Sound-modulations of visual motion perception implicate the cortico-vestibular brain.

A widely used example of the intricate (yet poorly understood) intertwining of multisensory signals in the brain is the audiovisual bounce inducing effect (ABE). This effect presents two identical objects moving along the azimuth with uniform motion and towards opposite directions. The perceptual interpretation of the motion is ambiguous and is modulated if a transient (sound) is presented in coincidence with the point of overlap of the two objects' motion trajectories. This phenomenon has long been written-off to simple attentional or decision-making mechanisms, although the neurological underpinnings for the effect are not well understood. Using behavioural metrics concurrently with event-related fMRI, we show that sound-induced modulations of motion perception can be further modulated by changing motion dynamics of the visual targets. The phenomenon engages the posterior parietal cortex and the parieto-insular-vestibular cortical complex, with a close correspondence of activity in these regions with behaviour. These findings suggest that the insular cortex is engaged in deriving a probabilistic perceptual solution through the integration of multisensory data.

Multimodal data acquisition at SARS-CoV-2 drive through screening centers: Setup description and experiences in Saarland, Germany.

SARS-CoV-2 drive through screening centers (DTSC) have been implemented worldwide as a fast and secure way of mass screening. We use DTSCs as a platform for the acquisition of multimodal datasets that are needed for the development of remote screening methods. Our acquisition setup consists of an array of thermal, infrared and RGB cameras as well as microphones and we apply methods from computer vision and computer audition for the contactless estimation of physiological parameters. We have recorded a multimodal dataset of DTSC participants in Germany for the development of remote screening methods and symptom identification. Acquisition in the early stages of a pandemic and in regions with high infection rates can facilitate and speed up the identification of infection specific symptoms and large-scale data acquisition at DTSC is possible without disturbing the flow of operation.

Fast variational alignment of non-flat 1D displacements for applications in neuroimaging.

BACKGROUND: In the context of signal analysis and pattern matching, alignment of 1D signals for the comparison of signal morphologies is an important problem. For image processing and computer vision, 2D optical flow (OF) methods find wide application for motion analysis and image registration and variational OF methods have been continuously improved over the past decades. NEW METHOD: We propose a variational method for the alignment and displacement estimation of 1D signals. We pose the estimation of non-flat displacements as an optimization problem with a similarity and smoothness term similar to variational OF estimation. To this end, we can make use of efficient optimization strategies that allow real-time applications on consumer grade hardware. RESULTS: We apply our method to two applications from functional neuroimaging: The alignment of 2-photon imaging line scan recordings and the denoising of evoked and event-related potentials in single trial matrices. We can report state of the art results in terms of alignment quality and computing speeds. EXISTING METHODS: Existing methods for 1D alignment target mostly constant displacements, do not allow native subsample precision or precise control over regularization or are slower than the proposed method. CONCLUSIONS: Our method is implemented as a MATLAB toolbox and is online available. It is suitable for 1D alignment problems, where high accuracy and high speed is needed and non-constant displacements occur.