Mixtures of general linear models for functional neuroimaging.
We set out a new general framework for making inferences from neuroimaging data, which includes a standard approach to neuroimaging analysis, statistical parametric mapping (SPM), as a special case. The model offers numerous conceptual and statistical advantages that derive from analyzing data at the "cluster level" rather than the "voxel level" and from explicit modeling of the shape and position of clusters of activation. This provides a natural and principled way to pool data from nearby voxels for parameter and variance-component estimation. The model can also be viewed as performing a spatio-temporal cluster analysis. The parameters of the model are estimated using an expectation maximization (EM) algorithm.
Penny
Will
W
Wellcome Department of Imaging Neuroscience, University College, 12 Queen Square, London WC1N 3BG, UK. wpenny@fil.ion.ucl.ac.uk
Friston
Karl
K
D000161:Acoustic Stimulation; D000465:Algorithms; D001307:Auditory Perception / Q000502:physiology; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D005072:Evoked Potentials, Auditory / Q000502:physiology; D005074:Evoked Potentials, Visual / Q000502:physiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D010775:Photic Stimulation; D014796:Visual Perception / Q000502:physiology