, De Oliveira Pena R, Mount RA, Xiao S, Zhou SL, Tseng HA, Gritton H, Shroff S, Kondabolu K, Cheung C, Wang Y, Piatkevich KD, Boyden ES, Mertz J, Hasselmo ME, Rotstein HG, Han X. Theta and gamma rhythmic coding through two spike output modes in the hippocampus during spatial navigation. Cell Rep 2023; 42:112906. [PMID: 37540599 PMCID: PMC10530698 DOI: 10.1016/j.celrep.2023.112906] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 05/31/2023] [Accepted: 07/14/2023] [Indexed: 08/06/2023] Open
Abstract
Hippocampal CA1 neurons generate single spikes and stereotyped bursts of spikes. However, it is unclear how individual neurons dynamically switch between these output modes and whether these two spiking outputs relay distinct information. We performed extracellular recordings in spatially navigating rats and cellular voltage imaging and optogenetics in awake mice. We found that spike bursts are preferentially linked to cellular and network theta rhythms (3-12 Hz) and encode an animal's position via theta phase precession, particularly as animals are entering a place field. In contrast, single spikes exhibit additional coupling to gamma rhythms (30-100 Hz), particularly as animals leave a place field. Biophysical modeling suggests that intracellular properties alone are sufficient to explain the observed input frequency-dependent spike coding. Thus, hippocampal neurons regulate the generation of bursts and single spikes according to frequency-specific network and intracellular dynamics, suggesting that these spiking modes perform distinct computations to support spatial behavior.
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Affiliation(s)
- Eric Lowet
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Daniel J Sheehan
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Ulises Chialva
- Departamento de Matemática, Universidad Nacional del Sur, Buenos Aires, Argentina
| | - Rodrigo De Oliveira Pena
- Federated Department of Biological Sciences, New Jersey Institute of Technology & Rutgers University, Newark, NJ, USA
| | - Rebecca A Mount
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sheng Xiao
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Samuel L Zhou
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Hua-An Tseng
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Howard Gritton
- Department of Comparative Biosciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Sanaya Shroff
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | - Cyrus Cheung
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Yangyang Wang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Kiryl D Piatkevich
- School of Life Sciences, Westlake University, Westlake Laboratory of Life Sciences and Biomedicine, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Edward S Boyden
- McGovern Institute for Brain Research and Howard Hughes Medical Institute, MIT, Cambridge, MA, USA
| | - Jerome Mertz
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Michael E Hasselmo
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Horacio G Rotstein
- Federated Department of Biological Sciences, New Jersey Institute of Technology & Rutgers University, Newark, NJ, USA
| | - Xue Han
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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Jadidi AF, Jensen W, Zarei AA, Lontis ER, Atashzar SF. From pulse width modulated TENS to cortical modulation: based on EEG functional connectivity analysis. Front Neurosci 2023; 17:1239068. [PMID: 37600002 PMCID: PMC10433172 DOI: 10.3389/fnins.2023.1239068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 07/18/2023] [Indexed: 08/22/2023] Open
Abstract
Modulation in the temporal pattern of transcutaneous electrical nerve stimulation (TENS), such as Pulse width modulated (PWM), has been considered a new dimension in pain and neurorehabilitation therapy. Recently, the potentials of PWM TENS have been studied on sensory profiles and corticospinal activity. However, the underlying mechanism of PWM TENS on cortical network which might lead to pain alleviation is not yet investigated. Therefore, we recorded cortical activity using electroencephalography (EEG) from 12 healthy subjects and assessed the alternation of the functional connectivity at the cortex level up to an hour following the PWM TENS and compared that with the effect of conventional TENS. The connectivity between eight brain regions involved in sensory and pain processing was calculated based on phase lag index and spearman correlation. The alteration in segregation and integration of information in the network were investigated using graph theory. The proposed analysis discovered several statistically significant network changes between PWM TENS and conventional TENS, such as increased local strength and efficiency of the network in high gamma-band in primary and secondary somatosensory sources one hour following stimulation. Our findings regarding the long-lasting desired effects of PWM TENS support its potential as a therapeutic intervention in clinical research.
