Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis
Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg It...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-01-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fnhum.2017.00637/full |
| _version_ | 1831644021356756992 |
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| author | Jlenia Toppi Jlenia Toppi Laura Astolfi Laura Astolfi Monica Risetti Alessandra Anzolin Alessandra Anzolin Silvia E. Kober Silvia E. Kober Guilherme Wood Guilherme Wood Donatella Mattia |
| author_facet | Jlenia Toppi Jlenia Toppi Laura Astolfi Laura Astolfi Monica Risetti Alessandra Anzolin Alessandra Anzolin Silvia E. Kober Silvia E. Kober Guilherme Wood Guilherme Wood Donatella Mattia |
| author_sort | Jlenia Toppi |
| collection | DOAJ |
| description | Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke. |
| first_indexed | 2024-12-19T13:11:52Z |
| format | Article |
| id | doaj.art-e0fbea1d143a4e03b97105c5d420289e |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-19T13:11:52Z |
| publishDate | 2018-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-e0fbea1d143a4e03b97105c5d420289e2022-12-21T20:19:55ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612018-01-011110.3389/fnhum.2017.00637274843Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical AnalysisJlenia Toppi0Jlenia Toppi1Laura Astolfi2Laura Astolfi3Monica Risetti4Alessandra Anzolin5Alessandra Anzolin6Silvia E. Kober7Silvia E. Kober8Guilherme Wood9Guilherme Wood10Donatella Mattia11Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, ItalyNeuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, ItalyDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, ItalyNeuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, ItalyNeuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, ItalyDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, ItalyNeuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, ItalyDepartment of Psychology, University of Graz, Graz, AustriaBioTechMed-Graz, Graz, AustriaDepartment of Psychology, University of Graz, Graz, AustriaBioTechMed-Graz, Graz, AustriaNeuroelectrical Imaging and Brain-Computer Interface Laboratory, Fondazione Santa Lucia IRCCS, Rome, ItalySeveral non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke.http://journal.frontiersin.org/article/10.3389/fnhum.2017.00637/fullEEGbrain networksworking memorysternberg taskconnectivitygraph theory |
| spellingShingle | Jlenia Toppi Jlenia Toppi Laura Astolfi Laura Astolfi Monica Risetti Alessandra Anzolin Alessandra Anzolin Silvia E. Kober Silvia E. Kober Guilherme Wood Guilherme Wood Donatella Mattia Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis Frontiers in Human Neuroscience EEG brain networks working memory sternberg task connectivity graph theory |
| title | Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis |
| title_full | Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis |
| title_fullStr | Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis |
| title_full_unstemmed | Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis |
| title_short | Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis |
| title_sort | different topological properties of eeg derived networks describe working memory phases as revealed by graph theoretical analysis |
| topic | EEG brain networks working memory sternberg task connectivity graph theory |
| url | http://journal.frontiersin.org/article/10.3389/fnhum.2017.00637/full |
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