Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord.

INTRODUCTION: Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is widely used for MS diagnosis and clinical follow-up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball-and-Stick models. METHODS: We analyzed spinal cord data acquired from multiple hospitals and extracted average diffusion MRI metrics per vertebral level using a collection of image processing methods and an atlas-based approach. We then performed a statistical analysis to evaluate the feasibility of these metrics for detecting lesions, exploring the usefulness of combining different metrics to improve accuracy. RESULTS: Our study demonstrates the sensitivity of each metric to underlying microstructure changes in MS patients. We show that selecting a specific subset of metrics, which provide complementary information, significantly improves the prediction score of lesion presence in the cervical spinal cord. Furthermore, the Ball-and-Stick model has the potential to provide novel information about the microstructure of damaged tissue. CONCLUSION: Our results suggest that diffusion measures, particularly combined measures, are sensitive in discriminating abnormal from healthy cervical vertebral levels in patients. This information could aid in improving MS diagnosis and clinical follow-up. Our study highlights the potential of the Ball-and-Stick model in providing additional insights into the microstructure of the damaged tissue.

An Iterative Centroid Approach for Diffeomorphic Online Atlasing.

Online atlasing, i.e., incrementing an atlas with new images as they are acquired, is key when performing studies on very large, or still being gathered, databases. Regular approaches to atlasing however do not focus on this aspect and impose a complete reconstruction of the atlas when adding images. We propose instead a diffeomorphic online atlasing method that allows gradual updates to an atlas. In this iterative centroid approach, we integrate new subjects in the atlas in an iterative manner, gradually moving the centroid of the images towards its final position. This leads to a computationally cheap approach since it only necessitates one additional registration per new subject added. We validate our approach on several experiments with three main goals: 1- to evaluate atlas image quality of the obtained atlases with sharpness and overlap measures, 2- to assess the deviation in terms of transformations with respect to a conventional atlasing method and 3- to compare its computational time with regular approaches of the literature. We demonstrate that the transformations divergence with respect to a state-of-the-art atlas construction method is small and reaches a plateau, that the two construction methods have the same ability to map subject homologous regions onto a common space and produce images of equivalent quality. The computational time of our approach is also drastically reduced for regular updates. Finally, we also present a direct extension of our method to update spatio-temporal atlases, especially useful for developmental studies.

Building memories on prior knowledge: behavioral and fMRI evidence of impairment in early Alzheimer's disease.

Impaired memory is a hallmark of prodromal Alzheimer's disease (AD). Prior knowledge associated with the memoranda improves memory in healthy individuals, but we ignore whether the same occurs in early AD. We used functional MRI to investigate whether prior knowledge enhances memory encoding in early AD, and whether the nature of this prior knowledge matters. Patients with early AD and Controls underwent a task-based fMRI experiment where they learned face-scene associations. Famous faces carried pre-experimental knowledge (PEK), while unknown faces with which participants were familiarized prior to learning carried experimental knowledge (EK). Surprisingly, PEK strongly enhanced subsequent memory in healthy controls, but importantly not in patients. Partly nonoverlapping brain networks supported PEK vs. EK associative encoding in healthy controls. No such networks were identified in patients. In addition, patients displayed impaired activation in a right sub hippocampal region where activity predicted successful associative memory formation for PEK stimuli. Despite the limited sample sizes of this study, these findings suggest that the role prior knowledge in new learning might have been so far overlooked and underestimated in AD patients. Prior knowledge may drive critical differences in the way healthy elderly and early AD patients learn novel associations.

Visual feedback improves movement illusions induced by tendon vibration after chronic stroke.

BACKGROUND: Illusion of movement induced by tendon vibration is commonly used in rehabilitation and seems valuable for motor rehabilitation after stroke, by playing a role in cerebral plasticity. The aim was to study if congruent visual cues using Virtual Reality (VR) could enhance the illusion of movement induced by tendon vibration of the wrist among participants with stroke. METHODS: We included 20 chronic stroke participants. They experienced tendon vibration of their wrist (100 Hz, 30 times) inducing illusion of movement. Three VR visual conditions were added to the vibration: a congruent moving virtual hand (Moving condition); a static virtual hand (Static condition); or no virtual hand at all (Hidden condition). The participants evaluated for each visual condition the intensity of the illusory movement using a Likert scale, the sensation of wrist's movement using a degree scale and they answered a questionnaire about their preferred condition. RESULTS: The Moving condition was significantly superior to the Hidden condition and to the Static condition in terms of illusion of movement (p < 0.001) and the wrist's extension (p < 0.001). There was no significant difference between the Hidden and the Static condition for these 2 criteria. The Moving condition was considered the best one to increase the illusion of movement (in 70% of the participants). Two participants did not feel any illusion of movement. CONCLUSIONS: This study showed the interest of using congruent cues in VR in order to enhance the consistency of the illusion of movement induced by tendon vibration among participants after stroke, regardless of their clinical severity. By stimulating the brain motor areas, this visuo-proprioceptive feedback could be an interesting tool in motor rehabilitation. Record number in Clinical Trials: NCT04130711, registered on October 17th 2019 ( https://clinicaltrials.gov/ct2/show/NCT04130711?id=NCT04130711&draw=2&rank=1 ).