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Affiliation(s)
- Armita Faghani Jadidi
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg East, Denmark
| | - Winnie Jensen
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg East, Denmark
| | - Ali Asghar Zarei
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg East, Denmark
| | - Eugen Romulus Lontis
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg East, Denmark
| | - S. Farokh Atashzar
- Department of Electrical and Computer Engineering, New York University, New York, NY, United States
- Department of Mechanical and Aerospace Engineering, New York University, New York, NY, United States
- Department of Biomedical Engineering, New York University, New York, NY, United States
- NYU WIRELESS, New York University (NYU), New York, NY, United States
- NYU Center for Urban Science and Progress (CUSP), New York University (NYU), New York, NY, United States
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Atilla F, Alimardani M, Kawamoto T, Hiraki K. Mother-child inter-brain synchrony during a mutual visual search task: A study of feedback valence and role. Soc Neurosci 2023; 18:232-244. [PMID: 37395457 DOI: 10.1080/17470919.2023.2228545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Indexed: 07/04/2023]
Abstract
Parent and child have been shown to synchronize their behaviors and physiology during social interactions. This synchrony is an important marker of their relationship quality and subsequently the child's social and emotional development. Therefore, understanding the factors that influence parent-child synchrony is an important undertaking. Using EEG hyperscanning, this study investigated brain-to-brain synchrony in mother-child dyads when they took turns performing a visual search task and received positive or negative feedback. In addition to the effect of feedback valence, we studied how their assigned role, i.e., observing or performing the task, influenced synchrony. Results revealed that mother-child synchrony was higher during positive feedback relative to negative feedback in delta and gamma frequency bands. Furthermore, a main effect was found for role in the alpha band with higher synchrony when a child observed their mother performing the task compared to when the mother observed their child. These findings reveal that a positive social context could lead a mother and child to synchronize more on a neural level, which could subsequently improve the quality of their relationship. This study provides insight into mechanisms that underlie mother-child brain-to-brain synchrony, and establishes a framework by which the impact of emotion and task demand on a dyad's synchrony can be investigated.
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Affiliation(s)
- Fred Atilla
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
| | - Maryam Alimardani
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
| | | | - Kazuo Hiraki
- Department of General Systems Studies, The University of Tokyo, Tokyo, Japan
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Xiao S, Cunningham WJ, Kondabolu K, Lowet E, Moya MV, Mount R, Ravasio C, Economo MN, Han X, Mertz J. Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.548930. [PMID: 37502929 PMCID: PMC10370169 DOI: 10.1101/2023.07.21.548930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Voltage imaging with cellular specificity has been made possible by the tremendous advances in genetically encoded voltage indicators (GEVIs). However, the kilohertz rates required for voltage imaging lead to weak signals. Moreover, out-of-focus fluorescence and tissue scattering produce background that both undermines signal-to-noise ratio (SNR) and induces crosstalk between cells, making reliable in vivo imaging in densely labeled tissue highly challenging. We describe a microscope that combines the distinct advantages of targeted illumination and confocal gating, while also maximizing signal detection efficiency. The resulting benefits in SNR and crosstalk reduction are quantified experimentally and theoretically. Our microscope provides a versatile solution for enabling high-fidelity in vivo voltage imaging at large scales and penetration depths, which we demonstrate across a wide range of imaging conditions and different GEVI classes.
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Affiliation(s)
- Sheng Xiao
- Department of Biomedical Engineering, Boston University, Boston MA 02215
| | | | | | - Eric Lowet
- Department of Biomedical Engineering, Boston University, Boston MA 02215
| | - Maria V. Moya
- Department of Biomedical Engineering, Boston University, Boston MA 02215
| | - Rebecca Mount
- Department of Biomedical Engineering, Boston University, Boston MA 02215
| | - Cara Ravasio
- Department of Biomedical Engineering, Boston University, Boston MA 02215
| | - Michael N. Economo
- Department of Biomedical Engineering, Boston University, Boston MA 02215
- Neurophotonics Center, Boston University, Boston MA, 02215
| | - Xue Han
- Department of Biomedical Engineering, Boston University, Boston MA 02215
- Neurophotonics Center, Boston University, Boston MA, 02215
| | - Jerome Mertz
- Department of Biomedical Engineering, Boston University, Boston MA 02215
- Neurophotonics Center, Boston University, Boston MA, 02215
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35
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Chaudhuri-Vayalambrone P, Rule ME, Bauza M, Krstulovic M, Kerekes P, Burton S, O'Leary T, Krupic J. Simultaneous representation of multiple time horizons by entorhinal grid cells and CA1 place cells. Cell Rep 2023; 42:112716. [PMID: 37402167 DOI: 10.1016/j.celrep.2023.112716] [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: 09/26/2022] [Revised: 04/08/2023] [Accepted: 06/13/2023] [Indexed: 07/06/2023] Open
Abstract
Grid cells and place cells represent the spatiotemporal continuum of an animal's past, present, and future locations. However, their spatiotemporal relationship is unclear. Here, we co-record grid and place cells in freely foraging rats. We show that average time shifts in grid cells tend to be prospective and are proportional to their spatial scale, providing a nearly instantaneous readout of a spectrum of progressively increasing time horizons ranging hundreds of milliseconds. Average time shifts of place cells are generally larger compared to grid cells and also increase with place field sizes. Moreover, time horizons display nonlinear modulation by the animal's trajectories in relation to the local boundaries and locomotion cues. Finally, long and short time horizons occur at different parts of the theta cycle, which may facilitate their readout. Together, these findings suggest that population activity of grid and place cells may represent local trajectories essential for goal-directed navigation and planning.
