1
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van den Bongard F, Gowik JK, Coenen J, Jakobsmeyer R, Reinsberger C. Exercise-induced central and peripheral sympathetic activity in a community-based group of epilepsy patients differ from healthy controls. Exp Brain Res 2024; 242:1301-1310. [PMID: 38551692 PMCID: PMC11108887 DOI: 10.1007/s00221-024-06792-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/22/2024] [Indexed: 05/23/2024]
Abstract
Ictal and interictal activity within the autonomic nervous system is characterized by a sympathetic overshoot in people with epilepsy. This autonomic dysfunction is assumed to be driven by alterations in the central autonomic network. In this study, exercise-induced changes of the interrelation of central and peripheral autonomic activity in patients with epilepsy was assessed. 21 patients with epilepsy (16 seizure-free), and 21 healthy matched controls performed an exhaustive bicycle ergometer test. Immediately before and after the exercise test, resting state electroencephalography measurements (Brain Products GmbH, 128-channel actiCHamp) of 5 min were carried out to investigate functional connectivity assessed by phase locking value in source space for whole brain, central autonomic network and visual network. Additionally, 1-lead ECG (Brain products GmbH) was performed to analyze parasympathetic (root mean square of successive differences (RMSSD) of the heart rate variability) and sympathetic activity (electrodermal activity (meanEDA)). MeanEDA increased (p < 0.001) and RMSSD decreased (p < 0.001) from pre to post-exercise in both groups. Correlation coefficients of meanEDA and central autonomic network functional connectivity differed significantly between the groups (p = 0.004) after exercise. Both patients with epilepsy and normal control subjects revealed the expected physiological peripheral autonomic responses to acute exhaustive exercise, but alterations of the correlation between central autonomic and peripheral sympathetic activity may indicate a different sympathetic reactivity after exercise in patients with epilepsy. The clinical relevance of this finding and its modulators (seizures, anti-seizure medication, etc.) still needs to be elucidated.
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Affiliation(s)
| | - Julia Kristin Gowik
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
| | - Jessica Coenen
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
| | - Rasmus Jakobsmeyer
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany
| | - Claus Reinsberger
- Institute of Sports Medicine, Paderborn University, Warburger Straße 100, 33098, Paderborn, Germany.
- Division of Sports Neurology & Neurosciences, Department of Neurology, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
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2
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Bonassi G, Zhao M, Samogin J, Mantini D, Marchese R, Contrino L, Tognetti P, Putzolu M, Botta A, Pelosin E, Avanzino L. Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:2875. [PMID: 38732980 PMCID: PMC11086305 DOI: 10.3390/s24092875] [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: 03/22/2024] [Revised: 04/24/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, β, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and β bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, β ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.
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Affiliation(s)
- Gaia Bonassi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, 16132 Genoa, Italy;
| | - Mingqi Zhao
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (M.Z.); (J.S.); (D.M.)
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Jessica Samogin
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (M.Z.); (J.S.); (D.M.)
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (M.Z.); (J.S.); (D.M.)
| | - Roberta Marchese
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy; (R.M.); (A.B.); (L.A.)
| | - Luciano Contrino
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, 16043 Chiavari, Italy; (L.C.); (P.T.)
| | - Paola Tognetti
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, 16043 Chiavari, Italy; (L.C.); (P.T.)
| | - Martina Putzolu
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Viale Benedetto XV 3, 16132 Genoa, Italy;
| | - Alessandro Botta
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy; (R.M.); (A.B.); (L.A.)
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, 16132 Genoa, Italy;
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy; (R.M.); (A.B.); (L.A.)
| | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy; (R.M.); (A.B.); (L.A.)
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Viale Benedetto XV 3, 16132 Genoa, Italy;
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3
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Marzetti L, Basti A, Guidotti R, Baldassarre A, Metsomaa J, Zrenner C, D’Andrea A, Makkinayeri S, Pieramico G, Ilmoniemi RJ, Ziemann U, Romani GL, Pizzella V. Exploring Motor Network Connectivity in State-Dependent Transcranial Magnetic Stimulation: A Proof-of-Concept Study. Biomedicines 2024; 12:955. [PMID: 38790917 PMCID: PMC11118810 DOI: 10.3390/biomedicines12050955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/19/2024] [Accepted: 04/20/2024] [Indexed: 05/26/2024] Open
Abstract
State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focus on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the Motor Network (MN) and how the MN state affects responses to the transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to the left M1 in eight subjects at rest, with simultaneous EEG. Motor-evoked potentials (MEPs) were measured from the right hand. The source space functional connectivity of the left M1 to the whole brain was assessed using the imaginary part of the phase locking value at the frequency of the sensorimotor μ-rhythm in a 1 s window before the pulse. Group-level connectivity revealed functional links between the left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At the single-subject level, this relation is more highly expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity-based NIBS.
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Affiliation(s)
- Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy;
| | - Alessio Basti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy;
| | - Johanna Metsomaa
- Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany (U.Z.)
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076 Aalto, Finland
| | - Christoph Zrenner
- Department of Neurology & Stroke, University of Tübingen, 72076 Tübingen, Germany
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Institute for Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON M6J 1H1, Canada
| | - Antea D’Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Saeed Makkinayeri
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Giulia Pieramico
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
| | - Risto J. Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076 Aalto, Finland
| | - Ulf Ziemann
- Hertie Institute for Clinical Brain Research, University of Tübingen, 72076 Tübingen, Germany (U.Z.)
- Department of Neurology & Stroke, University of Tübingen, 72076 Tübingen, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy;
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. d’Annunzio University of Chieti-Pescara, Via dei Vestini 31, 66100 Chieti, Italy;
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4
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Roshchupkina L, Wens V, Coquelet N, Urbain C, de Tiege X, Peigneux P. Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep. Sci Rep 2024; 14:7531. [PMID: 38553500 PMCID: PMC10980824 DOI: 10.1038/s41598-024-58123-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.
