IMAGE ANALYSIS OF FUNCTIONAL MAGNETIC RESONANCE IMAGING

Functional MRI can provide important new insights into the phenomenon of brain activity through improved temporal and spatial resolution as well as easiness and safety on repeated or prolonged examination. A revolutionary renewal on the knowledge of functional anatomy of the human brain based on the pre-fMRI technology is thus possible. However, the activity in the brain is so complex that the simplified model-dependent analysis methods may not be sufficient. Therefore, we have endeavored to establish data-driven tools and methodologies for fMRI. Insupervised algorithms based on self-organized artificial neural network and fuzzy logic are used to solve the problem of biased result or misled interpretation frequently encountered in statistical methods depending on prior knowledge of imposed experimental paradigm or assumed pattern of brain response to activation. With these new analysis methods, more accurate activation regions and physiological dynamics can be identified that makes further investigation possible and easier.

[1]  G. Radda,et al.  Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high field. , 1982, Biochimica et biophysica acta.

[2]  D. S. Williams,et al.  Magnetic resonance imaging of perfusion using spin inversion of arterial water. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[3]  J W Belliveau,et al.  Functional cerebral imaging by susceptibility‐contrast NMR , 1990, Magnetic resonance in medicine.

[4]  C. D. Coryell,et al.  The Magnetic Properties and Structure of Hemoglobin, Oxyhemoglobin and Carbonmonoxyhemoglobin , 1936, Proceedings of the National Academy of Sciences.

[5]  E. DeYoe,et al.  Functional magnetic resonance imaging (FMRI) of the human brain , 1994, Journal of Neuroscience Methods.

[6]  J F Moreau,et al.  Radiology at the Assistance Publique-Hôpitaux de Paris. , 1992, Investigative radiology.

[7]  S E Petersen,et al.  Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[8]  X. Hu,et al.  Evaluation of the early response in fMRI in individual subjects using short stimulus duration , 1997, Magnetic resonance in medicine.

[9]  J Hennig,et al.  Neural network‐based analysis of MR time series , 1999, Magnetic resonance in medicine.

[10]  S. Ogawa,et al.  Oxygenation‐sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields , 1990, Magnetic resonance in medicine.

[11]  Jyh-Horng Chen,et al.  Independent component analysis in the detection and correction of physiological artifacts in fMRI , 2001, NeuroImage.

[12]  Chung-Chih Lin,et al.  Model Free Functional MRI Analysis Using Kohonen Clustering Neural Network , 1999, IEEE Trans. Medical Imaging.

[13]  Karl J. Friston,et al.  Analysis of functional MRI time‐series , 1994, Human Brain Mapping.

[14]  E C Wong,et al.  Processing strategies for time‐course data sets in functional mri of the human brain , 1993, Magnetic resonance in medicine.

[15]  Chung-Chih Lin,et al.  Tissue segmentation-assisted analysis of fMRI for human motor response: an approach combining artificial neural network and fuzzy C means , 2001, Journal of Digital Imaging.

[16]  M. Raichle,et al.  Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Seong-Gi Kim.  Quantification of relative cerebral blood flow change by flow‐sensitive alternating inversion recovery (FAIR) technique: Application to functional mapping , 1995, Magnetic resonance in medicine.

[18]  B. Rosen,et al.  Functional mapping of the human visual cortex by magnetic resonance imaging. , 1991, Science.

[19]  R. Turner,et al.  Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[20]  D. Tank,et al.  Brain magnetic resonance imaging with contrast dependent on blood oxygenation. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Jan Sijbers,et al.  Adaptive anisotropic noise filtering for magnitude MR data , 1999, Medical Imaging.

[22]  M. Mintun,et al.  Nonoxidative glucose consumption during focal physiologic neural activity. , 1988, Science.

[23]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[24]  J. Mazziotta,et al.  Rapid Automated Algorithm for Aligning and Reslicing PET Images , 1992, Journal of computer assisted tomography.

[25]  P. Boesiger,et al.  A new correlation‐based fuzzy logic clustering algorithm for FMRI , 1998, Magnetic resonance in medicine.

[26]  Kai-Hsiang Chuang,et al.  Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy C-means , 1999, IEEE Transactions on Medical Imaging.

[27]  M. Raichle,et al.  A Stereotactic Method of Anatomical Localization for Positron Emission Tomography , 1985, Journal of computer assisted tomography.

[28]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[29]  S. Ogawa,et al.  Magnetic resonance imaging of blood vessels at high fields: In vivo and in vitro measurements and image simulation , 1990, Magnetic resonance in medicine.

[30]  G H Glover,et al.  Time course of odorant-induced activation in the human primary olfactory cortex. , 2000, Journal of neurophysiology.

[31]  Karl J. Friston,et al.  Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.

[32]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[33]  R Baumgartner,et al.  Fuzzy clustering of gradient‐echo functional MRI in the human visual cortex. Part I: Reproducibility , 1997, Journal of magnetic resonance imaging : JMRI.

[34]  T. L. Davis,et al.  Mr perfusion studies with t1‐weighted echo planar imaging , 1995, Magnetic resonance in medicine.

[35]  Peter T. Fox,et al.  The temporal response of the brain after eating revealed by functional MRI , 2000, Nature.

[36]  J. R. Baker,et al.  Magnetic Resonance Imaging Mapping of Brain Function: Human Visual Cortex , 1992, Investigative radiology.