Connectomics and new approaches for analyzing human brain functional connectivity

Oxford University Press (OUP) - Tập 4 - Trang 1-12 - 2015
R Cameron Craddock1,2, Rosalia L Tungaraza1, Michael P Milham1,2
1Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, USA
2Center for the Developing Brain, Child Mind Institute, New York, USA

Tóm tắt

Estimating the functional interactions between brain regions and mapping those connections to corresponding inter-individual differences in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a “big data” problem. Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimaging analysis algorithms and tools. This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems. Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.

Tài liệu tham khảo

Van Essen DC, Ugurbil K. The future of the human connectome. NeuroImage. 2012; 62(2):1299–310. doi:10.1016/j.neuroimage.2012.01.032.

Assaf Y, Alexander DC, Jones DK, Bizzi A, Behrens TEJ, Clark Ca, et al. The CONNECT project: Combining macro- and micro-structure. NeuroImage. 2013; 80:273–82. doi:10.1016/j.neuroimage.2013.05.055.

Jernigan TL, McCabe C, Chang L, Akshoomoff N, Newman E, Dale AM, et al, Pediatric Imaging Neurocognition and Genetics (PING) Study. Accessed 12 13 2014. http://pingstudy.ucsd.edu.

Satterthwaite TD, Elliott MA, Ruparel K, Loughead J, Prabhakaran K, Calkins ME, et al. Neuroimaging of the Philadelphia neurodevelopmental cohort. Neuroimage. 2014; 86:544–53. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3947233PMC3947233] [DOI:http://dx.doi.org/10.1016/j.neuroimage.2013.07.064] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/2392110123921101].

Buckner RL, Roffman JL, Smoller JW. Brain Genomics Superstruct Project (GSP). doi:10.7910/DVN/25833.

Nooner KB, Colcombe SJ, Tobe RH, Mennes M, Benedict MM, Moreno AL, et al. The nki-rockland sample: A model for accelerating the pace of discovery science in psychiatry. Front Neurosc. 2012; 6:152. doi:10.3389/fnins.2012.00152.

Craddock RC, Jbabdi S, Yan C-G, Vogelstein JT, Castellanos FX, Di Martino A, et al. Imaging human connectomes at the macroscale. Nat Methods. 2013; 10(6):524–39. doi:10.1038/nmeth.2482.

Varela F, Lachaux JP, Rodriguez E, Martinerie J. The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci. 2001; 2(4):229–39. [DOI:http://dx.doi.org/10.1038/3506755010.1038/35067550] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/1128374611283746].

Aguirre G. Number of neurons in a voxel. Accessed 12 13 2014. 2014. https://cfn.upenn.edu/aguirre/wiki/public:neurons_in_a_voxel.

Behrens TE, Sporns O. Human connectomics. Curr Opin Neurobiol. 2012; 22(1):144–53. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3294015PMC3294015] [DOI:http://dx.doi.org/10.1016/j.conb.2011.08.00510.1016/j.conb.2011.08.005] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/2190818321908183].

Kapur S, Phillips AG, Insel TR. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?Mol Psychiat. 2012; 17(12):1174–9. [DOI:http://dx.doi.org/10.1038/mp.2012.10510.1038/mp.2012.105] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/2286903322869033].

Hagmann P. From diffusion MRI to brain connectomics. PhD thesis. Lausanne: STI; 2005. doi:10.5075/epfl-thesis-3230. http://vpaa.epfl.ch/page14976.html.

Thirion B, Varoquaux G, Dohmatob E, Poline JB. Which fMRI clustering gives good brain parcellations?Front Neurosci. 2014; 8:167. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4076743PMC4076743] [DOI:http://dx.doi.org/10.3389/fnins.2014.0016710.3389/fnins.2014.00167] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/2507142525071425].

Eickhoff SB, Rottschy C, Kujovic M, Palomero-Gallagher N, Zilles K. Organizational principles of human visual cortex revealed by receptor mapping. Cereb Cortex. 2008; 18(11):2637–45. doi:10.1093/cercor/bhn024. http://cercor.oxfordjournals.org/content/18/11/2637.full.pdf+html.

Craddock RC, James GA, Iii PEH, Hu XP, Mayberg HS. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp.2012;33(8). doi:10.1002/hbm.21333.A.

Thirion B, Flandin G, Pinel P, Roche A, Ciuciu P, Poline JB. Dealing with the shortcomings of spatial normalization: multi-subject parcellation of fMRI datasets. Hum Brain Mapp. 2006; 27(8):678–93. [DOI:http://dx.doi.org/10.1002/hbm.20210] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/16281292].

