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Li N, Yang J, Long C, Lei X. Test-Retest Reliability of EEG Aperiodic Components in Resting and Mental Task States. Brain Topogr 2024; 37:961-971. [PMID: 39017780 DOI: 10.1007/s10548-024-01067-x] [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: 10/07/2023] [Accepted: 07/03/2024] [Indexed: 07/18/2024]
Abstract
Aperiodic activity is derived from the electroencephalography (EEG) power spectrum and reflects changes in the slope and shifts of the broadband spectrum. Studies have shown inconsistent test-retest reliability of the aperiodic components. This study systematically measured how the test-retest reliability of the aperiodic components was affected by data duration (1, 2, 3, 4, and 5 min), states (resting with eyes closed, resting with eyes open, performing mental arithmetic, recalling the events of the day, and mentally singing songs), and methods (the Fitting Oscillations and One-Over-F (FOOOF) and Linear Mixed-Effects Regression (LMER)) at both short (90-min) and long (one-month) intervals. The results showed that aperiodic components had fair, good, or excellent test-retest reliability (ranging from 0.53 to 0.91) at both short and long intervals. It is recommended that better reliability of the aperiodic components be obtained using data durations longer than 3 min, the resting state with eyes closed, the mental arithmetic task state, and the LMER method.
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Affiliation(s)
- Na Li
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, Chongqing, 400715, China
| | - Jingqi Yang
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, Chongqing, 400715, China
| | - Changquan Long
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, Chongqing, 400715, China.
| | - Xu Lei
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, Chongqing, 400715, China
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2
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Ghani U, Niazi I, Signal N, Kumari N, Amjad I, Haavik H, Taylor D. The influence of multiple cognitive workload levels of an exergame on dorsal attention network connectivity at the source level. Physiol Behav 2024; 284:114628. [PMID: 38977259 DOI: 10.1016/j.physbeh.2024.114628] [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: 04/22/2024] [Revised: 06/16/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024]
Abstract
This study investigates how adding a cognitive task on a balance board (exergame) affects connectivity in the dorsal attention network (DAN) during an exergame task. Healthy young adults performed a soccer ball-moving task by tilting a balance board with their feet while their brain activity was measured using electroencephalography (EEG). In this exergame, the speed of obstacles in front of the goal manipulated the cognitive workload. Higher speed means a higher cognitive workload. The study found significant changes in functional connectivity within DAN regions, specifically in the alpha band. During the shift from easy to medium cognitive task, we observed a significant increase in connectivity (p= 0.0436) between the right inferior temporal (ITG R) and the Left middle temporal (MTG L). During the transition from easy to hard cognitive tasks, strengthened interactions (p= 0.0324) between inferior temporal (ITG) and parsopercularis (pOPPER) were found. This suggests that the proposed balanceboard-based exergame enhances the functionality of specific brain regions, such as ITG and MTG regions, and improves connectivity in the frontal cortex. We also found a correlation between brain activity and performance data, highlighting that increased cognitive workload resulted in decreased performance and heightened frontal alpha activity. These findings align with research suggesting that adding cognitive games to physical activity-based tasks in rehabilitation programs can boost brain activity, resulting in improved decision-making and visual processing skills. This information can help clinicians tailor rehabilitation methods that target specific brain regions.
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Affiliation(s)
- Usman Ghani
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand; Department of health and sciences, Auckland University of Technology (AUT), New Zealand.
| | - Imran Niazi
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand; Department of health and sciences, Auckland University of Technology (AUT), New Zealand; Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Nada Signal
- Department of health and sciences, Auckland University of Technology (AUT), New Zealand
| | - Nitika Kumari
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
| | - Imran Amjad
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
| | - Heidi Haavik
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
| | - Denise Taylor
- Department of health and sciences, Auckland University of Technology (AUT), New Zealand
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3
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Hu X, Wang X, Long C, Lei X. Loneliness and brain rhythmic activity in resting state: an exploratory report. Soc Cogn Affect Neurosci 2024; 19:nsae052. [PMID: 39096513 PMCID: PMC11374414 DOI: 10.1093/scan/nsae052] [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: 01/12/2023] [Revised: 02/17/2024] [Accepted: 08/02/2024] [Indexed: 08/05/2024] Open
Abstract
Recent studies using resting-state functional magnetic resonance imaging have shown that loneliness is associated with altered blood oxygenation in several brain regions. However, the relationship between loneliness and changes in neuronal rhythm activity in the brain remains unclear. To evaluate brain rhythm, we conducted an exploratory resting-state electroencephalogram (EEG) study of loneliness. We recorded resting-state EEG signals from 139 participants (94 women; mean age = 19.96 years) and analyzed power spectrum density (PSD) and functional connectivity (FC) in both the electrode and source spaces. The PSD analysis revealed significant correlations between loneliness scores and decreased beta-band powers, which may indicate negative emotion, attention, reward, and/or sensorimotor processing. The FC analysis revealed a trend of alpha-band FC associated with individuals' loneliness scores. These findings provide new insights into the neural basis of loneliness, which will facilitate the development of neurobiologically informed interventions for loneliness.
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Affiliation(s)
- Xin Hu
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, 2 Tiansheng Rd., Beibei District, Chongqing 400715, China
| | - Xufang Wang
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, 2 Tiansheng Rd., Beibei District, Chongqing 400715, China
| | - Changquan Long
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, 2 Tiansheng Rd., Beibei District, Chongqing 400715, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, 2 Tiansheng Rd., Beibei District, Chongqing 400715, China
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4
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Earl EH, Goyal M, Mishra S, Kannan B, Mishra A, Chowdhury N, Mishra P. EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning. Clin Neurophysiol 2024; 164:130-137. [PMID: 38870669 DOI: 10.1016/j.clinph.2024.05.017] [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: 08/28/2023] [Revised: 04/02/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024]
Abstract
OBJECTIVE Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotional stimuli in major depressive disorder (MDD) in addition to resting state FC may help in improving the diagnostic accuracy of machine learning classification models. We explored the potential of EEG-based FC during resting state and emotional processing, for diagnosing MDD using machine learning approach. METHODS EEG was recorded during resting state and while watching emotionally contagious happy and sad videos in 24 drug-naïve MDD patients and 25 healthy controls. FC was quantified using the Phase Lag Index. Three Random Forest classifier models were constructed to classify MDD patients and healthy controls, Model-I incorporating FC features from the resting state and Model-II and Model-III incorporating FC features while watching happy and sad videos respectively. RESULTS Important features distinguishing MDD and healthy controls were from all frequency bands and represent functional connectivity between fronto-temporal, fronto-parietal and fronto occipital regions. The cross-validation accuracies for Model-I, Model-II and Model-III were 92.3%, 94.9% and 89.7% and test accuracies were 60%, 80% and 70% respectively. Incorporating emotionally contagious videos improved the classification accuracies. CONCLUSION Findings support EEG FC patterns during resting state and emotional processing along with machine learning can be used to diagnose MDD. Future research should focus on replicating and validating these results. SIGNIFICANCE EEG FC pattern combined with machine learning may be used for assisting in diagnosing MDD.
