A parsimonious statistical method to detect groupwise differentially expressed functional connectivity networks
dc.contributor.author | Chen, Shuo | |
dc.contributor.author | Kang, Jian | |
dc.contributor.author | Xing, Yishi | |
dc.contributor.author | Wang, Guoqing | |
dc.date.accessioned | 2017-04-14T15:09:18Z | |
dc.date.available | 2017-04-14T15:09:18Z | |
dc.date.issued | 2015-12 | |
dc.identifier.citation | Chen, Shuo; Kang, Jian; Xing, Yishi; Wang, Guoqing (2015). "A parsimonious statistical method to detect groupwise differentially expressed functional connectivity networks." Human Brain Mapping 36(12): 5196-5206. | |
dc.identifier.issn | 1065-9471 | |
dc.identifier.issn | 1097-0193 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/136357 | |
dc.description.abstract | Group‐level functional connectivity analyses often aim to detect the altered connectivity patterns between subgroups with different clinical or psychological experimental conditions, for example, comparing cases and healthy controls. We present a new statistical method to detect differentially expressed connectivity networks with significantly improved power and lower false‐positive rates. The goal of our method was to capture most differentially expressed connections within networks of constrained numbers of brain regions (by the rule of parsimony). By virtue of parsimony, the false‐positive individual connectivity edges within a network are effectively reduced, whereas the informative (differentially expressed) edges are allowed to borrow strength from each other to increase the overall power of the network. We develop a test statistic for each network in light of combinatorics graph theory, and provide p‐values for the networks (in the weak sense) by using permutation test with multiple‐testing adjustment. We validate and compare this new approach with existing methods, including false discovery rate and network‐based statistic, via simulation studies and a resting‐state functional magnetic resonance imaging case–control study. The results indicate that our method can identify differentially expressed connectivity networks, whereas existing methods are limited. Hum Brain Mapp 36:5196–5206, 2015. © 2015 Wiley Periodicals, Inc. | |
dc.publisher | Springer | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | connectivity | |
dc.subject.other | statistical power | |
dc.subject.other | family‐wise error | |
dc.subject.other | parsimony | |
dc.subject.other | network | |
dc.subject.other | fMRI | |
dc.title | A parsimonious statistical method to detect groupwise differentially expressed functional connectivity networks | |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Kinesiology and Sports | |
dc.subject.hlbsecondlevel | Neurosciences | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/136357/1/hbm23007_am.pdf | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/136357/2/hbm23007.pdf | |
dc.identifier.doi | 10.1002/hbm.23007 | |
dc.identifier.source | Human Brain Mapping | |
dc.identifier.citedreference | Sporns O ( 2012 ): From simple graphs to the connectome: Networks in neuroimaging. NeuroImage 62: 881 – 886. | |
dc.identifier.citedreference | Hayasaka S, Nichols TE ( 2004 ): Combining voxel intensity and cluster extent with permutation test framework. NeuroImage 23: 54. | |
dc.identifier.citedreference | Honey CJ, Sporns O ( 2008 ): Dynamical consequences of lesions in cortical networks. Hum Brain Mapp 29: 802 – 809. | |
dc.identifier.citedreference | Kim J, Wozniak JR, Mueller BA, Shen X, Pan W ( 2014 ): Comparison of statistical tests for group differences in brain functional networks. NeuroImage 101: 681 – 694. | |
