Ronel Veksler

Quantitative imaging assessment of blood-brain barrier permeability in humans

The blood–brain barrier (BBB) is a functional and structural barrier separating the intravascular and neuropil compartments of the brain. It characterizes the vascular bed and is essential for normal brain functions. Dysfunction in the BBB properties have been described in most common neurological disorders, such as stroke, traumatic injuries, intracerebral hemorrhage, tumors, epilepsy and neurodegenerative disorders. It is now obvious that the BBB plays an important role in normal brain activity, stressing the need for applicable imaging and assessment methods. Recent advancements in imaging techniques now make it possible to establish sensitive and quantitative methods for the assessment of BBB permeability. However, most of the existing techniques require complicated and demanding dynamic scanning protocols that are impractical and cannot be fulfilled in some cases. We review existing methods for the evaluation of BBB permeability, focusing on quantitative magnetic resonance-based approaches and discuss their drawbacks and limitations. In light of those limitations we propose two new approaches for BBB assessment with less demanding imaging sequences: the “post-pre” and the “linear dynamic” methods, both allow semi-quantitative permeability assessment and localization of dysfunctional BBB with simple/partial dynamic imaging protocols and easy-to-apply analysis algorithms. We present preliminary results and show an example which compares these new methods with the existing standard assessment method. We strongly believe that the establishment of such “easy to use” and reliable imaging methods is essential before BBB assessment can become a routine clinical tool. Large clinical trials are awaited to fully understand the significance of BBB permeability as a biomarker and target for treatment in neurological disorders.

Concussion, microvascular injury, and early tauopathy in young athletes after impact head injury and an impact concussion mouse model

The mechanisms underpinning concussion, traumatic brain injury (TBI) and chronic traumatic encephalopathy (CTE) are poorly understood. Using neuropathological analyses of brains from teenage athletes, a new mouse model of concussive impact injury, and computational simulations, Tagge et al. show that head injuries can induce TBI and early CTE pathologies independent of concussion.

Imaging Blood-Brain Barrier Dysfunction in Football Players

Imaging Blood-Brain Barrier Dysfunction in Football Players There has been an increasing awareness of the long-term neuropsychiatric pathologies associated with repeated mild traumatic brain injury (mTBI) and specifically sports-related concussive and subconcussive head impacts.1 While mTBI had been associated with diffusion tensor imaging evidence of diffusivity changes in soccer,2 American football, and hockey players,3 the mechanisms underlying the development of post-mTBI neurodegenerative complications are poorly understood. Accumulating evidence points to vascular pathology and dysfunction of the blood-brain barrier (BBB) as a potential link between severe TBI and neurodegeneration.4 Moreover, participation in American football has been associated with changes in blood proteins reflecting BBB leakage.5 Thus, here we set out to visualize the extent and location of BBB dysfunction in football players using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Blood-brain barrier imaging in human neuropathologies.

The blood-brain barrier (BBB) is essential for normal function of the brain, and its role in many brain pathologies has been the focus of numerous studies during the last decades. Dysfunction of the BBB is not only being shown in numerous brain diseases, but animal studies have indicated that it plays a direct key role in the genesis of neurovascular dysfunction and associated neurodegeneration. As such evidence accumulates, the need for robust and clinically applicable methods for minimally invasive assessment of BBB integrity is becoming urgent. This review provides an introduction to BBB imaging methods in the clinical scenario. First, imaging modalities are reviewed, with a focus on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We then proceed to review image analysis methods, including quantitative and semi-quantitative methods. The advantages and limitations of each approach are discussed, and future directions and questions are highlighted.

Imaging blood–brain barrier dysfunction as a biomarker for epileptogenesis

A biomarker that will enable the identification of patients at high-risk for developing post-injury epilepsy is critically required. Microvascular pathology and related blood-brain barrier dysfunction and neuroinflammation were shown to be associated with epileptogenesis after injury. Here we used prospective, longitudinal magnetic resonance imaging to quantitatively follow blood-brain barrier pathology in rats following status epilepticus, late electrocorticography to identify epileptic animals and post-mortem immunohistochemistry to confirm blood-brain barrier dysfunction and neuroinflammation. Finally, to test the pharmacodynamic relevance of the proposed biomarker, two anti-epileptogenic interventions were used; isoflurane anaesthesia and losartan. Our results show that early blood-brain barrier pathology in the piriform network is a sensitive and specific predictor (area under the curve of 0.96, P < 0.0001) for epilepsy, while diffused pathology is associated with a lower risk. Early treatments with either isoflurane anaesthesia or losartan prevented early microvascular damage and late epilepsy. We suggest quantitative assessment of blood-brain barrier pathology as a clinically relevant predictive, diagnostic and pharmaco!dynamics biomarker for acquired epilepsy.

Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences

HighlightsWe present a novel spatio‐temporal denoising framework, based on Deep Neural Networks (DNNs), for the analysis of DCE‐MRI sequences of the brain.Specifically, DCE‐MRI denoising facilitates the extraction of the pharmacokinetic (PK) parameters from the DCE‐MRI concentration curves (CTCs) and thus allows a quantitative assessment of blood‐brain barrier functionality.This is accomplished by an ensemble of expert DNNs (deep‐autoencoders), where each is trained on a specific subset of the input space to accommodate different noise characteristics and curve prototypes.We introduce a novel sampling scheme, for generating realistic training sets that faithfully model the DCE‐MRI data and accounts for statistical similarity of neighboring CTCs.Experimental results on synthesized realistic data demonstrate that PK parameters reconstruction is more accurate for denoised CTCs using our method in comparison to CTCs denoised by other methods.The proposed approach is also applied to real DCE‐MRI scans from patients with stroke and brain tumors and is shown to favorably compare to state‐of‐the‐art denoising methods. Graphical abstract Figure. No caption available. ABSTRACT Dynamic contrast‐enhanced MRI (DCE‐MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood‐brain barrier (BBB) permeability. Extraction of the pharmacokinetic (PK) parameters from the DCE‐MRI concentration curves allows quantitative assessment of the integrity of the BBB functionality. However, curve fitting required for the analysis of DCE‐MRI data is error‐prone as the dynamic scans are subject to non‐white, spatially‐dependent and anisotropic noise. We present a novel spatio‐temporal framework based on Deep Neural Networks (DNNs) to address the DCE‐MRI denoising challenges. This is accomplished by an ensemble of expert DNNs constructed as deep autoencoders, where each is trained on a specific subset of the input space to accommodate different noise characteristics and curve prototypes. Spatial dependencies of the PK dynamics are captured by incorporating the curves of neighboring voxels in the entire process. The most likely reconstructed curves are then chosen using a classifier DNN followed by a quadratic programming optimization. As clean signals (ground‐truth) for training are not available, a fully automatic model for generating realistic training sets with complex nonlinear dynamics is introduced. The proposed approach has been successfully applied to full and even temporally down‐sampled DCE‐MRI sequences, from two different databases, of stroke and brain tumor patients, and is shown to favorably compare to state‐of‐the‐art denoising methods.

Breakdown of Inter-Hemispheric Connectivity Is Associated with Posttraumatic Symptomatology and Memory Impairment

Altered brain anatomy in specific gray-matter regions has been shown in patients with posttraumatic stress disorder (PTSD). Recently, white-matter tracts have become a focus of research in PTSD. The corpus callosum (CC) is the principal white-matter fiber bundle, crucial in relaying sensory, motor and cognitive information between hemispheres. Alterations in CC fibers have been reported in PTSD and might be assumed to underlie substantial behavioral and cognitive sequelae; however most diffusion tensor imaging (DTI) studies in adult-onset PTSD failed to address the clinical correlates between imaging and PTSD symptoms severity, behavioral manifestation and cognitive functions. In the current study we examined (a) to what extent microstructural integrity of the CC is associated with memory performance and (b) whether imaging and cognitive parameters are associated with PTSD symptom severity. DTI data were obtained and fractional anisotropy (FA) values were computed for 16 patients and 14 controls. PTSD symptom severity was assessed by employing the clinician administered PTSD scale (CAPS) and memory was tested using a task probing item and associative memory for words and pictures. Significant correlations were found between PTSD symptoms severity, memory accuracy and reaction-time to CC FA values in the PTSD group. This study demonstrates meaningful clinical and cognitive correlates of microstructural connectivity. These results have implications for diagnostic tools and future studies aimed at identifying individuals at risk for PTSD.

Autosomal dominant epilepsy with auditory features : a new LGI1 family including a phenocopy with cortical dysplasia

We report a new family with autosomal dominant epilepsy with auditory features (ADEAF) including focal cortical dysplasia (FCD) in the proband. We aim to identify the molecular cause in this family and clarify the relationship between FCD and ADEAF. A large Iranian Jewish family including 14 individuals with epileptic seizures was phenotyped including high-resolution 3-T MRI. We performed linkage analysis and exome sequencing. LGI1, KANK1 and RELN were Sanger sequenced. Seizure semiology of 11 individuals was consistent with ADEAF. The proband underwent surgery for right mesiotemporal FCD. 3-T MRIs in four individuals were unremarkable. Linkage analysis revealed peaks on chromosome 9p24 (LOD 2.43) and 10q22–25 (LOD 2.04). A novel heterozygous LGI1 mutation was identified in all affected individuals except for the proband indicating a phenocopy. Exome sequencing did not reveal variants within the chromosome 9p24 region. Closely located variants in KANK1 and a RELN variant did not segregate with the phenotype. We provide detailed description of the phenotypic spectrum within a large ADEAF family with a novel LGI1 mutation that was conspicuously absent in the proband with FCD, demonstrating that despite identical clinical symptoms, phenocopies in ADEAF families may exist. This family illustrates that rare epilepsy syndromes within a single family can have both genetic and structural etiologies.

De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks

Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood brain barrier (BBB) permeability. Extraction of the pharmacokinetic (PK) parameters from the DCE-MRI washout curves allows quantitative assessment of the BBB functionality. Nevertheless, curve fitting required for the analysis of DCE-MRI data is error-prone as the dynamic scans are subject to non-white, spatially-dependent and anisotropic noise that does not fit standard noise models. The two existing approaches i.e. curve smoothing and image de-noising can either produce smooth curves but cannot guaranty fidelity to the PK model or cannot accommodate the high variability in noise statistics in time and space.

Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players.

We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows along-tract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players . Along-tract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements.