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Glomb K, Cabral J, Cattani A, Mazzoni A, Raj A, Franceschiello B. Computational Models in Electroencephalography. Brain Topogr 2021; 35:142-161. [PMID: 33779888 PMCID: PMC8813814 DOI: 10.1007/s10548-021-00828-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/19/2021] [Indexed: 12/17/2022]
Abstract
Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.
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Affiliation(s)
- Katharina Glomb
- Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Anna Cattani
- Department of Psychiatry, University of Wisconsin-Madison, Madison, USA.,Department of Biomedical and Clinical Sciences 'Luigi Sacco', University of Milan, Milan, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Ashish Raj
- School of Medicine, UCSF, San Francisco, USA
| | - Benedetta Franceschiello
- Department of Ophthalmology, Hopital Ophthalmic Jules Gonin, FAA, Lausanne, Switzerland.,CIBM Centre for Biomedical Imaging, EEG Section CHUV-UNIL, Lausanne, Switzerland.,Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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Hindriks R, Micheli C, Bosman CA, Oostenveld R, Lewis C, Mantini D, Fries P, Deco G. Source-reconstruction of the sensorimotor network from resting-state macaque electrocorticography. Neuroimage 2018; 181:347-358. [PMID: 29886144 DOI: 10.1016/j.neuroimage.2018.06.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 06/01/2018] [Accepted: 06/04/2018] [Indexed: 10/28/2022] Open
Abstract
The discovery of hemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electric cortical rhythms are organized into the same RSNs as hemodynamic signals. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or scalp electroencephalography (EEG), which limits the spatial resolution with which electrophysiological RSNs can be observed. Due to their close proximity to the cortical surface, electrocorticographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms, albeit at the expense of spatial coverage. In this study we propose using source-space spatial independent component analysis (spatial ICA) for identifying generators of resting-state cortical rhythms as recorded with ECoG and for reconstructing their functional connectivity. Network structure is assessed by two kinds of connectivity measures: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. By simulating rhythmic cortical generators, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated, which troubles the interpretation of lag-based connectivity measures. We illustrate the methodology on somatosensory beta rhythms recorded from a macaque monkey using ECoG. The methodology decomposes the resting-state sensorimotor network into three cortical generators, distributed across primary somatosensory and primary and higher-order motor areas. The generators display significant and reproducible amplitude correlations and phase-locking values with non-zero lags. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.
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Affiliation(s)
- R Hindriks
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra (UPF), Spain; Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - C Micheli
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5304, CNRS, Bron, France; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525, EN Nijmegen, the Netherlands
| | - C A Bosman
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525, EN Nijmegen, the Netherlands; Cognitive and System Neuroscience Group, Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, 1098, XH, Amsterdam, the Netherlands
| | - R Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525, EN Nijmegen, the Netherlands
| | - C Lewis
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt, Germany
| | - D Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium; Functional Neuroimaging Laboratory, IRCCS San Camillo Hospital, via Alberoni 70, 30126, Venice Lido, Italy
| | - P Fries
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525, EN Nijmegen, the Netherlands; Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt, Germany
| | - G Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra (UPF), Spain; Instituci Catalana de la Recerca i Estudis Avanats (ICREA), Universitat Pompeu Fabra, Spain
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