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International Journal of Robotics and Automation Technology

Articles

Vol. 5 (2018)

fMRI: A Benediction to Neuroscience

DOI
https://doi.org/10.31875/2409-9694.2018.05.3
Submitted
August 10, 2018
Published
10.08.2018

Abstract

Functional Magnetic Resonance Imaging (fMRI) is a looming technique utilized to study local brain functions in vivo on a large dimensional and temporal resolution. The technique is less expensive and completely noninvasive hence it has swiftly become one of the most preferred choices for brain mapping. It establishes on Magnetic Resonanc e Imaging and helps to identify neural correlations and brain-behavior relationship by detecting the changes in blood flow.fMRI is one of the most frequently used technique in the field of neuroscience which has provided researchers with unparalleled access to the brain in action.

The imaging data generated from different neuroimaging techniques (primarily fMRI) is a time series data. A typical fMRI study provides huge volume of noisy data with a complex spatio-temporal correlation configuration. Statistics play a vital stint in apprehending the attributes of the data and gaining appropriate conclusions that can be used and understood by neuroscientists.The data is huge and is characterized by volume, velocity, variety and veracity. These attributes makes it fall under big data further raising the issues of big data analytics.

Upcoming technologies such as cloud computing, Spark and massive parallel computational methods /algorithms could provide the possible solutions for analysis and mining of data. The review highlights fMRI as a source of Big Neuroimaging data, different databases & repositories where data is available, its role in healthcare, problems in the data analysis and how the present technologies provide possible solutions for data analysis.

