Temporal exponential random graph models of longitudinal brain networks after stroke Article Swipe
Catalina Obando
,
Charlotte Rosso
,
Joshua S. Siegel
,
Maurizio Corbetta
,
Fabrizio De Vico Fallani
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1098/rsif.2021.0850
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1098/rsif.2021.0850
Plasticity after stroke is a complex phenomenon. Functional reorganization occurs not only in the perilesional tissue but throughout the brain. However, the local connection mechanisms generating such global network changes remain largely unknown. To address this question, time must be considered as a formal variable of the problem rather than a simple repeated observation. Here, we hypothesized that the presence of temporal connection motifs, such as the formation of temporal triangles ( T ) and edges ( E ) over time, would explain large-scale brain reorganization after stroke. To test our hypothesis, we adopted a statistical framework based on temporal exponential random graph models (tERGMs), where the aforementioned temporal motifs were implemented as parameters and adapted to capture global network changes after stroke. We first validated the performance on synthetic time-varying networks as compared to standard static approaches. Then, using real functional brain networks, we showed that estimates of tERGM parameters were sufficient to reproduce brain network changes from 2 weeks to 1 year after stroke. These temporal connection signatures, reflecting within-hemisphere segregation ( T ) and between hemisphere integration ( E ), were associated with patients' future behaviour. In particular, interhemispheric temporal edges significantly correlated with the chronic language and visual outcome in subcortical and cortical stroke, respectively. Our results indicate the importance of time-varying connection properties when modelling dynamic complex systems and provide fresh insights into modelling of brain network mechanisms after stroke.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1098/rsif.2021.0850
- https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0850
- OA Status
- bronze
- Cited By
- 7
- References
- 80
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214878651
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4214878651Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1098/rsif.2021.0850Digital Object Identifier
- Title
-
Temporal exponential random graph models of longitudinal brain networks after strokeWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-01Full publication date if available
- Authors
-
Catalina Obando, Charlotte Rosso, Joshua S. Siegel, Maurizio Corbetta, Fabrizio De Vico FallaniList of authors in order
- Landing page
-
https://doi.org/10.1098/rsif.2021.0850Publisher landing page
- PDF URL
-
https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0850Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2021.0850Direct OA link when available
- Concepts
-
Stroke (engine), Neuroscience, Stroke recovery, Neuroplasticity, Computer science, Exponential random graph models, Network model, Connectome, Graph, Artificial intelligence, Psychology, Random graph, Functional connectivity, Theoretical computer science, Physics, Thermodynamics, RehabilitationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 2, 2022: 2Per-year citation counts (last 5 years)
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80Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.when | 218 |
| abstract_inverted_index.with | 185, 196 |
| abstract_inverted_index.year | 163 |
| abstract_inverted_index.Here, | 54 |
| abstract_inverted_index.Then, | 138 |
| abstract_inverted_index.These | 166 |
| abstract_inverted_index.after | 1, 86, 121, 164, 233 |
| abstract_inverted_index.based | 97 |
| abstract_inverted_index.brain | 84, 142, 155, 230 |
| abstract_inverted_index.edges | 75, 193 |
| abstract_inverted_index.first | 124 |
| abstract_inverted_index.fresh | 225 |
| abstract_inverted_index.graph | 102 |
| abstract_inverted_index.local | 22 |
| abstract_inverted_index.tERGM | 149 |
| abstract_inverted_index.time, | 80 |
| abstract_inverted_index.using | 139 |
| abstract_inverted_index.weeks | 160 |
| abstract_inverted_index.where | 105 |
| abstract_inverted_index.would | 81 |
| abstract_inverted_index.brain. | 19 |
| abstract_inverted_index.formal | 43 |
| abstract_inverted_index.future | 187 |
| abstract_inverted_index.global | 27, 118 |