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Affiliation(s)
- Estelle Havilla Earl
- Department of Physiology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Manish Goyal
- Department of Physiology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Shree Mishra
- Department of Psychiatry, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Balakrishnan Kannan
- Department of Physiology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Anushree Mishra
- Department of Psychiatry, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Nilotpal Chowdhury
- Department of Pathology, All India Institute of Medical Sciences, Rishikesh, Uttarakhand, India
| | - Priyadarshini Mishra
- Department of Physiology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India.
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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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Szakács H, Mutlu MC, Balestrieri G, Gombos F, Braun J, Kringelbach ML, Deco G, Kovács I. Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308364. [PMID: 38489748 DOI: 10.1002/advs.202308364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/22/2024] [Indexed: 03/17/2024]
Abstract
Adolescence is a timed process with an onset, tempo, and duration. Nevertheless, the temporal dimension, especially the pace of maturation, remains an insufficiently studied aspect of developmental progression. The primary objective is to estimate the precise influence of pubertal maturational tempo on the configuration of associative brain regions. To this end, the connection between maturational stages and the level of hierarchical organization of large-scale brain networks in 12-13-year-old females is analyzed. Skeletal maturity is used as a proxy for pubertal progress. The degree of maturity is defined by the difference between bone age and chronological age. To assess the level of hierarchical organization in the brain, the temporal dynamic of closed eye resting state high-density electroencephalography (EEG) in the alpha frequency range is analyzed. Different levels of hierarchical order are captured by the measured asymmetry in the directionality of information flow between different regions. The calculated EEG-based entropy production of participant groups is then compared with accelerated, average, and decelerated maturity. Results indicate that an average maturational trajectory optimally aligns with cerebral hierarchical order, and both accelerated and decelerated timelines result in diminished cortical organization. This suggests that a "Goldilocks rule" of brain development is favoring a particular maturational tempo.
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Affiliation(s)
- Hanna Szakács
- Laboratory for Psychological Research, Pázmány Péter Catholic University, 1 Mikszáth Square, Budapest, 1088, Hungary
- Semmelweis University Doctoral School, Division of Mental Health Sciences, 26 Üllői road, Budapest, 1085, Hungary
| | - Murat Can Mutlu
- Institute of Biology, Otto-von-Guericke University, 44 Leipziger Straße, 39120, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto-von-Guericke University, 44 Leipziger Straße, 39120, Magdeburg, Germany
| | - Giulio Balestrieri
- Center for Brain and Cognition, Universitat Pompeu Fabra, 25-27 Ramon Trias Fargas, Barcelona, 08005, Spain
| | - Ferenc Gombos
- Laboratory for Psychological Research, Pázmány Péter Catholic University, 1 Mikszáth Square, Budapest, 1088, Hungary
- HUN-REN-ELTE-PPKE Adolescent Development Research Group, 1 Mikszáth Kálmán Square, Budapest, 1088, Hungary
| | - Jochen Braun
- Institute of Biology, Otto-von-Guericke University, 44 Leipziger Straße, 39120, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto-von-Guericke University, 44 Leipziger Straße, 39120, Magdeburg, Germany
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Wellington Square, Oxford, OX3 9BX, UK
- Department of Psychiatry, University of Oxford, Wellington Square, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Nordre Ringgade 1, Aarhus, 8000, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra, 25-27 Ramon Trias Fargas, Barcelona, 08005, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, 122-140 Carrer de Tànger, Barcelona, 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), 23 Passeig de Lluís Companys, Barcelona, 08010, Spain
| | - Ilona Kovács
- HUN-REN-ELTE-PPKE Adolescent Development Research Group, 1 Mikszáth Kálmán Square, Budapest, 1088, Hungary
- Institute of Psychology, Faculty of Education and Psychology, Eötvös Loránd University, 25-27 Kazinczy Street, Budapest, 1075, Hungary
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Guo Q, Liu S, Wang L, Feng K, Yang S. Analysis of microstate features for Parkinson's disease based on reliability validation. J Neurosci Methods 2024; 406:110115. [PMID: 38531478 DOI: 10.1016/j.jneumeth.2024.110115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/13/2024] [Accepted: 03/20/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND Parkinson's disease (PD) is a disorder with abnormal changes in brain activity. The lack of objective indicators makes the assessment of PD progression difficult. Assessment of brain activity changes in PD may offer a potential solution. NEW METHOD Electroencephalogram (EEG) microstates reflect global dynamic changes in the brain. Therefore, we utilized microstates to assess changes in PD brain activity. However, the effect of epoch duration on the reliability of microstate analyses in PD is unclear. Thus, we first assessed the effect of data duration on the reliability of microstate topography and temporal features in PD and older healthy individuals. According to the reliability assessment, EEG epochs with high reliability were selected for microstate analysis in PD. Finally, we investigated the correlation between microstate features and clinical scales to determine whether these features could serve as objective indicators to evaluate PD progression. RESULTS Microstate analysis features that show high reliability for 3 min and above epoch durations. The topology of microstate D was significantly changed in PD compared to healthy controls, as well as the temporal features of microstates C and D. Additionally, the occurrence of C was negatively correlated with MoCA, and the duration of D was positively correlated with UPDRS. COMPARISON WITH EXISTING METHOD(S) High reliability of PD microstate features obtained by our approach. CONCLUSION EEG for PD microstate analysis should be at least 3 min. Microstate analysis is expected to provide new ideas and objective indicators for assessing Parkinson's disease progression in the clinical setting.
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Affiliation(s)
- Qingfang Guo
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
| | - Shuo Liu
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
| | - Lei Wang
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
| | - Keke Feng
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China.
| | - Shuo Yang
- Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China; State Key Laboratory of Reliable and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China.
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8
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Cao Q, Wang Y, Ji Y, He Z, Lei X. Resting-State EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia. Brain Topogr 2024; 37:388-396. [PMID: 36892651 DOI: 10.1007/s10548-023-00949-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/16/2023] [Indexed: 03/10/2023]
Abstract
Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.
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Affiliation(s)
- Qike Cao
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Yufang Ji
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Zhihui He
- The Ninth People's Hospital of Chongqing, Chongqing, 400700, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China.
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9
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Babiloni C, Jakhar D, Tucci F, Del Percio C, Lopez S, Soricelli A, Salvatore M, Ferri R, Catania V, Massa F, Arnaldi D, Famà F, Güntekin B, Yener G, Stocchi F, Vacca L, Marizzoni M, Giubilei F, Yıldırım E, Hanoğlu L, Hünerli D, Frisoni GB, Noce G. Resting state electroencephalographic alpha rhythms are sensitive to Alzheimer's disease mild cognitive impairment progression at a 6-month follow-up. Neurobiol Aging 2024; 137:19-37. [PMID: 38402780 DOI: 10.1016/j.neurobiolaging.2024.01.013] [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: 10/07/2022] [Revised: 10/31/2023] [Accepted: 01/26/2024] [Indexed: 02/27/2024]
Abstract
Are posterior resting-state electroencephalographic (rsEEG) alpha rhythms sensitive to the Alzheimer's disease mild cognitive impairment (ADMCI) progression at a 6-month follow-up? Clinical, cerebrospinal, neuroimaging, and rsEEG datasets in 52 ADMCI and 60 Healthy old seniors (equivalent groups for demographic features) were available from an international archive (www.pdwaves.eu). The ADMCI patients were arbitrarily divided into two groups: REACTIVE and UNREACTIVE, based on the reduction (reactivity) in the posterior rsEEG alpha eLORETA source activities from the eyes-closed to eyes-open condition at ≥ -10% and -10%, respectively. 75% of the ADMCI patients were REACTIVE. Compared to the UNREACTIVE group, the REACTIVE group showed (1) less abnormal posterior rsEEG source activity during the eyes-closed condition and (2) a decrease in that activity at the 6-month follow-up. These effects could not be explained by neuroimaging and neuropsychological biomarkers of AD. Such a biomarker might reflect abnormalities in cortical arousal in quiet wakefulness to be used for clinical studies in ADMCI patients using 6-month follow-ups.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino (FR), Italy.
