Model-independent method for fMRI analysis

• Published in: IEEE transactions on medical imaging Vol. 23; no. 3; pp. 285 - 296
• Main Authors: Soltanian-Zadeh, H., Peck, D.J., Hearshen, D.O., Lajiness-O'Neill, R.R.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activated; Adult; Algorithms; Brain - anatomy & histology; Brain - physiology; Brain Mapping - methods; Character generation; Cognition - physiology; Computer Simulation; Evoked Potentials - physiology; Humans; Image analysis; Image Enhancement - methods; Image generation; Image Interpretation, Computer-Assisted - methods; Image segmentation; Magnetic analysis; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical analysis; Mathematical models; Methods; Middle Aged; Models, Neurological; Neurons - cytology; Neurons - physiology; Pixel; Pixels; Reproducibility of Results; Sensitivity and Specificity; Signal generators; Signal to noise ratio; Signatures; Stochastic Processes; Studies; Time series; Vectors; Vectors (mathematics)
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2003.823064

This paper presents a fast method for delineation of activated areas of the brain from functional magnetic resonance imaging (fMRI) time series data. The steps of the work accomplished are as follows. 1) It is shown that the detection performance evaluated by the area under the receiver operating characteristic curve is directly related to the signal-to-noise ratio (SNR) of the composite image generated in the detection process. 2) Detection and segmentation of activated areas are formulated in a vector space framework. In this formulation, a linear transformation (image combination method) is shown to be desirable to maximize the SNR of the activated areas subject to the constraint of removing inactive areas. 3) An analytical solution for the problem is found. 4) Image pixel vectors and expected time series pattern (signature) for inactive pixels are used to calculate weighting vector and identify activated regions. 5) Signatures of the activated regions are used to segment different activities. 6) Segmented images by the proposed method are compared with those generated by the conventional methods (correlation, t-statistic, and z statistic). Detection performance and SNRs of the images are compared. The proposed approach outperforms the conventional methods of fMRI analysis. In addition, it is model-independent and does not require a priori knowledge of the fMRI response to the paradigm. Since the method is linear and most of the work is done analytically, numerical implementation and execution of the method are much faster than the conventional methods.