JoVE Logo
Faculty Resource Center

Sign In

Summary

Abstract

Introduction

Protocol

Representative Results

Discussion

Acknowledgements

Materials

References

Medicine

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published: June 26th, 2013

DOI:

10.3791/50319

1Center for Neurosciences, The Feinstein Institute for Medical Research

Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Neurodegenerative disorders have been extensively studied using techniques that localize and quantify abnormalities of brain metabolism as well as non-inferential methods that study regional interactions17. Data-driven multivariate analytical strategies such as principal component analysis (PCA)1,2,4,18 and independent component analysis (ICA)19,20, as well as supervised techniques such as partial least squares (PLS)21 and ordinal trends canonical variates analysis (OrT/CVA)22 can reveal characteristic patterns or "networks" of interrelated activity. The basics of multivariate procedures, particularly the scaled s....

Log in or to access full content. Learn more about your institution’s access to JoVE content here

1. Data Collection and Preprocessing

  1. The SSM/PCA method can be applied to single volume images obtained from various sources and modalities. Specifically, for on-site PET imaging of metabolism, prepare a suitable radionuclide tracer such as [18F]-fluorodeoxyglucose (FDG) and administer to each patient. Patients are usually scanned at rest with eyes open, following a fast of at least 12 hr, off medications.
  2. Scan each subject for individual or group assessment. For pattern derivation, scan .......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

A simple application of multivariate SSM/PCA analysis to derive a neuroimaging biomarker pattern for PD is illustrated below. PET FDG images of ten clinically diagnosed PD patients (6M/4F, 59y ± 7y sd) of variable diseased duration (9y ± 5y sd) and ten age and gender matched normal controls (6M/4F, 58y ±7y sd) were analyzed using our ssmpca routine. The twenty corresponding spatially pre-normalized images were initially selected under the categories disease subjects or controls along with th.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

The SSM/PCA model originally presented by Moeller et al.4 has evolved1-3 into a straightforward and robust technique for the analysis of neuroimaging data. However, there have been ambiguities in the application of this methodology that we have attempted to clarify here and in previous publications5-7,10. Some of these issues have been addressed in the text but are reemphasized here because of their importance. As detailed in the Introduction, SSM/PCA is primarily effective in re.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

This work was supported by Grant No. P50NS071675 (Morris K. Udall Center of Excellence in Parkinson's Disease Research at The Feinstein Institute for Medical Research) to D.E. from the National Institute of Neurological Disorders and Stroke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. The sponsor did not play a role in study design, collection, analysis and interpretation of data, writing of the report or in the decision to submit the paper for publication.

....

Log in or to access full content. Learn more about your institution’s access to JoVE content here

Name Company Catalog Number Comments
Name of Equipment Company Catalog Number Comments
Image Acquisition
PET Scanner GE Medical Systems GE Advance Any PET, PET/CT and PET/MRI Scanners from GE, Siemens and Philips
PC Workstations Lenovo Any http://www.lenovo.com/us/en/
Radiopharmaceuticals
[18F]-fluorodeoxyglucose Feinstein Institute for Medical Research Routine Production Also distributed by Cardinal Health http://www.cardinal.com/
Software
ScanVP Feinstein Institute for Medical Research Version 5.9.1, Version 6.2, To be released www.feinsteinneuroscience.org
SPM The UCL Institute of Neurology spm99-spm8 http://www.fil.ion.ucl.ac.uk/spm
Windows Microsoft Any
Matlab Mathworks Matlab Version 7.0, 7.3 http://www.mathworks.com/
JMP SAS Version 5 http://www.jmp.com/