Ryali S, Chen T, Supekar K, Menon V. Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty. Neuroimage. 2012; 59(4):3852–61. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3288428] [DOI:http://dx.doi.org/10.1016/j.neuroimage.2011.11.054] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/22155039].

Penny WD, Stephan KE, Daunizeau J, Rosa MJ, Friston KJ, Schofield TM, et al. Comparing families of dynamic causal models. PLoS Comput Biol. 2010; 6(3):1000709. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2837394] [DOI:http://dx.doi.org/10.1371/journal.pcbi.1000709] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/20300649].

Lohmann G, Erfurth K, Müller K, Turner R. Critical comments on dynamic causal modelling. NeuroImage. 2012; 59(3):2322–9. doi:10.1016/j.neuroimage.2011.09.025.

Deshpande G, Santhanam P, Hu X. Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data. Neuroimage. 2011; 54(2):1043–52. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2997120] [DOI:http://dx.doi.org/10.1016/j.neuroimage.2010.09.024] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/20850549].

Lv Y, Margulies DS, Cameron Craddock R, Long X, Winter B, Gierhake D, et al. Identifying the perfusion deficit in acute stroke with resting-state functional magnetic resonance imaging. Ann Neurol. 2013; 73(1):136–40. [DOI:http://dx.doi.org/10.1002/ana.23763] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/23378326.].

Craddock RC, Milham MP, LaConte SM. Predicting intrinsic brain activity. Neuroimage. 2013; 82:127–36. [DOI:http://dx.doi.org/10.1016/j.neuroimage.2013.05.072] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/23707580].

James GA, Lu ZL, VanMeter JW, Sathian K, Hu XP, Butler AJ. Changes in resting state effective connectivity in the motor network following rehabilitation of upper extremity poststroke paresis. Top Stroke Rehabil. 2009; 16(4):270–81. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3595191] [DOI:http://dx.doi.org/10.1310/tsr1604-270] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/19740732].

Brodersen KH, Schofield TM, Leff AP, Ong CS, Lomakina EI, Buhmann JM, et al. Generative embedding for model-based classification of fMRI data. PLoS Comput Biol. 2011; 7(6):1002079. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3121683] [DOI:http://dx.doi.org/10.1371/journal.pcbi.1002079] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/21731479].

Keilholz SD, Magnuson ME, Pan WJ, Willis M, Thompson GJ. Dynamic properties of functional connectivity in the rodent. Brain Connect. 2013; 3(1):31–40. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621313] [DOI:http://dx.doi.org/10.1089/brain.2012.0115] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/23106103].

Yang Z, Craddock RC, Margulies DS, Yan CG, Milham MP. Common intrinsic connectivity states among posteromedial cortex subdivisions: Insights from analysis of temporal dynamics. Neuroimage. 2014; 93 Pt 1:124–137. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4010223] [DOI:http://dx.doi.org/10.1016/j.neuroimage.2014.02.014] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/24560717].

Majeed W, Magnuson M, Hasenkamp W, Schwarb H, Schumacher EH, Barsalou L, et al. Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. Neuroimage. 2011; 54(2):1140–50. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2997178] [DOI:http://dx.doi.org/10.1016/j.neuroimage.2010.08.030] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/20728554].

Smith SM, Miller KL, Moeller S, Xu J, Auerbach EJ, Woolrich MW, et al. Temporally-independent functional modes of spontaneous brain activity. Proc Nat Acad Sci. 2012; 109(8):3131–6. doi:10.1073/pnas.1121329109. http://www.pnas.org/content/109/8/3131.full.pdf+html.

Castellanos FX, Di Martino A, Craddock RC, Mehta AD, Milham MP. Clinical applications of the functional connectome. Neuroimage. 2013; 80:527–40. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809093] [DOI:http://dx.doi.org/10.1016/j.neuroimage.2013.04.083] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/23631991].

Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex. 2012; 22(1):158–65. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3236795] [DOI:http://dx.doi.org/10.1093/cercor/bhr099] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/21616982].

Krienen FM, Yeo BT, Buckner RL. Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture. Philos Trans R Soc Lond B Biol Sci. 2014;369(1653). [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150301] [DOI:http://dx.doi.org/10.1098/rstb.2013.0526] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/25180304].