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Affiliation(s)
| | | | - Marius Bauza
- Sainsbury Wellcome Centre for Neural Circuits and Behavior, University College London, London W1T4JG, UK; Cambridge Phenotyping Limited, London NW1 9ND, UK
| | - Marino Krstulovic
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EG, UK
| | - Pauline Kerekes
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EG, UK
| | - Stephen Burton
- Sainsbury Wellcome Centre for Neural Circuits and Behavior, University College London, London W1T4JG, UK
| | - Timothy O'Leary
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
| | - Julija Krupic
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EG, UK; Cambridge Phenotyping Limited, London NW1 9ND, UK.
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Islam M, Lee T. Functional Connectivity Analysis in Multi-channel EEG for Emotion Detection with Phase Locking Value and 3D CNN. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083433 DOI: 10.1109/embc40787.2023.10340922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The noise-assisted multivariate Empirical mode decomposition (NA-MEMD) is applied to multi-channel EEG signals to obtain narrow-band scale-aligned intrinsic mode functions (IMFs) upon which functional connectivity analysis is performed. The connectivity pattern in relation to inherent functional activity of brain is estimated with the phase locking value (PLV). Instantaneous phase difference among different EEG channels gives PLV that is used to build the functional connectivity map. The connectivity map yields spatial-temporal feature representation which is taken as input of the proposed emotion detection system. The spatial-temporal features can be learned with a 3D convolutional neural network for classifying emotion states. The proposed system is evaluated on two publicly available DEAP and SEED dataset for binary and multi-class emotion classification. On detecting low versus high level in the valence and arousal dimensions, the attained accuracy values are 97.37% and 96.26% respectively. Meanwhile, this system yields 94.78% and 99.54% accuracy on multi-class task on DEAP and SEED, which outperform previously reported systems with other deep learning models and conventional EEG features.
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37
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Shroff SN, Lowet E, Sridhar S, Gritton HJ, Abumuaileq M, Tseng HA, Cheung C, Zhou SL, Kondabolu K, Han X. Striatal cholinergic interneuron membrane voltage tracks locomotor rhythms in mice. Nat Commun 2023; 14:3802. [PMID: 37365189 PMCID: PMC10293266 DOI: 10.1038/s41467-023-39497-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: 08/30/2022] [Accepted: 06/07/2023] [Indexed: 06/28/2023] Open
Abstract
Rhythmic neural network activity has been broadly linked to behavior. However, it is unclear how membrane potentials of individual neurons track behavioral rhythms, even though many neurons exhibit pace-making properties in isolated brain circuits. To examine whether single-cell voltage rhythmicity is coupled to behavioral rhythms, we focused on delta-frequencies (1-4 Hz) that are known to occur at both the neural network and behavioral levels. We performed membrane voltage imaging of individual striatal neurons simultaneously with network-level local field potential recordings in mice during voluntary movement. We report sustained delta oscillations in the membrane potentials of many striatal neurons, particularly cholinergic interneurons, which organize spikes and network oscillations at beta-frequencies (20-40 Hz) associated with locomotion. Furthermore, the delta-frequency patterned cellular dynamics are coupled to animals' stepping cycles. Thus, delta-rhythmic cellular dynamics in cholinergic interneurons, known for their autonomous pace-making capabilities, play an important role in regulating network rhythmicity and movement patterning.
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Affiliation(s)
- Sanaya N Shroff
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Eric Lowet
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Sudiksha Sridhar
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Howard J Gritton
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Department of Comparative Biosciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Hua-An Tseng
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Cyrus Cheung
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Samuel L Zhou
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | - Xue Han
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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Zhou L, Xie Y, Wang R, Fan Y, Wu Y. Dynamic segregation and integration of brain functional networks associated with emotional arousal. iScience 2023; 26:106609. [PMID: 37250309 PMCID: PMC10214403 DOI: 10.1016/j.isci.2023.106609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/12/2023] [Accepted: 03/31/2023] [Indexed: 05/31/2023] Open
Abstract
The organization of brain functional networks dynamically changes with emotional stimuli, but its relationship to emotional behaviors is still unclear. In the DEAP dataset, we used the nested-spectral partition approach to identify the hierarchical segregation and integration of functional networks and investigated the dynamic transitions between connectivity states under different arousal conditions. The frontal and right posterior parietal regions were dominant for network integration whereas the bilateral temporal, left posterior parietal, and occipital regions were responsible for segregation and functional flexibility. High emotional arousal behavior was associated with stronger network integration and more stable state transitions. Crucially, the connectivity states of frontal, central, and right parietal regions were closely related to arousal ratings in individuals. Besides, we predicted the individual emotional performance based on functional connectivity activities. Our results demonstrate that brain connectivity states are closely associated with emotional behaviors and could be reliable and robust indicators for emotional arousal.