  1. Moeller, J. R., Strother, S. C. A regional covariance approach to the analysis of functional patterns in positron emission tomographic data. J. Cereb. Blood Flow Metab. 11 (2), 121-135 (1991).
  2. Alexander, G. E., Moeller, J. R. Application of the scaled subprofile model to functional imaging in neuropsychiatric disorders: A principal component approach to modeling brain function in disease. Hum. Brain Mapp. 2, 1-16 (1994).
  3. Habeck, C. G. Basics of multivariate analysis in neuroimaging data. J. Vis. Exp. (41), e1988 (2010).
  4. Moeller, J. R., Strother, S. C., Sidtis, J. J., Rottenberg, D. A. Scaled subprofile model: a statistical approach to the analysis of functional patterns in positron emission tomographic data. J. Cereb. Blood Flow Metab. 7 (5), 649-658 (1987).
  5. Spetsieris, P. G., Eidelberg, D. Scaled subprofile modeling of resting state imaging data in Parkinson's disease: methodological issues. Neuroimage. 54 (4), 2899-2914 (2011).
  6. Dhawan, V., Tang, C. C., Ma, Y., Spetsieris, P., Eidelberg, D. Abnormal network topographies and changes in global activity: Absence of a causal relationship. Neuroimage. 63 (4), 1827-1832 (2012).
  7. Eidelberg, D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 32 (10), 548-557 (2009).
  8. Habeck, C., Foster, N. L., et al. Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease. Neuroimage. 40 (4), 1503-1515 (2008).
  9. Efron, B., Tibshirani, R. . An introduction to the bootstrap. , (1994).
  10. Ma, Y., Tang, C., Spetsieris, P., Dhawan, V., Eidelberg, D. Abnormal metabolic network activity in Parkinson's disease: test-retest reproducibility. J. Cereb. Blood Flow & Metab. 27 (3), 597-605 (2007).
  11. Ma, Y., Eidelberg, D. Functional imaging of cerebral blood flow and glucose metabolism in Parkinson's disease and Huntington's disease. Mol. Imaging Biol. 9 (4), 223-233 (2007).
  12. Tang, C. C., Poston, K. L., et al. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol. 9 (2), 149-158 (2010).
  13. Spetsieris, P. G., Ma, Y., Dhawan, V., Eidelberg, D. Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features. Neuroimage. 45 (4), 1241-1252 (2009).
  14. Tang, C. C., Poston, K. L., Dhawan, V., Eidelberg, D. Abnormalities in metabolic network activity precede the onset of motor symptoms in Parkinson's disease. J. Neurosci. 30 (3), 1049-1056 (2010).
  15. Mure, H., Hirano, S., et al. Parkinson's disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage. 54 (2), 1244-1253 (2011).
  16. Niethammer, M., Eidelberg, D. Metabolic brain networks in translational neurology: concepts and applications. Ann. Neurol. , (2012).
  17. Petersson, K. M., Nichols, T. E., Poline, J. B. Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 354 (1387), 1239-1260 (1999).
  18. Habeck, C., Stern, Y. Multivariate data analysis for neuroimaging data: overview and application to Alzheimer's disease. Cell Biochem. Biophys. 58 (2), 53-67 (2010).
  19. McKeown, M. J., Hansen, L. K., Sejnowsk, T. J. Independent component analysis of functional MRI: what is signal and what is noise. Current Opinion in Neurobiology. 13 (5), 620-629 (2003).
  20. Stone, J. V. Independent component analysis: an introduction. Trends Cogn. Sci. 6 (2), 59-64 (2002).
  21. McIntosh, A. R., Bookstein, F. L., Haxby, J. V., Grady, C. L. Spatial pattern analysis of functional brain images using partial least squares. Neuroimage. 3 (3 Pt. 1), 143-157 (1996).
  22. Habeck, C., Krakauer, J. W., et al. A new approach to spatial covariance modeling of functional brain imaging data: ordinal trend analysis. Neural Comput. 17 (7), 1602-1645 (2005).
  23. Habeck, C., Moeller, J. R. Intrinsic functional-connectivity networks for diagnosis: just beautiful pictures. Brain Connect. 1 (2), 99-103 (2011).
  24. Huang, C., Tang, C., et al. Changes in network activity with the progression of Parkinson's disease. Brain. 130, 1834-1846 (2007).
  25. Feigin, A., Tang, C., et al. Thalamic metabolism and symptom onset in preclinical Huntington's disease. Brain. 130, 2858-2867 (2007).
  26. Poston, K. L., Tang, C. C., et al. Network correlates of disease severity in multiple system atrophy. Neurology. 78 (16), 1237-1244 (2012).
  27. Biswal, B., Yetkin, F. Z., Haughton, V. M., Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. 34 (4), 537-541 (1995).
  28. Greicius, M. D., Krasnow, B., Reiss, A. L., Menon, V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America. 100 (1), 253-258 (2003).