| abstract_inverted_index.models | 103 |
| abstract_inverted_index.motifs | 109 |
| abstract_inverted_index.occurs | 9 |
| abstract_inverted_index.random | 101 |
| abstract_inverted_index.rather | 48 |
| abstract_inverted_index.remain | 30 |
| abstract_inverted_index.showed | 145 |
| abstract_inverted_index.simple | 51 |
| abstract_inverted_index.static | 136 |
| abstract_inverted_index.stroke | 2 |
| abstract_inverted_index.tissue | 15 |
| abstract_inverted_index.visual | 201 |
| abstract_inverted_index.adapted | 115 |
| abstract_inverted_index.address | 34 |
| abstract_inverted_index.adopted | 93 |
| abstract_inverted_index.between | 177 |
| abstract_inverted_index.capture | 117 |
| abstract_inverted_index.changes | 29, 120, 157 |
| abstract_inverted_index.chronic | 198 |
| abstract_inverted_index.complex | 5, 221 |
| abstract_inverted_index.dynamic | 220 |
| abstract_inverted_index.explain | 82 |
| abstract_inverted_index.largely | 31 |
| abstract_inverted_index.motifs, | 63 |
| abstract_inverted_index.network | 28, 119, 156, 231 |
| abstract_inverted_index.outcome | 202 |
| abstract_inverted_index.problem | 47 |
| abstract_inverted_index.provide | 224 |
| abstract_inverted_index.results | 210 |
| abstract_inverted_index.stroke, | 207 |
| abstract_inverted_index.stroke. | 87, 122, 165, 234 |
| abstract_inverted_index.systems | 222 |
| abstract_inverted_index.However, | 20 |
| abstract_inverted_index.compared | 133 |
| abstract_inverted_index.cortical | 206 |
| abstract_inverted_index.indicate | 211 |
| abstract_inverted_index.insights | 226 |
| abstract_inverted_index.language | 199 |
| abstract_inverted_index.networks | 131 |
| abstract_inverted_index.presence | 59 |
| abstract_inverted_index.repeated | 52 |
| abstract_inverted_index.standard | 135 |
| abstract_inverted_index.temporal | 61, 69, 99, 108, 167, 192 |
| abstract_inverted_index.unknown. | 32 |
| abstract_inverted_index.variable | 44 |
| abstract_inverted_index.(tERGMs), | 104 |
| abstract_inverted_index.estimates | 147 |
| abstract_inverted_index.formation | 67 |
| abstract_inverted_index.framework | 96 |
| abstract_inverted_index.modelling | 219, 228 |
| abstract_inverted_index.networks, | 143 |
| abstract_inverted_index.patients' | 186 |
| abstract_inverted_index.question, | 36 |
| abstract_inverted_index.reproduce | 154 |
| abstract_inverted_index.synthetic | 129 |
| abstract_inverted_index.triangles | 70 |
| abstract_inverted_index.validated | 125 |
| abstract_inverted_index.Functional | 7 |
| abstract_inverted_index.Plasticity | 0 |
| abstract_inverted_index.associated | 184 |
| abstract_inverted_index.behaviour. | 188 |
| abstract_inverted_index.connection | 23, 62, 168, 216 |
| abstract_inverted_index.considered | 40 |
| abstract_inverted_index.correlated | 195 |
| abstract_inverted_index.functional | 141 |
| abstract_inverted_index.generating | 25 |
| abstract_inverted_index.hemisphere | 178 |
| abstract_inverted_index.importance | 213 |
| abstract_inverted_index.mechanisms | 24, 232 |
| abstract_inverted_index.parameters | 113, 150 |
| abstract_inverted_index.properties | 217 |
| abstract_inverted_index.reflecting | 170 |
| abstract_inverted_index.sufficient | 152 |
| abstract_inverted_index.throughout | 17 |
| abstract_inverted_index.approaches. | 137 |
| abstract_inverted_index.exponential | 100 |
| abstract_inverted_index.hypothesis, | 91 |
| abstract_inverted_index.implemented | 111 |
| abstract_inverted_index.integration | 179 |
| abstract_inverted_index.large-scale | 83 |
| abstract_inverted_index.particular, | 190 |
| abstract_inverted_index.performance | 127 |
| abstract_inverted_index.phenomenon. | 6 |
| abstract_inverted_index.segregation | 172 |
| abstract_inverted_index.signatures, | 169 |
| abstract_inverted_index.statistical | 95 |
| abstract_inverted_index.subcortical | 204 |
| abstract_inverted_index.hypothesized | 56 |
| abstract_inverted_index.observation. | 53 |
| abstract_inverted_index.perilesional | 14 |
| abstract_inverted_index.time-varying | 130, 215 |
| abstract_inverted_index.respectively. | 208 |
| abstract_inverted_index.significantly | 194 |
| abstract_inverted_index.aforementioned | 107 |
| abstract_inverted_index.reorganization | 8, 85 |
| abstract_inverted_index.interhemispheric | 191 |
| abstract_inverted_index.within-hemisphere | 171 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.7020247 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |