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Takacs A, Toth-Faber E, Schubert L, Tárnok Z, Ghorbani F, Trelenberg M, Nemeth D, Münchau A, Beste C. Resting network architecture of theta oscillations reflects hyper-learning of sensorimotor information in Gilles de la Tourette syndrome. Brain Commun 2024; 6:fcae092. [PMID: 38562308 PMCID: PMC10984574 DOI: 10.1093/braincomms/fcae092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/01/2024] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
Gilles de la Tourette syndrome is a neurodevelopmental disorder characterized by motor and vocal tics. It is associated with enhanced processing of stimulus-response associations, including a higher propensity to learn probabilistic stimulus-response contingencies (i.e. statistical learning), the nature of which is still elusive. In this study, we investigated the hypothesis that resting-state theta network organization is a key for the understanding of superior statistical learning in these patients. We investigated the graph-theoretical network architecture of theta oscillations in adult patients with Gilles de la Tourette syndrome and healthy controls during a statistical learning task and in resting states both before and after learning. We found that patients with Gilles de la Tourette syndrome showed a higher statistical learning score than healthy controls, as well as a more optimal (small-world-like) theta network before the task. Thus, patients with Gilles de la Tourette syndrome had a superior facility to integrate and evaluate novel information as a trait-like characteristic. Additionally, the theta network architecture in Gilles de la Tourette syndrome adapted more to the statistical information during the task than in HC. We suggest that hyper-learning in patients with Gilles de la Tourette syndrome is likely a consequence of increased sensitivity to perceive and integrate sensorimotor information leveraged through theta oscillation-based resting-state dynamics. The study delineates the neural basis of a higher propensity in patients with Gilles de la Tourette syndrome to pick up statistical contingencies in their environment. Moreover, the study emphasizes pathophysiologically endowed abilities in patients with Gilles de la Tourette syndrome, which are often not taken into account in the perception of this common disorder but could play an important role in destigmatization.
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Affiliation(s)
- Adam Takacs
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden 01069, Germany
- Faculty of Medicine, University Neuropsychology Center, TU Dresden, Dresden 01069, Germany
| | - Eszter Toth-Faber
- Institute of Psychology, ELTE Eötvös Loránd University, Budapest 1064, Hungary
- Brain, Memory and Language Research Group, Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest 1117, Hungary
| | - Lina Schubert
- Institute of Systems Motor Science, University of Lübeck, Lübeck 23562, Germany
| | - Zsanett Tárnok
- Vadaskert Child and Adolescent Psychiatry Hospital and Outpatient Clinic, Budapest 1021, Hungary
| | - Foroogh Ghorbani
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden 01069, Germany
- Faculty of Medicine, University Neuropsychology Center, TU Dresden, Dresden 01069, Germany
| | - Madita Trelenberg
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden 01069, Germany
| | - Dezso Nemeth
- INSERM, Université Claude Bernard Lyon 1, CNRS, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Bron 69500, France
- NAP Research Group, Institute of Psychology, Eötvös Loránd University & Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest 1071, Hungary
- Department of Education and Psychology, Faculty of Social Sciences, University of Atlántico Medio, Las Palmas de Gran Canaria 35017, Spain
| | - Alexander Münchau
- Institute of Systems Motor Science, University of Lübeck, Lübeck 23562, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden 01069, Germany
- Faculty of Medicine, University Neuropsychology Center, TU Dresden, Dresden 01069, Germany
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Kavčič A, Demšar J, Georgiev D, Bon J, Soltirovska-Šalamon A. Age related changes and sex related differences of functional brain networks in childhood: A high-density EEG study. Clin Neurophysiol 2023; 150:216-226. [PMID: 37104911 DOI: 10.1016/j.clinph.2023.03.357] [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: 07/11/2022] [Revised: 02/11/2023] [Accepted: 03/18/2023] [Indexed: 04/29/2023]
Abstract
OBJECTIVE The aim of this study was to explore functional network age-related changes and sex-related differences during the early lifespan with a high-density resting state electroencephalography (rs-EEG). METHODS We analyzed two data sets of high-density rs-EEG in healthy children and adolescents. We recorded a 64-channel EEG and calculated functional connectomes in 27 participants aged 5-18 years. To validate our results, we used publicly available data and calculated functional connectomes in another 86 participants aged 6-18 years from a 128-channel rs-EEG. We were primarily interested in alpha frequency band, but we also analyzed theta and beta frequency bands. RESULTS We observed age-related increase of characteristic path, clustering coefficient and interhemispheric strength in the alpha frequency band of both data sets and in the beta frequency band of the larger validation data set. Age-related increase of global efficiency was seen in the theta band of the validation data set and in the alpha band of the test data set. Increase in small worldness was observed only in the alpha frequency band of the test data set. We also observed an increase of individual peak alpha frequency with age in both data sets. Sex-related differences were only observed in the beta frequency band of the larger validation data set, with females having higher values than same aged males. CONCLUSIONS Functional brain networks show indices of higher segregation, but also increasing global integration with maturation. Age-related changes are most prominent in the alpha frequency band. SIGNIFICANCE To the best of our knowledge, our study was the first to analyze maturation related changes and sex-related differences of functional brain networks with a high-density EEG and to compare functional connectomes generated from two diverse high-density EEG data sets. Understanding the age-related changes and sex-related differences of functional brain networks in healthy children and adolescents is crucial for identifying network abnormalities in different neurologic and psychiatric conditions, with the aim to identify possible markers for prognosis and treatment.