  29. Friston, K. J., Frith, C. D., Liddle, P. F., Frackowiak, R. S. Functional connectivity: the principal component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13, 5-14 (1993).
  30. Strother, S. C., Kanno, I., Rottenberg, D. A. Commentary and opinion: I. Principal component analysis, variance partitioning, and "functional connectivity". Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism. 15 (3), 353-360 (1995).
  31. Ekstrom, A. How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Research Reviews. 62 (2), 233-244 (2010).
  32. Logothetis, N. K. What we can do and what we cannot do with fMRI. Nature. 453 (7197), 869-878 (2008).
  33. Lin, T. P., Carbon, M., et al. Metabolic correlates of subthalamic nucleus activity in Parkinson's disease. Brain. 131 (Pt. 5), 1373-1380 (2008).
  34. Hirano, S., Asanuma, K., et al. Dissociation of metabolic and neurovascular responses to levodopa in the treatment of Parkinson's disease. J. Neurosci. 28 (16), 4201-4209 (2008).
  35. Feigin, A., Antonini, A., et al. Tc-99m ethylene cysteinate dimer SPECT in the differential diagnosis of parkinsonism. Mov. Disord. 17 (6), 1265-1270 (2002).
  36. Eckert, T., Van Laere, K., et al. Quantification of Parkinson's disease-related network expression with ECD SPECT. Eur. J. Nucl. Med. Mol. Imaging. 34 (4), 496-501 (2007).
  37. Ma, Y., Huang, C., et al. Parkinson's disease spatial covariance pattern: noninvasive quantification with perfusion MRI. J. Cereb. Blood Flow Metab. 30 (3), 505-509 (2010).
  38. Melzer, T. R., Watts, R., et al. Arterial spin labelling reveals an abnormal cerebral perfusion pattern in Parkinson's disease. Brain. 134 (Pt. 3), 845-855 (2011).
  39. Skidmore, F., Spetsieris, P., et al. Diagnosis of Parkinson's disease using resting state fMRI. , LB22 (2011).
  40. Peng, S., Wu, T., et al. A comparison study of Parkinson's disease-related patterns between FDG PET and fMRI at rest state. Neuroimage. 61, 5610 (2012).
  41. Brickman, A. M., Habeck, C., Zarahn, E., Flynn, J., Stern, Y. Structural MRI covariance patterns associated with normal aging and neuropsychological functioning. Neurobiol. Aging. 28 (2), 284-295 (2007).
  42. Bergfield, K. L., Hanson, K. D., et al. Age-related networks of regional covariance in MRI gray matter: reproducible multivariate patterns in healthy aging. Neuroimage. 49 (2), 1750-1759 (2010).
  43. Steffener, J., Brickman, A. M., Habeck, C. G., Salthouse, T. A. Cerebral blood flow and gray matter volume covariance patterns of cognition in aging. Human Brain Mapping. , (2012).
  44. Cangelosi, R., Goriely, A. Component retention in principal component analysis with application to cDNA microarray data. Biol. Direct. 2, 2 (2007).
  45. Akaike, H. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 19 (6), 716-723 (1974).
  46. Eidelberg, D., Moeller, J. R., et al. Assessment of disease severity in parkinsonism with fluorine-18-fluorodeoxyglucose and PET. J. Nucl. Med. 36 (3), 378-383 (1995).
  47. Ma, Y., Tang, C., Moeller, J. R., Eidelberg, D. Abnormal regional brain function in Parkinson's disease: truth or fiction. Neuroimage. 45 (2), 260-266 (2009).
  48. Habeck, C., Steffener, J., Rakitin, B., Stern, Y. Can the default-mode network be described with one spatial-covariance network. Brain Res. 1468, 38-51 (2012).
  49. Joliffe, I. T. Principal Components Analysis. Springer Series in Statistics. , (2002).
  50. Limpert, E., Stahel, W. A., Abbt, M. Log-normal distributions across the sciences: keys and clues. BioScience. 51 (5), 341-352 (2001).
  51. Huang, C., Mattis, P., et al. Metabolic abnormalities associated with mild cognitive impairment in Parkinson disease. Neurology. 70 (16 Pt. 2), 1470-1477 (2008).
  52. Mattis, P. J., Tang, C. C., Ma, Y., Dhawan, V., Eidelberg, D. Network correlates of the cognitive response to levodopa in Parkinson disease. Neurology. 77 (9), 858-865 (2011).
  53. Feigin, A., Kaplitt, M. G., et al. Modulation of metabolic brain networks after subthalamic gene therapy for Parkinson's disease. Proc. Natl. Acad. Sci. U.S.A. 104 (49), 19559-19564 (2007).
  54. Mure, H., Tang, C. C., et al. Improved sequence learning with subthalamic nucleus deep brain stimulation: evidence for treatment-specific network modulation. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 32 (8), 2804-2813 (2012).

This article has been published

Video Coming Soon

JoVE Logo

Privacy

Terms of Use

Policies

Research

Education

ABOUT JoVE

Copyright © 2024 MyJoVE Corporation. All rights reserved