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Affiliation(s)
- Lv Zhou
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
| | - Yong Xie
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
| | - Rong Wang
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- College of Science, Xi’an University of Science and Technology, Xi’an 710054, China
| | - Yongchen Fan
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
| | - Ying Wu
- School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an 710049, China
- National Demonstration Center for Experimental Mechanics Education, Xi’an Jiaotong University, Xi’an 710049, China
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39
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Zhang H, Hao Y, He H, Roberts N. EEG based brain functional connectivity analysis for post-autoimmune encephalitis (AE) patients with epilepsy. Epilepsy Res 2023; 193:107166. [PMID: 37216856 DOI: 10.1016/j.eplepsyres.2023.107166] [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: 03/06/2023] [Revised: 04/16/2023] [Accepted: 05/08/2023] [Indexed: 05/24/2023]
Abstract
Autoimmune Encephalitis (AE) refers to a group of conditions that occur when the body's immune system mistakenly attacks healthy brain cells, leading to inflammation of the brain. Seizures are a common symptom of AE and more than a third of patients experiencing seizures secondary to AE become epileptic over time. The objective of the present study is to identify biomarkers that can be used to identify those patients in whom AE will evolve into epilepsy. The bursts of abnormal electrical activity that occur during a seizure can be recorded by using Electroencephalography (EEG). In this work, common EEG (cEEG) and ambulatory EEG (aEEG) were recorded to compare the brain functional connectivity (FC) properties in post-AE patients with epilepsy patients and post-AE patients without epilepsy. The brain functional networks of spike waves were first constructed on the basis of Phase Locking Value (PLV). An analysis was then performed of the differences which existed in the FC properties of clustering coefficient, characteristic path length, global efficiency, local efficiency, and node degree between post-AE patients with epilepsy patients and post-AE patients without epilepsy. From the perspective of brain functional network analysis, post-AE patients with epilepsy showed a more complex network structure. Furthermore, the five FC properties have been found signification different, all FC property values of post-AE patients with epilepsy are higher than those of post-AE patients without epilepsy of cEEG and aEEG. Based on the extracted FC properties, five classifiers were used to classify them, and the results showed that all five FC properties could effectively distinguish between post-AE patients with epilepsy patients and post-AE patients without epilepsy in both cEEG and aEEG. These findings are potentially helpful for diagnosing whether a patient with AE will become epileptic.
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Affiliation(s)
- Huimin Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yong Hao
- Department of Neurology, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai 200127, China
| | - Hong He
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Neil Roberts
- Centre for Reproductive Health (CRH), School of Clinical Sciences, University of Edinburgh, Edinburgh EH16 4TJ, UK
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40
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Pinardi M, Schuler AL, Arcara G, Ferreri F, Marinazzo D, Di Pino G, Pellegrino G. Reduced connectivity of primary auditory and motor cortices during exposure to auditory white noise. Neurosci Lett 2023; 804:137212. [PMID: 36966962 DOI: 10.1016/j.neulet.2023.137212] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/27/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023]
Abstract
Auditory white noise (WN) is widely used in daily life for inducing sleep, and in neuroscience to mask unwanted environmental noise and cues. However, WN was recently reported to influence corticospinal excitability and behavioral performance. Here, we expand previous preliminary findings on the influence of WN exposure on cortical functioning, and we hypothesize that it may modulate cortical connectivity. We tested our hypothesis by performing magnetoencephalography in 20 healthy subjects. WN reduces cortical connectivity of the primary auditory and motor regions with very distant cortical areas, showing a right lateralized connectivity reduction for primary motor cortex. The present results, together with previous finding concerning WN impact on corticospinal excitability and behavioral performance, further support the role of WN as a modulator of cortical function. This suggest avoiding its unrestricted use as a masking tool, while purposely designed and controlled WN application could be exploited to harness brain function and to treat neuropsychiatric conditions.