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Affiliation(s)
- Liliia Roshchupkina
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium.
- UNI - ULB Neuroscience Institute, Brussels, Belgium.
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium.
- Faculté des Sciences Psychologiques et de l'Éducation, Campus du Solbosch - CP 191, Avenue F.D. Roosevelt, 50, 1050, Brussels, Belgium.
| | - Vincent Wens
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Nicolas Coquelet
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Charline Urbain
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
| | - Xavier de Tiege
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium
- UNI - ULB Neuroscience Institute, Brussels, Belgium
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5
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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:e2308364. [PMID: 38489748 DOI: 10.1002/advs.202308364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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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6
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Putzolu M, Samogin J, Bonassi G, Cosentino C, Mezzarobba S, Botta A, Avanzino L, Mantini D, Vato A, Pelosin E. Motor imagery ability scores are related to cortical activation during gait imagery. Sci Rep 2024; 14:5207. [PMID: 38433230 PMCID: PMC10909887 DOI: 10.1038/s41598-024-54966-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 02/19/2024] [Indexed: 03/05/2024] Open
Abstract
Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the θ, α, and β band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). Positive correlations were identified between VMIQ and avgERD of the middle cingulum in the β band and with avgERD of the left insula, right precentral area, and right middle occipital region in the θ band. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.
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Affiliation(s)
- Martina Putzolu
- Department of Experimental Medicine (DIMES), Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Jessica Samogin
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium
| | - Gaia Bonassi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal, and Child Health, University of Genoa, 16132, Genoa, Italy
| | - Carola Cosentino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal, and Child Health, University of Genoa, 16132, Genoa, Italy
| | - Susanna Mezzarobba
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal, and Child Health, University of Genoa, 16132, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Laura Avanzino
- Department of Experimental Medicine (DIMES), Section of Human Physiology, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, 3001, Leuven, Belgium
| | - Alessandro Vato
- Department of Biomedical Engineering, The Catholic University of America, Washington, DC, USA.
- National Center for Adaptive Neurotechnologies, Stratton VA Medical Center, Albany, NY, USA.
- College of Engineering and Applied Sciences, University at Albany - SUNY, Albany, NY, USA.
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal, and Child Health, University of Genoa, 16132, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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7
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Titone S, Samogin J, Peigneux P, Swinnen SP, Mantini D, Albouy G. Frequency-dependent connectivity in large-scale resting-state brain networks during sleep. Eur J Neurosci 2024; 59:686-702. [PMID: 37381891 DOI: 10.1111/ejn.16080] [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: 02/18/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 06/30/2023]
Abstract
Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during rapid eye movement (REM) sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. This study aimed to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3 and REM sleep (sleep stages scored using a semi-automatic procedure) in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks. In contrast, a reconnection occurred in the default mode and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier sleep cycles. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also illustrate a complex pattern of connectivity during REM sleep that is consistent with network- and frequency-specific breakdown and reconnection processes.
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Affiliation(s)
- Simon Titone
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- LBI-KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Jessica Samogin
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Philippe Peigneux
- Neuropsychology and Functional Neuroimaging Research Group (UR2NF) at the Centre for Research in Cognition and Neurosciences (CRCN), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Stephan P Swinnen
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- LBI-KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Dante Mantini
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Genevieve Albouy
- Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
- LBI-KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Health and Kinesiology, College of Health, University of Utah, Salt Lake City, Utah, USA
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8
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Conti M, Guerra A, Pierantozzi M, Bovenzi R, D'Onofrio V, Simonetta C, Cerroni R, Liguori C, Placidi F, Mercuri NB, Di Giuliano F, Schirinzi T, Stefani A. Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates. Mov Disord 2023; 38:2197-2208. [PMID: 37860930 DOI: 10.1002/mds.29615] [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: 05/03/2023] [Revised: 08/25/2023] [Accepted: 09/11/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD). OBJECTIVES In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms. METHODS We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach. RESULTS PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC. CONCLUSIONS Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Matteo Conti
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Andrea Guerra
- Parkinson and Movement Disorders Unit, Study Centre on Neurodegeneration (CESNE), Department of Neuroscience, University of Padova, Padua, Italy
| | - Mariangela Pierantozzi
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Roberta Bovenzi
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Valentina D'Onofrio
- Parkinson and Movement Disorders Unit, Study Centre on Neurodegeneration (CESNE), Department of Neuroscience, University of Padova, Padua, Italy
| | - Clara Simonetta
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Rocco Cerroni
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Claudio Liguori
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Fabio Placidi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Nicola Biagio Mercuri
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Francesca Di Giuliano
- Neuroradiology Unit, Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
| | - Tommaso Schirinzi
- Neurology Unit, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
| | - Alessandro Stefani
- Parkinson Centre, Department of Systems Medicine, University of Rome "Tor Vergata", Rome, Italy
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9
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Ambrosen KS, Fredriksson F, Anhøj S, Bak N, van Dellen E, Dominicus L, Lemvigh CK, Sørensen ME, Nielsen MØ, Bojesen KB, Fagerlund B, Glenthøj BY, Oranje B, Hansen LK, Ebdrup BH. Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography. Eur Arch Psychiatry Clin Neurosci 2023; 273:1785-1796. [PMID: 36729135 PMCID: PMC10713774 DOI: 10.1007/s00406-023-01550-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 01/09/2023] [Indexed: 02/03/2023]
Abstract
Schizophrenia is associated with aberrations in the Default Mode Network (DMN), but the clinical implications remain unclear. We applied data-driven, unsupervised machine learning based on resting-state electroencephalography (rsEEG) functional connectivity within the DMN to cluster antipsychotic-naïve patients with first-episode schizophrenia. The identified clusters were investigated with respect to psychopathological profile and cognitive deficits. Thirty-seven antipsychotic-naïve, first-episode patients with schizophrenia (mean age 24.4 (5.4); 59.5% males) and 97 matched healthy controls (mean age 24.0 (5.1); 52.6% males) underwent assessments of rsEEG, psychopathology, and cognition. Source-localized, frequency-dependent functional connectivity was estimated using Phase Lag Index (PLI). The DMN-PLI was factorized for each frequency band using principal component analysis. Clusters of patients were identified using a Gaussian mixture model and neurocognitive and psychopathological profiles of identified clusters were explored. We identified two clusters of patients based on the theta band (4-8 Hz), and two clusters based on the beta band (12-30 Hz). Baseline psychopathology could predict theta clusters with an accuracy of 69.4% (p = 0.003), primarily driven by negative symptoms. Five a priori selected cognitive functions conjointly predicted the beta clusters with an accuracy of 63.6% (p = 0.034). The two beta clusters displayed higher and lower DMN connectivity, respectively, compared to healthy controls. In conclusion, the functional connectivity within the DMN provides a novel, data-driven means to stratify patients into clinically relevant clusters. The results support the notion of biological subgroups in schizophrenia and endorse the application of data-driven methods to recognize pathophysiological patterns at earliest stage of this syndrome.
