Zhang, Jie, Cheng, Wei, Wang, ZhengGe, Zhang, ZhiQiang, Lu, Wenlian, Lu, GuangMing and Feng, Jianfeng. (2012) Pattern classification of large-scale functional brain networks : identification of informative neuroimaging markers for epilepsy. PLoS One, Vol.7 (No.5). e36733. ISSN 1932-6203

Preview

Text WRAP_Feng_journal.pone.0036733.pdf - Published Version Download (515Kb) | Preview

Abstract

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders.

Item Type:	Journal Article
Subjects:	Q Science > QP Physiology
Divisions:	Faculty of Science > Computer Science Faculty of Science > Centre for Scientific Computing
Library of Congress Subject Headings (LCSH):	Neurobehavioral disorders -- Diagnosis, Neurobehavioral disorders -- Magnetic resonance imaging, Pattern recognition systems, Brain -- Physiology
Journal or Publication Title:	PLoS One
Publisher:	Public Library of Science
ISSN:	1932-6203
Date:	2012
Volume:	Vol.7
Number:	No.5
Page Range:	e36733
Identification Number:	10.1371/journal.pone.0036733
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC), Nanjing General Hospital of Nanjing Military Command
Grant number:	61104143 (NSFC), 61004104 (NSFC), 30470510 (NSFC), 30800264 (NSFC), 30971019 (NSFC), 81171328 (NSFC), 81020108022 (NSFC), 07z030 (NGH), Q2008063 (NGH)
References:	1. Singh I, Rose N (2009) Biomarkers in psychiatry. Nature 460: 202–207. 2. Hahn T, Marquand AF, Ehlis AC, Dresler T, Kittel-Schneider S, et al. (2011) Integrating neurobiological markers of depression. Archives of General Psychiatry 68: 361. 3. Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. Neuroimage 19: 1273–1302. 4. Friston K, Fletcher P, Josephs O, Holmes A, Rugg M, et al. (1998) Event-related fMRI: characterizing differential responses. Neuroimage 7: 30–40. 5. Friston KJ, Holmes AP, Poline J, Grasby P, Williams S, et al. (1995) Analysis of fMRI time-series revisited. Neuroimage 2: 45–53. 6. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic resonance in medicine 34: 537–541. 7. Tao H, Guo S, Ge T, Kendrick KM, Xue Z, et al. (2011) Depression uncouples brain hate circuit. Mol Psychiatry. 8. Sporns O (2011) The non-random brain: efficiency, economy, and complex dynamics. Frontiers in Computational Neuroscience 5. 9. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10: 186–198. 10. Zhang D, Raichle ME (2010) Disease and the brain’s dark energy. Nature Reviews Neurology 6: 15–28. 11. Demirci O, Clark VP, Magnotta VA, Andreasen NC, Lauriello J, et al. (2008) A review of challenges in the use of fMRI for disease classification/characterization and a projection pursuit application from a multi-site fMRI schizophrenia study. Brain Imaging Behav 2: 207–226. 12. Dosenbach NUF, Nardos B, Cohen AL, Fair DA, Power JD, et al. (2010) Prediction of individual brain maturity using fMRI. Science 329: 1358–1361. 13. Magnin B, Mesrob L, Kinkingne´hun S, Pe´le´grini-Issac M, Colliot O, et al. (2009) Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51: 73–83. 14. Zhu CZ, Zang YF, Cao QJ, Yan CG, He Y, et al. (2008) Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. Neuroimage 40: 110–120. 15. Kawasaki Y, Suzuki M, Kherif F, Takahashi T, Zhou SY, et al. (2007) Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls. Neuroimage 34: 235–242. 16. Duncan JS (1997) Imaging and epilepsy. Brain 120: 339–377. 17. Duncan JS (2010) Imaging in the surgical treatment of epilepsy. Nature Reviews Neurology 6: 537–550. 18. Duncan JS, Sander JW, Sisodiya SM, Walker MC (2006) Adult epilepsy. The Lancet 367: 1087–1100. 19. Leopold DA, Murayama Y, Logothetis NK (2003) Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. Cerebral Cortex 13: 422–433. 20. Zang Y, Jiang T, Lu Y, He Y, Tian L (2004) Regional homogeneity approach to fMRI data analysis. Neuroimage 22: 394–400. 21. Luo C, Li Q, Lai Y, Xia Y, Qin Y, et al. (2011) Altered functional connectivity in default mode network in absence epilepsy: A resting-state fMRI study. Human brain mapping 32: 438–449. 22. Laufs H, Hamandi K, Salek-Haddadi A, Kleinschmidt AK, Duncan JS, et al. (2007) Temporal lobe interictal epileptic discharges affect cerebral activity in ‘‘default mode’’ brain regions. Human brain mapping 28: 1023–1032. 23. Blumenfeld H, Varghese G, Purcaro M, Motelow J, Enev M, et al. (2009) Cortical and subcortical networks in human secondarily generalized tonic–clonic seizures. Brain 132: 999–1012. 