Biology subjects

Thiele, A.

Publications and source records attributed to Thiele, A..

3 recordsLinked to original sources

bioRxiv2018

Behavioural and neural signatures of perceptual evidence accumulation are modulated by pupil-linked arousal

The timing and accuracy of perceptual decision making is exquisitely sensitive to fluctuations in arousal. Although extensive research has highlighted the role of neural evidence accumulation in forming decisions, our understanding of how arousal impacts these processes remains limited. Here we isolated electrophysiological signatures of evidence accumulation alongside signals reflecting target selection, attentional engagement and motor output and examined their modulation as a function of both tonic and phasic arousal, indexed by baseline and task-evoked pupil diameter, respectively. For both pupillometric measures, the relationship with reaction time was best described by a second-order, U-shaped, polynomial. Additionally, the two pupil measures were predictive of a unique set of EEG signatures that together represent multiple information processing steps of perceptual decision-making, including evidence accumulation. Finally, we found that behavioural variability associated with fluctuations in both tonic and phasic arousal was largely mediated by variability in evidence accumulation.

neuroscience↗

bioRxiv2017

An open resource for nonhuman primate imaging

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience.\n\nUnfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMate Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 13 independent data collections aggregated across 11 sites (total = 98 macaque monkeys). We also outline the unique pitfalls and challenges that should be considered in the analysis of the non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

neuroscience↗

bioRxiv2017

Non-parametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data

Directed connectivity inference has become a cornerstone in neuroscience following the recent progress in neuroimaging and elctrophysiological techniques to characterize anatomical and functional networks. This paper focuses on the detection of existing connections from the observed activity in networks of 50 to 150 nodes with linear feedback in discrete time. Through the variation of multiple network parameters, our numerical results indicate that directed connections - in the time domain - are more accurately estimated based on the coefficients obtained from multivariate autoregressive (MVAR) than Granger causality analysis, which is based on the error residuals of the same MVAR linear regression. Based on these findings, we propose a non-parametric significance test for connectivity detection, which achieves a good control of false positives (type 1 error) and is robust to various network topologies. When generating surrogate distributions, we compare the effects of circular shifts, random permutations and phase randomization of the observed time series, each breaking down covariances in a specific manner: the MVAR estimates from those shuffled covariances build a null-hypothesis distribution for each connection, from which the original connectivity estimate can be compared. We apply our method to multiunit activity data recorded from Utah electrode arrays in monkey and examine the detected interactions between 25 channels for a proof of concept. The results unravel a non-trivial underlying connectivity structure, which differentiates the effect of incoming and outgoing connections.

neuroscience↗