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Affiliation(s)
- Karen S Ambrosen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark.
| | - Fanny Fredriksson
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Simon Anhøj
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | | | - Edwin van Dellen
- Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Livia Dominicus
- Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Cecilie K Lemvigh
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Mikkel E Sørensen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Mette Ø Nielsen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kirsten B Bojesen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Birgitte Fagerlund
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Birte Y Glenthøj
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bob Oranje
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Lars K Hansen
- Department of Applied Mathematics and Computer Science, DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Bjørn H Ebdrup
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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10
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Scano A, Guanziroli E, Brambilla C, Amendola C, Pirovano I, Gasperini G, Molteni F, Spinelli L, Molinari Tosatti L, Rizzo G, Re R, Mastropietro A. A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation. Healthcare (Basel) 2023; 11:2282. [PMID: 37628480 PMCID: PMC10454517 DOI: 10.3390/healthcare11162282] [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: 07/04/2023] [Revised: 08/02/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.
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Affiliation(s)
- Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Eleonora Guanziroli
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Caterina Amendola
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.A.); (R.R.)
| | - Ileana Pirovano
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
| | - Giulio Gasperini
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Franco Molteni
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Lorenzo Spinelli
- Institute for Photonics and Nanotechnology (IFN), Italian National Research Council (CNR), Piazza Leonardo da Vinci 32, 20133 Milan, Italy;
| | - Lorenzo Molinari Tosatti
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Giovanna Rizzo
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
| | - Rebecca Re
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.A.); (R.R.)
- Institute for Photonics and Nanotechnology (IFN), Italian National Research Council (CNR), Piazza Leonardo da Vinci 32, 20133 Milan, Italy;
| | - Alfonso Mastropietro
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
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11
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Maffei A, Coccaro A, Jaspers-Fayer F, Goertzen J, Sessa P, Liotti M. EEG alpha band functional connectivity reveals distinct cortical dynamics for overt and covert emotional face processing. Sci Rep 2023; 13:9951. [PMID: 37337009 DOI: 10.1038/s41598-023-36860-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 06/13/2023] [Indexed: 06/21/2023] Open
Abstract
Current knowledge regarding how the focus of our attention during face processing influences neural responses largely comes from neuroimaging studies reporting on regional brain activations. The present study was designed to add novel insights to this research by studying how attention can differentially impact the way cortical regions interact during emotional face processing. High-density electroencephalogram was recorded in a sample of fifty-two healthy participants during an emotional face processing task. The task required participants to either attend to the expressions (i.e., overt processing) or attend to a perceptual distractor, which rendered the expressions task-irrelevant (i.e., covert processing). Functional connectivity in the alpha band was estimated in source space and modeled using graph theory to quantify whole-brain integration and segregation. Results revealed that overt processing of facial expressions is linked to reduced cortical segregation and increased cortical integration, this latter specifically for negative expressions of fear and sadness. Furthermore, we observed increased communication efficiency during overt processing of negative expressions between the core and the extended face processing systems. Overall, these findings reveal that attention makes the interaction among the nodes involved in face processing more efficient, also uncovering a connectivity signature of the prioritized processing mechanism of negative expressions, that is an increased cross-communication within the nodes of the face processing network.
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Affiliation(s)
- Antonio Maffei
- Department of Developmental Psychology and Socialisation, University of Padova, Padua, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padua, Italy
| | - Ambra Coccaro
- Department of Developmental Psychology and Socialisation, University of Padova, Padua, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padua, Italy
| | | | | | - Paola Sessa
- Department of Developmental Psychology and Socialisation, University of Padova, Padua, Italy
- Padova Neuroscience Center (PNC), University of Padova, Padua, Italy
| | - Mario Liotti
- Department of Developmental Psychology and Socialisation, University of Padova, Padua, Italy.
- Padova Neuroscience Center (PNC), University of Padova, Padua, Italy.
- Department of Psychology, Simon Fraser University, Burnaby, Canada.
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12
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Pusil S, Zegarra-Valdivia J, Cuesta P, Laohathai C, Cebolla AM, Haueisen J, Fiedler P, Funke M, Maestú F, Cheron G. Effects of spaceflight on the EEG alpha power and functional connectivity. Sci Rep 2023; 13:9489. [PMID: 37303002 DOI: 10.1038/s41598-023-34744-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 05/06/2023] [Indexed: 06/13/2023] Open
Abstract
Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight). DMN's alpha band power and FC were computed using eLORETA and phase-locking value. Eyes-opened (EO) and eyes-closed (EC) conditions were differentiated. We found a DMN alpha band power reduction during in-flight (EC: p < 0.001; EO: p < 0.05) and post-flight (EC: p < 0.001; EO: p < 0.01) when compared to pre-flight condition. FC strength decreased during in-flight (EC: p < 0.01; EO: p < 0.01) and post-flight (EC: ns; EO: p < 0.01) compared to pre-flight condition. The DMN alpha band power and FC strength reduction persisted until 20 days after landing. Spaceflight caused electrocerebral alterations that persisted after return to earth. Periodic assessment by EEG-derived DMN analysis has the potential to become a neurophysiologic marker of cerebral functional integrity during exploration missions to space.
