Abstract
An important application of resting state fMRI data has been to identify resting state networks (RSN). The conventional RSN studies attempted to discover consistent networks through functional connectivity analysis over the whole scan time, which implicitly assumes that RSNs are static. However, the brain undergoes dynamic functional state changes and the functional connectome patterns vary along with time, even in resting state. Hence, this study aims to characterize temporal brain dynamics in resting state. It utilizes the temporally dynamic functional connectome patterns to extract a set of resting state clusters and their corresponding RSNs based on the large-scale consistent, reproducible and predictable whole-brain reference system of dense individualized and common connectivity-based cortical landmarks (DICCCOL). Especially, an effective multi-view spectral clustering method was performed by treating each dynamic functional connectome pattern as one view, and this procedure was also applied on static multi-subject functional connectomes to obtain the static clusters for comparison. It turns out that some dynamic clusters exhibit high similarity with static clusters, suggesting the stability of those RSNs including the visual network and the default mode network. Moreover, two motor-related dynamic clusters show correspondence with one static cluster, which implies substantially more temporal variability of the motor resting network. Particularly, four dynamic clusters exhibited large differences in comparison with their corresponding static networks. Thus it is suggested that these four networks might play critically important roles in functional brain dynamics and interactions during resting state, offering novel insights into the brain function and its dynamics.
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References
Aertsen AM, Gerstein GL, Habib MK, Palm G (1989) Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J Neurophysiol 61(5):900–917
Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24:663–676
Assal M, Jagannathan K, Calhoun VD, Miller L, Stevens MC, Sahl R, O’Boyle JG, Schultz RT, Pearlson GD (2010) Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. Neuroimage 53(1):247–256
Beckman CF, DeLuca M, Devlin JT, Smith SM (2005) Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 360(1457):1001–1013
Bickel S, Scheffer T (2004) Multi-view clustering. Proceeding of the IEEE international conference on data mining
Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MR. Magn Reson Med 34(4):537–541
Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Conference on learning theory
Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38
Buzsáki G, Chen LS, Gage FH (1990) Spatial organization of physiological activity in the hippocampal region: relevance to memory formation. Prog Brain Res 83:257–268
Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14(3):140–151
Chang C, Glover GH (2010) Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50:81–98
Chaudhuri K, Kakade SM, Livescu K, Sridharan K (2009) Multi-view clustering via canonical correlation analysis. In: International conference on machine learning
Chen H, Li K, Zhu D, Jiang X, Yuan Y, Lv P, Zhang T, Guo L, Shen D, Liu T (2013) Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering. IEEE Trans on Med Imaging 32(9):1576–1586
Cordes D, Haughton VM, Arfanakis K, Wendt GJ, Turski PA, Moritz CH, Quigley MA, Meyerand ME (2000) Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR Am J Neuroradiol 21(9):1636–1644
Cordes D, Haughton VM, Carew JD, Arfanakis K, Maravilla K (2012) Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn Reson Imaging 20(4):305–317
Damoiseaux JS, Rombouts SARB, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF (2006) Consistent resting-state networks across healthy subjects. Proc Nat Acad Sci USA 103(37):13848–13853
De Luca M, Smith S, De Stefano N, Matthews PM (2005) Blood oxygenation level dependent contrast resting state networks are relevant to functional activity in the neocortical sensorimotor system. Exp Brain Res 167(4):587–594
De Luca M, Beckmann CF, De Stefano N, Matthews PM, Smith SM (2006) fMRI resting state networks define distinct modes of long-distance interactions in the human brain. NeuroImage 29(4):1359–1367
Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE TIP 15(12):3736–3745
Friston KJ, Frith CD, Liddle PF, Frackowiak RS (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13(1):5–14
Gembris D, Taylor JG, Schor S, Frings W, Suter D, Posse S (2000) Functional magnetic resonance imaging in real time (FIRE): sliding-window correlation analysis and reference-vector optimization. Magn Reson Med 43:259–268
Gilbert CD, Sigman M (2007) Brain states: top-down influences in sensory processing. Neuron 54(9):677–696
Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Nati Acad Sci USA 100(1):253–258
Greicius MD, Srivastava G, Reiss AL, Menon V (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Nati Acad Sci USA 101(13):4637–4642
Hampson M, Olson IR, Leung HC, Skudlarski P, Gore JC (2004) Changes in functional connectivity of human MT/V5 with visual motion input. NeuroReport 15(8):1315–1319
Jiang T, He Y, Zang Y, Weng X (2004) Modulation of functional connectivity during the resting state and the motor task. Hum Brain Mapp 22(1):63–71