Zalesky A, Cocchi L, Fornito A, Murray MM, Bullmore E. Connectivity differences in brain networks. NeuroImage. 2012; 60(2):1055–62. doi:10.1016/j.neuroimage.2012.01.068.

Craddock RC, Holtzheimer PE, Hu XP, Mayberg HS. Disease state prediction from resting state functional connectivity. Magn Reson Med. 2009; 62(6):1619–28. doi:10.1002/mrm.22159.

Thoma M, Cheng H, Gretton A, Han J, Kriegel H-P, Smola A, et al. Discriminative frequent subgraph mining with optimality guarantees. Stat Anal Data Min. 2010; 3(5):302–18. doi:10.1002/sam.v3:5.

Bogdanov P, Dereli N, Bassett D. Learning about Learning: Human Brain Sub-Network Biomarkers in fMRI Data. arXiv preprint arXiv: …. 2014. arXiv:1407.5590v1.

Richiardi J, Achard S, Bunke H, Van De Ville D. Machine learning with brain graphs: Predictive modeling approaches for functional imaging in systems neuroscience. Signal Process Mag IEEE. 2013; 30(3):58–70. doi:10.1109/MSP.2012.2233865.

Vogelstein JT, Roncal WG, Vogelstein RJ, Priebe CE. Graph classification using signal-subgraphs: Applications in statistical connectomics. IEEE Trans Pattern Anal Mach Intell. 2013; 35(7):1539–51. doi:10.1109/TPAMI.2012.235.

Vapnik VN, Vapnik V. Statistical Learning Theory vol. 2. New York: Wiley; 1998.

Centers for Disease Control and Prevention. Morbidity and Mortality Weekly Report (MMWR). Accessed 12 13 2014. http://www.cdc.gov/mmwr/.

Scott C, Blanchard G, Handy G, Pozzi S, Flaska M. ArXiv e-prints. 2013. 1303.1208.

Gates KM, Molenaar PC, Iyer SP, Nigg JT, Fair DA. Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks. PLoS ONE. 2014; 9(3):91322. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958357] [DOI:http://dx.doi.org/10.1371/journal.pone.0091322] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/24642753].

Mørup M. Applications of tensor (multiway array) factorizations and decompositions in data mining. Wiley Interdiscip Rev: Data Min Knowl Discov. 2011; 1(1):24–40. doi:10.1002/widm.1.

Franco AR, Ling J, Caprihan A, Calhoun VD, Jung RE, Heileman GL, et al. Multimodal and multi-tissue measures of connectivity revealed by joint independent component analysis. Selected Topics Signal Process IEEE J. 2008; 2(6):986–97.

Groves AR, Beckmann CF, Smith SM, Woolrich MW. Linked independent component analysis for multimodal data fusion. Neuroimage. 2011; 54(3):2198–217. [DOI:http://dx.doi.org/10.1016/j.neuroimage.2010.09.073] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/20932919].

Bottger J, Schurade R, Jakobsen E, Schaefer A, Margulies DS. Connexel visualization: a software implementation of glyphs and edge-bundling for dense connectivity data using brainGL. Front Neurosci. 2014; 8:15. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3941704] [DOI:http://dx.doi.org/10.3389/fnins.2014.00015] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/24624052].

Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ, Martin A, et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2012; 2(1):25–32. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3484684] [DOI:http://dx.doi.org/10.1089/brain.2012.0080] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/22432927].

LaConte S, Anderson J, Muley S, Ashe J, Frutiger S, Rehm K, et al. The evaluation of preprocessing choices in single-subject bold fmri using npairs performance metrics. NeuroImage. 2003; 18(1):10–27.

Dinov I, Lozev K, Petrosyan P, Liu Z, Eggert P, Pierce J. Neuroimaging study designs, computational analyses and data provenance using the loni pipeline. PLoS ONE. 2010; 5(9):13070. doi:10.1371/journal.pone.0013070.

Yan C-G, Zang Y-F. DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Front Syst Neurosci. 2010; 4:13.

Lavoie-Courchesne S, Rioux P, Chouinard-Decorte F, Sherif T, Rousseau M-E, Das S, et al.Integration of a neuroimaging processing pipeline into a pan-canadian computing grid. J Phys Conf Ser.2012;341(1). doi:10.1088/1742-6596/341/1/012032.

Craddock C, Sikka S, Cheung B, Khanuja R, Ghosh SS, Yan C, et al. Towards automated analysis of connectomes: The configurable pipeline for the analysis of connectomes (c-pac). Front Neuroinform.2013;(42). doi:10.3389/conf.fninf.2013.09.00042.