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Affiliation(s)
- Sandra Pusil
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
| | - Jonathan Zegarra-Valdivia
- Achucarro Basque Center for Neuroscience, Leioa, Spain
- Global Brain Health Institute (GBHI), University of California, San Francisco (UCSF), San Francisco, CA, USA
- Universidad Señor de Sipán, Chiclayo, Peru
| | - Pablo Cuesta
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Radiology, Rehabilitation, and Physiotherapy, Universidad Complutense de Madrid, Madrid, Spain
| | | | - Ana Maria Cebolla
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Michael Funke
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Instituto de Investigación Sanitario, Hospital Clínico San Carlos, Madrid, Spain
| | - Guy Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.
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13
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Putzolu M, Samogin J, Bonassi G, Cosentino C, Mezzarobba S, Botta A, Avanzino L, Mantini D, Vato A, Pelosin E. Are Motor Imagery Ability scores related to cortical activity during gait imagery? RESEARCH SQUARE 2023:rs.3.rs-2777321. [PMID: 37090654 PMCID: PMC10120778 DOI: 10.21203/rs.3.rs-2777321/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography (hdEEG) in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the β band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). VMIQ was positively correlated with avgERD of frontal and cingulate areas, whereas IA SCORE was positively correlated with avgERD of left inferior frontal and superior temporal regions. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.
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14
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Wong CHY, Liu J, Tao J, Chen LD, Yuan HL, Wong MNK, Xu YW, Lee TMC, Chan CCH. Causal influences of salience/cerebellar networks on dorsal attention network subserved age-related cognitive slowing. GeroScience 2022; 45:889-899. [PMID: 36401740 PMCID: PMC9886783 DOI: 10.1007/s11357-022-00686-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/31/2022] [Indexed: 11/21/2022] Open
Abstract
Age-related cognitive slowing is a prominent precursor of cognitive decline. Functional neuroimaging studies found that cognitive processing speed is associated with activation and coupling among frontal, parietal and cerebellar brain networks. However, how the reciprocal influences of inter- and intra-network coupling mediate age-related decline in processing speed remains insufficiently studied. This study examined how inter- and intra-brain network influences mediate age-related slowing. We were interested in the fronto-insular salience network (SN), frontoparietal dorsal attention network (DAN), cerebellar network (CN) and default mode network (DMN). Reaction time (RT) and functional MRI data from 84 participants (aged 18-75) were collected while they were performing the Arrow Task in visual or audial forms. At the subject level, effective connectivities (ECs) were estimated with regression dynamic causal modelling. At the group level, structural equation models (SEMs) were used to model latent speed based on age and the EC mediators. Age was associated with decreased speed and increased inter-network effective connectivity. The CN exerting influence on the DAN (CN → DAN EC) mediated, while the SN → DAN EC suppressed age-related slowing. The DMN and intra-network ECs did not seem to play significant roles in slowing due to ageing. Inter-network connectivity from the CN and SN to the DAN contributes to age-related slowing. The seemingly antagonizing influences of the CN and SN indicate that increased task-related automaticity and decreased effortful control on top-down attention would promote greater speed in older individuals.
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Affiliation(s)
- Clive H. Y. Wong
- grid.419993.f0000 0004 1799 6254Department of Psychology, The Education University of Hong Kong, New Territories, Tai Po, Hong Kong China ,grid.194645.b0000000121742757State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam Hong Kong, China ,grid.194645.b0000000121742757Laboratory of Neuropsychology and Human Neuroscience, Department of Psychology, The University of Hong Kong, Pokfulam Hong Kong, China
| | - Jiao Liu
- grid.411504.50000 0004 1790 1622National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian China ,Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, Fujian China ,grid.411504.50000 0004 1790 1622Traditional Chinese Medicine Rehabilitation Research Center of State Administration of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian China
| | - Jing Tao
- grid.411504.50000 0004 1790 1622National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian China ,grid.411504.50000 0004 1790 1622College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian China ,Fujian Collaborative Innovation Center for Rehabilitation Technology, Fuzhou, Fujian China
| | - Li-dian Chen
- grid.411504.50000 0004 1790 1622National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian China ,grid.411504.50000 0004 1790 1622College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian China ,Fujian Collaborative Innovation Center for Rehabilitation Technology, Fuzhou, Fujian China
| | - Huan-ling Yuan
- grid.16890.360000 0004 1764 6123Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom Hong Kong, China
| | - Mabel N. K. Wong
- grid.419993.f0000 0004 1799 6254Department of Psychology, The Education University of Hong Kong, New Territories, Tai Po, Hong Kong China ,grid.16890.360000 0004 1764 6123Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom Hong Kong, China
| | - Yan-wen Xu
- grid.263761.70000 0001 0198 0694Department of Rehabilitation Medicine, Affiliated Hospital of Soochow University, Wuxi, Jiangsu, China
| | - Tatia M. C. Lee
- grid.194645.b0000000121742757State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam Hong Kong, China ,grid.194645.b0000000121742757Laboratory of Neuropsychology and Human Neuroscience, Department of Psychology, The University of Hong Kong, Pokfulam Hong Kong, China
| | - Chetwyn C. H. Chan
- grid.419993.f0000 0004 1799 6254Department of Psychology, The Education University of Hong Kong, New Territories, Tai Po, Hong Kong China
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15
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Pascarella A, Gianni E, Abbondanza M, Armonaite K, Pitolli F, Bertoli M, L’Abbate T, Grifoni J, Vitulano D, Bruni V, Conti L, Paulon L, Tecchio F. Normalized compression distance to measure cortico-muscular synchronization. Front Neurosci 2022; 16:933391. [DOI: 10.3389/fnins.2022.933391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 10/19/2022] [Indexed: 11/12/2022] Open
Abstract
The neuronal functional connectivity is a complex and non-stationary phenomenon creating dynamic networks synchronization determining the brain states and needed to produce tasks. Here, as a measure that quantifies the synchronization between the neuronal electrical activity of two brain regions, we used the normalized compression distance (NCD), which is the length of the compressed file constituted by the concatenated two signals, normalized by the length of the two compressed files including each single signal. To test the NCD sensitivity to physiological properties, we used NCD to measure the cortico-muscular synchronization, a well-known mechanism to control movements, in 15 healthy volunteers during a weak handgrip. Independently of NCD compressor (Huffman or Lempel Ziv), we found out that the resulting measure is sensitive to the dominant-non dominant asymmetry when novelty management is required (p = 0.011; p = 0.007, respectively) and depends on the level of novelty when moving the non-dominant hand (p = 0.012; p = 0.024). Showing lower synchronization levels for less dexterous networks, NCD seems to be a measure able to enrich the estimate of functional two-node connectivity within the neuronal networks that control the body.