| | - Dharmendra Jakhar
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy; Department of Medical, Movement and Wellbeing Sciences, University of Naples Parthenope, Naples, Italy
| | | | | | | | - Federico Massa
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy; Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Dario Arnaldi
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy; Neurofisiopatologia, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Francesco Famà
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy; Neurofisiopatologia, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey; Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir University of Economics, Faculty of Medicine, Izmir, Turkey
| | | | | | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Ebru Yıldırım
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Duygu Hünerli
- Health Sciences Institute, Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
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10
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Bonacci MC, Sammarra I, Caligiuri ME, Sturniolo M, Martino I, Vizza P, Veltri P, Gambardella A. Quantitative analysis of visually normal EEG reveals spectral power abnormalities in temporal lobe epilepsy. Neurophysiol Clin 2024; 54:102951. [PMID: 38552384 DOI: 10.1016/j.neucli.2024.102951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 05/25/2024] Open
Abstract
OBJECTIVE To compare quantitative spectral parameters of visually-normal EEG between Mesial Temporal Lobe Epilepsy (MTLE) patients and healthy controls (HC). METHOD We enrolled 26 MTLE patients and 26 HC. From each recording we calculated total power of all frequency bands and determined alpha-theta (ATR) and alpha-delta (ADR) power ratios in different brain regions. Group-wise differences between spectral parameters were investigated (p < 0.05). To test for associations between spectral-power and cognitive status, we evaluated correlations between neuropsychological tests and quantitative EEG (qEEG) metrics. RESULTS In all comparisons, ATR and ADR were significantly decreased in MTLE patients compared to HC, particularly over the hemisphere ipsilateral to epileptic activity. A positive correlation was seen in MTLE patients between ATR in ipsilateral temporal lobe, and results of neuropsychological tests of auditory verbal learning (RAVLT and RAVLT-D), short term verbal memory (Digit span backwards), and executive function (Weigl's sorting test). ADR values in the contralateral posterior region correlated positively with RAVLT-D and Digit span backwards tests. DISCUSSION Results confirmed that the power spectrum of qEEG is shifted towards lower frequencies in MTLE patients compared to HC. CONCLUSION Of note, our results were found in visually-normal recordings, providing further evidence of the value of qEEG for longitudinal monitoring of MTLE patients over time. Exploratory analysis of associations between qEEG and neuropsychological data suggest this could be useful for investigating effects of antiseizure medications on cognitive integrity in patients.
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Affiliation(s)
| | - Ilaria Sammarra
- Institute of Neurology, Department of Medical and Surgical Sciences, University of Magna Graecia, Italy
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University Magna Graecia, Italy.
| | - Miriam Sturniolo
- Institute of Neurology, Department of Medical and Surgical Sciences, University of Magna Graecia, Italy
| | - Iolanda Martino
- U.O.C. Neurology, Renato Dulbecco University hospital, Italy
| | - Patrizia Vizza
- Department of Medical and Surgical Science, University of Magna Graecia, Italy
| | | | - Antonio Gambardella
- Institute of Neurology, Department of Medical and Surgical Sciences, University of Magna Graecia, Italy
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11
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Du Y, Ding H, Wu M, Chen F, Cai Z. MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition. Brain Sci 2024; 14:344. [PMID: 38671995 PMCID: PMC11048325 DOI: 10.3390/brainsci14040344] [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: 02/28/2024] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024] Open
Abstract
Emotion recognition using the electroencephalogram (EEG) has garnered significant attention within the realm of human-computer interaction due to the wealth of genuine emotional data stored in EEG signals. However, traditional emotion recognition methods are deficient in mining the connection between multi-domain features and fitting their advantages. In this paper, we propose a novel capsule Transformer network based on a multi-domain feature for EEG-based emotion recognition, referred to as MES-CTNet. The model's core consists of a multichannel capsule neural network(CapsNet) embedded with ECA (Efficient Channel Attention) and SE (Squeeze and Excitation) blocks and a Transformer-based temporal coding layer. Firstly, a multi-domain feature map is constructed by combining the space-frequency-time characteristics of the multi-domain features as inputs to the model. Then, the local emotion features are extracted from the multi-domain feature maps by the improved CapsNet. Finally, the Transformer-based temporal coding layer is utilized to globally perceive the emotion feature information of the continuous time slices to obtain a final emotion state. The paper fully experimented on two standard datasets with different emotion labels, the DEAP and SEED datasets. On the DEAP dataset, MES-CTNet achieved an average accuracy of 98.31% in the valence dimension and 98.28% in the arousal dimension; it achieved 94.91% for the cross-session task on the SEED dataset, demonstrating superior performance compared to traditional EEG emotion recognition methods. The MES-CTNet method, utilizing a multi-domain feature map as proposed herein, offers a broader observation perspective for EEG-based emotion recognition. It significantly enhances the classification recognition rate, thereby holding considerable theoretical and practical value in the EEG emotion recognition domain.
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Affiliation(s)
- Yuxiao Du
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China; (Y.D.); (H.D.); (Z.C.)
| | - Han Ding
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China; (Y.D.); (H.D.); (Z.C.)
| | - Min Wu
- School of Automation, China University of Geosciences, Wuhan 430074, China;
| | - Feng Chen
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China; (Y.D.); (H.D.); (Z.C.)
| | - Ziman Cai
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China; (Y.D.); (H.D.); (Z.C.)
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12
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Luo M, Gan Q, Huang Z, Jiang Y, Li K, Wu M, Yang D, Shao H, Chen Y, Fu Y, Chen Z. Changes in Mental Health and EEG Biomarkers of Undergraduates Under Different Patterns of Mindfulness. Brain Topogr 2024; 37:75-87. [PMID: 38145437 PMCID: PMC10771601 DOI: 10.1007/s10548-023-01026-y] [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: 11/17/2022] [Accepted: 11/20/2023] [Indexed: 12/26/2023]
Abstract
The effects of short-term mindfulness are associated with the different patterns (autonomic, audio guided, or experienced and certified mindfulness instructor guided mindfulness). However, robust evidence for reported the impacts of different patterns of mindfulness on mental health and EEG biomarkers of undergraduates is currently lacking. Therefore, we aimed to test the hypotheses that mindfulness training for undergraduates would improve mental health, and increase alpha power over frontal region and theta power over midline region at the single electrode level. We also describe the distinction among frequency bands patterns in different sites of frontal and midline regions. 70 participants were enrolled and assigned to either 5-day mindfulness or a waiting list group. Subjective questionnaires measured mental health and other psychological indicators, and brain activity was recorded during various EEG tasks before and after the intervention. The 5-day mindfulness training improved trait mindfulness, especially observing (p = 0.001, d = 0.96) and nonreactivity (p = 0.03, d = 0.56), sleep quality (p = 0.001, d = 0.91), and social support (p = 0.001, d = 0.95) while not in affect. Meanwhile, the expected increase in the alpha power of frontal sites (p < 0.017, d > 0.84) at the single electrode level was confirmed by the current data rather than the theta. Interestingly, the alteration of low-beta power over the single electrode of the midline (p < 0.05, d > 0.71) was difference between groups. Short-term mindfulness improves practitioners' mental health, and the potentially electrophysiological biomarkers of mindfulness on neuron oscillations were alpha activity over frontal sites and low-beta activity over midline sites.