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41
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Mittal P, Sao AK, Biswal B. Impact of amplitude and phase of fMRI time series for functional connectivity analysis. Magn Reson Imaging 2023; 102:26-37. [PMID: 37075867 DOI: 10.1016/j.mri.2023.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/29/2023] [Accepted: 04/16/2023] [Indexed: 04/21/2023]
Abstract
PURPOSE Several studies in the field of fMRI have reported the synchrony between the brain regions using instantaneous phase (IP) representation (derived from analytic representation of BOLD time series). We hypothesized that instantaneous amplitude (IA) representation from different brain regions might give additional information to the functional brain networks. To validate this, we explored this representation of resting state BOLD fMRI signal for deriving resting state networks (RSNs) and compared it with the IP representation based RSNs. METHOD Resting state fMRI data of 100 healthy adults (age = 20-35 years, 54 females) from the population of 500 Subjects of HCP dataset were studied. Data was acquired using a 3 T scanner in four runs (15-min each) with the phase encoding directions: Left to Right (LR), Right to Left (RL). These four runs were acquired in two sessions, and subjects were asked to keep their eyes open with a fixation on a white cross. The IA and IP representations were derived from a narrow-band filtered BOLD time series using Hilbert transforms and a seed-based approach is used to compute the RSNs in the brain. RESULTS The experimental results demonstrate that within the frequency range 0.01-0.1 Hz, IA representation based RSNs have the highest similarity score between the two sessions for the motor network. Whereas for fronto-parietal network, IP based activation maps have the highest similarity score for all the frequency bands. For higher frequency band (0.198-0.25 Hz) consistency of the obtained RSNs across two sessions reduced for both IA and IP representations. Fusion of IA and IP representations based RSNs in comparison to those of IP based representation, leads to 3-10% improvement in the similarity scores between the default mode network obtained for the two sessions. In addition, the same comparison demonstrates 15-20% improvement for the motor network in the frequency bands: 0.01-0.04 Hz, 0.04-0.07 Hz, slow5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz). It is also observed that the similarity score between two sessions using instantaneous frequency (IF) (derivative of unwrapped IP) representation in exploring functional connectivity (FC) networks is comparable with those obtained using IP representation. CONCLUSION Our findings suggest that IA-representation based measures can estimate RSNs with the reproducibility between the sessions comparable to that of the IP representation-based methods. This study demonstrates that IA and IP representations contain the complementary information of BOLD signal, and their fusion improves the results of FC.
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Affiliation(s)
| | - Anil K Sao
- Indian Institute of Technology Bhilai, India.
| | - Bharat Biswal
- New Jersey Institute of Technology, United States of America.
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42
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Morand-Beaulieu S, Wu J, Mayes LC, Grantz H, Leckman JF, Crowley MJ, Sukhodolsky DG. Increased Alpha-Band Connectivity During Tic Suppression in Children With Tourette Syndrome Revealed by Source Electroencephalography Analyses. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:241-250. [PMID: 33991741 PMCID: PMC8589865 DOI: 10.1016/j.bpsc.2021.05.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/08/2021] [Accepted: 05/04/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Tourette syndrome (TS) is a neurodevelopmental disorder involving chronic motor and phonic tics. Most individuals with TS can suppress their tics for at least a short period of time. Yet, the brain correlates of tic suppression are still poorly understood. METHODS In the current study, high-density electroencephalography was recorded during a resting-state and a tic suppression session in 72 children with TS. Functional connectivity between cortical regions was assessed in the alpha band (8-13 Hz) using an electroencephalography source connectivity method. Graph theory and network-based statistics were used to assess the global network topology and to identify brain regions showing increased connectivity during tic suppression. RESULTS Graph theoretical analyses revealed distinctive global network topology during tic suppression, relative to rest. Using network-based statistics, we found a subnetwork of increased connectivity during tic suppression (p < .001). That subnetwork encompassed many cortical areas, including the right superior frontal gyrus and the left precuneus, which are involved in the default mode network. We also found a condition-by-age interaction, suggesting age-mediated increases in connectivity during tic suppression. CONCLUSIONS These results suggest that children with TS suppress their tics through a brain circuit involving distributed cortical regions, many of which are part of the default mode network. Brain connectivity during tic suppression also increases as youths with TS mature. These results highlight a mechanism by which children with TS may control their tics, which could be relevant for future treatment studies.
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Affiliation(s)
| | - Jia Wu
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - Linda C Mayes
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - Heidi Grantz
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - James F Leckman
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - Michael J Crowley
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut
| | - Denis G Sukhodolsky
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut.
