Personal Income Performance Correlates with Brain Structural Network Modularity but Not Intelligence Quotient.

This study aims to use diffusion tensor imaging (DTI) in conjunction with brain graph techniques to define brain structural connectivity and investigate its association with personal income (PI) in individuals of various ages and intelligence quotients (IQ). MRI examinations were performed on 55 male subjects (mean age: 40.1 ± 9.

Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity.

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement.

Factorizer: A scalable interpretable approach to context modeling for medical image segmentation.

Convolutional Neural Networks (CNNs) with U-shaped architectures have dominated medical image segmentation, which is crucial for various clinical purposes. However, the inherent locality of convolution makes CNNs fail to fully exploit global context, essential for better recognition of some structures, e.g.

New multiple sclerosis lesion segmentation and detection using pre-activation U-Net.

Automated segmentation of new multiple sclerosis (MS) lesions in 3D MRI data is an essential prerequisite for monitoring and quantifying MS progression. Manual delineation of such lesions is time-consuming and expensive, especially because raters need to deal with 3D images and several modalities. In this paper, we propose Pre-U-Net, a 3D encoder-decoder architecture with pre-activation residual blocks, for the segmentation and detection of new MS lesions.

Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome.

The main goal of this study is to investigate the discrimination power of Grey Matter (GM) thickness connectome data between Multiple Sclerosis (MS) clinical profiles using statistical and Machine Learning (ML) methods. A dataset composed of 90 MS patients acquired at the MS clinic of Lyon Neurological Hospital was used for the analysis. Four MS profiles were considered, corresponding to Clinical Isolated Syndrome (CIS), Relapsing-Remitting MS (RRMS), Secondary Progressive MS (SPMS), and Primary Progressive MS (PPMS).

Case Report: True Motor Recovery of Upper Limb Beyond 5 Years Post-stroke.

Most of motor recovery usually occurs within the first 3 months after stroke. Herein is reported a remarkable late recovery of the right upper-limb motor function after a left middle cerebral artery stroke. This recovery happened progressively, from two to 12 years post-stroke onset, and along a proximo-distal gradient, including dissociated finger movements after 5 years.

T1/T2 ratio: A quantitative sensitive marker of brain tissue integrity in multiple sclerosis.

Background And Purpose: The aim of this study is to determine whether cerebral white matter (WM) microstructural damage, defined by decreased fractional anisotropy (FA) and increased axial (AD) and radial (RD) diffusivities, could be detected as accurately by measuring the T1/T2 ratio, in relapsing-remitting multiple sclerosis (RRMS) patients compared to healthy control (HC) subjects.

Methods: Twenty-eight RRMS patients and 24 HC subjects were included in this study. Region-based analysis based on the ICBM-81 diffusion tensor imaging (DTI) atlas WM labels was performed to compare T1/T2 ratio to DTI values in normal-appearing WM (NAWM) regions of interest.

Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity.

Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system characterized by demyelination and neurodegeneration processes. It leads to different clinical courses and degrees of disability that need to be anticipated by the neurologist for personalized therapy. Recently, machine learning (ML) techniques have reached a high level of performance in brain disease diagnosis and/or prognosis, but the decision process of a trained ML system is typically nontransparent.

Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis.

Background And Objective: Machine learning frameworks have demonstrated their potentials in dealing with complex data structures, achieving remarkable results in many areas, including brain imaging. However, a large collection of data is needed to train these models. This is particularly challenging in the biomedical domain since, due to acquisition accessibility, costs and pathology related variability, available datasets are limited and usually imbalanced.

Signal Intensity Evaluation in the Dentate Nucleus and Subcortical Gray Matter : Effect of Several Administrations of Gadoterate Meglumine in Multiple Sclerosis.

Purpose: Several studies reported gadolinium deposition in the dentate nuclei (DN) and the globus pallidus (GP) that was associated to linear GBCA administrations rather than macrocyclic. It is therefore imperative to evaluate and assess the safety of cumulative administration of gadoterate meglumine (macrocyclic). Thus, T1-weighted images (T1WI) of multiple sclerosis (MS) patients longitudinally followed for 4 years were retrospectively analyzed.

White matter microarchitecture and structural network integrity correlate with children intelligence quotient.

The neural substrate of high intelligence performances remains not well understood. Based on diffusion tensor imaging (DTI) which provides microstructural information of white matter fibers, we proposed in this work to investigate the relationship between structural brain connectivity and intelligence quotient (IQ) scores. Fifty-seven children (8-12 y.