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16
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Karahan E, Tait L, Si R, Özkan A, Szul MJ, Graham KS, Lawrence AD, Zhang J. The interindividual variability of multimodal brain connectivity maintains spatial heterogeneity and relates to tissue microstructure. Commun Biol 2022; 5:1007. [PMID: 36151363 PMCID: PMC9508245 DOI: 10.1038/s42003-022-03974-w] [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: 11/02/2021] [Accepted: 09/09/2022] [Indexed: 11/09/2022] Open
Abstract
Humans differ from each other in a wide range of biometrics, but to what extent brain connectivity varies between individuals remains largely unknown. By combining diffusion-weighted imaging (DWI) and magnetoencephalography (MEG), this study characterizes the inter-subject variability (ISV) of multimodal brain connectivity. Structural connectivity is characterized by higher ISV in association cortices including the core multiple-demand network and lower ISV in the sensorimotor cortex. MEG ISV exhibits frequency-dependent signatures, and the extent of MEG ISV is consistent with that of structural connectivity ISV in selective macroscopic cortical clusters. Across the cortex, the ISVs of structural connectivity and beta-band MEG functional connectivity are negatively associated with cortical myelin content indexed by the quantitative T1 relaxation rate measured by high-resolution 7 T MRI. Furthermore, MEG ISV from alpha to gamma bands relates to the hindrance and restriction of the white-matter tissue estimated by DWI microstructural models. Our findings depict the inter-relationship between the ISV of brain connectivity from multiple modalities, and highlight the role of tissue microstructure underpinning the ISV.
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Affiliation(s)
- Esin Karahan
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Luke Tait
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Ruoguang Si
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Ayşegül Özkan
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Maciek J Szul
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Bron, France.,Université Claude Bernard Lyon I, Lyon, France
| | - Kim S Graham
- Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom. .,Department of Computer Science, Swansea University, Swansea, United Kingdom.
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17
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Masina F, Montemurro S, Marino M, Manzo N, Pellegrino G, Arcara G. State-dependent tDCS modulation of the somatomotor network: A MEG study. Clin Neurophysiol 2022; 142:133-142. [PMID: 36037749 DOI: 10.1016/j.clinph.2022.07.508] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/13/2022] [Accepted: 07/30/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Transcranial direct current stimulation (tDCS) is a non-invasive technique widely used to investigate brain excitability and activity. However, the variability in both brain and behavioral responses to tDCS limits its application for clinical purposes. This study aims to shed light on state-dependency, a phenomenon that contributes to the variability of tDCS. METHODS To this aim, we investigated changes in spectral activity and functional connectivity in somatomotor regions after Real and Sham tDCS using generalized additive mixed models (GAMMs), which allowed us to investigate how modulation depends on the initial state of the brain. RESULTS Results showed that changes in spectral activity, but not connectivity, in the somatomotor regions depend on the initial state of the brain, confirming state-dependent effects. Specifically, we found a non-linear interaction between stimulation conditions (Real vs Sham) and initial state: a reduction of alpha and beta power was observed only in participants that had higher alpha and beta power before Real tDCS. CONCLUSIONS This study highlights the importance of considering state-dependency to tDCS and shows how it can be taken into account with appropriate statistical models. SIGNIFICANCE Our findings bear insight into tDCS mechanisms, potentially leading to discriminate between tDCS responders and non-responders.
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Affiliation(s)
| | | | - Marco Marino
- IRCCS San Camillo Hospital, Venice, Italy; Department of Movement Sciences, Research Center for Motor Control and Neuroplasticity, KU Leuven, Belgium.
| | - Nicoletta Manzo
- IRCCS San Camillo Hospital, Venice, Italy; Department of Human Neurosciences, Sapienza, University of Rome, Rome, Italy.
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18
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Petkoski S, Jirsa VK. Normalizing the brain connectome for communication through synchronization. Netw Neurosci 2022; 6:722-744. [PMID: 36607179 PMCID: PMC9810372 DOI: 10.1162/netn_a_00231] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/10/2022] [Indexed: 01/10/2023] Open
Abstract
Networks in neuroscience determine how brain function unfolds, and their perturbations lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual, particle-wave, perspective, integrate time delays due to finite transmission speeds, and derive a normalization of the connectome. When applied to the database of the Human Connectome Project, it explains the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N = 100) and are a fundamental network property within the wave picture. The normalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms. These two perspectives are orthogonal, but not incommensurable, when understood within the novel, here-proposed, generalized framework of structural connectivity.
