Graph Analysis of EEG Functional Connectivity Networks During a Letter-Speech Sound Binding Task in Adult Dyslexics Article Swipe
Gorka Fraga González
,
Dirk J. A. Smit
,
Melle J. W. van der Molen
,
Jurgen Tijms
,
Cornelis J. Stam
,
Eco J. C. de Geus
,
Maurits W. van der Molen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyg.2021.767839
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyg.2021.767839
We performed an EEG graph analysis on data from 31 typical readers (22.27 ± 2.53 y/o) and 24 dyslexics (22.99 ± 2.29 y/o), recorded while they were engaged in an audiovisual task and during resting-state. The task simulates reading acquisition as participants learned new letter-sound mappings via feedback. EEG data was filtered for the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands. We computed the Phase Lag Index (PLI) to provide an estimate of the functional connectivity between all pairs of electrodes per band. Then, networks were constructed using a Minimum Spanning Tree (MST), a unique sub-graph connecting all nodes (electrodes) without loops, aimed at minimizing bias in between groups and conditions comparisons. Both groups showed a comparable accuracy increase during task blocks, indicating that they correctly learned the new associations. The EEG results revealed lower task-specific theta connectivity, and lower theta degree correlation over both rest and task recordings, indicating less network integration in dyslexics compared to typical readers. This pattern suggests a role of theta oscillations in dyslexia and may reflect differences in task engagement between the groups, although robust correlations between MST metrics and performance indices were lacking.
Related Topics
Concepts
Electroencephalography
Resting state fMRI
Graph
Task (project management)
Speech recognition
Psychology
Spanning tree
Functional connectivity
Computer science
Pattern recognition (psychology)
Artificial intelligence
Audiology
Cognitive psychology
Mathematics
Neuroscience
Combinatorics
Theoretical computer science
Management
Economics
Medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpsyg.2021.767839
- OA Status
- gold
- Cited By
- 13
- References
- 87
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3214060876
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3214060876Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fpsyg.2021.767839Digital Object Identifier
- Title
-
Graph Analysis of EEG Functional Connectivity Networks During a Letter-Speech Sound Binding Task in Adult DyslexicsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-19Full publication date if available
- Authors
-
Gorka Fraga González, Dirk J. A. Smit, Melle J. W. van der Molen, Jurgen Tijms, Cornelis J. Stam, Eco J. C. de Geus, Maurits W. van der MolenList of authors in order
- Landing page
-
https://doi.org/10.3389/fpsyg.2021.767839Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fpsyg.2021.767839Direct OA link when available
- Concepts
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Electroencephalography, Resting state fMRI, Graph, Task (project management), Speech recognition, Psychology, Spanning tree, Functional connectivity, Computer science, Pattern recognition (psychology), Artificial intelligence, Audiology, Cognitive psychology, Mathematics, Neuroscience, Combinatorics, Theoretical computer science, Management, Economics, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 6, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
87Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2047984762, https://openalex.org/W2094022175, https://openalex.org/W2464812788, https://openalex.org/W2625682306, https://openalex.org/W6679850065, https://openalex.org/W3081236147, https://openalex.org/W2110065044, https://openalex.org/W2103366774, https://openalex.org/W2061950760, https://openalex.org/W2162546175, https://openalex.org/W1999653836, https://openalex.org/W2142875089, https://openalex.org/W2135842712, https://openalex.org/W2117995966, https://openalex.org/W2128495200, https://openalex.org/W2940976841, https://openalex.org/W2084446158, https://openalex.org/W1964831789, https://openalex.org/W2082813604, https://openalex.org/W2057858168, https://openalex.org/W2067393309, https://openalex.org/W2046630687, https://openalex.org/W2969893586, https://openalex.org/W2888986025, https://openalex.org/W2468478141, https://openalex.org/W2345475267, https://openalex.org/W1902335101, https://openalex.org/W2000370549, https://openalex.org/W2152737963, https://openalex.org/W2007634388, https://openalex.org/W2062526841, https://openalex.org/W2139068289, https://openalex.org/W1985175961, https://openalex.org/W2604397136, https://openalex.org/W2547823113, https://openalex.org/W4245970497, https://openalex.org/W2040087316, https://openalex.org/W1979750429, https://openalex.org/W1991058081, https://openalex.org/W2017150121, https://openalex.org/W2095836449, https://openalex.org/W1996538430, https://openalex.org/W2767137929, https://openalex.org/W2098330912, https://openalex.org/W2095259243, https://openalex.org/W2162911385, https://openalex.org/W1970248807, https://openalex.org/W2473535606, https://openalex.org/W2072608027, https://openalex.org/W1965680834, https://openalex.org/W2952105217, https://openalex.org/W2105909330, https://openalex.org/W3174987447, https://openalex.org/W1569332048, https://openalex.org/W3174821991, https://openalex.org/W2021895865, https://openalex.org/W2104624711, https://openalex.org/W2507937192, https://openalex.org/W2089755222, https://openalex.org/W2033193852, https://openalex.org/W1972428529, https://openalex.org/W2019885188, https://openalex.org/W2144218267, https://openalex.org/W2026240569, https://openalex.org/W2060985254, https://openalex.org/W2004299646, https://openalex.org/W2807853440, https://openalex.org/W2892304293, https://openalex.org/W2168219758, https://openalex.org/W2063497316, https://openalex.org/W2109582949, https://openalex.org/W2148155494, https://openalex.org/W2076347184, https://openalex.org/W1973705647, https://openalex.org/W2914282280, https://openalex.org/W1967675906, https://openalex.org/W2049543102, https://openalex.org/W2006283047, https://openalex.org/W1988813696, https://openalex.org/W2999838611, https://openalex.org/W2555723931, https://openalex.org/W2149984477, https://openalex.org/W2204002479, https://openalex.org/W2135572147, https://openalex.org/W2131519171, https://openalex.org/W2898858517, https://openalex.org/W3093300511 |
| referenced_works_count | 87 |
| abstract_inverted_index.a | 95, 100, 122, 169 |
| abstract_inverted_index.24 | 17 |
| abstract_inverted_index.31 | 9 |
| abstract_inverted_index.We | 0, 68 |
| abstract_inverted_index.an | 2, 29, 77 |
| abstract_inverted_index.as | 40 |
| abstract_inverted_index.at | 110 |
| abstract_inverted_index.in | 28, 113, 160, 174, 180 |
| abstract_inverted_index.of | 79, 86, 171 |
| abstract_inverted_index.on | 6 |
| abstract_inverted_index.to | 75, 163 |
| abstract_inverted_index.± | 13, 20 |
| abstract_inverted_index.EEG | 3, 48, 138 |
| abstract_inverted_index.Hz) | 66 |
| abstract_inverted_index.Lag | 72 |
| abstract_inverted_index.MST | 190 |
| abstract_inverted_index.The | 35, 137 |
| abstract_inverted_index.all | 84, 104 |
| abstract_inverted_index.and | 16, 32, 63, 116, 145, 153, 176, 192 |
| abstract_inverted_index.for | 52 |
| abstract_inverted_index.may | 177 |
| abstract_inverted_index.new | 43, 135 |
| abstract_inverted_index.per | 88 |
| abstract_inverted_index.the | 53, 70, 80, 134, 184 |
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| abstract_inverted_index.2.53 | 14 |
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| abstract_inverted_index.data | 7, 49 |
| abstract_inverted_index.from | 8 |
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| abstract_inverted_index.over | 150 |
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| abstract_inverted_index.that | 130 |
| abstract_inverted_index.they | 25, 131 |
| abstract_inverted_index.were | 26, 92, 195 |
| abstract_inverted_index.y/o) | 15 |
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| abstract_inverted_index.Index | 73 |
| abstract_inverted_index.Phase | 71 |