The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study.

Objective.In this study, we assessed the impact of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) neurofeedback (NF) on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on successful activation of targeted cortical regions. However, given the crucial role of brain network reorganization for stroke recovery, our broader aim was to assess connectivity changes after an NF training protocol targeting localized motor areas.Approach.We considered changes in fMRI connectivity after a multisession EEG-fMRI NF training targeting ipsilesional motor areas in nine stroke patients. We applied the dynamic causal modeling and parametric empirical Bayes frameworks for the estimation of effective connectivity changes. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas.Main results.Our results indicate that NF upregulation of targeted areas (ipsilesional supplementary and primary motor areas) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task.Significance.To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.

Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset.

MRI plays a crucial role in multiple sclerosis diagnostic and patient follow-up. In particular, the delineation of T2-FLAIR hyperintense lesions is crucial although mostly performed manually - a tedious task. Many methods have thus been proposed to automate this task. However, sufficiently large datasets with a thorough expert manual segmentation are still lacking to evaluate these methods. We present a unique dataset for MS lesions segmentation evaluation. It consists of 53 patients acquired on 4 different scanners with a harmonized protocol. Hyperintense lesions on FLAIR were manually delineated on each patient by 7 experts with control on T2 sequence, and gathered in a consensus segmentation for evaluation. We provide raw and preprocessed data and a split of the dataset into training and testing data, the latter including data from a scanner not present in the training dataset. We strongly believe that this dataset will become a reference in MS lesions segmentation evaluation, allowing to evaluate many aspects: evaluation of performance on unseen scanner, comparison to individual experts performance, comparison to other challengers who already used this dataset, etc.

Influence of the visuo-proprioceptive illusion of movement and motor imagery of the wrist on EEG cortical excitability among healthy participants.

INTRODUCTION: Motor Imagery (MI) is a powerful tool to stimulate sensorimotor brain areas and is currently used in motor rehabilitation after a stroke. The aim of our study was to evaluate whether an illusion of movement induced by visuo-proprioceptive immersion (VPI) including tendon vibration (TV) and Virtual moving hand (VR) combined with MI tasks could be more efficient than VPI alone or MI alone on cortical excitability assessed using Electroencephalography (EEG). METHODS: We recorded EEG signals in 20 healthy participants in 3 different conditions: MI tasks involving their non-dominant wrist (MI condition); VPI condition; and VPI with MI tasks (combined condition). Each condition lasted 3 minutes, and was repeated 3 times in randomized order. Our main judgment criterion was the Event-Related De-synchronization (ERD) threshold in sensori-motor areas in each condition in the brain motor area. RESULTS: The combined condition induced a greater change in the ERD percentage than the MI condition alone, but no significant difference was found between the combined and the VPI condition (p = 0.07) and between the VPI and MI condition (p = 0.20). CONCLUSION: This study demonstrated the interest of using a visuo-proprioceptive immersion with MI rather than MI alone in order to increase excitability in motor areas of the brain. Further studies could test this hypothesis among patients with stroke to provide new perspectives for motor rehabilitation in this population.

Multiparametric Analysis of Cerebral Development in Preterm Infants Using Magnetic Resonance Imaging.