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Affiliation(s)
- Spase Petkoski
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France,* Corresponding Authors: ;
| | - Viktor K. Jirsa
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France,* Corresponding Authors: ;
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19
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Fusina F, Marino M, Spironelli C, Angrilli A. Ventral Attention Network Correlates With High Traits of Emotion Dysregulation in Community Women - A Resting-State EEG Study. Front Hum Neurosci 2022; 16:895034. [PMID: 35721362 PMCID: PMC9205637 DOI: 10.3389/fnhum.2022.895034] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 04/27/2022] [Indexed: 11/26/2022] Open
Abstract
In recent years, many studies have focused on resting-state brain activity, and especially on functional connectivity (FC), an approach that typically describes the statistical interdependence of activity in distant brain regions through specific networks. Our aim was to study the neurophysiological correlates of emotion dysregulation. Therefore, we expected that both the Default Mode Network (DMN), and the Ventral Attention Network (VAN) would have been involved. Indeed, the latter plays a role in the automatic orienting of attention towards biologically salient stimuli and includes key regions for emotion control and modulation. Starting from a community sample of 422 female students, we selected 25 women with high traits of emotion dysregulation (HD group) and 25 with low traits (LD group). They underwent a 64-channel EEG recording during a five-minute resting state with eyes open. Seed-based FC was computed on the EEG Alpha band (8-13 Hz) as a control band, and on EEG Gamma power (30-50 Hz) as the relevant measure. The power within each network and inter-network connectivity (Inter-NC) was also calculated. Analysis of the EEG Gamma band revealed, in the HD group, higher levels of Inter-NC between the VAN and all other resting-state networks as compared with the LD group, while no differences emerged in the Alpha band. Concerning correlations, Alpha power in the VAN was negatively correlated in the HD group with affective lability (ALS-18 questionnaire), both for total score (ρ = -0.52, p FDR < 0.01) and the Depression/Elation subscale) ρ = -0.45, p FDR < 0.05). Consistent with this, in the Gamma band, a positive correlation was found between VAN spectral power and the Depression/Elation subscale of ALS-18, again in the HD group only (ρ = 0.47, p FDR < 0.05). In conclusion, both resting state FC and network power in the VAN were found to be related to high emotion dysregulation, even in our non-clinical sample with high traits. Emotion dysregulation was characterized, in the EEG gamma band, by a VAN strongly connected to all other networks, a result that points, in women prone to emotion dysregulation, to a strong automatic orienting of attention towards their internal state, bodily sensations, and emotionally intense related thoughts.
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Affiliation(s)
- Francesca Fusina
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of General Psychology, University of Padua, Padua, Italy
| | - Marco Marino
- Department of Movement Sciences, Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
- IRCCS San Camillo Hospital, Venice, Italy
| | - Chiara Spironelli
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of General Psychology, University of Padua, Padua, Italy
| | - Alessandro Angrilli
- Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of General Psychology, University of Padua, Padua, Italy
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20
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Porcaro C, Marino M, Carozzo S, Russo M, Ursino M, Valentinaruggiero, Ragno C, Proto S, Tonin P. Fractal Dimension Feature as a Signature of Severity in Disorders of Consciousness: An EEG Study. Int J Neural Syst 2022; 32:2250031. [DOI: 10.1142/s0129065722500319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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21
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Connectivity in Large-Scale Resting-State Brain Networks Is Related to Motor Learning: A High-Density EEG Study. Brain Sci 2022; 12:brainsci12050530. [PMID: 35624919 PMCID: PMC9138969 DOI: 10.3390/brainsci12050530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/12/2022] [Accepted: 04/19/2022] [Indexed: 11/28/2022] Open
Abstract
Previous research has shown that resting-state functional connectivity (rsFC) between different brain regions (seeds) is related to motor learning and motor memory consolidation. Using high-density electroencephalography (hdEEG), we addressed this question from a brain network perspective. Specifically, we examined frequency-dependent functional connectivity in resting-state networks from twenty-nine young healthy participants before and after they were trained on a motor sequence learning task. Consolidation was assessed with an overnight retest on the motor task. Our results showed training-related decreases in gamma-band connectivity within the motor network, and between the motor and functionally distinct resting-state networks including the attentional network. Brain-behavior correlation analyses revealed that baseline beta, delta, and theta rsFC were related to subsequent motor learning and memory consolidation such that lower connectivity within the motor network and between the motor and several distinct resting-state networks was correlated with better learning and overnight consolidation. Lastly, training-related increases in beta-band connectivity between the motor and the visual networks were related to greater consolidation. Altogether, our results indicate that connectivity in large-scale resting-state brain networks is related to—and modulated by—motor learning and memory consolidation processes. These finding corroborate previous seed-based connectivity research and provide evidence that frequency-dependent functional connectivity in resting-state networks is critically linked to motor learning and memory consolidation.
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22
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Zhao M, Bonassi G, Samogin J, Taberna GA, Pelosin E, Nieuwboer A, Avanzino L, Mantini D. Frequency-dependent modulation of neural oscillations across the gait cycle. Hum Brain Mapp 2022; 43:3404-3415. [PMID: 35384123 PMCID: PMC9248303 DOI: 10.1002/hbm.25856] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/08/2022] [Accepted: 03/22/2022] [Indexed: 12/14/2022] Open
Abstract
Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait‐related modulations of neural activity, we collected high‐density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event‐related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG‐based brain–body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders.
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Affiliation(s)
- Mingqi Zhao
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Gaia Bonassi
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Chiavari, Italy
| | - Jessica Samogin
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | | | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genova, Genova, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
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23
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Putzolu M, Samogin J, Cosentino C, Mezzarobba S, Bonassi G, Lagravinese G, Vato A, Mantini D, Avanzino L, Pelosin E. Neural oscillations during motor imagery of complex gait: an HdEEG study. Sci Rep 2022; 12:4314. [PMID: 35279682 PMCID: PMC8918338 DOI: 10.1038/s41598-022-07511-x] [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: 09/17/2021] [Accepted: 01/20/2022] [Indexed: 11/15/2022] Open
Abstract
The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of α- and β-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed α and β bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, α- and β-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found α- and β-band ERDs in the DT compared with the UW condition. Finally, in the β band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related α- and β-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases.
