Adaptive strategy for the statistical analysis of connectomes.
We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2011-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3150413?pdf=render |
| _version_ | 1798044504603754496 |
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| author | Djalel Eddine Meskaldji Marie-Christine Ottet Leila Cammoun Patric Hagmann Reto Meuli Stephan Eliez Jean Philippe Thiran Stephan Morgenthaler |
| author_facet | Djalel Eddine Meskaldji Marie-Christine Ottet Leila Cammoun Patric Hagmann Reto Meuli Stephan Eliez Jean Philippe Thiran Stephan Morgenthaler |
| author_sort | Djalel Eddine Meskaldji |
| collection | DOAJ |
| description | We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores. |
| first_indexed | 2024-04-11T23:05:16Z |
| format | Article |
| id | doaj.art-49f832abd5204faeadc38977251d8f96 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T23:05:16Z |
| publishDate | 2011-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-49f832abd5204faeadc38977251d8f962022-12-22T03:58:02ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0168e2300910.1371/journal.pone.0023009Adaptive strategy for the statistical analysis of connectomes.Djalel Eddine MeskaldjiMarie-Christine OttetLeila CammounPatric HagmannReto MeuliStephan EliezJean Philippe ThiranStephan MorgenthalerWe study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores.http://europepmc.org/articles/PMC3150413?pdf=render |
| spellingShingle | Djalel Eddine Meskaldji Marie-Christine Ottet Leila Cammoun Patric Hagmann Reto Meuli Stephan Eliez Jean Philippe Thiran Stephan Morgenthaler Adaptive strategy for the statistical analysis of connectomes. PLoS ONE |
| title | Adaptive strategy for the statistical analysis of connectomes. |
| title_full | Adaptive strategy for the statistical analysis of connectomes. |
| title_fullStr | Adaptive strategy for the statistical analysis of connectomes. |
| title_full_unstemmed | Adaptive strategy for the statistical analysis of connectomes. |
| title_short | Adaptive strategy for the statistical analysis of connectomes. |
| title_sort | adaptive strategy for the statistical analysis of connectomes |
| url | http://europepmc.org/articles/PMC3150413?pdf=render |
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