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Dowdall JR, Vinck M. Coherence fails to reliably capture inter-areal interactions in bidirectional neural systems with transmission delays. Neuroimage 2023; 271:119998. [PMID: 36863546 PMCID: PMC7614400 DOI: 10.1016/j.neuroimage.2023.119998] [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: 08/20/2022] [Revised: 02/05/2023] [Accepted: 02/27/2023] [Indexed: 03/04/2023] Open
Abstract
Accurately measuring and quantifying the underlying interactions between brain areas is crucial for understanding the flow of information in the brain. Of particular interest in the field of electrophysiology is the analysis and characterization of the spectral properties of these interactions. Coherence and Granger-Geweke causality are well-established, commonly used methods for quantifying inter-areal interactions, and are thought to reflect the strength of inter-areal interactions. Here we show that the application of both methods to bidirectional systems with transmission delays is problematic, especially for coherence. Under certain circumstances, coherence can be completely abolished despite there being a true underlying interaction. This problem occurs due to interference caused in the computation of coherence, and is an artifact of the method. We motivate an understanding of the problem through computational modelling and numerical simulations. In addition, we have developed two methods that can recover the true bidirectional interactions in the presence of transmission delays.
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Affiliation(s)
- Jarrod Robert Dowdall
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany; Robarts Research Institute, Western University, London, Ontario, Canada.
| | - Martin Vinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany; Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University, Nijmegen, The Netherlands.
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Quantitative High Density EEG Brain Connectivity Evaluation in Parkinson's Disease: The Phase Locking Value (PLV). J Clin Med 2023; 12:jcm12041450. [PMID: 36835985 PMCID: PMC9967371 DOI: 10.3390/jcm12041450] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
INTRODUCTION The present study explores brain connectivity in Parkinson's disease (PD) and in age matched healthy controls (HC), using quantitative EEG analysis, at rest and during a motor tasks. We also evaluated the diagnostic performance of the phase locking value (PLV), a measure of functional connectivity, in differentiating PD patients from HCs. METHODS High-density, 64-channels, EEG data from 26 PD patients and 13 HC were analyzed. EEG signals were recorded at rest and during a motor task. Phase locking value (PLV), as a measure of functional connectivity, was evaluated for each group in a resting state and during a motor task for the following frequency bands: (i) delta: 2-4 Hz; (ii) theta: 5-7 Hz; (iii) alpha: 8-12 Hz; beta: 13-29 Hz; and gamma: 30-60 Hz. The diagnostic performance in PD vs. HC discrimination was evaluated. RESULTS Results showed no significant differences in PLV connectivity between the two groups during the resting state, but a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. Comparing the resting state versus the motor task for each group, only HCs showed a higher PLV connectivity in the delta band during motor task. A ROC curve analysis for HC vs. PD discrimination, showed an area under the ROC curve (AUC) of 0.75, a sensitivity of 100%, and a negative predictive value (NPV) of 100%. CONCLUSIONS The present study evaluated the brain connectivity through quantitative EEG analysis in Parkinson's disease versus healthy controls, showing a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. This neurophysiology biomarkers showed the potentiality to be explored in future studies as a potential screening biomarker for PD patients.
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Almohammad HA, Chertoff ME, Ferraro JA, Diaz FJ. Auditory nerve phase-locked response recorded from normal hearing adults using electrocochleography. Int J Audiol 2023; 62:172-181. [PMID: 35130459 DOI: 10.1080/14992027.2021.2024283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE The auditory nerve overlapped waveform response (ANOW), a new measure that can be recorded non-invasively from humans, holds promise for providing more accurate assessment of low frequency hearing thresholds than currently used objective measures. This research aims to investigate the robustness and the nature of the ANOW response in humans. DESIGN Repeated within-session recordings of the ANOW response using low-frequency Tone Bursts (TBs) were obtained at multiple stimulus levels. ANOW's absolute amplitude and phase locking value (PLV) measures were analysed to obtain normative data and to test the reliability of the ANOW response. STUDY SAMPLE Thirteen normal hearing adults within the age range of 25 to 40 years. RESULTS ANOW response was obtained to both 250 Hz and 500 Hz TBs and was traced down to 30-40 dB nHL. ANOW response showed significantly higher amplitude and stronger phase locking using 250 Hz TB compared to 500 Hz TB. High degree of test retest reliability of the ANOW response was found using 250 Hz TB at presentation levels higher than 40 dB nHL. CONCLUSIONS ANOW response is recordable noninvasively using low-frequency TBs and shows higher robustness as the stimulus frequency decreases.