| abstract_inverted_index.Then, | 90 |
| abstract_inverted_index.aimed | 109 |
| abstract_inverted_index.alpha | 60 |
| abstract_inverted_index.band. | 89 |
| abstract_inverted_index.delta | 54 |
| abstract_inverted_index.graph | 4 |
| abstract_inverted_index.lower | 141, 146 |
| abstract_inverted_index.nodes | 105 |
| abstract_inverted_index.pairs | 85 |
| abstract_inverted_index.theta | 57, 143, 147, 172 |
| abstract_inverted_index.using | 94 |
| abstract_inverted_index.while | 24 |
| abstract_inverted_index.y/o), | 22 |
| abstract_inverted_index.(22.27 | 12 |
| abstract_inverted_index.(22.99 | 19 |
| abstract_inverted_index.(4–8 | 58 |
| abstract_inverted_index.(MST), | 99 |
| abstract_inverted_index.bands. | 67 |
| abstract_inverted_index.degree | 148 |
| abstract_inverted_index.during | 33, 126 |
| abstract_inverted_index.groups | 115, 120 |
| abstract_inverted_index.loops, | 108 |
| abstract_inverted_index.robust | 187 |
| abstract_inverted_index.showed | 121 |
| abstract_inverted_index.unique | 101 |
| abstract_inverted_index.(8–13 | 61 |
| abstract_inverted_index.Minimum | 96 |
| abstract_inverted_index.between | 83, 114, 183, 189 |
| abstract_inverted_index.blocks, | 128 |
| abstract_inverted_index.engaged | 27 |
| abstract_inverted_index.groups, | 185 |
| abstract_inverted_index.indices | 194 |
| abstract_inverted_index.learned | 42, 133 |
| abstract_inverted_index.metrics | 191 |
| abstract_inverted_index.network | 158 |
| abstract_inverted_index.pattern | 167 |
| abstract_inverted_index.provide | 76 |
| abstract_inverted_index.readers | 11 |
| abstract_inverted_index.reading | 38 |
| abstract_inverted_index.reflect | 178 |
| abstract_inverted_index.results | 139 |
| abstract_inverted_index.typical | 10, 164 |
| abstract_inverted_index.without | 107 |
| abstract_inverted_index.(0.5–4 | 55 |
| abstract_inverted_index.(13–30 | 65 |
| abstract_inverted_index.Spanning | 97 |
| abstract_inverted_index.accuracy | 124 |
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| abstract_inverted_index.analysis | 5 |
| abstract_inverted_index.compared | 162 |
| abstract_inverted_index.computed | 69 |
| abstract_inverted_index.dyslexia | 175 |
| abstract_inverted_index.estimate | 78 |
| abstract_inverted_index.filtered | 51 |
| abstract_inverted_index.increase | 125 |
| abstract_inverted_index.lacking. | 196 |
| abstract_inverted_index.mappings | 45 |
| abstract_inverted_index.networks | 91 |
| abstract_inverted_index.readers. | 165 |
| abstract_inverted_index.recorded | 23 |
| abstract_inverted_index.revealed | 140 |
| abstract_inverted_index.suggests | 168 |
| abstract_inverted_index.correctly | 132 |
| abstract_inverted_index.dyslexics | 18, 161 |
| abstract_inverted_index.feedback. | 47 |
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| abstract_inverted_index.functional | 81 |
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| abstract_inverted_index.minimizing | 111 |
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| abstract_inverted_index.audiovisual | 30 |
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| abstract_inverted_index.performance | 193 |
| abstract_inverted_index.recordings, | 155 |
| abstract_inverted_index.(electrodes) | 106 |
| abstract_inverted_index.comparisons. | 118 |
| abstract_inverted_index.connectivity | 82 |
| abstract_inverted_index.correlations | 188 |
| abstract_inverted_index.letter-sound | 44 |
| abstract_inverted_index.oscillations | 173 |
| abstract_inverted_index.participants | 41 |
| abstract_inverted_index.associations. | 136 |
| abstract_inverted_index.connectivity, | 144 |
| abstract_inverted_index.task-specific | 142 |
| abstract_inverted_index.resting-state. | 34 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5022882073 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I202697423, https://openalex.org/I4210100468, https://openalex.org/I887064364 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8799999952316284 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.79843465 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |