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..

The brain signature of paracetamol in healthy volunteers: a double-blind randomized trial

International audienceBackground: Paracetamol’s (APAP) mechanism of action suggests the implication of supraspinal structures but no neuroimaging study has been performed in humans.Methods and results: This randomized, double-blind, crossover, placebo-controlled trial in 17 healthy volunteers (NCT01562704) aimed to evaluate how APAP modulates pain-evoked functional magnetic resonance imaging signals. We used behavioral measures and functional magnetic resonance imaging to investigate the response to experimental thermal stimuli with APAP or placebo administration. Region-of-interest analysis revealed that activity in response to noxious stimulation diminished with APAP compared to placebo in prefrontal cortices, insula, thalami, anterior cingulate cortex, and periaqueductal gray matter.Conclusion: These findings suggest an inhibitory effect of APAP on spinothalamic tracts leading to a decreased activation of higher structures, and a top-down influence on descending inhibition. Further binding and connectivity studies are needed to evaluate how APAP modulates pain, especially in the context of repeated administration to patients with pain

Comparative study of MRI biomarkers in the substantia nigra to discriminate idiopathic Parkinson disease

BACKGROUND AND PURPOSE: Several new MR imaging techniques have shown promising results in patients with Parkinson disease; however, the comparative diagnostic values of these measures at the individual level remain unclear. Our aim was to compare the diagnostic value of MR imaging biomarkers of substantia nigra damage for distinguishing patients with Parkinson disease from healthy volunteers. MATERIALS AND METHODS: Thirty-six patients and 20 healthy volunteers were prospectively included. The MR imaging protocol at 3T included 3D T2-weighted and T1-weighted neuromelanin-sensitive images, diffusion tensor images, and R2* mapping. T2* high-resolution images were also acquired at 7T to evaluate the dorsal nigral hyperintensity sign. Quantitative analysis was performed using ROIs in the substantia nigra drawn manually around the area of high signal intensity on neuromelanin-sensitive images and T2-weighted images. Visual analysis of the substantia nigra neuromelanin-sensitive signal intensity and the dorsolateral nigral hyperintensity on T2* images was performed. RESULTS: There was a significant decrease in the neuromelanin-sensitive volume and signal intensity in patients with Parkinson disease. There was also a significant decrease in fractional anisotropy and an increase in mean, axial, and radial diffusivity in the neuromelanin-sensitive substantia nigra at 3T and a decrease in substantia nigra volume on T2* images. The combination of substantia nigra volume, signal intensity, and fractional anisotropy in the neuromelanin-sensitive substantia nigra allowed excellent diagnostic accuracy (0.93). Visual assessment of both substantia nigra dorsolateral hyperintensity and neuromelanin-sensitive images had good diagnostic accuracy (0.91 and 0.86, respectively). CONCLUSIONS: The combination of neuromelanin signal and volume changes with fractional anisotropy measurements in the substantia nigra showed excellent diagnostic accuracy. Moreover, the high diagnostic accuracy of visual assessment of substantia nigra changes using dorsolateral hyperintensity analysis or neuromelanin-sensitive signal changes indicates that these techniques are promising for clinical practice

Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI

Early and accurate diagnosis of parkinsonian syndromes is critical to provide appropriate care to patients and for inclusion in therapeutic trials. The red nucleus is a structure of the midbrain that plays an important role in these disorders. It can be visualized using iron-sensitive magnetic resonance imaging (MRI) sequences. Different iron-sensitive contrasts can be produced with MRI. Combining such multimodal data has the potential to improve segmentation of the red nucleus. Current multimodal segmentation algorithms are computationally consuming, cannot deal with missing modalities and need annotations for all modalities. In this paper, we propose a new model that integrates prior knowledge from different contrasts for red nucleus segmentation. The method consists of three main stages. First, it disentangles the image into high-level information representing the brain structure, and low-frequency information representing the contrast. The high-frequency information is then fed into a network to learn anatomical features, while the list of multimodal low-frequency information is processed by another module. Finally, feature fusion is performed to complete the segmentation task. The proposed method was used with several iron-sensitive contrasts (iMag, QSM, R2*, SWI). Experiments demonstrate that our proposed model substantially outperforms a baseline UNet model when the training set size is very small