Köhler S, Crane J, Milner B (2002) Differential contributions of the parahippocampal place area and the anterior hippocampus to human memory for scenes. Hippocampus 12(6):718–723
Kumar A, Daumé H (2011) A co-training approach for multi-view spectral clustering. ICML-11
Larson-Prior LJ, Zempel JM, Nolan TS, Prior FW, Snyder AZ, Raichle ME (2009) Cortical network functional connectivity in the descent to sleep. Proc Natl Acad Sci USA 106(11):4489–4494
Li X, Zhu D, Jiang X, Jin C, Zhang X, Guo L, Zhang J, Hu X, Li L, Liu T (2013) Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients. Hum Brain Mapp. doi:10.1002/hbm.22290
Lowe MJ, Mock BJ, Sorenson JA (1998) Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage 7(2):119–132
Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. Med Imaging IEEE Trans 16(2):187–198
Mairal J, Elad M, Sapiro G (2008) Sparse representation for color image restoration. IEEE TIP 17(1):53–69
Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2009) Non-local sparse models for image restoration. ICCV
Packard MG, Knowlton BJ (2002) Learning and memory functions of the basal ganglia. Annu Rev Neurosci 25:563–593
Raichle ME, Snyder AZ (2007) A default mode of brain function: a brief history of an evolving idea. Neuroimage 37(4):1083–1090
Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc Natl Acad Sci USA 98(2):676–682
Sakoğlu Ü, Pearlson G, Kiehl K, Wang YM, Michael A, Calhoun V (2010) A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. Magn Reson Mater Phys Biol Med 23:351–366
Schwarz GE (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464
Shi J, Malik J (2000) Normalized cuts and image segmentation. Pattern Anal Mach Intel IEEE Trans 22(8):888–905
Smith SM, Miller KL, Moeller S, Xu J, Auerbach EJ, Woolrich MW, Beckmann CF, Jenkinson M, Andersson J, Glasser MF, Van Essen DC, Feinberg DA, Yacoub ESM, Ugurbil K (2012) Temporally-independent functional modes of spontaneous brain activity. Proc Natl Acad Sci USA 109:3131–3136
Sorg C, Riedl V, Mühlau M, Calhoun VD, Eichele T, Läer L, Drzezga A, Förstl H, Kurz A, Zimmer C, Wohlschläger AM (2007) Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proc Natl Acad Sci USA 104(47):18760–18765
Squire LR, Schacter DL (2002) The neuropsychology of memory. Guilford Press, New York, USA
Starck J, Elad M, Donoho D (2005) Image decomposition via the combination of sparse representation and a variational approach. IEEE Trans Image Process 14(10):1570–1582
Thirion B, Dodel S, Poline JB (2006) Detection of signal synchronizations in resting-state fMRI datasets. Neuroimage 29(1):321–327
Van den Heuvel M, Mandl R, Hulshoff Pol H (2008) Normalized cut group clustering of resting-state fMRI data. PLos ONE 3(4):e2001
Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416
Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S (2010) Sparse representation for computer vision and pattern recognition. PIEEE 98(6):1031–1044
Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. CVPR
Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. ICCV
Yuan Y, Jiang X, Zhu D, Chen H, Li K, Lv P, Yu X, Li X, Zhang S, Zhang T, Hu X, Han J, Guo L, Liu T (2013) Meta-analysis of functional roles of DICCCOLs. Neuroinformatics 11(1):47–63
Zhang Q, Li BX (2010) Discriminative K-SVD for dictionary learning in face recognition. CVPR
Zhang X, Guo L, Li X, Zhu D, Li K, Sun Z, Jin C, Hu X, Han J, Zhao Q, Li L, Liu T (2012) Characterization of task-free/task-performance Brain States. MICCAI, Nice, France
Zhang X, Guo L, Li X, Zhang T, Zhu D, Li K, Chen H, Lv J, Jin C, Zhao Q, Li L, Liu T (2013) Characterization of task-free and task-performance brain states via functional connectome patterns. Med Image Anal 17(8):1106–1122
Zhu D, Li K, Faraco CC, Deng F, Zhang D, Jiang X, Chen H, Guo L, Miller LS, Liu T (2011) Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles. NeuroImage 59(2):1382–1393
Zhu D, Li K, Guo L, Jiang X, Zhang T, Zhang D, Chen H, Deng F, Faraco C, Jin C, Wee CY, Yuan Y, Lv P, Yin Y, Hu X, Duan L, Hu X, Han J, Wang L, Shen D, Miler LS, Li L, Liu T (2012) DICCCOL: dense Individualized and common connectivity-based cortical landmarks. Cereb Cortex 23(4):786–800
Acknowledgments
T Liu was supported by the NIH R01 DA-033393, NIH R01 AG-042599, NSF CAREER Award IIS-1149260, and NSF BME-1302089. L Guo was supported by NSFC 61273362 and 61333017. X Zhang and J Lv were supported by the China Government Scholarship and the Doctorate Foundation of Northwestern Polytechnical University. X Hu was supported by the National Science Foundation of China under Grant 61103061, China Postdoctoral Science Foundation under Grant 20110490174 and 2012T50819. Lingjiang Li was supported by The National Natural Science Foundation of China (30830046) and The National 973 Program of China (2009 CB918303).
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Xin Zhang, Xiang Li and Changfeng Jin contributed equally to this work.
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Zhang, X., Li, X., Jin, C. et al. Identifying and Characterizing Resting State Networks in Temporally Dynamic Functional Connectomes. Brain Topogr 27, 747–765 (2014). https://doi.org/10.1007/s10548-014-0357-7
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DOI: https://doi.org/10.1007/s10548-014-0357-7