24. Waites AB, Briellmann RS, Saling MM, Abbott DF, Jackson GD (2006) Functional connectivity networks are disrupted in left temporal lobe epilepsy. Ann Neurol 59: 335–343. 25. Richiardi J, Eryilmaz H, Schwartz S, Vuilleumier P, Van De Ville D (2011) Decoding brain states from fMRI connectivity graphs. Neuroimage 56: 616–626. 26. Bullmore ET, Bassett DS (2011) Brain graphs: graphical models of the human brain connectome. Annual review of clinical psychology 7: 113–140. 27. Shirer W, Ryali S, Rykhlevskaia E, Menon V, Greicius M (2012) Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebral Cortex 22: 158–165. 28. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, et al. (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15: 273–289. 29. Behzadi Y, Restom K, Liau J, Liu TT (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37: 90–101. 30. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2011) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 31. Van Dijk KRA, Sabuncu MR, Buckner RL (2011) The influence of head motion on intrinsic functional connectivity MRI. Neuroimage. 32. Tononi G, Sporns O, Edelman GM (1994) A measure for brain complexity: relating functional segregation and integration in the nervous system. Proceedings of the National Academy of Sciences 91: 5033. 33. MacQueen J (1967) Some methods for classification and analysis of multivariate observations; California, USA. 14 p. 34. Tibshirani R (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological). pp 267–288. 35. Zhang K, Gray JW, Parvin B (2010) Sparse multitask regression for identifying common mechanism of response to therapeutic targets. Bioinformatics 26: i97–i105. 36. Sun T, Walsh CA (2006) Molecular approaches to brain asymmetry and handedness. Nature Reviews Neuroscience 7: 655–662. 37. Ge T, Kendrick KM, Feng J (2009) A novel extended Granger causal model approach demonstrates brain hemispheric differences during face recognition learning. PLoS computational biology 5: e1000570. 38. Gazzaniga MS (2005) Forty-five years of split-brain research and still going strong. Nature Reviews Neuroscience 6: 653–659. 39. Liu H, Stufflebeam SM, Sepulcre J, Hedden T, Buckner RL (2009) Evidence from intrinsic activity that asymmetry of the human brain is controlled by multiple factors. Proceedings of the National Academy of Sciences 106: 20499–20503. 40. Tomasi D, Volkow ND (2011) Laterality Patterns of Brain Functional Connectivity: Gender Effects. Cerebral Cortex. 41. Wang Z, Mechanic-Hamilton D, Pluta J, Glynn S, Detre JA (2009) Function lateralization via measuring coherence laterality. Neuroimage 47: 281–288. 42. Taylor KS, Seminowicz DA, Davis KD (2009) Two systems of resting state connectivity between the insula and cingulate cortex. Human brain mapping 30: 2731–2745. 43. Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M (2007) Electrophysiological signatures of resting state networks in the human brain. Proceedings of the National Academy of Sciences 104: 13170. 44. Lundberg S, Eeg-Olofsson O, Raininko R, Eeg-Olofsson KE (1999) Hippocampal asymmetries and white matter abnormalities on MRI in benign childhood epilepsy with centrotemporal spikes. Epilepsia 40: 1808–1815. 45. Francks C, Maegawa S, Laure´n J, Abrahams BS, Velayos-Baeza A, et al. (2007) LRRTM1 on chromosome 2p12 is a maternally suppressed gene that is associated paternally with handedness and schizophrenia. Mol Psychiatry 12: 1129–1139. 46. Herbert MR, Ziegler D, Deutsch C, O’Brien L, Kennedy D, et al. (2005) Brain asymmetries in autism and developmental language disorder: a nested wholebrain analysis. Brain 128: 213–226. 47. Bonelli SB, Powell R, Yogarajah M, Thompson PJ, Symms MR, et al. (2009) Preoperative amygdala fMRI in temporal lobe epilepsy. Epilepsia 50: 217–227. 48. Hayasaka S, Laurienti PJ (2010) Comparison of characteristics between regionand voxel-based network analyses in resting-state fMRI data. Neuroimage 50: 499–508. 49. Zalesky A, Fornito A, Harding IH, Cocchi L, Yu¨ cel M, et al. (2010) Whole-brain anatomical networks: Does the choice of nodes matter? Neuroimage 50: 970–983. 50. Douw L, Baayen JC, Klein M, Velis D, Alpherts WC, et al. (2009) Functional connectivity in the brain before and during intra-arterial amobarbital injection (Wada test). Neuroimage 46: 584–588.
URI:	http://wrap.warwick.ac.uk/id/eprint/49430

Request changes to a record

Actions (login required)

View Item