Classifying cognitive profiles using machine learning with privileged information in mild cognitive impairment

Hanin H. Alahmadi, Yuan Shen*, Shereen Fouad, Caroline Di B. Luft, Peter Bentham, Zoe Kourtzi, Peter Tino, Concha Bielza, Li Su, Lubica Benuskova

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    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 Generalized 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 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 a probabilistic sequence 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 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.

    Original languageEnglish
    Article number117
    JournalFrontiers in Computational Neuroscience
    Publication statusPublished - 17 Nov 2016

    Bibliographical note

    Funding Information:
    PT and YS were supported by EPSRC grant no EP/L000296/1 "Personalized Medicine through Learning in the Model Space." This work was supported by grants to ZK from the Biotechnology and Biological Sciences Research Council (H012508), the Leverhulme Trust (RF-2011-378), and the (European Community?s) Seventh Framework Programme (FP7/2007-2013) under agreement PITN-GA- 2011-290011.


    • Discriminative feature extraction
    • Fmri graph feature
    • Learning vector quantization
    • Learning with privileged information
    • Linear discriminant analysis
    • Supervised metric learning


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