OBJECTIVES: The severity of neurocognitive impairment increases with prematurity. However, its mechanisms remain poorly understood. Our aim was firstly to identify multiparametric magnetic resonance imaging (MRI) markers that differ according to the degree of prematurity, and secondly to evaluate the impact of clinical complications on these markers. MATERIALS AND METHODS: We prospectively enrolled preterm infants who were divided into two groups according to their degree of prematurity: extremely preterm (<28 weeks' gestational age) and very preterm (28-32 weeks' gestational age). They underwent a multiparametric brain MRI scan at term-equivalent age including morphological, diffusion tensor and arterial spin labeling (ASL) perfusion sequences. We quantified overall and regional volumes, diffusion parameters, and cerebral blood flow (CBF). We then compared the parameters for the two groups. We also assessed the effects of clinical data and potential MRI morphological abnormalities on those parameters. RESULTS: Thirty-four preterm infants were included. Extremely preterm infants (n = 13) had significantly higher frontal relative volumes (p = 0.04), frontal GM relative volumes (p = 0.03), and regional CBF than very preterm infants, but they had lower brainstem and insular relative volumes (respectively p = 0.008 and 0.04). Preterm infants with WM lesions on MRI had significantly lower overall GM CBF (13.3 ± 2 ml/100 g/min versus 17.7 ± 2.5, < ml/100 g/min p = 0.03). CONCLUSION: Magnetic resonance imaging brain scans performed at term-equivalent age in preterm infants provide quantitative imaging parameters that differ with respect to the degree of prematurity, related to brain maturation.

WITHDRAWN: Alternative bimodal unimodal neurofeedback training to induce cerebral reorganization after chronic stroke: A proof-of-concept case report.

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Interpolation and Averaging of Diffusion MRI Multi-Compartment Models.

Multi-compartment models (MCM) are increasingly used to characterize the brain white matter microstructure from diffusion-weighted imaging (DWI). Their use in clinical studies is however limited by the inability to resample an MCM image towards a common reference frame, or to construct atlases from such brain microstructure models. We propose to solve this problem by first identifying that these two tasks amount to the same problem. We propose to tackle it by viewing it as a simplification problem, solved thanks to spectral clustering and the definition of semi-metrics between several usual compartments encountered in the MCM literature. This generic framework is evaluated for two models: the multi-tensor model where individual fibers are modeled as individual tensors and the diffusion direction imaging (DDI) model that differentiates intra- and extra-axonal components of each fiber. Results on simulated data, simulated transformations and real data show the ability of our method to well interpolate MCM images of these types. We finally present as an application an MCM template of normal controls constructed using our approach.

Influence of virtual reality visual feedback on the illusion of movement induced by tendon vibration of wrist in healthy participants.

INTRODUCTION: Illusion of movement induced by tendon vibration is an effective approach for motor and sensory rehabilitation in case of neurological impairments. The aim of our study was to investigate which modality of visual feedback in Virtual Reality (VR) associated with tendon vibration of the wrist could induce the best illusion of movement. METHODS: We included 30 healthy participants in the experiment. Tendon vibration inducing illusion of movement (wrist extension, 100Hz) was applied on their wrist during 3 VR visual conditions (10 times each): a moving virtual hand corresponding to the movement that the participants could feel during the tendon vibration (Moving condition), a static virtual hand (Static condition), or no virtual hand at all (Hidden condition). After each trial, the participants had to quantify the intensity of the illusory movement on a Likert scale, the subjective degree of extension of their wrist and afterwards they answered a questionnaire. RESULTS: There was a significant difference between the 3 visual feedback conditions concerning the Likert scale ranking and the degree of wrist's extension (p<0.001). The Moving condition induced a higher intensity of illusion of movement and a higher sensation of wrist's extension than the Hidden condition (p<0.001 and p<0.001 respectively) than that of the Static condition (p<0.001 and p<0.001 respectively). The Hidden condition also induced a higher intensity of illusion of movement and a higher sensation of wrist's extension than the Static condition (p<0.01 and p<0.01 respectively). The preferred condition to facilitate movement's illusion was the Moving condition (63.3%). CONCLUSIONS: This study demonstrated the importance of carefully selecting a visual feedback to improve the illusion of movement induced by tendon vibration, and the increase of illusion by adding VR visual cues congruent to the illusion of movement. Further work will consist in testing the same hypothesis with stroke patients.

Acquisition Duration in Resting-State Arterial Spin Labeling. How Long Is Enough?

Resting-state Arterial Spin Labeling (rs-ASL) is a rather confidential method compared to resting-state BOLD. As ASL allows to quantify the cerebral blood flow, unlike BOLD, rs-ASL can lead to significant clinical subject-scaled applications. Despite directly impacting clinical practicability and functional networks estimation, there is no standard for rs-ASL regarding the acquisition duration. Our work here focuses on assessing the feasibility of ASL as an rs-fMRI method and on studying the effect of the acquisition duration on the estimation of functional networks. To this end, we acquired a long 24 min 30 s rs-ASL sequence and investigated how estimations of six typical functional brain networks evolved with respect to the acquisition duration. Our results show that, after a certain acquisition duration, the estimations of all functional networks reach their best and are stabilized. Since, for clinical application, the acquisition duration should be the shortest possible, we suggest an acquisition duration of 14 min, i.e., 240 volumes with our sequence parameters, as it covers the functional networks estimation stabilization.