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Affiliation(s)
- Miaoling Luo
- Medical Faculty, Kunming University of Science and Technology, Kunming, China
- Brain Science and Visual Cognition Research Center, Medical School of Kunming University of Science and Technology, Kunming, China
| | - Quan Gan
- Medical Faculty, Kunming University of Science and Technology, Kunming, China
- Brain Science and Visual Cognition Research Center, Medical School of Kunming University of Science and Technology, Kunming, China
- Faculté de Médecine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Ziyang Huang
- Medical Faculty, Kunming University of Science and Technology, Kunming, China
| | - Yunxiong Jiang
- Medical Faculty, Kunming University of Science and Technology, Kunming, China
| | - Kebin Li
- Medical Faculty, Kunming University of Science and Technology, Kunming, China
| | - Minxiang Wu
- Medical Faculty, Kunming University of Science and Technology, Kunming, China
| | - Dongxiao Yang
- Medical Faculty, Kunming University of Science and Technology, Kunming, China
| | - Heng Shao
- Department of Geriatrics, The First People's Hospital of Yunnan Province, Kunming, China
| | - Yanmei Chen
- Medical Faculty, Kunming University of Science and Technology, Kunming, China
- Brain Science and Visual Cognition Research Center, Medical School of Kunming University of Science and Technology, Kunming, China
| | - Yu Fu
- Medical Faculty, Kunming University of Science and Technology, Kunming, China
- Brain Science and Visual Cognition Research Center, Medical School of Kunming University of Science and Technology, Kunming, China
| | - Zhuangfei Chen
- Medical Faculty, Kunming University of Science and Technology, Kunming, China.
- Brain Science and Visual Cognition Research Center, Medical School of Kunming University of Science and Technology, Kunming, China.
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13
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Hao Z, Zhai X, Peng B, Cheng D, Zhang Y, Pan Y, Dou W. CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG. Neuroimage 2023; 282:120405. [PMID: 37820859 DOI: 10.1016/j.neuroimage.2023.120405] [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: 06/20/2023] [Revised: 09/19/2023] [Accepted: 10/08/2023] [Indexed: 10/13/2023] Open
Abstract
Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.
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Affiliation(s)
- Zexuan Hao
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Xiaoxue Zhai
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Bo Peng
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Dandan Cheng
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Yanlin Zhang
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
| | - Yu Pan
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China.
| | - Weibei Dou
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China.
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14
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Kumar WS, Ray S. Healthy ageing and cognitive impairment alter EEG functional connectivity in distinct frequency bands. Eur J Neurosci 2023; 58:3432-3449. [PMID: 37559505 DOI: 10.1111/ejn.16114] [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: 05/11/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023]
Abstract
Functional connectivity (FC) indicates the interdependencies between brain signals recorded from spatially distinct locations in different frequency bands, which is modulated by cognitive tasks and is known to change with ageing and cognitive disorders. Recently, the power of narrow-band gamma oscillations induced by visual gratings have been shown to reduce with both healthy ageing and in subjects with mild cognitive impairment (MCI). However, the impact of ageing/MCI on stimulus-induced gamma FC has not been well studied. We recorded electroencephalogram (EEG) from a large cohort (N = 229) of elderly subjects (>49 years) while they viewed large cartesian gratings to induce gamma oscillations and studied changes in alpha and gamma FC with healthy ageing (N = 218) and MCI (N = 11). Surprisingly, we found distinct differences across age and MCI groups in power and FC. With healthy ageing, alpha power did not change but FC decreased significantly. MCI reduced gamma but not alpha FC significantly compared with age and gender matched controls, even when power was matched between the two groups. Overall, our results suggest distinct effects of ageing and disease on EEG power and FC, suggesting different mechanisms underlying ageing and cognitive disorders.
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Affiliation(s)
| | - Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bengaluru, India
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15
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Lian J, Xu L, Song T, Peng Z, Zhang Z, An X, Chen S, Zhong X, Shao Y. Reduced Resting-State EEG Power Spectra and Functional Connectivity after 24 and 36 Hours of Sleep Deprivation. Brain Sci 2023; 13:949. [PMID: 37371427 DOI: 10.3390/brainsci13060949] [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: 05/12/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Total sleep deprivation (TSD) leads to cognitive decline; however, the neurophysiological mechanisms underlying resting-state electroencephalogram (EEG) changes after TSD remain unclear. In this study, 42 healthy adult participants were subjected to 36 h of sleep deprivation (36 h TSD), and resting-state EEG data were recorded at baseline, after 24 h of sleep deprivation (24 h TSD), and after 36 h TSD. The analysis of resting-state EEG at baseline, after 24 h TSD, and after 36 h TSD using source localization analysis, power spectrum analysis, and functional connectivity analysis revealed a decrease in alpha-band power and a significant increase in delta-band power after TSD and impaired functional connectivity in the default mode network, precuneus, and inferior parietal lobule. The cortical activities of the precuneus, inferior parietal lobule, and superior parietal lobule were significantly reduced, but no difference was found between the 24 h and 36 h TSD groups. This may indicate that TSD caused some damage to the participants, but this damage temporarily slowed during the 24 h to 36 h TSD period.
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Affiliation(s)
- Jie Lian
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Lin Xu
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Tao Song
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Ziyi Peng
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Zheyuan Zhang
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Xin An
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Shufang Chen
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Xiao Zhong
- School of Psychology, Beijing Sport University, Beijing 100084, China
| | - Yongcong Shao
- School of Psychology, Beijing Sport University, Beijing 100084, China
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16
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Kim H, Wang IN, Park JS, Kim KT, Kong J, Kim JB, Kim DJ. Inherent seizure susceptibility in patients with antihistamine-induced acute symptomatic seizure: a resting-state EEG analysis. Sci Rep 2023; 13:9146. [PMID: 37277514 DOI: 10.1038/s41598-023-36415-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/03/2023] [Indexed: 06/07/2023] Open
Abstract
We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.
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Affiliation(s)
- Hayom Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - In-Nea Wang
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Jun-Su Park
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Keun-Tae Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jooheon Kong
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
| | - Dong-Joo Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
- NeuroTx, Co., Ltd., Seoul, Republic of Korea.
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea.