References

  1. Dolinski K. and Troyanskaya OG. Implications of Big Data for cell biology. Molecular biology of the cell 2015; 26(14): 2575- 2578. https://doi.org/10.1091/mbc.E13-12-0756
  2. Kalinichenko LA, Vol'nova AA, Gordov EP, Kiselyova NN, Kovaleva DA, Malkov OYE et al. Data access challenges for data intensive research in Russia. Informatika i EePrimeneniya [Informatics and its Applications
  3. Van Horn JD and Toga AW. Human neuroimaging as a "Big Data" science. Brain imaging and behavior 2014; 8(2): 323- 331. https://doi.org/10.1007/s11682-013-9255-y
  4. Sejnowski TJ, Churchland PS and Movshon JA. Putting big data to good use in neuroscience. Nature neuroscience 2014; 17(11): 1440-1441 https://doi.org/10.1038/nn.3839
  5. Turk-Browne NB. Functional interactions as big data in the human brain. Science 2013; 342(6158); 580-584. https://doi.org/10.1126/science.1238409
  6. Andreu-Perez J, Poon CC, Merrifield RD, Wong ST and Yang GZ. Big data for health. IEEE journal of biomedical and health informatics 2015; 19(4): 193-1208. https://doi.org/10.1109/JBHI.2015.2450362
  7. Fan J, Han F and Liu H. Challenges of big data analysis. National science review 2014; 1(2): 293-314. https://doi.org/10.1093/nsr/nwt032
  8. Cox RW and Hyde JS. Software tools for analysis and visualization of fMRI data. NMR in Biomedicine 1997; 10(45): 171-178. https://doi.org/10.1002/(SICI)1099- 1492(199706/08)10:4/53.0.CO;2-L
  9. Sarraf S and Ostadhashem M. December. Big data application in functional magnetic resonance imaging using apache spark. In Future Technologies Conference (FTC) IEEE 2016; 281-284. https://doi.org/10.1109/FTC.2016.7821623
  10. Poldrack RA, Barch DM, Mitchell J, Wager T, Wagner AD, Devlin JT et al. Toward open sharing of task-based fMRI data: the Open fMRI project. Frontiers in neuroinformatics 21 2013; 7: 12. https://doi.org/10.3389/fninf.2013.00012
  11. Van Horn JD, Grethe JS, Kostelec P, Woodward JB, Aslam JA, Rus D, et al. The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the challenges and rewards of large-scale databasing of neuroimaging studies. Philosophical Transactions of the Royal Society of London B: Biological Sciences 2001; 356(1412): 1323-1339. https://doi.org/10.1098/rstb.2001.0916
  12. Van Horn JD and Gazzaniga MS. Why share data? Lessons learned from the fMRIDC. Neuroimage 2013; 82: 677-682 https://doi.org/10.1016/j.neuroimage.2012.11.010
  13. Varma DR. Managing DICOM images: Tips and tricks for the radiologist. The Indian journal of radiology & imaging 2012; 22(1): 4. https://doi.org/10.4103/0971-3026.95396
  14. Ashby FG. 2011. Statistical analysis of fMRI data. MIT press. https://doi.org/10.7551/mitpress/8764.001.0001
  15. Boubela RN, Kalcher K, Huf W, Našel C and Moser E. 2015. Big data approaches for the analysis of large-scale fMRI data using apache spark and GPU processing: A demonstration on resting-state fMRI data from the human connectome project. Frontiers in neuroscience, 9. https://doi.org/10.3389/fnins.2015.00492
  16. Polzehl J and Tabelow K. 2007. fmri: A Package for Analyzing fmri Data
  17. Eloyan A, Li S, Muschelli J, Pekar JJ, Mostofsky SH and Caffo BS. Analytic programming with fMRI data: A quick-start guide for statisticians using R. PloS one 2014; 9(2): 89470. https://doi.org/10.1371/journal.pone.0089470
  18. Visscher KM and Weissman DH. Would the field of cognitive neuroscience be advanced by sharing functional MRI data?. BMC medicine 2011; 9(1): p.34. https://doi.org/10.1186/1741-7015-9-34
  19. Hanke M, Halchenko YO, Sederberg PB, Hanson SJ, Haxby JV and Pollmann S. PyMVPA: a python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics 2009; 7(1): 37-53. https://doi.org/10.1007/s12021-008-9041-y
  20. Mei Yen MAN, Mei Sin ONG, Mohd Saberi Mohamad SD, Ghazali SULONG JY and Fauzan Khairi CHE. A review on the bioinformatics tools for neuroimaging. The Malaysian journal of medical sciences: MJMS, 2015; 22(Spec Issue): p.9.
  21. Papademetris X, Jackowski MP, Rajeevan N, DiStasio M, Okuda H, Constable RT and Staib LH. BioImage Suite: An integrated medical image analysis suite: An update. The insight journal 2006; p.209.
  22. Oakes TR, Johnstone T, Walsh KO, Greischar LL, Alexander AL, Fox AS and Davidson RJ. Comparison of fMRI motion correction software tools. Neuroimage, 2005; 28(3): 529-543. https://doi.org/10.1016/j.neuroimage.2005.05.058
  23. Detre JA and Floyd TF. 2001. Functional MRI and its applications to the clinical neurosciences. The neuroscientist, 7(1), pp.64-79. https://doi.org/10.1177/107385840100700110
  24. Matthews PM, Honey GD and Bullmore ET. Applications of fMRI in translational medicine and clinical practice. Nature Reviews Neuroscience 2006; 7(9): 732-744. https://doi.org/10.1038/nrn1929
  25. Matthews PM. Clinical Applications of fMRI. In fMRI: From Nuclear Spins to Brain Functions 2015; (pp. 611-632). Springer US. https://doi.org/10.1007/978-1-4899-7591-1_21
  26. Wishart HA, Saykin AJ and McAllister TW. Functional magnetic resonance imaging: emerging clinical applications. Current psychiatry reports 2002; 4(5): 338-345. https://doi.org/10.1007/s11920-002-0081-y
  27. Aronen HJ, Korvenoja A, Martinkauppi S, Perkiö J, Karonen J and Carlson S. Clinical applications of functional magnetic resonance imaging. International Journal of Bioelectromagnetism 1999; 1(1): 23-34.
  28. Orringer DA, Vago DR and Golby AJ. September. Clinical applications and future directions of functional MRI. In Seminars in neurology 2012; 32(04): 466-475. Thieme Medical Publishers. https://doi.org/10.1055/s-0032-1331816
  29. Fox RJ, Beall E, Bhattacharyya P, Chen JT and Sakaie K. 2011. Advanced MRI in multiple sclerosis: current status and future challenges.Neurologic clinics, 29(2), pp.357-380. https://doi.org/10.1016/j.ncl.2010.12.011
  30. Alahmadi A, Tur Gomez C, Samson R, Pardini M, Zeidman P, D'Angelo E et al. 2017. Different functional networks observed in multiple sclerosis during rest and motor task fMRI. Frontiers in Cellular Neuroscience. https://doi.org/10.3389/conf.fncel.2017.37.00006
  31. M Filippi and MA. Rocca, "Application of fMRI to the study of multiple sclerosis: An update," Future Neurology, vol. 3, no. 2, pp. 141-151, Mar. 2008 https://doi.org/10.2217/14796708.3.2.141
  32. Iannetti GD and Wise RG. 2007. BOLD functional MRI in disease and pharmacological studies: room for improvement?. Magnetic resonance imaging, 25(6), pp.978- 988. https://doi.org/10.1016/j.mri.2007.03.018
  33. Wise RG and Tracey I. 2006. The role of fMRI in drug discovery. Journal of Magnetic Resonance Imaging, 23(6), pp.862-876 https://doi.org/10.1002/jmri.20584
  34. Alyass A, Turcotte M and Meyre D. 2015. From big data analysis to personalized medicine for all: challenges and opportunities.BMC medical genomics, 8(1), p.33 https://doi.org/10.1186/s12920-015-0108-y
  35. Trifonova OP, Il'in VA, Kolker EV and Lisitsa AV. 2013. Big data in biology and medicine.ActaNaturae (англоязычнаяверсия), 5(3 (18).
  36. Borsook D, Becerra L and Hargreaves R. 2006. A role for fMRI in optimizing CNS drug development. Nature Reviews Drug Discovery, 5(5), pp.411-425. https://doi.org/10.1038/nrd2027
  37. Faro SH and Mohamed FB eds. 2006. Functional MRI: basic principles and clinical applications. Springer Science & Business Media
  38. Zhan X and Yu R. 2015. A window into the brain: advances in psychiatric fMRI. BioMed research international, 2015 https://doi.org/10.1155/2015/542467
  39. Sahara Y, Kobayashi T, Toya H and Suzuki T. 2012. Applications of functional magnetic resonance imaging for analysis of oral functions. Journal of Oral Biosciences, 54(2), pp.101-106. https://doi.org/10.1016/j.job.2012.01.008
  40. Detre JA. 2004. fMRI: applications in epilepsy. Epilepsia, 45(s4), pp.26-31. https://doi.org/10.1111/j.0013-9580.2004.04006.x
  41. Bookheimer SY. 1996. Functional MRI applications in clinical epilepsy.Neuroimage, 4(3), pp.S139-S146 https://doi.org/10.1006/nimg.1996.0064
  42. Kesavadas C and Thomas B. 2008. Clinical applications of functional MRI in epilepsy. The Indian journal of radiology & imaging, 18(3), p.210. https://doi.org/10.4103/0971-3026.41829
