Human VMPFC encodes early signatures of confidence in perceptual decisions, bioRxiv, 2017-11-26
AbstractChoice confidence, an individual’s internal estimate of judgment accuracy, plays a critical role in adaptive behaviour. Despite its importance, the early (decisional) stages of confidence processing remain underexplored. Here, we recorded simultaneous EEGfMRI while participants performed a direction discrimination task and rated their confidence on each trial. Using multivariate single-trial discriminant analysis of the EEG, we identified a stimulus- and accuracy-independent component encoding confidence, appearing prior to subjects’ choice and explicit confidence report. The trial-to-trial variability in this EEG-derived confidence signal was uniquely associated with fMRI responses in the ventromedial prefrontal cortex (VMPFC), a region not typically associated with confidence for perceptual decisions. Furthermore, we showed that the VMPFC was functionally coupled with regions of the prefrontal cortex that support neural representations of confidence during explicit metacognitive report. Our results suggest that the VMPFC encodes an early confidence readout, preceding and potentially informing metacognitive evaluation and learning, by acting as an implicit valuereward signal.
biorxiv neuroscience 0-100-users 2017Sensory cortex is optimised for prediction of future input, bioRxiv, 2017-11-25
Neurons in sensory cortex are tuned to diverse features in natural scenes. But what determines which features neurons become selective to? Here we explore the idea that neuronal selectivity is optimised to represent features in the recent past of sensory input that best predict immediate future inputs. We tested this hypothesis using simple feedforward neural networks, which were trained to predict the next few video or audio frames in clips of natural scenes. The networks developed receptive fields that closely matched those of real cortical neurons, including the oriented spatial tuning of primary visual cortex, the frequency selectivity of primary auditory cortex and, most notably, in their temporal tuning properties. Furthermore, the better a network predicted future inputs the more closely its receptive fields tended to resemble those in the brain. This suggests that sensory processing is optimised to extract those features with the most capacity to predict future input.Impact statementPrediction of future input explains diverse neural tuning properties in sensory cortex.
biorxiv neuroscience 0-100-users 2017Eye 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 2017The Generation and Propagation of the Human Alpha Rhythm, bioRxiv, 2017-11-19
AbstractThe alpha rhythm is the longest studied brain oscillation and has been theorized to play a key role in cognition. Still, its physiology is poorly understood. In this study, we used micro and macro electrodes in surgical epilepsy patients to measure the intracortical and thalamic generators of the alpha rhythm during quiet wakefulness. We first found that alpha in posterior cortex propagates from higher-order anterosuperior areas towards the occipital pole, consistent with alpha effecting top-down processing. This cortical alpha leads pulvinar alpha, complicating prevailing theories of a thalamic pacemaker. Finally, alpha is dominated by currents and firing in supragranular cortical layers. Together, these results suggest that the alpharhythm likely reflects short-range supragranular feedback which propagates from higher to lower-order cortex and cortex to thalamus. These physiological insights suggest how alpha could mediate feedback throughout the thalamocortical system.
biorxiv neuroscience 0-100-users 2017Resting-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 2017Theta and alpha oscillations are traveling waves in the human neocortex, bioRxiv, 2017-11-14
SummaryHuman cognition requires the coordination of neural activity across widespread brain networks. Here we describe a new mechanism for large-scale coordination in the human brain traveling waves of theta and alpha oscillations. Examining direct brain recordings from neurosurgical patients performing a memory task, we found contiguous clusters of cortex in individual patients with oscillations at specific frequencies between 2 to 15 Hz. These clusters displayed spatial phase gradients, indicating that the oscillations were traveling waves that propagated across the cortex at ∼0.25-0.75 ms. Traveling waves were relevant behaviorally because their propagation correlated with task events and was more consistent when subjects performed the task well. Our findings suggest that traveling waves can be modeled by a network of coupled oscillators because the direction of wave propagation correlated with the spatial orientation of local frequency gradients. These findings suggest a role for traveling waves in supporting brain connectivity by organizing neural processes across space and time.
biorxiv neuroscience 100-200-users 2017