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Affiliation(s)
- Hana A Almohammad
- Department of Rehabilitation Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Mark E Chertoff
- Department of Hearing and Speech, University of Kansas Medical Center, Kansas City, KS, USA
| | - John A Ferraro
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, USA
| | - Francisco J Diaz
- Department of Hearing and Speech, University of Kansas Medical Center, Kansas City, KS, USA
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Cousyn L, Messaoud RB, Lehongre K, Frazzini V, Lambrecq V, Adam C, Mathon B, Navarro V, Chavez M. Daily resting-state intracranial EEG connectivity for seizure risk forecasts. Epilepsia 2023; 64:e23-e29. [PMID: 36481871 DOI: 10.1111/epi.17480] [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: 05/10/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
Forecasting seizure risk aims to detect proictal states in which seizures would be more likely to occur. Classical seizure prediction models are trained over long-term electroencephalographic (EEG) recordings to detect specific preictal changes for each seizure, independently of those induced by shifts in states of vigilance. A daily single measure-during a vigilance-controlled period-to estimate the risk of upcoming seizure(s) would be more convenient. Here, we evaluated whether intracranial EEG connectivity (phase-locking value), estimated from daily vigilance-controlled resting-state recordings, could allow distinguishing interictal (no seizure) from preictal (seizure within the next 24 h) states. We also assessed its relevance for daily forecasts of seizure risk using machine learning models. Connectivity in the theta band was found to provide the best prediction performances (area under the curve ≥ .7 in 80% of patients), with accurate daily and prospective probabilistic forecasts (mean Brier score and Brier skill score of .13 and .72, respectively). More efficient ambulatory clinical application could be considered using mobile EEG or chronic implanted devices.
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Affiliation(s)
- Louis Cousyn
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Rémy Ben Messaoud
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- INRIA, ARAMIS Project-Team, Paris, France
| | - Katia Lehongre
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
| | - Valerio Frazzini
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Virginie Lambrecq
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Claude Adam
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
| | - Bertrand Mathon
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Sorbonne University, Paris, France
- Department of Neurosurgery, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
| | - Vincent Navarro
- Department of Neurology, Epilepsy Unit, Public Hospital Network of Paris, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
- Center of Reference for Rare Epilepsies, Pitié-Salpêtrière Hospital, Paris, France
- Sorbonne University, Paris, France
| | - Mario Chavez
- Paris Brain Institute, ICM (INSERM-U1127, CNRS-UMR7225), Paris, France
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Ren Z, Zhao Y, Han X, Yue M, Wang B, Zhao Z, Wen B, Hong Y, Wang Q, Hong Y, Zhao T, Wang N, Zhao P. An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features. Front Neurosci 2023; 16:1060814. [PMID: 36711136 PMCID: PMC9878185 DOI: 10.3389/fnins.2022.1060814] [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: 10/15/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Objective Cognitive impairment (CI) is a common disorder in patients with epilepsy (PWEs). Objective assessment method for diagnosing CI in PWEs would be beneficial in reality. This study proposed to construct a diagnostic model for CI in PWEs using the clinical and the phase locking value (PLV) functional connectivity features of the electroencephalogram (EEG). Methods PWEs who met the inclusion and exclusion criteria were divided into a cognitively normal (CON) group (n = 55) and a CI group (n = 76). The 23 clinical features and 684 PLV EEG features at the time of patient visit were screened and ranked using the Fisher score. Adaptive Boosting (AdaBoost) and Gradient Boosting Decision Tree (GBDT) were used as algorithms to construct diagnostic models of CI in PWEs either with pure clinical features, pure PLV EEG features, or combined clinical and PLV EEG features. The performance of these models was assessed using a five-fold cross-validation method. Results GBDT-built model with combined clinical and PLV EEG features performed the best with accuracy, precision, recall, F1-score, and an area under the curve (AUC) of 90.11, 93.40, 89.50, 91.39, and 0.95%. The top 5 features found to influence the model performance based on the Fisher scores were the magnetic resonance imaging (MRI) findings of the head for abnormalities, educational attainment, PLV EEG in the beta (β)-band C3-F4, seizure frequency, and PLV EEG in theta (θ)-band Fp1-Fz. A total of 12 of the top 5% of features exhibited statistically different PLV EEG features, while eight of which were PLV EEG features in the θ band. Conclusion The model constructed from the combined clinical and PLV EEG features could effectively identify CI in PWEs and possess the potential as a useful objective evaluation method. The PLV EEG in the θ band could be a potential biomarker for the complementary diagnosis of CI comorbid with epilepsy.
