Learning to Interpret Cognitive States from fMRI Brain Images
Proceedings of the 2009 conference on Computational Intelligence and Bioengineering: Essays in Memory of Antonina Starita
Comparing classification methods for longitudinal fmri studies
Neural Computation
Multiclass sparse Bayesian regression for fMRI-based prediction
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Journal of Cognitive Neuroscience
Intelligent data analysis of intelligent systems
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Functional neuroimaging and psychology: What have you done for me lately?
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
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Over the past decade, object recognition work has confounded voxel response detection with potential voxel class identification. Consequently, the claim that there are areas of the brain that are necessary and sufficient for object identification cannot be resolved with existing associative methods (e.g., the general linear model) that are dominant in brain imaging methods. In order to explore this controversy we trained full brain (40,000 voxels) single TR (repetition time) classifiers on data from 10 subjects in two different recognition tasks on the most controversial classes of stimuli (house and face) and show 97.4% median out-of-sample (unseen TRs) generalization. This performance allowed us to reliably and uniquely assay the classifier's voxel diagnosticity in all individual subjects' brains. In this two-class case, there may be specific areas diagnostic for house stimuli (e.g., LO) or for face stimuli (e.g., STS); however, in contrast to the detection results common in this literature, neither the fusiform face area nor parahippocampal place area is shown to be uniquely diagnostic for faces or places, respectively.