Lao, J. | Search bioRxiv

bioRxiv2018

Neural representations of faces are tuned to eye movements

Eye movements provide a functional signature of how human vision is achieved. Many recent studies have reported idiosyncratic visual sampling strategies during face recognition. Whether these inter-individual differences are mirrored by idiosyncratic neural responses has not been investigated yet. Here, we tracked observers eye movements during face recognition; additionally, we obtained an objective index of neural face discrimination through EEG that was recorded while subjects fixated different facial information.\n\nAcross all observers, we found that those facial features that were fixated longer during face recognition elicited stronger neural face discrimination responses. This relationship occurred independently of inter-individual differences in fixation biases. Our data show that eye movements play a functional role during face processing by providing the neural system with information that is diagnostic to a specific observer. The effective processing of face identity involves idiosyncratic, rather than universal representations.

neuroscience↗

bioRxiv2018

Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal

BackgroundfMRI provides spatial resolution that is unmatched by any non-invasive neuroimaging technique. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic based fMRI signal.\n\nNew MethodsWe present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed using pairs of single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect.\n\nResultsWe demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of univariate differences. Using Monte Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and at the single subject level, FWER was either at or significantly below 5%. For the group level, we reached the desired power with 18 subjects and 12 trials; for the single subject scenario, 14 trials were required to achieve comparable power.\n\nComparison with existing methodstMVPA adds a temporal multivariate dimension to the tools available for fMRI analysis, enabling investigations of the evolution of neural representations over time. Moreover, tMVPA permits performing single subject inferential statistics by considering single-trial distribution.\n\nConclusionThe growing interest in fMRI temporal dynamics, motivated by recent evidence suggesting that the BOLD signal carries temporal information at a finer scale than previously thought, advocates the need for analytical tools, such as the tMVPA approach proposed here, tailored to investigating BOLD temporal information.

neuroscience↗