Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation

Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the gestational process. In this context, there is a clear need for segmentation techniques that are robust to variations in image contrast and to the spatial configuration of anatomical structures. In this work, we evaluate the potential of synthetic learning, a contrast-independent model trained using synthetic images generated from the ground truth labels of very few subjects.We base our experiments on the dataset released by the developmental Human Connectome Project, for which high-quality T1- and T2-weighted images are available for more than 700 babies aged between 26 and 45 weeks post-conception. First, we confirm the impressive performance of a standard Unet trained on a few T2-weighted volumes, but also confirm that such models learn intensity-related features specific to the training domain. We then evaluate the synthetic learning approach and confirm its robustness to variations in image contrast by reporting the capacity of such a model to segment both T1- and T2-weighted images from the same individuals. However, we observe a clear influence of the age of the baby on the predictions. We improve the performance of this model by enriching the synthetic training set with realistic motion artifacts and over-segmentation of the white matter. Based on extensive visual assessment, we argue that the better performance of the model trained on real T2w data may be due to systematic errors in the ground truth. We propose an original experiment combining two definitions of the ground truth allowing us to show that learning from real data will reproduce any systematic bias from the training set, while synthetic models can avoid this limitation. Overall, our experiments confirm that synthetic learning is an effective solution for segmenting neonatal brain MRI. Our adapted synthetic learning approach combines key features that will be instrumental for large multi-site studies and clinical applications

Hopfield learning rule with high capacity storage of time-correlated patterns

A new local and incremental learning rule is examined for its ability to store patterns from a time series in an attractor neural network. This learning rule has a higher capacity than the Hebb rule, and suffers significantly less capacity loss as the correlation between patterns increases. 1 Introduction There are two rules regularly used for training Hopfield networks. The most common of these, the Hebb rule, suffers severe degradation when training patterns are correlated. As a result, practitioners have generally reverted to using the pseudo-inverse rule in such circumstances. However the pseudo-inverse method suffers from significant problems. Firstly, it is not incremental: if a new pattern is to be trained, all the old patterns have to be retrained. Secondly it is not local: the network cannot naturally be trained in a parallel manner, and so is not easily amenable to high speed parallel techniques. Lastly the training method is slow because it involves inverting an m \Theta m..

Apathy Associated With Impaired Recognition of Happy Facial Expressions in Huntington's Disease.

OBJECTIVES: Previous research has demonstrated an association between emotion recognition and apathy in several neurological conditions involving fronto-striatal pathology, including Parkinson's disease and brain injury. In line with these findings, we aimed to determine whether apathetic participants with early Huntington's disease (HD) were more impaired on an emotion recognition task compared to non-apathetic participants and healthy controls. METHODS: We included 43 participants from the TRACK-HD study who reported apathy on the Problem Behaviours Assessment - short version (PBA-S), 67 participants who reported no apathy, and 107 controls matched for age, sex, and level of education. During their baseline TRACK-HD visit, participants completed a battery of cognitive and psychological tests including an emotion recognition task, the Hospital Depression and Anxiety Scale (HADS) and were assessed on the PBA-S. RESULTS: Compared to the non-apathetic group and the control group, the apathetic group were impaired on the recognition of happy facial expressions, after controlling for depression symptomology on the HADS and general disease progression (Unified Huntington's Disease Rating Scale total motor score). This was despite no difference between the apathetic and non-apathetic group on overall cognitive functioning assessed by a cognitive composite score. CONCLUSIONS: Impairment of the recognition of happy expressions may be part of the clinical picture of apathy in HD. While shared reliance on frontostriatal pathways may broadly explain associations between emotion recognition and apathy found across several patient groups, further work is needed to determine what relationships exist between recognition of specific emotions, distinct subtypes of apathy and underlying neuropathology. (JINS, 2019, 25, 453-461)

Individual differences in prefrontal cortical activation on the Tower of London planning task: implication for effortful processing

International audienceSolving challenging ('effortful') problems is known to involve the dorsal and dorsolateral prefrontal cortex in normal volunteers, although there is considerable individual variation. In this functional magnetic resonance imaging study, we show that healthy subjects with different levels of performance in the Tower of London planning task exhibit different patterns of brain activation. All subjects exhibited significant bilateral activation in the dorsolateral prefrontal cortex, the anterior and posterior cingulate areas and the parietal cortex. However, 'standard performers' (performance 70% correct) differed in the patterns of activation exhibited. Superior performers showed a significantly more spatially extended activation in the left dorsolateral prefrontal cortex than did standard performers, whereas the latter group tended to show increased activation of the anterior cingulate region

In vivo 1H MRS study in microlitre voxels in the hippocampus of a mouse model of Down syndrome at 11.7 T

International audienc