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Affiliation(s)
- Alja Kavčič
- Division of Pediatrics, Department of Neonatology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Jure Demšar
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia; Faculty of Computer and Information Sciences, University of Ljubljana, Ljubljana, Slovenia
| | - Dejan Georgiev
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Jurij Bon
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia; University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia
| | - Aneta Soltirovska-Šalamon
- Division of Pediatrics, Department of Neonatology, University Medical Centre Ljubljana, Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Slovenia.
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Bosch TJ, Espinoza AI, Mancini M, Horak FB, Singh A. Functional Connectivity in Patients With Parkinson’s Disease and Freezing of Gait Using Resting-State EEG and Graph Theory. Neurorehabil Neural Repair 2022; 36:715-725. [DOI: 10.1177/15459683221129282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background Although many studies have shown abnormalities in brain structure and function in people with Parkinson’s disease (PD), we still have a poor understanding of how brain structure and function relates to freezing of gait (FOG). Graph theory analysis of electroencephalography (EEG) can explore the relationship between brain network structure and gait function in PD. Methods Scalp EEG signals of 83 PD (42 PDFOG+ and 41 PDFOG−) and 42 healthy controls were recorded in an eyes-opened resting-state. The phase lag index was calculated for each electrode pair in different frequency bands, but we focused our analysis on the theta-band and performed global analyses along with nodal analyses over a midfrontal channel. The resulting connectivity matrices were converted to weighted graphs, whose structure was characterized using strength and clustering coefficient measurements, our main outcomes. Results We observed increased global strength and increased global clustering coefficient in people with PD compared to healthy controls in the theta-band, though no differences were observed in midfrontal nodal strength and midfrontal clustering coefficient. Furthermore, no differences in global nor midfrontal nodal strength nor global clustering coefficients were observed between PDFOG+ and PDFOG− in the theta-band. However, PDFOG+ exhibited a significantly diminished midfrontal nodal clustering coefficient in the theta-band compared to PDFOG−. Furthermore, FOG scores were negatively correlated with midfrontal nodal clustering coefficient in the theta-band. Conclusion The present findings support the involvement of midfrontal theta oscillations in FOG symptoms in PD and the sensitivity of graph metrics to characterize functional networks in PDFOG+.
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Affiliation(s)
- Taylor J. Bosch
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA
| | | | - Martina Mancini
- Department of Neurology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Fay B. Horak
- Department of Neurology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Arun Singh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA
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Góngora L, Paglialonga A, Mastropietro A, Rizzo G, Barbieri R. A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals. SENSORS 2022; 22:s22134747. [PMID: 35808250 PMCID: PMC9269473 DOI: 10.3390/s22134747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 02/05/2023]
Abstract
Connectivity among different areas within the brain is a topic that has been notably studied in the last decade. In particular, EEG-derived measures of effective connectivity examine the directionalities and the exerted influences raised from the interactions among neural sources that are masked out on EEG signals. This is usually performed by fitting multivariate autoregressive models that rely on the stationarity that is assumed to be maintained over shorter bits of the signals. However, despite being a central condition, the selection process of a segment length that guarantees stationary conditions has not been systematically addressed within the effective connectivity framework, and thus, plenty of works consider different window sizes and provide a diversity of connectivity results. In this study, a segment-size-selection procedure based on fourth-order statistics is proposed to make an informed decision on the appropriate window size that guarantees stationarity both in temporal and spatial terms. Specifically, kurtosis is estimated as a function of the window size and used to measure stationarity. A search algorithm is implemented to find the segments with similar stationary properties while maximizing the number of channels that exhibit the same properties and grouping them accordingly. This approach is tested on EEG signals recorded from six healthy subjects during resting-state conditions, and the results obtained from the proposed method are compared to those obtained using the classical approach for mapping effective connectivity. The results show that the proposed method highlights the influence that arises in the Default Mode Network circuit by selecting a window of 4 s, which provides, overall, the most uniform stationary properties across channels.
