The (in)stability of functional brain network measures across thresholds
The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous me...
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| Veröffentlicht in: | NeuroImage (Orlando, Fla.) Fla.), 2015-09, Vol.118, p.651-661 |
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| creator | Garrison, Kathleen A. Scheinost, Dustin Finn, Emily S. Shen, Xilin Constable, R. Todd |
| description | The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.
•Network measures were found to be unstable across absolute thresholds.•For instance, the direction of significant group differences can reverse across thresholds.•Network measures were found to be more stable across proportional thresholds.•Caution should be used when applying thresholds to functional connectivity data. |
| doi_str_mv | 10.1016/j.neuroimage.2015.05.046 |
| format | Article |
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•Network measures were found to be unstable across absolute thresholds.•For instance, the direction of significant group differences can reverse across thresholds.•Network measures were found to be more stable across proportional thresholds.•Caution should be used when applying thresholds to functional connectivity data.</description><subject>Adult</subject><subject>Age</subject><subject>Aged</subject><subject>Alzheimer's disease</subject><subject>Brain - physiology</subject><subject>Brain Mapping - methods</subject><subject>Efficiency</subject><subject>Female</subject><subject>Functional connectivity</subject><subject>Graph theory</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Models, Neurological</subject><subject>Nerve Net - physiology</subject><subject>Network analysis</subject><subject>Neuroimaging</subject><subject>Resting state</subject><subject>Threshold</subject><subject>Young Adult</subject><issn>1053-8119</issn><issn>1095-9572</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkUtv1DAUhS1ERUvhL6BIbNpFBl_Hzw0SVECRKrEpa8txbjoeMnaxk6L--3qYUh4bKl3Jtvzd4-tzCGmAroCCfLNZRVxyClt3hStGQaxoLS6fkCOgRrRGKPZ0txddqwHMIXleyoZSaoDrZ-SQScqAgT4i55drbE5CPC2z68MU5tsmjc24RD-HFN3U9NmF2EScf6T8rdmiK0vG0jifUynNvK6HdZqG8oIcjG4q-PJ-PSZfP364PDtvL758-nz27qL1UsLcdqCRDhQ9H7XUYDwK6XSvjKNKC4q9RG9kHY4ZxoTnUspOqF4owb1SnHXH5O1e93rptzh4jHN2k73O1Yx8a5ML9u-bGNb2Kt1YLgTXna4CJ_cCOX1fsMx2G4rHaXIR01IsKGBKdYLDI1CqtTFM0Yq-_gfdpCVXA_cUCACQldJ76qd7GceHuYHaXbJ2Y38na3fJWlqL71pf_fnvh8ZfUVbg_R7A6v5NwGyLDxg9DiGjn-2Qwv9fuQMnW7ku</recordid><startdate>20150901</startdate><enddate>20150901</enddate><creator>Garrison, Kathleen A.</creator><creator>Scheinost, Dustin</creator><creator>Finn, Emily S.</creator><creator>Shen, Xilin</creator><creator>Constable, R. 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Todd</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The (in)stability of functional brain network measures across thresholds</atitle><jtitle>NeuroImage (Orlando, Fla.)</jtitle><addtitle>Neuroimage</addtitle><date>2015-09-01</date><risdate>2015</risdate><volume>118</volume><spage>651</spage><epage>661</epage><pages>651-661</pages><issn>1053-8119</issn><eissn>1095-9572</eissn><abstract>The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.
•Network measures were found to be unstable across absolute thresholds.•For instance, the direction of significant group differences can reverse across thresholds.•Network measures were found to be more stable across proportional thresholds.•Caution should be used when applying thresholds to functional connectivity data.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>26021218</pmid><doi>10.1016/j.neuroimage.2015.05.046</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Age Aged Alzheimer's disease Brain - physiology Brain Mapping - methods Efficiency Female Functional connectivity Graph theory Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Models, Neurological Nerve Net - physiology Network analysis Neuroimaging Resting state Threshold Young Adult |
| title | The (in)stability of functional brain network measures across thresholds |
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