dc.identifier.citedreference | Marrelec G, Bellec P, Krainik A, Duffau H, Pélégrini‐Issac M, Lehéricy S, Doyon J ( 2008 ): Regions, systems, and the brain: Hierarchical measures of functional integration in fMRI. Med Image Anal 12: 484 – 496. | |
dc.identifier.citedreference | Park HJ, Friston K ( 2013 ): Structural and functional brain networks: From connections to cognition. Science 342: 1238411. | |
dc.identifier.citedreference | Rubinov M, Sporns O ( 2010 ): Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52: 1059 – 1069. | |
dc.identifier.citedreference | Rubinov M, Sporns O ( 2011 ): Weight‐conserving characterization of complex functional brain networks. NeuroImage 56: 2068 – 2079. | |
dc.identifier.citedreference | Shehzad Z, Kelly C, Reiss PT, Cameron Craddock R, Emerson JW, McMahon K, Copland DA, Castellanos FX, Milham MP ( 2014 ): A multivariate distance‐based analytic framework for connectome‐wide association studies. NeuroImage 93: 74 – 94. | |
dc.identifier.citedreference | Simpson SL, Laurienti PJ ( 2015 ): A two‐part mixed‐effects modeling framework for analyzing whole‐brain network data. NeuroImage 113: 310 – 319. | |
dc.identifier.citedreference | Simpson SL, Hayasaka S, Laurienti PJ ( 2011 ): Exponential random graph modeling for complex brain networks. PLoS One 6: e20039. | |
dc.identifier.citedreference | Simpson SL, Moussa MN, Laurienti PJ ( 2012 ): An exponential random graph modeling approach to creating group‐based representative whole‐brain connectivity networks. NeuroImage 60: 1117 – 1126. | |
dc.identifier.citedreference | Simpson SL, Bowman FD, Laurienti PJ ( 2013a ): Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain. Stat Survey 7: 1. | |
dc.identifier.citedreference | Simpson SL, Lyday RG, Hayasaka S, Marsh AP, Laurienti PJ ( 2013b ): A permutation testing framework to compare groups of brain networks. Front Comput Neurosci 7: 171. | |
dc.identifier.citedreference | Sporns O ( 2011 ): The human connectome: A complex network. Ann N Y Acad Sci 1224: 109 – 125. | |
dc.identifier.citedreference | Tyszka JM, Kennedy DP, Paul LK, Adolphs R ( 2014 ): Largely typical patterns of resting‐state functional connectivity in high‐functioning adults with autism. Cerebral Cortex 24: 1894 – 1905. | |
dc.identifier.citedreference | Tzourio‐Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M ( 2002 ): Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single‐subject brain. NeuroImage 15: 273 – 289. | |
dc.identifier.citedreference | van den Heuvel MP, Stam CJ, Boersma M, Pol HH ( 2008 ): Small‐world and scale‐free organization of voxel‐based resting‐state functional connectivity in the human brain. NeuroImage 43: 528 – 539. | |
dc.identifier.citedreference | van den Heuvel MP, Stam CJ, Kahn RS, Pol HEH ( 2009 ): Efficiency of functional brain networks and intellectual performance. J Neurosci 29: 7619 – 7624. | |
dc.identifier.citedreference | van den Heuvel MP, Mandl RC, Stam CJ, Kahn RS, Pol HEH ( 2010 ): Aberrant frontal and temporal complex network structure in schizophrenia: A graph theoretical analysis. J Neurosci 30: 15915 – 15926. | |
dc.identifier.citedreference | Varoquaux G, Craddock RC ( 2013 ): Learning and comparing functional connectomes across subjects. NeuroImage 80: 405 – 415. | |
dc.identifier.citedreference | Von Luxburg U ( 2007 ): A tutorial on spectral clustering. Stat Comput 17: 395 – 416. | |
dc.identifier.citedreference | Xia M, Wang J, He Y ( 2013 ): BrainNet Viewer: A network visualization tool for human brain connectomics. PLoS One 8: e68910. | |
dc.identifier.citedreference | Zalesky A, Fornito A, Bullmore ET ( 2010 ): Network‐based statistic: Identifying differences in brain networks. NeuroImage 53: 1197 – 1207. | |
