NeuroImage 84 (2014) 657–671 Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/ynimg Spatial–temporal modelling of fMRI data through spatially regularize

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalised Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a ``Learning with privileged information'' approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants.MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls based on the learning performance and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on the learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal for structured stimuli is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI

Implied Motion from Form in the Human Visual Cortex

When cartoonists use speed lines—also called motion streaks—to suggest the speed of a stationary object, they use form to imply motion. The goal of this study was to investigate the mechanisms that mediate the percept of implied motion in the human visual cortex. In an adaptation functional imaging paradigm we presented Glass patterns that, just like speed lines, imply motion but do not on average contain coherent motion energy. We found selective adaptation to these patterns in the human motion complex, the lateral occipital complex (LOC), and earlier visual areas. Glass patterns contain both local orientation features and global structure. To disentangle these aspects we performed a control experiment using Glass patterns with minimal local orientation differences but large global structure differences. This experiment showed that selectivity for Glass patterns arises in part in areas beyond V1 and V2. Interestingly, the selective adaptation transferred from implied motion stimuli to similar real motion patterns in dorsal but not ventral areas. This suggests that the same subpopulations of cells in dorsal areas that are selective for implied motion are also selective for real motion. In other words, these cells are invariant with respect to the cue (implied or real) that generates the motion. We conclude that the human motion complex responds to Glass patterns as if they contain coherent motion. This, presumably, is the reason why these patterns appear to move coherently. The LOC, however, has different cells that respond to the structure of real motion patterns versus implied motion patterns. Such a differential response may allow ventral areas to further analyze the structure of global patterns