A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback.

Neurofeedback (NF) and brain-computer interface (BCI) applications rely on the registration and real-time feedback of individual patterns of brain activity with the aim of achieving self-regulation of specific neural substrates or control of external devices. These approaches have historically employed visual stimuli. However, in some cases vision is unsuitable or inadequately engaging. Other sensory modalities, such as auditory or haptic feedback have been explored, and multisensory stimulation is expected to improve the quality of the interaction loop. Moreover, for motor imagery tasks, closing the sensorimotor loop through haptic feedback may be relevant for motor rehabilitation applications, as it can promote plasticity mechanisms. This survey reviews the various haptic technologies and describes their application to BCIs and NF. We identify major trends in the use of haptic interfaces for BCI and NF systems and discuss crucial aspects that could motivate further studies.

Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies.

Clinical multicenter imaging studies are frequent and rely on a wide range of existing tools for sharing data and processing pipelines. This is not the case for preclinical (small animal) studies. Animal population imaging is still in infancy, especially because a complete standardization and control of initial conditions in animal models across labs is still difficult and few studies aim at standardization of acquisition and post-processing techniques. Clearly, there is a need of appropriate tools for the management and sharing of data, post-processing and analysis methods dedicated to small animal imaging. Solutions developed for Human imaging studies cannot be directly applied to this specific domain. In this paper, we present the Small Animal Shanoir (SAS) solution for supporting animal population imaging using tools compatible with open data. The integration of automated workflow tools ensures accessibility and reproducibility of research outputs. By sharing data and imaging processing tools, hosted by SAS, we promote data preparation and tools for reproducibility and reuse, and participation in multicenter or replication "open science" studies contributing to the improvement of quality science in preclinical domain. SAS is a first step for promoting open science for small animal imaging and a contribution to the valorization of data and pipelines of reference.

Simultaneous EEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integration.

Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. This represents one of the first simultaneous recording of EEG and fMRI for NF and here we present the first open access bi-modal NF dataset integrating EEG and fMRI. We believe that it will be a valuable tool to (1) advance and test methodologies for multi-modal data integration, (2) improve the quality of NF provided, (3) improve methodologies for de-noising EEG acquired under MRI and (4) investigate the neuromarkers of motor-imagery using multi-modal information.

Unsupervised Domain Adaptation With Optimal Transport in Multi-Site Segmentation of Multiple Sclerosis Lesions From MRI Data.

Automatic segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) images is essential for clinical assessment and treatment planning of MS. Recent years have seen an increasing use of Convolutional Neural Networks (CNNs) for this task. Although these methods provide accurate segmentation, their applicability in clinical settings remains limited due to a reproducibility issue across different image domains. MS images can have highly variable characteristics across patients, MRI scanners and imaging protocols; retraining a supervised model with data from each new domain is not a feasible solution because it requires manual annotation from expert radiologists. In this work, we explore an unsupervised solution to the problem of domain shift. We present a framework, Seg-JDOT, which adapts a deep model so that samples from a source domain and samples from a target domain sharing similar representations will be similarly segmented. We evaluated the framework on a multi-site dataset, MICCAI 2016, and showed that the adaptation toward a target site can bring remarkable improvements in a model performance over standard training.

A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients.

Traditional rehabilitation techniques present limitations and the majority of patients show poor 1-year post-stroke recovery. Thus, Neurofeedback (NF) or Brain-Computer-Interface applications for stroke rehabilitation purposes are gaining increased attention. Indeed, NF has the potential to enhance volitional control of targeted cortical areas and thus impact on motor function recovery. However, current implementations are limited by temporal, spatial or practical constraints of the specific imaging modality used. In this pilot work and for the first time in literature, we applied bimodal EEG-fMRI NF for upper limb stroke recovery on four stroke-patients with different stroke characteristics and motor impairment severity. We also propose a novel, multi-target training approach that guides the training towards the activation of the ipsilesional primary motor cortex. In addition to fMRI and EEG outcomes, we assess the integrity of the corticospinal tract (CST) with tractography. Preliminary results suggest the feasibility of our approach and show its potential to induce an augmented activation of ipsilesional motor areas, depending on the severity of the stroke deficit. Only the two patients with a preserved CST and subcortical lesions succeeded in upregulating the ipsilesional primary motor cortex and exhibited a functional improvement of upper limb motricity. These findings highlight the importance of taking into account the variability of the stroke patients' population and enabled to identify inclusion criteria for the design of future clinical studies.