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17
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Soyuhos O, Baldauf D. Functional connectivity fingerprints of the frontal eye field and inferior frontal junction suggest spatial versus nonspatial processing in the prefrontal cortex. Eur J Neurosci 2023; 57:1114-1140. [PMID: 36789470 DOI: 10.1111/ejn.15936] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 01/28/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023]
Abstract
Neuroimaging evidence suggests that the frontal eye field (FEF) and inferior frontal junction (IFJ) govern the encoding of spatial and nonspatial (such as feature- or object-based) representations, respectively, both during visual attention and working memory tasks. However, it is still unclear whether such contrasting functional segregation is also reflected in their underlying functional connectivity patterns. Here, we hypothesized that FEF has predominant functional coupling with spatiotopically organized regions in the dorsal ('where') visual stream whereas IFJ has predominant functional connectivity with the ventral ('what') visual stream. We applied seed-based functional connectivity analyses to temporally high-resolving resting-state magnetoencephalography (MEG) recordings. We parcellated the brain according to the multimodal Glasser atlas and tested, for various frequency bands, whether the spontaneous activity of each parcel in the ventral and dorsal visual pathway has predominant functional connectivity with FEF or IFJ. The results show that FEF has a robust power correlation with the dorsal visual pathway in beta and gamma bands. In contrast, anterior IFJ (IFJa) has a strong power coupling with the ventral visual stream in delta, beta and gamma oscillations. Moreover, while FEF is phase-coupled with the superior parietal lobe in the beta band, IFJa is phase-coupled with the middle and inferior temporal cortex in delta and gamma oscillations. We argue that these intrinsic connectivity fingerprints are congruent with each brain region's function. Therefore, we conclude that FEF and IFJ have dissociable connectivity patterns that fit their respective functional roles in spatial versus nonspatial top-down attention and working memory control.
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Affiliation(s)
- Orhan Soyuhos
- Centre for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy.,Center for Neuroscience, University of California, Davis, California, USA
| | - Daniel Baldauf
- Centre for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
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18
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Wang Y, Duan W, Dong D, Ding L, Lei X. A test-retest resting, and cognitive state EEG dataset during multiple subject-driven states. Sci Data 2022; 9:566. [PMID: 36100589 PMCID: PMC9470564 DOI: 10.1038/s41597-022-01607-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 08/02/2022] [Indexed: 11/23/2022] Open
Abstract
Here we present a test-retest dataset of electroencephalogram (EEG) acquired at two resting (eyes open and eyes closed) and three subject-driven cognitive states (memory, music, subtraction) with both short-term (within 90 mins) and long-term (one-month apart) designs. 60 participants were recorded during three EEG sessions. Each session includes EEG and behavioral data along with rich samples of behavioral assessments testing demographic, sleep, emotion, mental health and the content of self-generated thoughts (mind wandering). This data enables the investigation of both intra- and inter-session variability not only limited to electrophysiological changes, but also including alterations in resting and cognitive states, at high temporal resolution. Also, this dataset is expected to add contributions to the reliability and validity of EEG measurements with open resource.
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Affiliation(s)
- Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Wei Duan
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Lihong Ding
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China.
- National Demonstration Center for Experimental Psychology Education (Southwest University), Chongqing, 400715, China.
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Heitmann H, Gil Ávila C, Nickel MM, Ta Dinh S, May ES, Tiemann L, Hohn VD, Tölle TR, Ploner M. Longitudinal resting-state electroencephalography in patients with chronic pain undergoing interdisciplinary multimodal pain therapy. Pain 2022; 163:e997-e1005. [PMID: 35050961 PMCID: PMC9393803 DOI: 10.1097/j.pain.0000000000002565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/10/2021] [Accepted: 12/03/2021] [Indexed: 11/27/2022]
Abstract
ABSTRACT Chronic pain is a major healthcare issue posing a large burden on individuals and society. Converging lines of evidence indicate that chronic pain is associated with substantial changes of brain structure and function. However, it remains unclear which neuronal measures relate to changes of clinical parameters over time and could thus monitor chronic pain and treatment responses. We therefore performed a longitudinal study in which we assessed clinical characteristics and resting-state electroencephalography data of 41 patients with chronic pain before and 6 months after interdisciplinary multimodal pain therapy. We specifically assessed electroencephalography measures that have previously been shown to differ between patients with chronic pain and healthy people. These included the dominant peak frequency; the amplitudes of neuronal oscillations at theta, alpha, beta, and gamma frequencies; as well as graph theory-based measures of brain network organization. The results show that pain intensity, pain-related disability, and depression were significantly improved after interdisciplinary multimodal pain therapy. Bayesian hypothesis testing indicated that these clinical changes were not related to changes of the dominant peak frequency or amplitudes of oscillations at any frequency band. Clinical changes were, however, associated with an increase in global network efficiency at theta frequencies. Thus, changes in chronic pain might be reflected by global network changes in the theta band. These longitudinal insights further the understanding of the brain mechanisms of chronic pain. Beyond, they might help to identify biomarkers for the monitoring of chronic pain.
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Affiliation(s)
- Henrik Heitmann
- Technical University of Munich (TUM), School of Medicine, Department of Neurology, Munich, Germany
- TUM, School of Medicine, TUM-Neuroimaging Center, Munich, Germany
- TUM, School of Medicine, Center for Interdisciplinary Pain Medicine, Munich, Germany
| | - Cristina Gil Ávila
- Technical University of Munich (TUM), School of Medicine, Department of Neurology, Munich, Germany
- TUM, School of Medicine, TUM-Neuroimaging Center, Munich, Germany
| | - Moritz M. Nickel
- Technical University of Munich (TUM), School of Medicine, Department of Neurology, Munich, Germany
- TUM, School of Medicine, TUM-Neuroimaging Center, Munich, Germany
| | - Son Ta Dinh
- Technical University of Munich (TUM), School of Medicine, Department of Neurology, Munich, Germany
- TUM, School of Medicine, TUM-Neuroimaging Center, Munich, Germany
| | - Elisabeth S. May
- Technical University of Munich (TUM), School of Medicine, Department of Neurology, Munich, Germany
- TUM, School of Medicine, TUM-Neuroimaging Center, Munich, Germany
| | - Laura Tiemann
- Technical University of Munich (TUM), School of Medicine, Department of Neurology, Munich, Germany
- TUM, School of Medicine, TUM-Neuroimaging Center, Munich, Germany
| | - Vanessa D. Hohn
- Technical University of Munich (TUM), School of Medicine, Department of Neurology, Munich, Germany
- TUM, School of Medicine, TUM-Neuroimaging Center, Munich, Germany
| | - Thomas R. Tölle
- Technical University of Munich (TUM), School of Medicine, Department of Neurology, Munich, Germany
- TUM, School of Medicine, Center for Interdisciplinary Pain Medicine, Munich, Germany
| | - Markus Ploner
- Technical University of Munich (TUM), School of Medicine, Department of Neurology, Munich, Germany
- TUM, School of Medicine, TUM-Neuroimaging Center, Munich, Germany
- TUM, School of Medicine, Center for Interdisciplinary Pain Medicine, Munich, Germany
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20
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Zhang Y, Ye L, Cao L, Song W. Resting-state electroencephalography changes in poststroke patients with visuospatial neglect. Front Neurosci 2022; 16:974712. [PMID: 36033611 PMCID: PMC9399887 DOI: 10.3389/fnins.2022.974712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 07/22/2022] [Indexed: 11/13/2022] Open
Abstract
Background This study aimed to explore the electrophysiological characteristics of resting-state electroencephalography (rsEEG) in patients with visuospatial neglect (VSN) after stroke. Methods A total of 44 first-event sub-acute strokes after right hemisphere damage (26 with VSN and 18 without VSN) were included. Besides, 18 age-matched healthy participants were used as healthy controls. The resting-state electroencephalography (EEG) of 64 electrodes was recorded to obtain the power of the spectral density of different frequency bands. The global delta/alpha ratio (DAR), DAR over the affected hemispheres (DARAH), DAR over the unaffected hemispheres (DARUH), and the pairwise-derived brain symmetry index (pdBSI; global and four bands) were compared between groups and receiver operating characteristic (ROC) curve analysis was conducted. The Barthel index (BI), Fugl-Meyer motor function assessment (FMA), and Berg balance scale (BBS) were used to assess the functional state of patients. Visuospatial neglect was assessed using a battery of standardized tests. Results We found that patients with VSN performed poorly compared with those without VSN. Analysis of rsEEG revealed increased delta and theta power and decreased alpha and beta power in stroke patients with VSN. Compared to healthy controls and poststroke non-VSN patients, patients with VSN showed a higher DAR (P < 0.001), which was significantly positively correlated with the BBS (DAR: r = –0.522, P = 0.006; DARAH: r = –0.521, P = 0.006; DARUH: r = –0.494, P = 0.01). The line bisection task was positively correlated with DAR (r = 0.458, P = 0.019) and DARAH (r = 0.483, P = 0.012), while the star cancellation task was only positively correlated with DARAH (r = 0.428, P = 0.029). DARAH had the best discriminating value between VSN and non-VSN, with an area under the curve (AUC) of 0.865. Patients with VSN showed decreased alpha power in the parietal and occipital areas of the right hemisphere. A higher parieto-occipital pdBSIalpha was associated with a worse line bisection task (r = 0.442, P = 0.024). Conclusion rsEEG may be a useful tool for screening for stroke patients with visuospatial neglect, and DAR and parieto-occipital pdBSIalpha may be useful biomarkers for visuospatial neglect after stroke.
