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

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

Hemispheric differences between left and right supramarginal gyrus for pitch and rhythm memory

Functional brain imaging studies and non-invasive brain stimulation methods have shown the importance of the left supramarginal gyrus (SMG) for pitch memory. The extent to which this brain region plays a crucial role in memory for other auditory material remains unclear. Here, we sought to investigate the role of the left and right SMG in pitch and rhythm memory in non-musicians. Anodal or sham transcranial direct current stimulation (tDCS) was applied over the left SMG (Experiment 1) and right SMG (Experiment 2) in two different sessions. In each session participants completed a pitch and rhythm recognition memory task immediately after tDCS. A significant facilitation of pitch memory was revealed when anodal stimulation was applied over the left SMG. No significant effects on pitch memory were found for anodal tDCS over the right SMG or sham condition. For rhythm memory the opposite pattern was found; anodal tDCS over the right SMG led to an improvement in performance, but anodal tDCS over the left SMG had no significant effect. These results highlight a different hemispheric involvement of the SMG in auditory memory processing depending on auditory material that is encoded

A machine learning approach to predict perceptual decisions: an insight into face pareidolia

The perception of an external stimulus not only depends upon the characteristics of the stimulus but is also influenced by the ongoing brain activity prior to its presentation. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images, while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial. Using differential hemispheric asymmetry features based on large-scale neural oscillations in a machine learning classifier, we demonstrated that prestimulus brain activities could achieve a classification accuracy, discriminating face from no-face perception, of 75% across trials. The time–frequency features representing hemispheric asymmetry yielded the best classification performance, and prestimulus alpha oscillations were found to be mostly involved in predicting perceptual decision. These findings suggest a mechanism of how prior expectations in the prestimulus period may affect post-stimulus decision making

The value of an action: Impact of motor behaviour on outcome processing and stimulus preference

While influences of Pavlovian associations on instrumental behaviour are well established, we still do not know how motor actions affect the formation of Pavlovian associations. To address this question, we designed a task in which participants were presented with neutral stimuli, half of which were paired with an active response, half with a passive waiting period. Stimuli had an 80% chance of predicting either a monetary gain or loss. We compared the feedback-related negativity (FRN) in response to predictive stimuli and outcomes, as well as directed phase synchronization before and after outcome presentation between trials with versus without a motor response. We found a larger FRN amplitude in response to outcomes presented after a motor response (active trials). This effect was driven by a positive deflection in active reward trials, which was absent in passive reward trials. Connectivity analysis revealed that the motor action reversed the direction of the phase synchronization at the time of the feedback presentation: Top-down information flow during the outcome anticipation phase in active trials, but bottom-up information flow in passive trials. This main effect of action was mirrored in behavioural data showing that participants preferred stimuli associated with an active response. Our findings suggest an influence of neural systems that initiate motor actions on neural systems involved in reward processing. We suggest that motor actions might modulate the brain responses to feedback by affecting the dynamics of brain activity towards optimizing the processing of the resulting action outcome.Royal Society (RGS\R1\191344)