Cox RW, Ashburner J, Breman H, Fissell K, Haselgrove C, Holmes CJ, et al. A (sort of) new image data format standard: NifTI-1. In: Proceedings Organization of Human Brain Mapping 10th Annual Meeting, Budapest, Hungary. Budapest, Hungary: 2004.

Amdahl GM. Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the April 18-20, 1967, Spring Joint Computer Conference. AFIPS ’67 (Spring). New York, NY, USA: ACM: 1967. p. 483–485. doi:10.1145/1465482.1465560. http://doi.acm.org/10.1145/1465482.1465560.

O’Driscoll A, Daugelaite J, Sleator RD. ‘big data’, hadoop and cloud computing in genomics. J Biomed Inform. 2013; 46(5):774–81. doi:10.1016/j.jbi.2013.07.001.

CPAC. Configurable Pipeline for the Analysis of Connectomes Amazon Machine Instance. Accessed 12 13 2014. 2014. https://github.com/FCP-INDI/ndar-dev/blob/master/aws_walkthrough.md.

NITRC. NITRC Computational Environment. Accessed 01 14 2015. 2014. https://aws.amazon.com/marketplace/pp/B00AW0MBLO/ref=mkt_ste_l2_hls_f1?nc2=h_l3_hl.

Hernandez D. Now You Can Build Google’s $1M Artificial Brain on the Cheap. Wired. 2013; 6(3):413–421.

Eklund A, Dufort P, Villani M, LaConte S. Broccoli: Software for fast fmri analysis on many-core cpus and gpus. Front Neuroinform. 2014; 8:24.

Delgado J, Moure JC, Vives-Gilabert Y, Delfino M, Espinosa A, Gomez-Anson B. Improving the execution performance of FreeSurfer : a new scheduled pipeline scheme for optimizing the use of CPU and GPU resources. Neuroinformatics. 2014; 12(3):413–21.

Eklund A, Dufort P, Forsberg D, Laconte SM. Medical image processing on the GPU - Past, present and future. Med Image Anal. 2013; 17(8):1073–94. doi:10.1016/j.media.2013.05.008.

Eklund A, Friman O, Andersson M, Knutsson H. A gpu accelerated interactive interface for exploratory functional connectivity analysis of fmri data. In: Image Processing (ICIP), 2011 18th IEEE International Conference On: 2011. p. 1589–1592. doi:10.1109/ICIP.2011.6115753.

Eklund A, Andersson M, Knutsson H. Fast random permutation tests enable objective evaluation of methods for single-subject FMRI analysis. Int J Biomed Imaging. 2011; 2011:627947. doi:10.1155/2011/627947.

Eklund A, Andersson M, Josephson C, Johannesson M, Knutsson H. Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets. NeuroImage. 2012; 61(3):565–78. doi:10.1016/j.neuroimage.2012.03.093.

Munshi A, Gaster B, Mattson TG, Fung J, Ginsburg D. OpenCL Programming Guide, 1st edn. Boston, MA: Addison-Wesley Professional; 2011.

Craddock RC, Bellec P. Preprocessed Connectomes Project (PCP). Accessed 12 13 2014. 2014. http://preprocessed-connectomes-project.github.io.

Fox PT, Lancaster JL. Opinion: Mapping context and content: the BrainMap model. Nat Rev Neurosci. 2002; 3(4):319–21. [DOI:http://dx.doi.org/10.1038/nrn789] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/11967563].

Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat Methods. 2011; 8(8):665–70. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146590] [DOI:http://dx.doi.org/10.1038/nmeth.1635] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/21706013].

Gorgolewski C, Yarkoni T, Schwarz Y, Maumet C, Margulies D. Neurovault. Accessed 12 13 2014. 2014. http://www.neurovault.org.

Toro R. Brainspell. Accessed 12 13 2014. 2014. http://brainspell.org.

of Health Blueprint for Neuroscience Research, N.I. Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC). Accessed 12 13 2014. 2006. http://www.nitrc.org.

Milham MP, Fair D, Mennes M, Mostofsky SH. The adhd-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci.2012;6(62). doi:10.3389/fnsys.2012.00062.

Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiat. 2014; 19(6):659–67. [PubMed Central:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4162310] [DOI:http://dx.doi.org/10.1038/mp.2013.78] [PubMed:http://www.ncbi.nlm.nih.gov/pubmed/23774715].

Poldrack R. OpenfMRI. Accessed 12 13 2014. 2014. https://openfmri.org/.