Biology subjects

Marquardt, I.

Publications and source records attributed to Marquardt, I..

3 recordsLinked to original sources

bioRxiv2019

Motion displaces population receptive fields in the direction opposite to motion

Motion signals can bias the perceived position of visual stimuli. While the apparent position of a stimulus is biased in the direction of motion, electro-physiological studies have shown that the receptive field (RF) of neurons is shifted in the direction opposite to motion, at least in cats and macaque monkeys. In humans, it remains unclear how motion signals affect population RF (pRF) estimates. We addressed this question using psychophysical measurements and functional magnetic resonance imaging (fMRI) at 7 Tesla. We systematically varied two factors: the motion direction of the carrier pattern (inward, outward and flicker motion) and the contrast of the mapping stimulus (low and high stimulus contrast). We observed that while physical positions were identical across all conditions, presence of low-contrast motion, but not high-contrast motion, shifted perceived stimulus position in the direction of motion. Correspondingly, we found that pRF estimates in early visual cortex were shifted against the direction of motion for low-contrast stimuli but not for high stimulus contrast. We offer an explanation in form of a model for why apertures are perceptually shifted in the direction of motion even though pRFs shift in the opposite direction.

neuroscience↗

bioRxiv2019

Depth-resolved ultra-high field fMRI reveals feedback contributions to surface motion perception

Human visual surface perception has neural correlates in early visual cortex, but the extent to which feedback contributes to this activity is not well known. Feedback projections preferentially enter superficial and deep anatomical layers, while avoiding the middle layer, which provides a hypothesis for the cortical depth distribution of fMRI activity related to feedback in early visual cortex. Here, we presented human participants uniform surfaces on a dark, textured background. The grey surface in the left hemifield was either perceived as static or moving based on a manipulation in the right hemifield. Physically, the surface was identical in the left visual hemifield, so any difference in percept likely was related to feedback. Using ultra-high field fMRI, we report the first evidence for a depth distribution of activation in line with feedback during the (illusory) perception of surface motion. Our results fit with a signal re-entering in superficial depths of V1, followed by a feedforward sweep of the re-entered information through V2 and V3, as suggested by activity centred in the middle-depth levels of the latter areas. This positive modulation of the BOLD signal due to illusory surface motion was on top of a strong negative BOLD response in the cortical representation of the surface stimuli, which depended on the presence of texture in the background. Hence, the magnitude and sign of the BOLD response to the surface strongly depended on background properties, and was additionally modulated by the presence or absence of illusory motion perception in a manner compatible with feedback. In summary, the present study demonstrates the potential of depth resolved fMRI in tackling biomechanical questions on perception that so far were only within reach of invasive animal experimentation.

neuroscience↗

bioRxiv2018

A scalable method to improve gray matter segmentation at ultra high field MRI

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

neuroscience↗