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24
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Samogin J, Rueda Delgado L, Taberna GA, Swinnen SP, Mantini D. Age-related differences of frequency-dependent functional connectivity in brain networks and their link to motor performance. Brain Connect 2022; 12:686-698. [DOI: 10.1089/brain.2021.0135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Jessica Samogin
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Laura Rueda Delgado
- Trinity College Institute of Neuroscience, 71434, Dublin, Ireland
- Cumulus Neuroscience, Ltd. , Dublin, Ireland
| | - Gaia Amaranta Taberna
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Stephan P. Swinnen
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute , Leuven, Belgium
| | - Dante Mantini
- Leuven, Belgium
- Research Center for Movement Control and Neuroplasticity, Department of Movement Sciences, KU Leuven, Leuven, Belgium
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25
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Lorenzini L, van Wingen G, Cerliani L. Atypically high influence of subcortical activity on primary sensory regions in autism. Neuroimage Clin 2022; 32:102839. [PMID: 34624634 PMCID: PMC8503568 DOI: 10.1016/j.nicl.2021.102839] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/20/2021] [Accepted: 09/21/2021] [Indexed: 12/20/2022]
Abstract
The age-dependent decrease of subcortico-cortical connectivity is attenuated in ASD. Primary sensory regions remain less segregated from subcortical activity in ASD. This could underlie an excessive amount of sensory input relayed to the cortex.
Background Hypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from the persistent overconnectivity between primary sensory regions and deep brain nuclei in both children and adults with ASD – as reported by several previous studies – which could reflect a decreased or arrested maturation of brain connectivity. However, it has not yet been investigated whether this overconnectivity can be modelled as an excessive directional influence of subcortical brain activity on primary sensory cortical regions in ASD, with respect to age-matched typically developing (TD) individuals. Methods To this aim, we used dynamic causal modelling to estimate (1) the directional influence of subcortical activity on cortical processing and (2) the functional segregation of primary sensory cortical regions from subcortical activity in 166 participants with ASD and 193 TD participants from the Autism Brain Imaging Data Exchange (ABIDE). We then specifically tested the hypothesis that the age-related changes of these indicators of brain connectivity would differ between the two groups. Results We found that in TD participants age was significantly associated with decreased influence of subcortical activity on cortical processing, paralleled by an increased functional segregation of cortical sensory processing from subcortical activity. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD. Conclusion This atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory input relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.
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Affiliation(s)
- Luigi Lorenzini
- Dept. of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Meibergdreef 5, 1105AZ Amsterdam, The Netherlands; Dept. Radiology and Nuclear Medicine, Amsterdam UMC, VU University, Amsterdam Neuroscience, De Boelelaan 1117, 1081HV Amsterdam, The Netherlands.
| | - Guido van Wingen
- Dept. of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Meibergdreef 5, 1105AZ Amsterdam, The Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018WT, University of Amsterdam, The Netherlands
| | - Leonardo Cerliani
- Dept. of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Meibergdreef 5, 1105AZ Amsterdam, The Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018WT, University of Amsterdam, The Netherlands; Netherlands Institute for Neuroscience, Social Brain Lab, Meibergdreef 47, 1105BA Amsterdam, The Netherlands
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26
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Zhao M, Bonassi G, Guarnieri R, Pelosin E, Nieuwboer A, Avanzino L, Mantini D. A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data. J Neural Eng 2021; 18. [PMID: 34874319 DOI: 10.1088/1741-2552/ac4084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/06/2021] [Indexed: 11/12/2022]
Abstract
Objective.Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which-depending on their extent-may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts.Approach.In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches.Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts.Significance.Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.
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Affiliation(s)
- Mingqi Zhao
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium
| | - Gaia Bonassi
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, 16043 Chiavari, Italy
| | - Roberto Guarnieri
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium.,Icometrix, 3012 Leuven, Belgium
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health, University of Genova, 16132 Genova, Italy.,Ospedale Policlinico San Martino, IRCCS, 16132 Genoa, Italy
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, KU Leuven, 3001 Leuven, Belgium
| | - Laura Avanzino
- Ospedale Policlinico San Martino, IRCCS, 16132 Genoa, Italy.,Department of Experimental Medicine, Section of Human Physiology, University of Genoa, 16132 Genoa, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
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27
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Resting state network connectivity is attenuated by fMRI acoustic noise. Neuroimage 2021; 247:118791. [PMID: 34920084 DOI: 10.1016/j.neuroimage.2021.118791] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 10/21/2021] [Accepted: 12/07/2021] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION During the past decades there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI however does not provide the ideal environmental setting, as participants are continuously exposed to noise generated by MRI coils during acquisition of Echo Planar Imaging (EPI). We investigated the effect of EPI noise on resting state activity and connectivity using magnetoencephalography (MEG), by reproducing the acoustic characteristics of rs-fMRI environment during the recordings. As compared to fMRI, MEG has little sensitivity to brain activity generated in deep brain structures, but has the advantage to capture both the dynamic of cortical magnetic oscillations with high temporal resolution and the slow magnetic fluctuations highly correlated with BOLD signal. METHODS Thirty healthy subjects were enrolled in a counterbalanced design study including three conditions: a) silent resting state (Silence), b) resting state upon EPI noise (fMRI), and c) resting state upon white noise (White). White noise was employed to test the specificity of fMRI noise effect. The amplitude envelope correlation (AEC) in alpha band measured the connectivity of seven Resting State Networks (RSN) of interest (default mode network, dorsal attention network, language, left and right auditory and left and right sensory-motor). Vigilance dynamic was estimated from power spectral activity. RESULTS fMRI and White acoustic noise consistently reduced connectivity of cortical networks. The effects were widespread, but noise and network specificities were also present. For fMRI noise, decreased connectivity was found in the right auditory and sensory-motor networks. Progressive increase of slow theta-delta activity related to drowsiness was found in all conditions, but was significantly higher for fMRI . Theta-delta significantly and positively correlated with variations of cortical connectivity. DISCUSSION rs-fMRI connectivity is biased by unavoidable environmental factors during scanning, which warrant more careful control and improved experimental designs. MEG is free from acoustic noise and allows a sensitive estimation of resting state connectivity in cortical areas. Although underutilized, MEG could overcome issues related to noise during fMRI, in particular when investigation of motor and auditory networks is needed.
