Surfing the Brain—An Overview of Wavelet-Based Techniques for fMRI Data Analysis

The measurement of brain activity in a noninvasive way is an essential element in modern neurosciences. Modalities such as electroencephalography (EEG) and magnetoencephalography (MEG) recently gained interest, but two classical techniques remain predominant. One of them is positron emission tomography (PET), which is costly and lacks temporal resolution but allows the design of tracers for specific tasks; the other main one is functional magnetic resonance imaging (fMRI), which is more affordable than PET from a technical, financial, and ethical point of view, but which suffers from poor contrast and low signal-to-noise ratio (SNR). For this reason, advanced methods have been devised to perform the statistical analysis of fMRI data.