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Affiliation(s)
- Zhe Ren
- Department of Neurology, Zhengzhou University People’s Hospital, Zhengzhou, Henan, China
| | - Yibo Zhao
- Department of Neurology, Zhengzhou University People’s Hospital, Zhengzhou, Henan, China
| | - Xiong Han
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Xiong Han,
| | - Mengyan Yue
- Department of Rehabilitation, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Bin Wang
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China
| | - Bin Wen
- School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Yang Hong
- Department of Neurology, People’s Hospital of Henan University, Zhengzhou, Henan, China
| | - Qi Wang
- Department of Neurology, Zhengzhou University People’s Hospital, Zhengzhou, Henan, China
| | - Yingxing Hong
- Department of Neurology, People’s Hospital of Henan University, Zhengzhou, Henan, China
| | - Ting Zhao
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Na Wang
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Pan Zhao
- Department of Neurology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Park C, Youn I, Han S. Single-lead ECG based autonomic nervous system assessment for meditation monitoring. Sci Rep 2022; 12:22513. [PMID: 36581715 PMCID: PMC9800362 DOI: 10.1038/s41598-022-27121-x] [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: 05/10/2022] [Accepted: 12/26/2022] [Indexed: 12/30/2022] Open
Abstract
We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by breathing is a dominant component of HRV, but its frequency depends on an individual's breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p < 0.000005) and decreased (p < 0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity.
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Affiliation(s)
- Chanki Park
- grid.36303.350000 0000 9148 4899Future and Basic Technology Research Division, ICT Creative Research Laboratory, Electronics and Telecommunications Research Institute, CybreBrain Research Section, Daejeon, 34129 Republic of Korea
| | - Inchan Youn
- grid.35541.360000000121053345Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792 Republic of Korea ,grid.35541.360000000121053345Division of Bio‑Medical Science and Technology, Korea Institute of Science and Technology School, Seoul, 02792 Republic of Korea ,grid.289247.20000 0001 2171 7818KHU-KIST Department of Converging Science and Technology, Kyung Hee University, Seoul, Seongbuk-gu 02447 Republic of Korea
| | - Sungmin Han
- grid.35541.360000000121053345Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792 Republic of Korea ,grid.35541.360000000121053345Division of Bio‑Medical Science and Technology, Korea Institute of Science and Technology School, Seoul, 02792 Republic of Korea
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Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A Systematic Literature Review. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120781. [PMID: 36550987 PMCID: PMC9774931 DOI: 10.3390/bioengineering9120781] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 12/13/2022]
Abstract
Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.
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Kazemi R, Rostami R, Nasiri Z, Hadipour AL, Kiaee N, Coetzee JP, Philips A, Brown R, Seenivasan S, Adamson MM. Electrophysiological and behavioral effects of unilateral and bilateral rTMS; A randomized clinical trial on rumination and depression. J Affect Disord 2022; 317:360-372. [PMID: 36055535 DOI: 10.1016/j.jad.2022.08.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Rumination is significantly frequent in major depressive disorder (MDD). However, not a lot of studies have investigated the effects of repetitive transcranial magnetic stimulation (rTMS) on rumination. METHODS 61 participants with a minimum Hamilton Depression Rating Scale (HAM-D) score of 20 were randomly assigned to sham, bilateral stimulation (BS) or unilateral stimulation (US) groups. EEG, The Ruminative Response Scale (RRS), and HAM-D were administered before and after the 20 sessions of rTMS. Phase locked values (PLV) were calculated as a measure of connectivity. RESULTS There was a significant decrease in HAM-D scores in both BS and US. In responders, BS and US differed significantly in RRS total scores, with greater reduction in BS. PLV significantly changed in the default mode network (DMN) in delta, theta, alpha, and beta in BS, in responders of which PLV decreased in the DMN in beta and gamma. Positive correlations between PLV and brooding in delta and theta, and negative correlations between PLV and reflection were found in theta, alpha, and beta. In US, connectivity in the DMN increased in beta, and PLV increased in theta and beta, and decreased in alpha and beta in its responders. Positive correlations between PLV and brooding in the delta and theta, as well as negative correlations between PLV and reflection in theta were observed in the DMN. CONCLUSION US and BS resulted in different modulations in the DMN, however, both could alleviate both rumination and depression. Reductions in the beta and alpha frequency bands in the DMN can be considered as potential EEG-based markers of response to bilateral and unilateral rTMS, respectively.
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Affiliation(s)
- Reza Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran.
| | - Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran; Atieh Clinical Neuroscience Center, Tehran, Iran
| | - Zahra Nasiri
- Atieh Clinical Neuroscience Center, Tehran, Iran
| | - Abed L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - Nasim Kiaee
- Atieh Clinical Neuroscience Center, Tehran, Iran
| | - John P Coetzee
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Angela Philips
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Randi Brown
- Department of Psychology, Palo Alto University, Palo Alto, CA, USA
| | - Srija Seenivasan
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Maheen M Adamson
- Rehabilitation Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
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