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21
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Detrending Moving Average, Power Spectral Density, and Coherence: Three EEG-Based Methods to Assess Emotion Irradiation during Facial Perception. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Understanding brain reactions to facial expressions can help in explaining emotion-processing and memory mechanisms. The purpose of this research is to examine the dynamics of electrical brain activity caused by visual emotional stimuli. The focus is on detecting changes in cognitive mechanisms produced by negative, positive, and neutral expressions on human faces. Three methods were used to study brain reactions: power spectral density, detrending moving average (DMA), and coherence analysis. Using electroencephalogram (EEG) recordings from 48 subjects while presenting facial image stimuli from the International Affective Picture System, the topographic representation of the evoked responses was acquired and evaluated to disclose the specific EEG-based activity patterns in the cortex. The theta and beta systems are two key cognitive systems of the brain that are activated differently on the basis of gender. The obtained results also demonstrate that the DMA method can provide information about the cortical networks’ functioning stability, so it can be coupled with more prevalent methods of EEG analysis.
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22
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Strijbis EMM, Timar YSS, Schoonhoven DN, Nauta IM, Kulik SD, de Ruiter LRJ, Schoonheim MM, Hillebrand A, Stam CJ. State Changes During Resting-State (Magneto)encephalographic Studies: The Effect of Drowsiness on Spectral, Connectivity, and Network Analyses. Front Neurosci 2022; 16:782474. [PMID: 35784839 PMCID: PMC9245543 DOI: 10.3389/fnins.2022.782474] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background A common problem in resting-state neuroimaging studies is that subjects become drowsy or fall asleep. Although this could drastically affect neurophysiological measurements, such as magnetoencephalography (MEG), its specific impact remains understudied. We aimed to systematically investigate how often drowsiness is present during resting-state MEG recordings, and how the state changes alter quantitative estimates of oscillatory activity, functional connectivity, and network topology. Methods About 8-min MEG recordings of 19 healthy subjects, split into ~13-s epochs, were scored for the presence of eyes-open (EO), alert eyes-closed (A-EC), or drowsy eyes-closed (D-EC) states. After projection to source-space, results of spectral, functional connectivity, and network analyses in 6 canonical frequency bands were compared between these states on a global and regional levels. Functional connectivity was analyzed using the phase lag index (PLI) and corrected amplitude envelope correlation (AECc), and network topology was analyzed using the minimum spanning tree (MST). Results Drowsiness was present in >55% of all epochs that did not fulfill the AASM criteria for sleep. There were clear differences in spectral results between the states (A-EC vs. D-EC) and conditions (EO vs. A-EC). The influence of state and condition was far less pronounced for connectivity analyses, with only minimal differences between D-EC and EO in the AECc in the delta band. There were no effects of drowsiness on any of the MST measures. Conclusions Drowsiness during eyes-closed resting-state MEG recordings is present in the majority of epochs, despite the instructions to stay awake. This has considerable influence on spectral properties, but much less so on functional connectivity and network topology. These findings are important for interpreting the results of EEG/MEG studies using spectral analyses in neurological disease, where recordings should be evaluated for the presence of drowsiness. For connectivity analyses or studies on network topology, this seems of far less importance.
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Affiliation(s)
- Eva M. M. Strijbis
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- *Correspondence: Eva M. M. Strijbis
| | - Yannick S. S. Timar
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Deborah N. Schoonhoven
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ilse M. Nauta
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Shanna D. Kulik
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Lodewijk R. J. de Ruiter
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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Sun Y, Xu Y, Lv J, Liu Y. Self- and Situation-Focused Reappraisal are not homogeneous: Evidence from behavioral and brain networks. Neuropsychologia 2022; 173:108282. [PMID: 35660514 DOI: 10.1016/j.neuropsychologia.2022.108282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 05/13/2022] [Accepted: 05/27/2022] [Indexed: 11/20/2022]
Abstract
Reappraisal is an effective emotion regulation strategy which can be divided into self- and situation-focused subtypes. Previous studies have produced inconsistent findings on the moderating effects and neural mechanisms of reappraisal; thus, further research is necessary to clarify these inconsistencies. In this study, a total of 44 participants were recruited and randomly assigned to two groups. 23 participants were assigned to the self-focused group, while 21 participants were assigned to the situation-focused group. The participants' resting EEG data were collected for 6 minutes before the experiment began, followed by an emotional regulation task. During this task, participants were asked to view emotion-provoking images under four emotion regulation conditions (View, Watch, Increase, and Decrease). Late positive potential (LPP) was obtained when these emotional images were observed. LPP is an effective physiological indicator of emotion regulation, enabling this study to explore emotion regulation under different reappraisal strategies, as well as the functional connectivity and node efficiency within the brain. It was found that, in terms of the effect on emotion regulation, situation-focused reappraisal was significantly better than self-focused reappraisal at enhancing the valence of negative emotion, while self-focused reappraisal was significantly better than situation-focused reappraisal at increasing the arousal of negative emotion. In terms of neural mechanisms, multiple brain regions such as the anterior cingulate cortex, the frontal lobe, the parahippocampal gyrus, parts of the temporal lobe, and parts of the parietal lobe were involved in both reappraisal processes. In addition, there were some differences in brain regions associated with different forms of cognitive reappraisal. Self-focused reappraisal was associated with the posterior cingulate gyrus, fusiform gyrus, and lingual gyrus, and situation-focused reappraisal was associated with the parietal lobule, anterior central gyrus, and angular gyrus. In conclusion, this research demonstrates that self- and situation-focused reappraisal are not homogenous in terms of their effects and neural mechanisms and clarifies the uncertainties over their regulatory effects. Different types of reappraisal activate different brain regions when used, and the functional connectivity or node efficiency of these brain regions seems to be a suitable indicator for assessing the effects of different types of reappraisal.
