Search

Most diffusion MRI (dMRI) studies of disease rely on statistical comparisons between large groups of patients and healthy controls to infer altered tissue state. Such studies often require data from a significant number of patients before robust inferences can be made, and clinical heterogeneity can greatly challenge their discriminative power. Moreover, for clinicians and researchers studying small datasets, rare cases, or individual patients, this approach is clearly inappropriate. There is a clear and unmet need to shift away from the current standard approach of group-wise comparisons to methods with the sensitivity for detection of altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalised-medicine in translational imaging. To this end, Detect was developed to advance dMRI-based Tractometry towards single-subject analysis. By: 1) operating on the manifold of white matter pathways; and 2) learning normative microstructural features to better discriminate patients from controls, our framework captures idiosyncrasies in patterns along brain white matter pathways in the individual. This novel approach paves the way from traditional group-based comparisons to true personalised radiology, taking microstructural imaging from the bench to the bedside.

Dystonia is a hyperkinetic movement disorder involving repetitive or sustained muscle contractions, resulting in abnormal postures. Proposed underlying pathophysiological mechanisms include neuronal network dysfunction, disruption to inhibitory processes and impaired synaptic plasticity. Here, we perform a systematic literature review of diffusion MRI brain studies of genetic and idiopathic forms of dystonia, aiming to identify brain regions and pathways consistently implicated in dystonia. Embase and PubMed databases were searched (to October 2021) for terms relating to diffusion MRI and idiopathic and inherited forms of dystonia. Of 40 identified studies, 10 assessed inherited, 24 idiopathic and 6 mixed types dystonia. The imaging methodology employed was variable, with key limitations including data acquisi- tion and processing steps. Study cohorts were also often small, particularly for Mendelian inherited forms. The most consistent abnormalities identified were lower FA (fractional anisotropy) and reduced number of tractography streamlines in white matter connecting the brainstem, cerebellum, basal ganglia and sensorimotor cortex. Findings were consistent across genetic and idiopathic forms of dystonia, although greater variability was seen in findings from idiopathic cohorts. Overall, microstructural white matter dif- ferences between dystonia cohorts and controls are evident, however the imaging measures used are often non-specific, with broad potential to its underlying biological meaning.

In case of refractory localized temporal lobe epilepsy the anterior part of the temporal lobe and parts of the mesial structures can be removed with epilepsy surgery. After anterior temporal lobe resection approximately 60 to 80% of the patients become seizure free. However, a visual field deficit in the upper quadrant of the visual field is a complication that arises due to disruption of the most anterior part of the optic radiation (Meyer's loop) in up to 100% of the cases. In case of a severe visual field deficit, patients are not allowed to drive anymore after surgery. In this study we tackle the difficult task of mapping the highly curved fibers of Meyer's loop to assist the neurosurgical planning. Tractography seeding is done by fMRI for the calcarine sulcus, and manual outlining of the lateral geniculate nucleus. The fibers of the central, upper and lower visual areas could be discriminated on their specific lengths and curvatures.

For high-resolution diffusion-MRI, strong gradients combined with fast readout are desired. In this study, we propose a gradient system architecture for such strong gradients and fast readouts without nerve stimulation by implementing a filter topology that allows for both low and high frequency gradient waveforms. Two gradient inserts with different specifications are realized and two proposed modulation strategies are implemented in the amplifier firmware. Field measurements demonstrated the desired dual-mode capability of the system. With one gradient insert, a gradient amplitude of 300 mT/m and slew rates well above 10.000 T/m/s can be reached with only 500A&330V as amplifier output.

International audience; Understanding the mapping between structural and functional brain connectivity is essential for understanding how cognitive processes emerge from their morphological substrates. Many studies have investigated the problem from an eigendecomposition viewpoint, however, few have taken a deep learning viewpoint, even less studies have been engaged within the framework of graph neural networks (GNNs). As deep learning has produced significant results in several fields, there has been an increasing interest in applying neural networks to graph problems. In this paper, we investigate the structural connectivity and functional connectivity mapping within a deep learning GNNs based framework, including graph convolutional networks (GCN) and graph transformer networks (GTN). To our knowledge, this original GTN based framework has never been studied in the context of structure-function and brain connectivity mapping. To achieve this goal, we use a GNNs based encoder-decoder system, where the encoder takes structural connectivity (SC) matrix as input and generates a latent representation of each node in a lower dimension, then the decoder uses the latent representation to reconstruct or predict the associated functional connectivity (FC) matrix. Besides comparing different encoders for node embedding, we also demonstrate that a decoder, which projects lower dimension vectors onto higher dimensional space, can improve the model performance. Our experiments demonstrate that both GCN encoder and GTN encoder combined with the proposed decoder can provide better results on our data than the previously proposed GCN autoencoder model. GTN encoder is also shown to be much more effective when it comes to noisy data and outliers.