Combining cues for estimating slant in perception and action

Smeets, J.B.J. [Promotor]Brenner, E.M. [Copromotor

Slant cues are processed with different latencies for the online control of movement

For the online control of movement, it is important to respond fast. The extent to which cues are effective in guiding our actions might therefore depend on how quickly they provide new information. We compared the latency to alter a movement when monocular and binocular cues indicated that the surface slant had changed. We found that subjects adjusted their movement in response to three types of information: information about the new slant from the monocular image, information about the new slant from binocular disparity, and information about the change in slant from the change in the monocular image. Responses to changes in the monocular image were approximately 40 ms faster than responses to a new slant estimate from binocular disparity and about 90 ms faster than responses to a new slant estimate from the monocular image. Considering these delays, adjustments of ongoing movements to changes in slant will usually be initiated by changes in the monocular image. The response will later be refined on the basis of combined binocular and monocular estimates of slant. © ARVO

A longitudinal cohort study on quality of life in stroke patients and their partners: Restore4Stroke Cohort

BACKGROUND: Stroke is a major cause of disability in the Western world. Its long-term consequences have a negative impact on the quality of life of both the patients and their partners. AIM: The aim of the Restore4Stroke Cohort study is to investigate the changes in quality of life of stroke patients and their partners over time, and to determine factors predicting quality of life in several domains, especially personal and environmental factors. METHOD: Multicentre prospective longitudinal cohort study. Inclusion and the first assessment take place during hospital stay in the first week post-stroke. Follow-up assessments take place at two months, six months, one year, and two years post-stroke. Recruitment of 500 patients from stroke units in six participation hospitals is foreseen. If the patient has a partner, he or she is also asked to participate in the study. OUTCOMES: The main outcome is quality of life, considered from a health-related quality of life and domain-specific quality of life perspective. Factors predicting long-term quality of life will be determined by taking into account the health condition (pre-stroke health condition and stroke-related health condition), personal factors (e.g. coping and illness cognitions), and environmental factors (e.g. caregiver burden and social support). DISCUSSION: This study is expected to provide information about the changes in quality of life of stroke patients and their partners over time. Furthermore, the identification of factors predicting quality of life can be used to improve rehabilitation care and develop new interventions for stroke patients and their partners

Time-varying effective EEG source connectivity: the optimization of model parameters*

Adaptive estimation methods based on general Kalman filter are powerful tools to investigate brain networks dynamics given the non-stationary nature of neural signals. These methods rely on two parameters, the model order p and adaptation constant c, which determine the resolution and smoothness of the time-varying multivariate autoregressive estimates. A sub-optimal filtering may present consistent biases in the frequency domain and temporal distortions, leading to fallacious interpretations. Thus, the performance of these methods heavily depends on the accurate choice of these two parameters in the filter design. In this work, we sought to define an objective criterion for the optimal choice of these parameters. Since residual- and information-based criteria are not guaranteed to reach an absolute minimum, we propose to study the partial derivatives of these functions to guide the choice of p and c. To validate the performance of our method, we used a dataset of human visual evoked potentials during face perception where the generation and propagation of information in the brain is well understood and a set of simulated data where the ground truth is available

Large-Scale 3-5 Hz Oscillation Constrains the Expression of Neocortical Fast Ripples in a Mouse Model of Mesial Temporal Lobe Epilepsy.

Large-scale slow oscillations allow the integration of neuronal activity across brain regions during sensory or cognitive processing. However, evidence that this form of coding also holds for pathological networks, such as for distributed networks in epileptic disorders, does not yet exist. Here, we show in a mouse model of unilateral hippocampal epilepsy that epileptic fast ripples generated in the neocortex distant from the primary focus occur during transient trains of interictal epileptic discharges. During these epileptic paroxysms, local phase-locking of neuronal firing and a phase-amplitude coupling of the epileptic discharges over a slow oscillation at 3-5 Hz are detected. Furthermore, the buildup of the slow oscillation begins in the bihippocampal network that includes the focus, which synchronizes and drives the activity across the large-scale epileptic network into the frontal cortex. This study provides the first functional description of the emergence of neocortical fast ripples in hippocampal epilepsy and shows that cross-frequency coupling might be a fundamental mechanism underlying the spreading of epileptic activity

Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis.

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth

Gene therapy in the Netherlands

- Extensive research is ongoing worldwide on the clinical utility of gene therapy, particularly for the treatment of cancer and genetic disorders. - Two gene therapy products have already been approved recently in China. - Clinical experience with gene therapy has also been accumulating in the Netherlands: over 200 Dutch patients have now been treated in clinical trials. - Published results indicate that gene therapy is generally safe. - Gene therapy appears to be effective for some genetic disorders, such as severe combined immune deficiency and haemophilia B. - The efficacy of gene therapy, particularly in the treatment of cancer, appears to be limited up till now.</p