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Affiliation(s)
- Leonardo Góngora
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;
| | - Alessia Paglialonga
- Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni (IEIIT), Consiglio Nazionale delle Ricerche (CNR), 20133 Milan, Italy;
| | - Alfonso Mastropietro
- Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), 20054 Segrate, Italy; (A.M.); (G.R.)
| | - Giovanna Rizzo
- Istituto di Tecnologie Biomediche (ITB), Consiglio Nazionale delle Ricerche (CNR), 20054 Segrate, Italy; (A.M.); (G.R.)
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;
- Correspondence:
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Yu H, Ba S, Guo Y, Guo L, Xu G. Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm. Brain Sci 2022; 12:brainsci12020194. [PMID: 35203957 PMCID: PMC8870302 DOI: 10.3390/brainsci12020194] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 02/01/2023] Open
Abstract
Motor imagery (MI) refers to the mental rehearsal of movement in the absence of overt motor action, which can activate or inhibit cortical excitability. EEG mu/beta oscillations recorded over the human motor cortex have been shown to be consistently suppressed during both the imagination and performance of movements, although the specific effect on brain function remains to be confirmed. In this study, Granger causality (GC) was used to construct the brain functional network of subjects during motor imagery and resting state based on EEG in order to explore the effects of motor imagery on brain function. Parameters of the brain functional network were compared and analyzed, including degree, clustering coefficient, characteristic path length and global efficiency of EEG mu/beta rhythm in different states. The results showed that the clustering coefficient and efficiency of EEG mu/beta rhythm decreased significantly during motor imagery (p < 0.05), while degree distribution and characteristic path length increased significantly (p < 0.05), mainly concentrated in the frontal lobe and sensorimotor area. For the resting state after motor imagery, the changes of brain functional characteristics were roughly similar to those of the task state. Therefore, it is concluded that motor imagery plays an important role in activation of cortical excitability.
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Affiliation(s)
- Hongli Yu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (L.G.); (G.X.)
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; (S.B.); (Y.G.)
- Correspondence: ; Tel.: +86-137-5249-0401
| | - Sidi Ba
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; (S.B.); (Y.G.)
| | - Yuxue Guo
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; (S.B.); (Y.G.)
| | - Lei Guo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (L.G.); (G.X.)
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; (S.B.); (Y.G.)
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (L.G.); (G.X.)
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; (S.B.); (Y.G.)
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Orkan Olcay B, Özgören M, Karaçalı B. On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels. Neural Netw 2021; 143:452-474. [PMID: 34273721 DOI: 10.1016/j.neunet.2021.06.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 05/04/2021] [Accepted: 06/18/2021] [Indexed: 10/21/2022]
Abstract
Accurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Δt, the time lag between maximally synchronized signal segments τ, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the inter-channel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes.
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Affiliation(s)
- B Orkan Olcay
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
| | - Murat Özgören
- Department of Biophysics, Faculty of Medicine, Near East University, 99138, Nicosia, Cyprus.
| | - Bilge Karaçalı
- Department of Electrical and Electronics Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
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Edwards DJ, Trujillo LT. An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States. Brain Sci 2021; 11:330. [PMID: 33808022 PMCID: PMC7998369 DOI: 10.3390/brainsci11030330] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/26/2021] [Accepted: 03/03/2021] [Indexed: 12/20/2022] Open
Abstract
Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4-7 Hz, alpha: 8-13 Hz, low beta: 14-20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.
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Affiliation(s)
- Dalton J. Edwards
- Department of Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX 75080-3021, USA;
- Department of Psychology, Texas State University, San Marcos, TX 78666, USA
| | - Logan T. Trujillo
- Department of Psychology, Texas State University, San Marcos, TX 78666, USA
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