dc.identifier.citedreference | Zalesky A, Fornito A, Bullmore E ( 2012a ): On the use of correlation as a measure of network connectivity. NeuroImage 60: 2096 – 2106. | |
dc.identifier.citedreference | Zalesky A, Cocchi L, Fornito A, Murray MM, Bullmore E ( 2012b ): Connectivity differences in brain networks. NeuroImage 60: 1055 – 1062. | |
dc.identifier.citedreference | Achard S, Salvador R, Whitcher B, Suckling J, Bullmore ED ( 2006 ): A resilient, low‐frequency, small‐world human brain functional network with highly connected association cortical hubs. J Neurosci 26: 63 – 72. | |
dc.identifier.citedreference | Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST ( 2011 ): Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci U S A 108: 7641 – 7646. | |
dc.identifier.citedreference | Bassett DS, Nelson BG, Mueller BA, Camchong J, Lim KO ( 2012 ): Altered resting state complexity in schizophrenia. NeuroImage 59: 2196 – 2207. | |
dc.identifier.citedreference | Braun U, Plichta MM, Esslinger C, Sauer C, Haddad L, Grimm O, Mier D, Mohnke S, Heinz A, Erk S, Walter H, Seiferth N, Kirsch P, Meyer‐Lindenberg A ( 2012 ): Test–retest reliability of resting‐state connectivity network characteristics using fMRI and graph theoretical measures. NeuroImage 59: 1404 – 1412. | |
dc.identifier.citedreference | Bullmore E, Sporns O ( 2009 ): Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10: 186 – 198. | |
dc.identifier.citedreference | Cherkassky VL, Kana RK, Keller TA, Just MA ( 2006 ): Functional connectivity in a baseline resting‐state network in autism. Neuroreport 17: 1687 – 1690. | |
dc.identifier.citedreference | Craddock RC, Holtzheimer PE, Hu XP, Mayberg HS ( 2009 ): Disease state prediction from resting state functional connectivity. Magn Reson Med 62: 1619 – 1628. | |
dc.identifier.citedreference | Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M, Deen B, Delmonte S, Dinstein I, Ertl‐Wagner B, Fair DA, Gallagher L, Kennedy DP, Keown CL, Keysers C, Lainhart JE, Lord C, Luna B, Menon V, Minshew NJ, Monk CS, Mueller S, Müller RA, Nebel MB, Nigg JT, O’Hearn K, Pelphrey KA, Peltier SJ, Rudie JD, Sunaert S, Thioux M, Tyszka JM, Uddin LQ, Verhoeven JS, Wenderoth N, Wiggins JL, Mostofsky SH, Milham MP ( 2014 ): The autism brain imaging data exchange: Towards a large‐scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19: 659 – 667. | |
dc.identifier.citedreference | Fan J, Han X, Gu W ( 2012 ): Estimating false discovery proportion under arbitrary covariance dependence. J Am Stat Assoc 107: 1019 – 1035. | |
dc.identifier.citedreference | Fornito A, Zalesky A, Pantelis C, Bullmore ET ( 2012 ): Schizophrenia, neuroimaging and connectomics. NeuroImage 62: 2296 – 2314. | |
dc.identifier.citedreference | Fornito A, Zalesky A, Breakspear M ( 2013 ): Graph analysis of the human connectome: Promise, progress, and pitfalls. NeuroImage 80: 426 – 444. | |
dc.identifier.citedreference | Ginestet CE, Fournel AP, Simmons A ( 2014 ): Statistical network analysis for functional MRI: Summary networks and group comparisons. Front Comput Neurosci, 8: 51. | |
dc.identifier.citedreference | Guo S, Kendrick KM, Yu R, Wang HLS, Feng J ( 2014 ): Key functional circuitry altered in schizophrenia involves parietal regions associated with sense of self. Hum Brain Mapp 35: 123 – 139. | |
dc.identifier.citedreference | Hagen L, Kahng AB ( 1992 ): New spectral methods for ratio cut partitioning and clustering. IEEE Trans Computer‐Aided Des 11: 1074 – 1085. | |
dc.identifier.citedreference | Hastie T, Tibshirani R, Friedman J ( 2009 ): The Elements of Statistical Learning, Vol. 2, No. 1. New York: Springer. | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.