Longitudinal changes in functional connectivity of cortico-basal ganglia networks in manifests and premanifest huntington's disease

Huntington's disease (HD) is a genetic neurological disorder resulting in cognitive and motor impairments. We evaluated the longitudinal changes of functional connectivity in sensorimotor, associative and limbic cortico-basal ganglia networks. We acquired structural MRI and resting-state fMRI in three visits one year apart, in 18 adult HD patients, 24 asymptomatic mutation carriers (preHD) and 18 gender- and age-matched healthy volunteers from the TRACK-HD study. We inferred topological changes in functional connectivity between 182 regions within cortico-basal ganglia networks using graph theory measures. We found significant differences for global graph theory measures in HD but not in preHD. The average shortest path length (L) decreased, which indicated a change toward the random network topology. HD patients also demonstrated increases in degree k, reduced betweeness centrality bc and reduced clustering C. Changes predominated in the sensorimotor network for bc and C and were observed in all circuits for k. Hubs were reduced in preHD and no longer detectable in HD in the sensorimotor and associative networks. Changes in graph theory metrics (L, k, C and bc) correlated with four clinical and cognitive measures (symbol digit modalities test, Stroop, Burden and UHDRS). There were no changes in graph theory metrics across sessions, which suggests that these measures are not reliable biomarkers of longitudinal changes in HD. preHD is characterized by progressive decreasing hub organization, and these changes aggravate in HD patients with changes in local metrics. HD is characterized by progressive changes in global network interconnectivity, whose network topology becomes more random over time. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc

Introducing Soft Topology Constraints in Deep Learning-based Segmentation using Projected Pooling Loss

International audienceDeep learning methods have achieved impressive results for 3D medical image segmentation. However, when the network is only guided by voxel-level information, it may provide anatomically aberrant segmentations. When performing manual segmentations, experts heavily rely on prior anatomical knowledge. Topology is an important prior information due to its stability across patients. Recently, several losses based on persistent homology were proposed to constrain topology. Persistent homology offers a principled way to control topology. However, it is computationally expensive and complex to implement, in particular in 3D. In this paper, we propose a novel loss function to introduce topological priors in deep learning-based segmentation, which is fast to compute and easy to implement. The loss performs a projected pooling within two steps. We first focus on errors from a global perspective by using 3D MaxPooling to obtain projections of 3D data onto three planes: axial, coronal and sagittal. Then, 2D MaxPooling layers with different kernel sizes are used to extract topological features from the multi-view projections. These two steps are combined using only MaxPooling, thus ensuring the efficiency of the loss function. Our approach was evaluated in several medical image datasets (spleen, heart, hippocampus, red nucleus). It allowed reducing topological errors and, in some cases, improving voxel-level accuracy

The sooner the better: clinical and neural correlates of impulsive choice in Tourette disorder.

Reward sensitivity has been suggested as one of the central pathophysiological mechanisms in Tourette disorder. However, the subjective valuation of a reward by introduction of delay has received little attention in Tourette disorder, even though it has been suggested as a trans-diagnostic feature of numerous neuropsychiatric disorders. We aimed to assess delay discounting in Tourette disorder and to identify its brain functional correlates. We evaluated delayed discounting and its brain functional correlates in a large group of 54 Tourette disorder patients and 31 healthy controls using a data-driven approach. We identified a subgroup of 29 patients with steeper reward discounting, characterised by a higher burden of impulse-control disorders and a higher level of general impulsivity compared to patients with normal behavioural performance or to controls. Reward discounting was underpinned by resting-state activity of a network comprising the orbito-frontal, cingulate, pre-supplementary motor area, temporal and insular cortices, as well as ventral striatum and hippocampus. Within this network, (i) lower connectivity of pre-supplementary motor area with ventral striatum predicted a higher impulsivity and a steeper reward discounting and (ii) a greater connectivity of pre-supplementary motor area with anterior insular cortex predicted steeper reward discounting and more severe tics. Overall, our results highlight the heterogeneity of the delayed reward processing in Tourette disorder, with steeper reward discounting being a marker of burden in impulsivity and impulse control disorders, and the pre-supplementary motor area being a hub region for the delay discounting, impulsivity and tic severity