Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods
Graph theory is increasingly being used to study brain connectivity across the spectrum of Alzheimer's disease (AD), but prior findings have been inconsistent, likely reflecting methodological differences. We systematically investigated how methods of graph creation (i.e., type of correlation m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2015-01-01
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| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221315821500008X |
| _version_ | 1819175893418901504 |
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| author | David J. Phillips Alec McGlaughlin David Ruth Leah R. Jager Anja Soldan |
| author_facet | David J. Phillips Alec McGlaughlin David Ruth Leah R. Jager Anja Soldan |
| author_sort | David J. Phillips |
| collection | DOAJ |
| description | Graph theory is increasingly being used to study brain connectivity across the spectrum of Alzheimer's disease (AD), but prior findings have been inconsistent, likely reflecting methodological differences. We systematically investigated how methods of graph creation (i.e., type of correlation matrix and edge weighting) affect structural network properties and group differences. We estimated the structural connectivity of brain networks based on correlation maps of cortical thickness obtained from MRI. Four groups were compared: 126 cognitively normal older adults, 103 individuals with Mild Cognitive Impairment (MCI) who retained MCI status for at least 3 years (stable MCI), 108 individuals with MCI who progressed to AD-dementia within 3 years (progressive MCI), and 105 individuals with AD-dementia. Small-world measures of connectivity (characteristic path length and clustering coefficient) differed across groups, consistent with prior studies. Groups were best discriminated by the Randić index, which measures the degree to which highly connected nodes connect to other highly connected nodes. The Randić index differentiated the stable and progressive MCI groups, suggesting that it might be useful for tracking and predicting the progression of AD. Notably, however, the magnitude and direction of group differences in all three measures were dependent on the method of graph creation, indicating that it is crucial to take into account how graphs are constructed when interpreting differences across diagnostic groups and studies. The algebraic connectivity measures showed few group differences, independent of the method of graph construction, suggesting that global connectivity as it relates to node degree is not altered in early AD. |
| first_indexed | 2024-12-22T21:02:06Z |
| format | Article |
| id | doaj.art-b3c98e5d986d404f91bd5af122f90927 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-22T21:02:06Z |
| publishDate | 2015-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-b3c98e5d986d404f91bd5af122f909272022-12-21T18:12:49ZengElsevierNeuroImage: Clinical2213-15822015-01-017C37739010.1016/j.nicl.2015.01.007Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methodsDavid J. Phillips0Alec McGlaughlin1David Ruth2Leah R. Jager3Anja Soldan4Department of Mathematics, United States Naval Academy, Annapolis, MD 21401, USADepartment of Mathematics, United States Naval Academy, Annapolis, MD 21401, USADepartment of Mathematics, United States Naval Academy, Annapolis, MD 21401, USADepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USADepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USAGraph theory is increasingly being used to study brain connectivity across the spectrum of Alzheimer's disease (AD), but prior findings have been inconsistent, likely reflecting methodological differences. We systematically investigated how methods of graph creation (i.e., type of correlation matrix and edge weighting) affect structural network properties and group differences. We estimated the structural connectivity of brain networks based on correlation maps of cortical thickness obtained from MRI. Four groups were compared: 126 cognitively normal older adults, 103 individuals with Mild Cognitive Impairment (MCI) who retained MCI status for at least 3 years (stable MCI), 108 individuals with MCI who progressed to AD-dementia within 3 years (progressive MCI), and 105 individuals with AD-dementia. Small-world measures of connectivity (characteristic path length and clustering coefficient) differed across groups, consistent with prior studies. Groups were best discriminated by the Randić index, which measures the degree to which highly connected nodes connect to other highly connected nodes. The Randić index differentiated the stable and progressive MCI groups, suggesting that it might be useful for tracking and predicting the progression of AD. Notably, however, the magnitude and direction of group differences in all three measures were dependent on the method of graph creation, indicating that it is crucial to take into account how graphs are constructed when interpreting differences across diagnostic groups and studies. The algebraic connectivity measures showed few group differences, independent of the method of graph construction, suggesting that global connectivity as it relates to node degree is not altered in early AD.http://www.sciencedirect.com/science/article/pii/S221315821500008XGraph theoryStructural MRIAlzheimer's diseaseMild cognitive impairmentConnectomicsCortical thickness networks |
| spellingShingle | David J. Phillips Alec McGlaughlin David Ruth Leah R. Jager Anja Soldan Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods NeuroImage: Clinical Graph theory Structural MRI Alzheimer's disease Mild cognitive impairment Connectomics Cortical thickness networks |
| title | Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods |
| title_full | Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods |
| title_fullStr | Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods |
| title_full_unstemmed | Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods |
| title_short | Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods |
| title_sort | graph theoretic analysis of structural connectivity across the spectrum of alzheimer s disease the importance of graph creation methods |
| topic | Graph theory Structural MRI Alzheimer's disease Mild cognitive impairment Connectomics Cortical thickness networks |
| url | http://www.sciencedirect.com/science/article/pii/S221315821500008X |
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