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28
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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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Babaeeghazvini P, Rueda-Delgado LM, Gooijers J, Swinnen SP, Daffertshofer A. Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography. Front Hum Neurosci 2021; 15:721206. [PMID: 34690718 PMCID: PMC8529047 DOI: 10.3389/fnhum.2021.721206] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 09/10/2021] [Indexed: 11/13/2022] Open
Abstract
Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.
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Affiliation(s)
- Parinaz Babaeeghazvini
- Department of Human Movements Sciences, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Science Institute (AMS), Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Institute for Brain and Behaviour Amsterdam (iBBA), Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Laura M. Rueda-Delgado
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Jolien Gooijers
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Stephan P. Swinnen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Andreas Daffertshofer
- Department of Human Movements Sciences, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Science Institute (AMS), Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Institute for Brain and Behaviour Amsterdam (iBBA), Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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30
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Duma GM, Danieli A, Vettorel A, Antoniazzi L, Mento G, Bonanni P. Investigation of dynamic functional connectivity of the source reconstructed epileptiform discharges in focal epilepsy: A graph theory approach. Epilepsy Res 2021; 176:106745. [PMID: 34428725 DOI: 10.1016/j.eplepsyres.2021.106745] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/26/2021] [Accepted: 08/17/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The aim of the present study is to investigate with noninvasive methods the modulation of dynamic functional connectivity during interictal epileptiform discharge (IED). METHOD We reconstructed the cortical source of the EEG recorded IED of 17 patients with focal epilepsy. We then computed dynamic connectivity using the time resolved phase locking value (PLV). We derived graph theory indices (i.e. degree, strength, local efficiency, clustering coefficient and global efficiency). Finally, we selected the atlas node with the maximum activation as the IED cortical source investigating the graph indices dynamics in theta, alpha, beta and gamma frequency bands. RESULTS We observed IED-locked modulations of the graph indexes depending on the frequency bands. We detected a modulation of the strength, clustering coefficient, local and global efficiency both in theta and in alpha bands, which also displayed modulations of the degree index. In the beta band only the global efficiency was modulated by the IED, while no effects were detected in the gamma band. Finally, we found a correlation between alpha and theta local efficiency, as well as alpha global efficiency, and the epilepsy duration. SIGNIFICANCE Our findings suggest that the neural synchronization is not limited to the IED cortical source, but implies a phase synchronization across multiple brain areas. We hypothesize that the aberrant electrical activity originating from the IED locus is spread amongst the other network nodes throughout the low frequency bands (i.e. theta and alpha). Moreover, IED-dependent increase in the global efficiency indicates that the IED interfere with the whole network functioning. We finally discussed possible application of this methodology for future investigation.
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Affiliation(s)
- Gian Marco Duma
- Department of General Psychology, University of Padova, Italy; Epilepsy and Clinical Neurophysiology Unit, Scientific Institute, IRCCS "E. Medea", Conegliano, TV, Italy.
| | - Alberto Danieli
- Epilepsy and Clinical Neurophysiology Unit, Scientific Institute, IRCCS "E. Medea", Conegliano, TV, Italy
| | - Airis Vettorel
- Epilepsy and Clinical Neurophysiology Unit, Scientific Institute, IRCCS "E. Medea", Conegliano, TV, Italy
| | - Lisa Antoniazzi
- Epilepsy and Clinical Neurophysiology Unit, Scientific Institute, IRCCS "E. Medea", Conegliano, TV, Italy
| | - Giovanni Mento
- Department of General Psychology, University of Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Italy
| | - Paolo Bonanni
- Epilepsy and Clinical Neurophysiology Unit, Scientific Institute, IRCCS "E. Medea", Conegliano, TV, Italy
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Xu N, Shan W, Qi J, Wu J, Wang Q. Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity. Front Hum Neurosci 2021; 15:649074. [PMID: 34276321 PMCID: PMC8283278 DOI: 10.3389/fnhum.2021.649074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 06/07/2021] [Indexed: 11/21/2022] Open
Abstract
Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source-space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.
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Affiliation(s)
- Na Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Qi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianping Wu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
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32
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Taberna GA, Samogin J, Marino M, Mantini D. Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies. Brain Sci 2021; 11:brainsci11060741. [PMID: 34204868 PMCID: PMC8226780 DOI: 10.3390/brainsci11060741] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/23/2021] [Accepted: 05/31/2021] [Indexed: 11/16/2022] Open
Abstract
Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.
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Affiliation(s)
- Gaia Amaranta Taberna
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
| | - Jessica Samogin
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
| | - Marco Marino
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium; (G.A.T.); (J.S.); (M.M.)
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
- Correspondence: ; Tel.: +32-16-37-29-09
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Samogin J, Marino M, Porcaro C, Wenderoth N, Dupont P, Swinnen SP, Mantini D. Frequency-dependent functional connectivity in resting state networks. Hum Brain Mapp 2020; 41:5187-5198. [PMID: 32840936 PMCID: PMC7670639 DOI: 10.1002/hbm.25184] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 08/07/2020] [Indexed: 12/27/2022] Open
Abstract
Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large‐scale networks, called resting‐state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency‐dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band‐limited power correlations from high‐density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.
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Affiliation(s)
- Jessica Samogin
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium
| | - Marco Marino
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Camillo Porcaro
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy.,Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.,Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.,Research in Advanced Neurorehabilitation, S. Anna Istitute, Crotone, Italy
| | - Nicole Wenderoth
- Neural Control of Movement Lab, Department of Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Patrick Dupont
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium.,Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium
| | - Stephan P Swinnen
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
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