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 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.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

Social synchronization of brain activity increases during eye-contact

Humans make eye-contact to extract information about other people’s mental states, recruiting dedicated brain networks that process information about the self and others. Recent studies show that eye-contact increases the synchronization between two brains but do not consider its effects on activity within single brains. Here we investigate how eye-contact affects the frequency and direction of the synchronization within and between two brains and the corresponding network characteristics. We also evaluate the functional relevance of eye-contact networks by comparing inter- and intra-brain networks of friends vs. strangers and the direction of synchronization between leaders and followers. We show that eye-contact increases higher inter- and intra-brain synchronization in the gamma frequency band. Network analysis reveals that some brain areas serve as hubs linking within- and between-brain networks. During eye-contact, friends show higher inter-brain synchronization than strangers. Dyads with clear leader/follower roles demonstrate higher synchronization from leader to follower in the alpha frequency band. Importantly, eye-contact affects synchronization between brains more than within brains, demonstrating that eye-contact is an inherently social signal. Future work should elucidate the causal mechanisms behind eye-contact induced synchronization

The return trip effect: Why the return trip often seems to take less time

Three studies confirm the existence of the return trip effect: The return trip often seems shorter than the initial trip, even though the distance traveled and the actual time spent traveling are identical. A pretest shows that people indeed experience a return trip effect regularly, and the effect was found on a bus trip (Study 1), a bicycle trip (Study 2), and when participants watched a video of someone else traveling (Study 3). The return trip effect also existed when another, equidistant route was taken on the return trip, showing that it is not familiarity with the route that causes this effect. Rather, it seems that a violation of expectations causes this effect

The Effects of Previous Misestimation of Task Duration on Estimating Future Task Duration

It is a common time management problem that people underestimate the duration of tasks, which has been termed the "planning fallacy." To overcome this, it has been suggested that people should be informed about how long they previously worked on the same task. This study, however, tests whether previous misestimation also affects the duration estimation of a novel task, even if the feedback is only self-generated. To test this, two groups of participants performed two unrelated, laboratory-based tasks in succession. Learning was manipulated by permitting only the experimental group to retrospectively estimate the duration of the first task before predicting the duration of the second task. Results showed that the experimental group underestimated the duration of the second task less than the control group, which indicates a general kind of learning from previous misestimation. The findings imply that people could be trained to carefully observe how much they misestimate task duration in order to stimulate learning. The findings are discussed in relation to the anchoring account of task duration misestimation and the memory-bias account of the planning fallacy. © 2014 Springer Science+Business Media New York

Cognitive Performance and Heart Rate Variability: The Influence of Fitness Level

In the present study, we investigated the relation between cognitive performance and heart rate variability as a function of fitness level. We measured the effect of three cognitive tasks (the psychomotor vigilance task, a temporal orienting task, and a duration discrimination task) on the heart rate variability of two groups of participants: a high-fit group and a low-fit group. Two major novel findings emerged from this study. First, the lowest values of heart rate variability were found during performance of the duration discrimination task, compared to the other two tasks. Second, the results showed a decrement in heart rate variability as a function of the time on task, although only in the low-fit group. Moreover, the high-fit group showed overall faster reaction times than the low-fit group in the psychomotor vigilance task, while there were not significant differences in performance between the two groups of participants in the other two cognitive tasks. In sum, our results highlighted the influence of cognitive processing on heart rate variability. Importantly, both behavioral and physiological results suggested that the main benefit obtained as a result of fitness level appeared to be associated with processes involving sustained attention.This research was supported by the Spanish Ministerio de Educación y Cultura with a predoctoral grant (FPU-AP2010-3630) to the first author, Spanish grants SEJ2007-63645 from the Junta de Andalucía to Daniel Sanabria, Mikel Zabala and Esther Morales, and the CSD2008-00048 CONSOLIDER INGENIO (Dirección General de Investigación) to Daniel Sanabria