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Affiliation(s)
- Yan Sun
- School of Psychology, Liaoning Normal University, Dalian, 116029, China
| | - Yuanyuan Xu
- School of Psychology, Liaoning Normal University, Dalian, 116029, China
| | - Jiaojiao Lv
- School of Psychology, Liaoning Normal University, Dalian, 116029, China; Department of Psychology, Shanxi Datong University, Datong, 037009, China
| | - Yan Liu
- School of Psychology, Liaoning Normal University, Dalian, 116029, China.
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Alteration of cortical functional networks in mood disorders with resting-state electroencephalography. Sci Rep 2022; 12:5920. [PMID: 35396563 PMCID: PMC8993886 DOI: 10.1038/s41598-022-10038-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/24/2022] [Indexed: 01/10/2023] Open
Abstract
Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal; however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs. Our findings might suggest more altered cortical functional network in patients with BD than in those with MDD.
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25
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Li M, Wang Y, Lopez-Naranjo C, Hu S, Reyes RCG, Paz-Linares D, Areces-Gonzalez A, Hamid AIA, Evans AC, Savostyanov AN, Calzada-Reyes A, Villringer A, Tobon-Quintero CA, Garcia-Agustin D, Yao D, Dong L, Aubert-Vazquez E, Reza F, Razzaq FA, Omar H, Abdullah JM, Galler JR, Ochoa-Gomez JF, Prichep LS, Galan-Garcia L, Morales-Chacon L, Valdes-Sosa MJ, Tröndle M, Zulkifly MFM, Abdul Rahman MRB, Milakhina NS, Langer N, Rudych P, Koenig T, Virues-Alba TA, Lei X, Bringas-Vega ML, Bosch-Bayard JF, Valdes-Sosa PA. Harmonized-Multinational qEEG norms (HarMNqEEG). Neuroimage 2022; 256:119190. [PMID: 35398285 DOI: 10.1016/j.neuroimage.2022.119190] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/23/2022] [Accepted: 04/05/2022] [Indexed: 12/14/2022] Open
Abstract
This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.
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Affiliation(s)
- Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Carlos Lopez-Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiang Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230601, China
| | | | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - Aini Ismafairus Abd Hamid
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Alexander N Savostyanov
- Humanitarian Institute, Novosibirsk State University, Novosibirsk 630090, Russia; Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia
| | | | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany; Center for Stroke Research, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Carlos A Tobon-Quintero
- Grupo Neuropsicología y Conducta - GRUNECO, Faculty of Medicine, Universidad de Antioquia, Colombia; Research Department, Institución Prestadora de Servicios de Salud IPS Universitaria, Colombia
| | - Daysi Garcia-Agustin
- Cuban Center for Neurocience, La Habana, Cuba; The Cuban center aging longevity and health, Havana Cuba
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, China
| | | | - Faruque Reza
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hazim Omar
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Jafri Malin Abdullah
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Universiti Sains Malaysia Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Janina R Galler
- Division of Pediatric Gastroenterology and Nutrition, Massachusetts General Hospital for Children, Boston, MA, United States Massachusetts General Hospital for Children, Boston, MA, United States
| | - John F Ochoa-Gomez
- Grupo Neuropsicología y Conducta - GRUNECO, Faculty of Medicine, Universidad de Antioquia, Colombia; Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Leslie S Prichep
- Research & Development, BrainScope Company, Inc. Bethesda, MD, United States; Department of Psychiatry (Ret.), Brain Research Laboratories, NYU School of Medicine, New York, NY, United States
| | | | - Lilia Morales-Chacon
- Department of Clinical Neurophysiology, International Center for Neurological Restoration, Playa, Havana 11300, Cuba
| | | | - Marius Tröndle
- Department of Methods of Plasticity Research, Institute of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program "Dynamic of Healthy Aging", University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Mohd Faizal Mohd Zulkifly
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Muhammad Riddha Bin Abdul Rahman
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Nerus 21300, Malaysia
| | - Natalya S Milakhina
- Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia
| | - Nicolas Langer
- Department of Methods of Plasticity Research, Institute of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program "Dynamic of Healthy Aging", University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Pavel Rudych
- Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Department of Information Technologies Novosibirsk State University, Novosibirsk 630090, Russia; Federal Research Center for Information and Computational Technologies, Biomedical Data Processing Lab, Novosibirsk 630090, Russia
| | - Thomas Koenig
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | | | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba.
| | - Jorge F Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada.
| | - Pedro Antonio Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba.
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Zhu Y, Zhu G, Li B, Yang Y, Zheng X, Xu Q, Li X. Abnormality of Functional Connections in the Resting State Brains of Schizophrenics. Front Hum Neurosci 2022; 16:799881. [PMID: 35355584 PMCID: PMC8959982 DOI: 10.3389/fnhum.2022.799881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
To explore the change of brain connectivity in schizophrenics (SCZ), the resting-state EEG source functional connections of SCZ and healthy control (HC) were investigated in this paper. Different band single-layer networks, multilayer networks, and improved multilayer networks were constructed and their topological attributes were extracted. The topological attributes of SCZ and HC were automatically distinguished using ensemble learning methods called Ensemble Learning based on Trees and Soft voting method, and the effectiveness of different network construction methods was compared based on the classification accuracy. The results showed that the classification accuracy was 89.38% for α band network, 82.5% for multilayer network, and 86.88% for improved multilayer network. Comparing patients with SCZ to those with Alzheimer's disease (AD), the classification accuracy of improved multilayer network was the highest, which was 88.12%. The power spectrum in the α band of SCZ was significantly lower than HC, whereas there was no significant difference between SCZ and AD. This indicated that the improved multilayer network can effectively distinguish SCZ and other groups not only when their power spectrum was significantly different. The results also suggested that the improved multilayer topological attributes were regarded as biological markers in the clinical diagnosis of patients with schizophrenia and even other mental disorders.
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Affiliation(s)
- Yan Zhu
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Geng Zhu
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Bin Li
- Shanghai Yangpu District Mental Health Center, Shanghai, China
| | - Yueqi Yang
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiaohan Zheng
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Qi Xu
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiaoou Li
- College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China
- College of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Wiesman AI, da Silva Castanheira J, Baillet S. Stability of spectral estimates in resting-state magnetoencephalography: Recommendations for minimal data duration with neuroanatomical specificity. Neuroimage 2021; 247:118823. [PMID: 34923132 PMCID: PMC8852336 DOI: 10.1016/j.neuroimage.2021.118823] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/19/2021] [Accepted: 12/14/2021] [Indexed: 12/20/2022] Open
Abstract
The principle of resting-state paradigms is appealing and practical for collecting data from impaired patients and special populations, especially if data collection times can be minimized. To achieve this goal, researchers need to ensure estimated signal features of interest are robust. In electro- and magnetoencephalography (EEG, MEG) we are not aware of any studies of the minimal length of data required to yield a robust one-session snapshot of the frequency-spectrum derivatives that are typically used to characterize the complex dynamics of the brain’s resting-state. We aimed to fill this knowledge gap by studying the stability of common spectra measures of resting-state MEG source time series obtained from large samples of single-session recordings from shared data repositories featuring different recording conditions and instrument technologies (OMEGA: N = 107; Cam-CAN: N = 50). We discovered that the rhythmic and arrhythmic spectral properties of intrinsic brain activity can be robustly estimated in most cortical regions when derived from relatively short segments of 30-s to 120-s of resting-state data, regardless of instrument technology and resting-state paradigm. Using an adapted leave-one-out approach and Bayesian analysis, we also provide evidence that the stability of spectral features over time is unaffected by age, sex, handedness, and general cognitive function. In summary, short MEG sessions are sufficient to yield robust estimates of frequency-defined brain activity during resting-state. This study may help guide future empirical designs in the field, particularly when recording times need to be minimized, such as with patient or special populations.
