Network topology changes in chronic mild traumatic brain injury (mTBI)
Background: In mild traumatic brain injury (mTBI), diffuse axonal injury results in disruption of functional networks in the brain and is thought to be a major contributor to cognitive dysfunction even years after trauma. Objective: Few studies have assessed longitudinal changes in network topology...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158221001352 |
| _version_ | 1819109351740145664 |
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| author | Elias Boroda Michael Armstrong Casey S. Gilmore Carrie Gentz Alicia Fenske Mark Fiecas Tim Hendrickson Donovan Roediger Bryon Mueller Randy Kardon Kelvin Lim |
| author_facet | Elias Boroda Michael Armstrong Casey S. Gilmore Carrie Gentz Alicia Fenske Mark Fiecas Tim Hendrickson Donovan Roediger Bryon Mueller Randy Kardon Kelvin Lim |
| author_sort | Elias Boroda |
| collection | DOAJ |
| description | Background: In mild traumatic brain injury (mTBI), diffuse axonal injury results in disruption of functional networks in the brain and is thought to be a major contributor to cognitive dysfunction even years after trauma. Objective: Few studies have assessed longitudinal changes in network topology in chronic mTBI. We utilized a graph theoretical approach to investigate alterations in global network topology based on resting-state functional connectivity in veterans with chronic mTBI. Methods: 50 veterans with chronic mTBI (mean of 20.7 yrs. from trauma) and 40 age-matched controls underwent two functional magnetic resonance imaging scans 18 months apart. Graph theory analysis was used to quantify network topology measures (density, clustering coefficient, global efficiency, and modularity). Hierarchical linear mixed models were used to examine longitudinal change in network topology. Results: With all network measures, we found a significant group × time interaction. At baseline, brain networks of individuals with mTBI were less clustered (p = 0.03) and more modular (p = 0.02) than those of HC. Over time, the mTBI networks became more densely connected (p = 0.002), with increased clustering (p = 0.001) and reduced modularity (p < 0.001). Network topology did not change across time in HC. Conclusion: These findings demonstrate that brain networks of individuals with mTBI remain plastic decades after injury and undergo significant changes in network topology even at the later phase of the disease. |
| first_indexed | 2024-12-22T03:24:27Z |
| format | Article |
| id | doaj.art-5a84b6a3a12d48a4a38d36b933d74a5e |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-22T03:24:27Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-5a84b6a3a12d48a4a38d36b933d74a5e2022-12-21T18:40:38ZengElsevierNeuroImage: Clinical2213-15822021-01-0131102691Network topology changes in chronic mild traumatic brain injury (mTBI)Elias Boroda0Michael Armstrong1Casey S. Gilmore2Carrie Gentz3Alicia Fenske4Mark Fiecas5Tim Hendrickson6Donovan Roediger7Bryon Mueller8Randy Kardon9Kelvin Lim10Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA; Corresponding author at: Department of Psychiatry and Behavioral Sciences, University of Minnesota, Suite 516, 717 Delaware Street SE, Minneapolis, MN 55414, USA.Minneapolis VA Health Care System, Minneapolis, MN, USAMinneapolis VA Health Care System, Minneapolis, MN, USAMinneapolis VA Health Care System, Minneapolis, MN, USAMinneapolis VA Health Care System, Minneapolis, MN, USACenter for the Prevention and Treatment of Visual Loss, Iowa City VA Healthcare System, Iowa City, IA, USAUniversity of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USADepartment of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USADepartment of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USAUniversity of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USADepartment of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA; Minneapolis VA Health Care System, Minneapolis, MN, USA; School of Public Health, Department of Biostatistics, University of Minnesota, Minneapolis, MN, USABackground: In mild traumatic brain injury (mTBI), diffuse axonal injury results in disruption of functional networks in the brain and is thought to be a major contributor to cognitive dysfunction even years after trauma. Objective: Few studies have assessed longitudinal changes in network topology in chronic mTBI. We utilized a graph theoretical approach to investigate alterations in global network topology based on resting-state functional connectivity in veterans with chronic mTBI. Methods: 50 veterans with chronic mTBI (mean of 20.7 yrs. from trauma) and 40 age-matched controls underwent two functional magnetic resonance imaging scans 18 months apart. Graph theory analysis was used to quantify network topology measures (density, clustering coefficient, global efficiency, and modularity). Hierarchical linear mixed models were used to examine longitudinal change in network topology. Results: With all network measures, we found a significant group × time interaction. At baseline, brain networks of individuals with mTBI were less clustered (p = 0.03) and more modular (p = 0.02) than those of HC. Over time, the mTBI networks became more densely connected (p = 0.002), with increased clustering (p = 0.001) and reduced modularity (p < 0.001). Network topology did not change across time in HC. Conclusion: These findings demonstrate that brain networks of individuals with mTBI remain plastic decades after injury and undergo significant changes in network topology even at the later phase of the disease.http://www.sciencedirect.com/science/article/pii/S2213158221001352TBIfMRIGraph theory |
| spellingShingle | Elias Boroda Michael Armstrong Casey S. Gilmore Carrie Gentz Alicia Fenske Mark Fiecas Tim Hendrickson Donovan Roediger Bryon Mueller Randy Kardon Kelvin Lim Network topology changes in chronic mild traumatic brain injury (mTBI) NeuroImage: Clinical TBI fMRI Graph theory |
| title | Network topology changes in chronic mild traumatic brain injury (mTBI) |
| title_full | Network topology changes in chronic mild traumatic brain injury (mTBI) |
| title_fullStr | Network topology changes in chronic mild traumatic brain injury (mTBI) |
| title_full_unstemmed | Network topology changes in chronic mild traumatic brain injury (mTBI) |
| title_short | Network topology changes in chronic mild traumatic brain injury (mTBI) |
| title_sort | network topology changes in chronic mild traumatic brain injury mtbi |
| topic | TBI fMRI Graph theory |
| url | http://www.sciencedirect.com/science/article/pii/S2213158221001352 |
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