fMRI pattern classification using neuroanatomically constrained boosting

No abstract

This paper introduces a multivariate Bayesian (MVB) scheme to decode or recognise brain states from neuroimages. It resolves the illposed many-to-one mapping, from voxel values or data features to a target variable, using a parametric empirical or hierarchical Bayesian model. This model is inverted using standard variational techniques, in this case expectation maximisation, to furnish the model evidence and the conditional density of the model's parameters. This allows one to compare different models or hypotheses about the mapping from functional or structural anatomy to perceptual and behavioural consequences (or their deficits). We frame this approach in terms of decoding measured brain states to predict or classify outcomes using the rhetoric established in pattern classification of neuroimaging data. However, the aim of MVB is not to predict (because the outcomes are known) but to enable inference on different models of structurefunction mappings; such as distributed and sparse representations. This allows one to optimise the model itself and produce predictions that outperform standard pattern classification approaches, like support vector machines.Technically, the model inversion and inference uses the same empirical Bayesian procedures developed for ill-posed inverse problems (e.g., source reconstruction in EEG). However, the MVB scheme used here extends this approach to include a greedy search for sparse solutions. It reduces the problem to the same form used in Gaussian process modelling, which affords a generic and efficient scheme for model optimisation and evaluating model evidence. We illustrate MVB using simulated and real data, with a special focus on model comparison; where models can differ in the form of the mapping (i.e., neuronal representation) within one region, or in the (combination of) regions per se.

a b s t r a c t a r t i c l e i n f oUsing an fMRI-based classification approach and the structural equation modeling (SEM) method, this study examined the neural bases of atypical planning and execution processes involved in stuttering. Twelve stuttering speakers and 12 controls were asked to name pictures under different conditions (single-syllable, multi-syllable, or repeated-syllable) in the scanner. The contrasts between conditions provided information about planning and execution processes. The classification analysis showed that, as compared to nonstuttering controls, stuttering speakers' atypical planning of speech was evident in their neural activities in the bilateral inferior frontal gyrus (IFG) and right putamen and their atypical execution of speech was evident in their activations in the right cerebellum and insula, left premotor area (PMA), and angular gyrus (AG). SEM results further revealed two parallel neural circuits-the basal ganglia-IFG/PMA circuit and the cerebellum-PMA circuit-that were involved in atypical planning and execution processes of stuttering, respectively. The AG appeared to be involved in the interface of atypical planning and execution in stuttering. These results are discussed in terms of their implications to the theories about stuttering and to clinical applications.