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Affiliation(s)
- Alex I Wiesman
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal QC, Canada.
| | | | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal QC, Canada
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Garro F, Chiappalone M, Buccelli S, De Michieli L, Semprini M. Neuromechanical Biomarkers for Robotic Neurorehabilitation. Front Neurorobot 2021; 15:742163. [PMID: 34776920 PMCID: PMC8579108 DOI: 10.3389/fnbot.2021.742163] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 09/22/2021] [Indexed: 02/06/2023] Open
Abstract
One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.
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Affiliation(s)
- Florencia Garro
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Michela Chiappalone
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
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Iandolo R, Semprini M, Sona D, Mantini D, Avanzino L, Chiappalone M. Investigating the spectral features of the brain meso-scale structure at rest. Hum Brain Mapp 2021; 42:5113-5129. [PMID: 34331365 PMCID: PMC8449100 DOI: 10.1002/hbm.25607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 07/16/2021] [Accepted: 07/18/2021] [Indexed: 12/02/2022] Open
Abstract
Recent studies provide novel insights into the meso-scale organization of the brain, highlighting the co-occurrence of different structures: classic assortative (modular), disassortative, and core-periphery. However, the spectral properties of the brain meso-scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso-scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source-localized high-density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block-model (WSBM) to capture the landscape of meso-scale structures across the frequency domain. We found that different meso-scale modalities co-exist and are diversely organized over the frequency spectrum. Specifically, we found a core-periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30-50 Hz). We further described other features of the meso-scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and core-peripheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity-invariance). In conclusion, we observed that the brain spontaneous activity shows frequency-specific meso-scale organization, which may support spatially distributed and local information processing.
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Affiliation(s)
- Riccardo Iandolo
- Rehab Technologies LabIstituto Italiano di TecnologiaGenovaItaly
- Present address:
Department of Neuromedicine and Movement ScienceFaculty of Medicine, Norwegian University of Science and TechnologyTrondheimNorway
| | | | - Diego Sona
- Pattern Analysis & Computer VisionIstituto Italiano di TecnologiaGenovaItaly
- Data Science for Health, Center for Digital Health and WellbeingFondazione Bruno KesslerTrentoItaly
| | - Dante Mantini
- Research Center for Motor Control and NeuroplasticityKU LeuvenLeuvenBelgium
- Brain Imaging and Neural Dynamics Research GroupIRCCS San Camillo HospitalVeneziaItaly
| | - Laura Avanzino
- Department of Experimental Medicine, Section of Human PhysiologyUniversity of GenovaGenovaItaly
- Ospedale Policlinico San MartinoIRCCSGenovaItaly
| | - Michela Chiappalone
- Rehab Technologies LabIstituto Italiano di TecnologiaGenovaItaly
- Present address:
Department of Informatics, Bioengineering, Robotics and System EngineeringUniversity of GenovaGenovaItaly
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Zhao X, Chen X, Duan W, Zhao W, Lei X. Impaired alpha rhythm in bad sleeper during nap: A high-density electroencephalogram study. J Sleep Res 2021; 31:e13419. [PMID: 34131989 DOI: 10.1111/jsr.13419] [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: 03/22/2021] [Revised: 04/30/2021] [Accepted: 05/14/2021] [Indexed: 11/30/2022]
Abstract
As the pace of life accelerates, poor sleep quality has become a common health problem. A wealth of sleep studies based on electroencephalogram (EEG) have found that poor sleep quality has decreased alpha rhythm during night-time sleep. However, whether the EEG rhythm during an afternoon nap has a similar impairment has seldom been investigated. By recruiting 35 subjects for a 90-min nap, we explored the relationship between sleep quality and the alpha rhythm, and the contribution of naps to subjective sleep experience. We found that the power of alpha rhythm in the occipital lobe negatively correlated with sleep quality. However, there was no correlation between the sleep quality at night and the sleep duration of the nap, nor the sleep quality and the proportion of each sleep period of a nap. Our present results indicated that the quality of night-time sleep did not affect the macrostructure of the nap. Interestingly, the negative correlation between the alpha rhythm of a nap and sleep quality at night continuously existed in Wake, N1 sleep, and N2 sleep, and had the most substantial value during N2 sleep of a nap. This implies that an impaired occipital lobe alpha rhythm is a robust biomarker for poor sleep. The research is of particular significance for developing electrical neural stimulation therapy to improve sleep quality, especially for the stimuli regions for selection and the time windows for implementation.
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Affiliation(s)
- Xiangyue Zhao
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China
| | - Xinyuan Chen
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China
| | - Wei Duan
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China
| | - Wenrui Zhao
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, China
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The Prediction of Acute Postoperative Pain Based on Neural Oscillations Measured before the Surgery. Neural Plast 2021; 2021:5543974. [PMID: 33897775 PMCID: PMC8052183 DOI: 10.1155/2021/5543974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/05/2021] [Accepted: 03/16/2021] [Indexed: 11/17/2022] Open
Abstract
Even with an improved understanding of pain mechanisms and advances in perioperative pain management, inadequately controlled postoperative pain remains. Predicting acute postoperative pain based on presurgery physiological measures could provide valuable insights into individualized, effective analgesic strategies, thus helping improve the analgesic efficacy. Considering the strong correlation between pain perception and neural oscillations, we hypothesize that acute postoperative pain could be predicted by neural oscillations measured shortly before the surgery. Here, we explored the relationship between neural oscillations 2 hours before the thoracoscopic surgery and the subjective intensity of acute postoperative pain. The spectral power density of resting-state beta and gamma band oscillations at the frontocentral region was significantly different between patients with different levels of acute postoperative pain (i.e., low pain vs. moderate/high pain). A positive correlation was also observed between the spectral power density of resting-state beta and gamma band oscillations and subjective reports of postoperative pain. Then, we predicted the level of acute postoperative pain based on features of neural oscillations using machine learning techniques, which achieved a prediction accuracy of 92.54% and a correlation coefficient between the real pain intensities and the predicted pain intensities of 0.84. Altogether, the prediction of acute postoperative pain based on neural oscillations measured before the surgery is feasible and could meet the clinical needs in the future for better control of postoperative pain and other unwanted negative effects. The study was registered on the Clinical Trial Registry (https://clinicaltrials.gov/ct2/show/NCT03761576?term=NCT03761576&draw=2&rank=1) with the registration number NCT03761576.
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