Regional brain development analysis through registration using anisotropic similarity, a constrained affine transformation.

We propose a novel method to quantify brain growth in 3 arbitrary orthogonal directions of the brain or its sub-regions through linear registration. This is achieved by introducing a 9 degrees of freedom (dof) transformation called anisotropic similarity which is an affine transformation with constrained scaling directions along arbitrarily chosen orthogonal vectors. This gives the opportunity to extract scaling factors describing brain growth along those directions by registering a database of subjects onto a common reference. This information about directional growth brings insights that are not usually available in longitudinal volumetric analysis. The interest of this method is illustrated by studying the anisotropic regional and global brain development of 308 healthy subjects betwen 0 and 19 years old. A gender comparison of those scaling factors is also performed for four age-intervals. We demonstrate through these applications the stability of the method to the chosen reference and its ability to highlight growth differences accros regions and gender.

New OFSEP recommendations for MRI assessment of multiple sclerosis patients: Special consideration for gadolinium deposition and frequent acquisitions.

PURPOSE: New multiple sclerosis (MS) disease-modifying therapies (DMTs), which exert beneficial effects through prevention of relapse, limitation of disability progression, and improvement of patients' quality of life, have recently emerged. Nonetheless, these DMTs are not without associated complications (severe adverse events like. progressive multifocal leukoencephalopathy). Patient follow-up requires regular clinical evaluations and close monitoring with magnetic resonance imaging (MRI). Detection of new T2 lesions and potential brain atrophy measurements contribute to the evaluation of treatment effectiveness. Current MRI protocols for MS recommend the acquisition of an annual gadolinium (Gd) enhanced MRI, resulting in administration of high volume of contrast agents over time and Gd accumulation in the brain. METHODS: A consensus report was established by neuroradiologists and neurologists from the French Observatory of MS, which aimed at reducing the number of Gd injections required during MS patient follow-up. RECOMMENDATIONS: The French Observatory of MS recommends the use of macrocyclic Gd enhancement at time of diagnosis, when a new DMT is introduced, at 6-month re-baseline, and when previous scans are unavailable for comparison. Gd administration can be performed as an option in case of relapse or suspicion of intercurrent disease such as progressive multifocal leukoencephalopathy. Other follow-up MRIs do not require contrast enhancement, provided current and previous MRI acquisitions follow the same standardized protocol including 3D FLAIR sequences.

Prenatal exposure to glycol ethers and motor inhibition function evaluated by functional MRI at the age of 10 to 12 years in the PELAGIE mother-child cohort.

BACKGROUND: Pregnant women are ubiquitously exposed to organic solvents, such as glycol ethers. Several studies suggest potential developmental neurotoxicity following exposure to glycol ethers with a lack of clarity of possible brain mechanisms. OBJECTIVES: We investigated the association between urinary levels of glycol ethers of women during early pregnancy and motor inhibition function of their 10- to 12-year-old children by behavioral assessment and brain imaging. METHODS: Exposure to glycol ethers was assessed by measuring six metabolites in urine (<19 weeks of gestation) of 73 pregnant women of the PELAGIE mother-child cohort (France). Maternal urinary levels were classified as low, medium, or high. Children underwent functional magnetic resonance imaging (fMRI) examinations during which motor inhibition function was assessed with a Go/No-Go task. Analyses were performed using linear regression for task performance and generalized linear mixed-effect models for brain activation, FWER-corrected for multiple testing at the spatial cluster level. Confounders were considered by restriction and a priori adjustment. RESULTS: Higher maternal butoxyacetic acid (BAA) urinary concentrations were associated with poorer child performance (ß = -1.1; 95% CI: -1.9, -0.2 for high vs low). There was also a trend for ethoxyacetic acid (EAA) towards poorer performance (ß = -0.3; 95% CI: -0.7, 0.01). Considering inhibition demand, there were increased activity in occipital regions in association with moderate EAA (left cuneus) and moderate methoxyacetic acid (MAA) (right precuneus). When children succeeded to inhibit, high ethoxyethoxyacetic acid (EEAA) and moderate phenoxyacetic acid (PhAA) levels were associated with differential activity in frontal cortex, involved in inhibition network. DISCUSSION: Prenatal urinary levels of two glycol ether metabolites were associated with poorer Go/No-Go task performance. Differential activations were observed in the brain motor inhibition network in relation with successful inhibition, but not with cognitive demand. Nevertheless, there is no consistence between performance indicators and cerebral activity results. Other studies are highly necessary given the ubiquity of glycol ether exposure.