Skip to main content
NCBI home page
Human Brain Mapping logoLink to Human Brain Mapping
. 1998 Dec 7;6(3):160–188. doi: 10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1

Analysis of fMRI data by blind separation into independent spatial components

Martin J Mckeown 1,✉, Scott Makeig 2,3, Greg G Brown 4, Tzyy‐Ping Jung 1, Sandra S Kindermann 4, Anthony J Bell 1, Terrence J Sejnowski 1,5
PMCID: PMC6873377  PMID: 9673671

Abstract

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129–1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color‐naming, the Brown and Peterson word/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three‐dimensional locations (a component “map”), and a unique associated time course of activation. Given data from 144 time points collected during a 6‐min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40‐sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task‐related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task‐related, quasiperiodic, or slowly varying. By utilizing higher‐order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth‐order decomposition technique (Comon [1994]: Signal Processing 36:11–20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task‐related activation. For each subject, the time courses and active regions of the task‐related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task‐related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask‐related signal components, movements, and other artifacts, as well as consistently or transiently task‐related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks. Hum. Brain Mapping 6:160–188, 1998. © 1998 Wiley‐Liss, Inc.

Keywords: functional magnetic resonance imaging, independent component analysis, higher‐order statistics

Full Text

The Full Text of this article is available as a PDF (1.8 MB).