Intracellular Phosphate and ATP Depletion Measured by Magnetic Resonance Spectroscopy in Patients Receiving Maintenance Hemodialysis.

Background: The precise origin of phosphate that is removed during hemodialysis remains unclear; only a minority comes from the extracellular space. One possibility is that the remaining phosphate originates from the intracellular compartment, but there have been no available data from direct assessment of intracellular phosphate in patients undergoing hemodialysis.

Methods: We used phosphorus magnetic resonance spectroscopy to quantify intracellular inorganic phosphate (Pi), phosphocreatine (PCr), and ATP.

Corrigendum: Topological Modification of Brain Networks Organization in Children With High Intelligence Quotient: A Resting-State fMRI Study.

[This corrects the article DOI: 10.3389/fnhum.2019.

Tensor Based Blind Source Separation in Longitudinal Magnetic Resonance Imaging Analysis.

Study of white matter (WM) fiber-bundles is a crucial challenge in the investigation of neurological diseases like multiple sclerosis (MS). In this activity, the amount of data to process is huge, and an automated approach to carry out this task is in order.In this paper we show how tensor-based blind source separation (BSS) techniques can be successfully applied to model complex anatomical brain structures.

Prediction of Multiple Sclerosis Patient Disability from Structural Connectivity using Convolutional Neural Networks.

Prediction of disability progression in multiple sclerosis patients is a critical component of their management. In particular, one challenge is to identify and characterize a patient profile who may benefit of efficient treatments. However, it is not yet clear whether a particular relation exists between the brain structure and the disability status.

Topological Modification of Brain Networks Organization in Children With High Intelligence Quotient: A Resting-State fMRI Study.

The idea that intelligence is embedded not only in a single brain network, but instead in a complex, well-optimized system of complementary networks, has led to the development of whole brain network analysis. Using graph theory to analyze resting-state functional MRI data, we investigated the brain graph networks (or brain networks) of high intelligence quotient (HIQ) children. To this end, we computed the "hub disruption index κ," an index sensitive to graph network modifications.

Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks.

Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices.

Evidence of axonal damage in cerebellar peduncles without T2-lesions in multiple sclerosis.

Background And Aim: Cerebellar peduncles (CP) can be probed by diffusion tensor imaging (DTI) to evaluate the integrity of cerebellar afferent and efferent networks. Damage to the CP in multiple sclerosis (MS) could lead to serious cognitive and mobility impairment. The aim of this study was to investigate the extent and the clinical impact of CP damage in MS.

Weekly enhanced T1-weighted MRI with Gadobutrol injections in MS patients: Is there a signal intensity increase in the dentate nucleus and the globus pallidus?

Background And Purpose: Gadolinium-based contrast agents (GBCAs) administration have drastically improved the accuracy of Multiple Sclerosis (MS) diagnosis by highlighting any damage to the brain blood barrier, thereby differentiating between active and non-active lesions. Following multiple administrations of GBCAs, several MS studies have reported a signal intensity (SI) increase on unenhanced T1-weighted images in certain brain regions such as the dentate nucleus (DN) and the globus pallidus (GP). Our aim was therefore to determine the accumulation of macrocyclic GBCAs on enhanced T1-weighted images SI in the DN and the GP of MS patients injected eight times.

Weekly follow up of acute lesions in three early multiple sclerosis patients using MR spectroscopy and diffusion.

Object: Pathophysiological mechanisms underlying multiple sclerosis (MS) lesion formation, including inflammation, demyelination/remyelination and axonal damage, and their temporal evolution are still not clearly understood. To this end, three acute white matter lesions were monitored using a weekly multimodal magnetic resonance (MR) protocol.

Materials And Methods: Three untreated patients with early relapsing-remitting MS and one healthy control subject were followed weekly for two months.

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features.

The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.

Hemispheric Differences in White Matter Microstructure between Two Profiles of Children with High Intelligence Quotient vs. Controls: A Tract-Based Spatial Statistics Study.

The main goal of this study was to investigate and compare the neural substrate of two children's profiles of high intelligence quotient (HIQ). Two groups of HIQ children were included with either a homogeneous (Hom-HIQ: = 20) or a heterogeneous IQ profile (Het-HIQ: = 24) as defined by a significant difference between verbal comprehension index and perceptual reasoning index. Diffusion tensor imaging was used to assess white matter (WM) microstructure while tract-based spatial statistics (TBSS) analysis was performed to detect and localize WM regional differences in fractional anisotropy (FA), mean diffusivity, axial (AD), and radial diffusivities.