  43. Pihlajamäki M and Sperling RA. 2008. fMRI: Use in early Alzheimer's disease and in clinical trials. https://doi.org/10.2217/14796708.3.4.409
  44. Matthews PM and Jezzard P. 2004. Functional magnetic resonance imaging. Journal of Neurology, Neurosurgery & Psychiatry, 75(1), pp.6-12.
  45. Sperling R. 2005. fMRI features of AD: Distinguishing Alzheimer's disease from normal aging and other dementias with fMRI. Alzheimer's & Dementia, 1(1), p.S5. https://doi.org/10.1016/j.jalz.2005.06.048
  46. Yoshiura T. 2001. [Clinical applications of functional magnetic resonance imaging
  47. Pihlajamäki M and Sperling RA. 2008. fMRI: use in early Alzheimer's disease and in clinical trials https://doi.org/10.2217/14796708.3.4.409
  48. Damoiseaux JS. 2012. Resting-state fMRI as a biomarker for Alzheimer's disease?. Alzheimer's research & therapy, 4(2), p.8. https://doi.org/10.1186/alzrt106
  49. Tahmasian M, Bettray LM, van Eimeren T, Drzezga A, Timmermann L, Eickhoff CR, Eickhoff SB and Eggers C. 2015. A systematic review on the applications of resting-state fMRI in Parkinson's disease: Does dopamine replacement therapy play a role?. Cortex, 73, pp.80-105. https://doi.org/10.1016/j.cortex.2015.08.005
  50. MD Phillips, "Functional MRI Evaluation of Parkinson's Disease," Grantome, 20-Apr-2000. [Online
  51. Belle A, Thiagarajan R, Soroushmehr SM, Navidi F, Beard DA and Najarian K. 2015. Big data analytics in healthcare. BioMed research international, 2015. https://doi.org/10.1155/2015/370194
  52. Choudhury S, Fishman JR, McGowan ML and Juengst ET. 2014. Big data, open science and the brain: lessons learned from genomics. Frontiers in human neuroscience, 8, p.239. https://doi.org/10.3389/fnhum.2014.00239
  53. Marx V. 2013. Biology: The big challenges of big data. Nature, 498(7453), pp.255-260. https://doi.org/10.1038/498255a
  54. Van Horn JD and Gazzaniga MS. 2002. Databasing fMRI studies-towards a'discoveryscience'of brain function. Nature Reviews Neuroscience, 3(4), pp.314-318. https://doi.org/10.1038/nrn788
  55. Poldrack Russell A and Krzysztof J Gorgolewski. "Making big data open: data sharing in neuroimaging." Nature neuroscience 17, no. 11 (2014): 1510-1517. https://doi.org/10.1038/nn.3818
  56. Rajeshwari D. 2015. State of the Art of Big Data Analytics: A Survey. International Journal of Computer Applications, 120(22). https://doi.org/10.5120/21395-4456
  57. Ji C, Li Y, Qiu W, Awada U and Li K. 2012, December. Big data processing in cloud computing environments.In 2012 12th International Symposium on Pervasive Systems, Algorithms and Networks (pp. 17-23).IEEE. https://doi.org/10.1109/I-SPAN.2012.9
  58. Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A and Khan SU. 2015. The rise of "big data" on cloud computing: Review and open research issues. Information Systems, 47, pp.98-115 https://doi.org/10.1016/j.is.2014.07.006
  59. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I and Zaharia M. 2010. A view of cloud computing. Communications of the ACM, 53(4), pp.50-58. https://doi.org/10.1145/1721654.1721672
  60. Kambatla K, Kollias G, Kumar V and Grama A. 2014. Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), pp.2561-2573. https://doi.org/10.1016/j.jpdc.2014.01.003
  61. Chen, M., Mao, S. and Liu Y. 2014. Big data: a survey. Mobile Networks and Applications, 19(2), pp.171-209. https://doi.org/10.1007/s11036-013-0489-0
  62. O'Driscoll A, Daugelaite J and Sleator RD. 2013. 'Big data', Hadoop and cloud computing in genomics. Journal of biomedical informatics, 46(5), pp.774-781. https://doi.org/10.1016/j.jbi.2013.07.001
  63. Han Z and Zhang Y. 2015, December. Spark: A Big Data Processing Platform Based on Memory Computing. In Parallel Architectures, Algorithms and Programming (PAAP), 2015 Seventh International Symposium on (pp. 172-176). IEEE. https://doi.org/10.1109/PAAP.2015.41
  64. Singh A, Khare V, Singh S, Mehra N, Jain CK, Akhter S. "Analysis and Identification of Parkinson disease based on fMRI", International Journal of Engineering Technology, Management and Applied Sciences, Vol 5,Issue 1 Jan 2017, ISSN 2349-4476