References

  1. Amari S, Cichocki A, Yang H (1996): A new learning algorithm for blind signal separation. Adv Neural Information Processing Syst 8: 757–763. [Google Scholar]
  2. Bandettini PA, Wong EC, Hinks RS, Tikofsky RS, Hyde JS (1992): Time course EPI of human brain function during task activation. Magn Reson Med 25: 390–397. [DOI] [PubMed] [Google Scholar]
  3. Bandettini PA, Jesmanowicz A, Wong EC, Hyde JS (1993): Processing strategies for time‐course data sets in functional MRI of the human brain. Magn Reson Med 30: 161–173. [DOI] [PubMed] [Google Scholar]
  4. Bell AJ, Sejnowski TJ (1995): An information‐maximization approach to blind separation and blind deconvolution. Neural Comput 7: 1129–1159. [DOI] [PubMed] [Google Scholar]
  5. Bench CJ, Frith CD, Grasby PM, Friston KJ, Paulesu E, Frackowiak RS, Dolan RJ (1993): Investigations of the functional anatomy of attention using the Stroop test. Neuropsychologia 31: 907–922. [DOI] [PubMed] [Google Scholar]
  6. Binder JR (1997): Neuroanatomy of language processing studied with functional MRI. Clin Neurosci 4: 87–94. [PubMed] [Google Scholar]
  7. Biswal B, DeYoe AE, Hyde JS (1996): Reduction of physiological fluctuations in fMRI using digital filters. Magn Reson Med 35: 107–13. [DOI] [PubMed] [Google Scholar]
  8. Boynton GM, Engel SA, Glover GH, Heeger DJ (1996): Linear systems analysis of functional magnetic resonance imaging in human V1. J Neurosci 16: 4207–4221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Brammer MJ, Wright IC, Woodruff PWR, Williams SCR, Simmons A, Bullmore ET (1997): Wavelet analysis of periodic and nonperiodic experimental designs in functional magnetic resonance imaging of the brain [abstract]. Neuroimage 5: 479. [Google Scholar]
  10. Chichibu S, Ohta Y, Chikugo T, Suzuki T (1995): Temporal and spatial properties of slow waves in the electroencephalogram of spontaneously hypertensive rats. Clin Exp Pharmacol Physiol [Suppl] 22: 288–289. [DOI] [PubMed] [Google Scholar]
  11. Comon P (1994): Independent component analysis: A new concept? Signal Processing 36: 11–20. [Google Scholar]
  12. Cox RW (1996): AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29: 162–173. [DOI] [PubMed] [Google Scholar]
  13. Duffield JS, de Silva RN, Grant R (1994): Pure alexia without agraphia: A classical cortical syndrome revisited. Scott Med J 39: 178–179. [DOI] [PubMed] [Google Scholar]
  14. Friston KJ (1995): Commentary and opinion: II. Statistical parametric mapping: Ontology and current issues. J Cereb Blood Flow Metab 15: 361–370. [DOI] [PubMed] [Google Scholar]
  15. Friston KJ (1996): Statistical Parametric Mapping and Other Analyses of Functional Imaging Data In: Toga AW, Mazziotta JC. (eds): Brain Mapping: The Methods. San Diego: Academic Press, pp 363–396. [Google Scholar]
  16. Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R (1996): Movement‐related effects in fMRI time‐series. Magn Reson Med 35: 346–355. [DOI] [PubMed] [Google Scholar]
  17. Gardner E (1975): Fundamentals of Neurology, 6th ed. Philadelphia: W. B. Saunders. [Google Scholar]
  18. Jackson JE (1991): A User's Guide to Principal Components, New York: John Wiley & Sons, Inc. [Google Scholar]
  19. Jung T‐P, Humphries C, Lee T‐W, Makeig S, McKeown MJ, Iragui V, Sejnowski T (1998): Extended ICA removes artifacts from Electroencepholographic Recordings. Adv Neural Information Processing Systems, Vol 10 (in press). [Google Scholar]
  20. Jutten C, Herault J (1991): Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Process 24: 1–10. [Google Scholar]
  21. Kwong KK (1995): Functional magnetic resonance imaging with echo planar imaging. Magn Reson Q 11: 1–20. [PubMed] [Google Scholar]
  22. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, Kennedy DN, Hoppel BE, Cohen MS, Turner R, Cheug H‐M, Brady TJ, Rosen BR (1992): Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci USA 89: 5675–5679. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Le TH, Hu X (1996): Retrospective estimation and correction of physiological artifacts in fMRI by direct extraction of physiological activity from MR data. Magn Reson Med 35: 290–298. [DOI] [PubMed] [Google Scholar]
  24. Lezak MD (1995): Neuropsychological Assessment, 3rd ed. New York: Oxford University Press. [Google Scholar]
  25. Makeig S, Bell AJ, Jung T‐P, Ghahremani D, Sejnowski TJ (1997): Blind separation of auditory event related responses into independent components. Proc Natl Acad Sci USA 94: 10979–10984. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Manoach DS, Schlaug G, Siewert B, Darby DG, Bly BM, Benfield A, Edelman RR, Warach S (1997): Prefrontal cortex fMRI signal changes are correlated with working memory load. Neuroreport 8: 545–549. [DOI] [PubMed] [Google Scholar]
  27. McKeown MJ, Jung T‐P, Makeig S, Brown GG, Kindermann SS, Lee T‐W, Sejnowski TJ (1998): Spatially independent activity patterns in functional magnetic resonance imaging data during the Stroop color‐naming task. Proc Natl Acad Sci USA 95: 803–810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mitra PP, Ogawa S, Hu X, Ugurbil K (1997): The nature of spatiotemporal changes in cerebral hemodynamics as manifested in functional magnetic resonance imaging. Magn Reson Med 37: 511–518. [DOI] [PubMed] [Google Scholar]
  29. Moeller JR, Strother SC (1991): Aregional covariance approach to the analysis of functional patterns in position emission tomographic data. J Cereb Blood Flow Metab 11: A121–A135. [DOI] [PubMed] [Google Scholar]
  30. Nathaniel‐James DA, Fletcher P, Frith CD (1997): The functional anatomy of verbal initiation and suppression using the Hayling test. Neuropsychologia 35: 559–566. [DOI] [PubMed] [Google Scholar]
  31. Nobre AC, Sebestyen GN, Gitelman DR, Mesulam MM, Frackowiak RS, Frith CD (1997): Functional localization of the system for visuospatial attention using positron emission tomography. Brain 120: 515–533. [DOI] [PubMed] [Google Scholar]
  32. Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle H, Ugurbil K (1992): Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging. Proc Natl Acad Sci USA 89: 5951–5955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Peterson L, Peterson M (1959): Short‐term retention of individual verbal items. J Exp Psychol 58: 193–198. [DOI] [PubMed] [Google Scholar]
  34. Phelps EA, Hyder F, Blamire AM, Shulman RG (1997): FMRI of the prefrontal cortex during overt verbal fluency. Neuroreport 8: 561–565. [DOI] [PubMed] [Google Scholar]
  35. Phillips CG, Zeki S, Barlow HB (1984): Localization of function in the cerebral cortex. Past, present and future. Brain 107: 327–361. [PubMed] [Google Scholar]
  36. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992): Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. Cambridge: Cambridge University Press. [Google Scholar]
  37. Quint DJ, Gilmore JL (1992): Alexia without agraphia. Neuroradiology 34: 210–214. [DOI] [PubMed] [Google Scholar]
  38. Stuart A, Ord J (1991): Kendall's Advanced Theory of Statistics, New York: Oxford University Press. [Google Scholar]
  39. Takahashi N, Kawamura M, Shinotou H, Hirayama K, Kaga K, Shindo M (1992): Pure word deafness due to left hemisphere damage. Cortex 28: 295–303. [DOI] [PubMed] [Google Scholar]
  40. Tulving E, Markowitsch HJ, Craik FE, Habib R, Houle S (1996): Novelty and familiarity activations in PET studies of memory encoding and retrieval. Cereb Cortex 6: 71–79. [DOI] [PubMed] [Google Scholar]
  41. Wayenberg JL, Hasaerts D, Franco P, Valente F, Massager N (1995): Anterior fontanelle pressure variations during sleep in healthy infants. Sleep 18: 223–228. [DOI] [PubMed] [Google Scholar]
  42. Wong E, Bandettini P, Hyde J (1992): Echo‐planar imaging of the human brain using a three axis local gradient coil In: Abstracts of the Proceedings of the Eleventh Annual Meeting of the Society of Magnetic Resonance in Medicine, Berkeley: (Society of Magnetic Resonance in Medicine; ). [Google Scholar]

Articles from Human Brain Mapping are provided here courtesy of Wiley

RESOURCES