Eye movement-related confounds in neural decoding of visual working memory representations, bioRxiv, 2017-11-21

AbstractThe study of visual working memory (VWM) has recently seen revitalization with the emergence of new insights and theories regarding its neural underpinnings. One crucial ingredient responsible for this progress is the rise of neural decoding techniques. These techniques promise to uncover the representational contents of neural signals, as well as the underlying code and the dynamic profile thereof. Here, we aimed to contribute to the field by subjecting human volunteers to a combined VWMimagery task, while recording and decoding their neural signals as measured by MEG. At first sight, the results seem to provide evidence for a persistent, stable representation of the memorandum throughout the delay period. However, control analyses revealed that these findings can be explained by subtle, VWM-specific eye movements. As a potential remedy, we demonstrate the use of a functional localizer, which was specifically designed to target bottom-up sensory signals and as such avoids eye movements, to train the neural decoders. This analysis revealed a sustained representation for approximately 1 second, but no longer throughout the entire delay period. We conclude by arguing for more awareness of the potentially pervasive and ubiquitous effects of eye movement-related confounds.Significance statementVisual working memory is an important aspect of higher cognition and has been subject of much investigation within the field of cognitive neuroscience. Over recent years, these studies have increasingly relied on the use of neural decoding techniques. Here, we show that neural decoding may be susceptible to confounds induced by stimulus-specific eye movements. Such eye movements during working memory have been reported before, and may in fact be a common phenomenon. Given the widespread use of neural decoding and the potentially contaminating effects of eye movements, we therefore believe that our results are of significant relevance for the field.

biorxiv neuroscience 0-100-users 2017

Resting-state functional brain connectivity best predicts the personality dimension of openness to experience, bioRxiv, 2017-11-14

AbstractPersonality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the “Big Five”, as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (testretest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (“α” and “β”) from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

biorxiv neuroscience 100-200-users 2017

 

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