The fundamental challenge in the analysis of functional MRI experiments is to identify voxels that show signal changes varying with changing brain states. This is a difficult problem: firstly because the signal to noise ratio is generally poor, with the activation signal being often no larger than the noise level; secondly, the neurophysiology which couples the underlying brain activity to the measured response in FMRI is complex and generally poorly understood; and thirdly, the noise consists of a complex blend of spatio-temporal deterministic and stochastic components due to physiological and scanner-based artefacts.
Advances in data modelling have the potential to greatly increase our ability to detect neural activations and investigate brain function using FMRI. This section reviews research carried out by FMRIB to tackle these issues. This includes our research within the model-based voxelwise general linear model (GLM) for modelling single-session data (FILM), and the Bayesian method for analysing multiple sessions/subjects (FLAME). FEAT is a complete GUI-based tool for model-based FMRI analysis, built around FILM and FLAME, as well as other low-level tools such as FLIRT image registration.
Complementary to the model-based analyses, MELODIC uses independent component analysis to carry out ``temporal model-free'' exploratory analysis. This approach is able to identify signal and structured noise in FMRI data without needing to be given a temporal model.
We complete this section with recent development in the area of inference (``thresholding''), using spatial mixture modelling to address some of the limitations associated with the current common practice of linear data smoothing and null hypothesis testing.