Quantifying consistency of Event Related Potentials across subjects based on single-trial topographic analysis

Introduction: Non-invasive brain imaging techniques often contrast experimental conditions across a cohort of participants, obfuscating distinctions in individual performance and brain mechanisms that are better characterised by the inter-trial variability. To overcome such limitations, we developed topographic analysis methods for single-trial EEG data [1]. So far this was typically based on time-frequency analysis of single-electrode data or single independent components. The method's efficacy is demonstrated for event-related responses to environmental sounds, hitherto studied at an average event-related potential (ERP) level. Methods: Nine healthy subjects participated to the experiment. Auditory meaningful sounds of common objects were used for a target detection task [2]. On each block, subjects were asked to discriminate target sounds, which were living or man-made auditory objects. Continuous 64-channel EEG was acquired during the task. Two datasets were considered for each subject including single-trial of the two conditions, living and man-made. The analysis comprised two steps. In the first part, a mixture of Gaussians analysis [3] provided representative topographies for each subject. In the second step, conditional probabilities for each Gaussian provided statistical inference on the structure of these topographies across trials, time, and experimental conditions. Similar analysis was conducted at group-level. Results: Results show that the occurrence of each map is structured in time and consistent across trials both at the single-subject and at group level. Conducting separate analyses of ERPs at single-subject and group levels, we could quantify the consistency of identified topographies and their time course of activation within and across participants as well as experimental conditions. A general agreement was found with previous analysis at average ERP level. Conclusions: This novel approach to single-trial analysis promises to have impact on several domains. In clinical research, it gives the possibility to statistically evaluate single-subject data, an essential tool for analysing patients with specific deficits and impairments and their deviation from normative standards. In cognitive neuroscience, it provides a novel tool for understanding behaviour and brain activity interdependencies at both single-subject and at group levels. In basic neurophysiology, it provides a new representation of ERPs and promises to cast light on the mechanisms of its generation and inter-individual variability.

Weight status and gender-related differences in motor skills and in child care - based physical activity in young children.

Over the last decades, a decline in motor skills and in physical activity and an increase in obesity has been observed in children. However, there is a lack of data in young children. We tested if differences in motor skills and in physical activity according to weight or gender were already present in 2- to 4-year-old children. Fifty-eight child care centers in the French part of Switzerland were randomly selected for the Youp'là bouge study. Motor skills were assessed by an obstacle course including 5 motor skills, derived from the Zurich Neuromotor Assessment test. Physical activity was measured with accelerometers (GT1M, Actigraph, Florida, USA) using age-adapted cut-offs. Weight status was assessed using the International Obesity Task Force criteria (healthy weight vs overweight) for body mass index (BMI). Of the 529 children (49% girls, 3.4 ± 0.6 years, BMI 16.2 ± 1.2 kg/m2), 13% were overweight. There were no significant weight status-related differences in the single skills of the obstacle course, but there was a trend (p = 0.059) for a lower performance of overweight children in the overall motor skills score. No significant weight status-related differences in child care-based physical activity were observed. No gender-related differences were found in the overall motor skills score, but boys performed better than girls in 2 of the 5 motor skills (p ≤ 0.04). Total physical activity as well as time spent in moderate-vigorous and in vigorous activity during child care were 12-25% higher and sedentary activity 5% lower in boys compared to girls (all p < 0.01). At this early age, there were no significant weight status- or gender-related differences in global motor skills. However, in accordance to data in older children, child care-based physical activity was higher in boys compared to girls. These results are important to consider when establishing physical activity recommendations or targeting health promotion interventions in young children.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.