Data-driven analyses of an fMRI study of a subject experiencing phosphenes

• Published in: Journal of magnetic resonance imaging Vol. 31; no. 4; pp. 821 - 828
• Main Authors: Missimer, John H., Seitz, Rüdiger J., Kleiser, Raimund
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.04.2010
• Subjects: Brain Infarction - diagnosis; Brain Infarction - pathology; Brain Mapping - methods; Female; fMRI; Humans; Image Processing, Computer-Assisted - methods; independent component analysis; Magnetic Resonance Imaging - methods; Middle Aged; Neural Networks (Computer); Occipital Lobe - pathology; Phosphenes; Principal Component Analysis; Visual Cortex - pathology
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.22122

Purpose: To compare two data‐driven methods of statistical image analysis, principal and independent component analysis (PCA, ICA), in identifying neural networks related to the transient occurrence of phosphenes experienced by a female patient subsequent to a brain infarct. Materials and Methods: An initial functional magnetic resonance imaging (fMRI) session consisted of two acquisitions: one of the patient experiencing phosphenes and a second responding to a well‐defined visual stimulation paradigm. A second fMRI session 6 months later, when the patient no longer experienced phosphenes, consisted of an acquisition in which no stimulation was presented. Analysis of correlations between the temporal expression coefficients and models of the hemodynamic response identified salient components. Spectral analysis confirmed the identification. The phosphene model was based solely on the subjective report of the patient. Results: Both methods revealed occipital cortical and subcortical areas known to be sites for visual information‐processing during stimulation, as did SPM. In addition, higher‐order visual areas such as the precuneus and the lateral parietal cortex were implicated in the PCA of the phosphenes. Conclusion: The analyses suggest the capability of data‐driven approaches to identify the brain structures involved in these transient, spontaneous visual events. J. Magn. Reson. Imaging 2010;31:821–828. ©2010 Wiley‐Liss, Inc.