Activation detection in functional MRI using model-free technique based on CCA-ICA analysis.
The model-based approach for detecting the fMRI activations involves assumptions about the hemodynamic response function. If such assumptions are incorrect or incomplete, this may result in biased estimates of the true response, posing a significant obstacle to the practicality of the technique. In this work, a simple yet robust model-free technique is proposed for detecting the fMRI activations. The idea of the proposed model is to convert one of the model-based fMRI tools, namely canonical correlation analysis (CCA), to model-free with help of independent component analysis (ICA). In particular, ICA provides accurate reference functions for CCA instead of the harmonics originally used. This combination enables the elimination of the limitations of both techniques and provides a model-free approach for data analysis. Results from both numerical simulations and real fMRI data sets confirm the practicality and robustness of the proposed method.
El-Shabrawy
Nour
N
Biomedical Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt.
Mohamed
Ahmed S
AS
Youssef
Abou-Bakr M
AB
Kadah
Yasser M
YM
D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009044:Motor Cortex / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic