OBJECTIVE: Recently, the 7 Tesla (7 T) Epilepsy Task Force published recommendations for 7 T magnetic resonance imaging (MRI) in patients with pharmaco-resistant focal epilepsy in pre-surgical evaluation. The objective of this study was to implement and evaluate this consensus protocol with respect to both its practicability and its diagnostic value/potential lesion delineation surplus effect over 3 T MRI in the pre-surgical work-up of patients with pharmaco-resistant focal onset epilepsy.
METHODS: The 7 T MRI protocol consisted of T1-weighted, T2-weighted, high-resolution-coronal T2-weighted, fluid-suppressed, fluid-and-white-matter-suppressed, and susceptibility-weighted imaging, with an overall duration of 50 min. Two neuroradiologists independently evaluated the ability of lesion identification, the detection confidence for these identified lesions, and the lesion border delineation at 7 T compared to 3 T MRI.
RESULTS: Of 41 recruited patients > 12 years of age, 38 were successfully measured and analyzed. Mean detection confidence scores were non-significantly higher at 7 T (1.95 ± 0.84 out of 3 versus 1.64 ± 1.19 out of 3 at 3 T, p = 0.050). In 50% of epilepsy patients measured at 7 T, additional findings compared to 3 T MRI were observed. Furthermore, we found improved border delineation at 7 T in 88% of patients with 3 T-visible lesions. In 19% of 3 T MR-negative cases a new potential epileptogenic lesion was detected at 7 T.
CONCLUSIONS: The diagnostic yield was beneficial, but with 19% new 7 T over 3 T findings, not major. Our evaluation revealed epilepsy outcomes worse than ILAE Class 1 in two out of the four operated cases with new 7 T findings.

There is a paradigm shift in our understanding of white matter hyperintensities (WMH) found on brain imaging. They were once thought to be a normal phenomenon of ageing and, therefore, warranted no further investigation. However, evidence now suggests these lesions are markers of poor brain and cardiovascular health, portending an increased risk of stroke, cognitive decline, depression and death. Nevertheless, no specific guidelines exist for the management of incidentally found WMH for general medical practitioners and other clinicians ordering brain magnetic resonance imaging scans for diverse clinical indications. Informed by a literature review and expert opinion gleaned from stroke neurologists, medical and imaging specialists, and general practitioners, we present our consensus statement to guide the management of incidentally found WMH in adults.
When incidental WMH are found on brain imaging: Perform a detailed history and examination to screen for neurological events. Investigate for potential undiagnosed or undertreated cardiovascular risk factors, especially hypertension and diabetes mellitus. Commence intensive and individualised cardiovascular risk management when risk factors are uncovered. Treat underlying risk factors via accepted guidelines but note that antiplatelet and anticoagulant medications should not be prescribed for incidental WMH in the absence of an alternative indication.
A brain health opportunity. We consider the discovery of incidental WMH on brain imaging to represent an opportunity to investigate for common cardiovascular risk factors and to optimise brain health. This can be commenced and monitored by the general practitioner or physician without delay in waiting for an outpatient neurology review.

BACKGROUND: The leukodystrophy \"Vanishing White Matter\" (VWM) is an orphan disease with neurological decline and high mortality. Currently, VWM has no approved treatments, but advances in understanding pathophysiology have led to identification of promising therapies. Several investigational medicinal products are either in or about to enter clinical trial phase. Clinical trials in VWM pose serious challenges, as VWM has an episodic disease course; disease phenotype is highly heterogeneous and predictable only for early onset; and study power is limited by the small patient numbers. To address these challenges and accelerate therapy delivery, the VWM Consortium, a group of academic clinicians with expertise in VWM, decided to develop a core protocol to function as a template for trials, to improve trial design and facilitate sharing of control data, while permitting flexibility regarding other trial details. Overall aims of the core protocol are to collect safety, tolerability, and efficacy data for treatment assessment and marketing authorization.
METHODS: To develop the core protocol, the VWM Consortium designated a committee, including clinician members of the VWM Consortium, family and patient group advocates, and experts in statistics, clinical trial design and alliancing with industries. We drafted three age-specific protocols, to stratify into more homogeneous patient groups, of ages ≥ 18 years, ≥ 6 to < 18 years and < 6 years. We chose double-blind, randomized, placebo-controlled design for patients aged ≥ 6 years; and open-label non-randomized natural-history-controlled design for patients < 6 years. The protocol describes study populations, age-specific endpoints, inclusion and exclusion criteria, study schedules, sample size determinations, and statistical considerations.
CONCLUSIONS: The core protocol provides a shared uniformity across trials, enables a pool of shared controls, and reduces the total number of patients necessary per trial, limiting the number of patients on placebo. All VWM clinical trials are suggested to adhere to the core protocol. Other trial components such as choice of primary outcome, pharmacokinetics, pharmacodynamics, and biomarkers are flexible and unconstrained by the core protocol. Each sponsor is responsible for their trial execution, while the control data are handled by a shared research organization. This core protocol benefits the efficiency of parallel and consecutive trials in VWM, and we hope accelerates time to availability of treatments for VWM.
BACKGROUND: NA. From a scientific and ethical perspective, it is strongly recommended that all interventional trials using this core protocol are registered in a clinical trial register.

BACKGROUND: Vascular cognitive impairment (VCI) is a heterogeneous entity with multiple aetiologies, all linked to underlying vascular disease. Among these, VCI related to subcortical small vessel disease (SSVD) is emerging as a major homogeneous subtype. Its progressive course raises the need for biomarker identification and/or development for adequate therapeutic interventions to be tested. In order to shed light in the current status on biochemical markers for VCI-SSVD, experts in field reviewed the recent evidence and literature data.
METHODS: The group conducted a comprehensive search on Medline, PubMed and Embase databases for studies published until 15.01.2017. The proposal on current status of biochemical markers in VCI-SSVD was reviewed by all co-authors and the draft was repeatedly circulated and discussed before it was finalized.
RESULTS: This review identifies a large number of biochemical markers derived from CSF and blood. There is a considerable overlap of VCI-SSVD clinical symptoms with those of Alzheimer\'s disease (AD). Although most of the published studies are small and their findings remain to be replicated in larger cohorts, several biomarkers have shown promise in separating VCI-SSVD from AD. These promising biomarkers are closely linked to underlying SSVD pathophysiology, namely disruption of blood-CSF and blood-brain barriers (BCB-BBB) and breakdown of white matter myelinated fibres and extracellular matrix, as well as blood and brain inflammation. The leading biomarker candidates are: elevated CSF/blood albumin ratio, which reflects BCB/BBB disruption; altered CSF matrix metalloproteinases, reflecting extracellular matrix breakdown; CSF neurofilment as a marker of axonal damage, and possibly blood inflammatory cytokines and adhesion molecules. The suggested SSVD biomarker deviations contrasts the characteristic CSF profile in AD, i.e. depletion of amyloid beta peptide and increased phosphorylated and total tau.
CONCLUSIONS: Combining SSVD and AD biomarkers may provide a powerful tool to identify with greater precision appropriate patients for clinical trials of more homogeneous dementia populations. Thereby, biomarkers might promote therapeutic progress not only in VCI-SSVD, but also in AD.

The human braingraph or the connectome is the object of an intensive research today. The advantage of the graph-approach to brain science is that the rich structures, algorithms and definitions of graph theory can be applied to the anatomical networks of the connections of the human brain. In these graphs, the vertices correspond to the small (1-1.5 cm2) areas of the gray matter, and two vertices are connected by an edge, if a diffusion-MRI based workflow finds fibers of axons, running between those small gray matter areas in the white matter of the brain. One main question of the field today is discovering the directions of the connections between the small gray matter areas. In a previous work we have reported the construction of the Budapest Reference Connectome Server http://connectome.pitgroup.org from the data recorded in the Human Connectome Project of the NIH. The server generates the consensus braingraph of 96 subjects in Version 2, and of 418 subjects in Version 3, according to selectable parameters. After the Budapest Reference Connectome Server had been published, we recognized a surprising and unforeseen property of the server. The server can generate the braingraph of connections that are present in at least k graphs out of the 418, for any value of k = 1, 2, …, 418. When the value of k is changed from k = 418 through 1 by moving a slider at the webserver from right to left, certainly more and more edges appear in the consensus graph. The astonishing observation is that the appearance of the new edges is not random: it is similar to a growing shrub. We refer to this phenomenon as the Consensus Connectome Dynamics. We hypothesize that this movement of the slider in the webserver may copy the development of the connections in the human brain in the following sense: the connections that are present in all subjects are the oldest ones, and those that are present only in a decreasing fraction of the subjects are gradually the newer connections in the individual brain development. An animation on the phenomenon is available at https://youtu.be/yxlyudPaVUE. Based on this observation and the related hypothesis, we can assign directions to some of the edges of the connectome as follows: Let Gk + 1 denote the consensus connectome where each edge is present in at least k+1 graphs, and let Gk denote the consensus connectome where each edge is present in at least k graphs. Suppose that vertex v is not connected to any other vertices in Gk+1, and becomes connected to a vertex u in Gk, where u was connected to other vertices already in Gk+1. Then we direct this (v, u) edge from v to u.

For accurate EEG/MEG source analysis it is necessary to model the head volume conductor as realistic as possible. This includes the distinction of the different conductive compartments in the human head. In this study, we investigated the influence of modeling/not modeling the conductive compartments skull spongiosa, skull compacta, cerebrospinal fluid (CSF), gray matter, and white matter and of the inclusion of white matter anisotropy on the EEG/MEG forward solution. Therefore, we created a highly realistic 6-compartment head model with white matter anisotropy and used a state-of-the-art finite element approach. Starting from a 3-compartment scenario (skin, skull, and brain), we subsequently refined our head model by distinguishing one further of the above-mentioned compartments. For each of the generated five head models, we measured the effect on the signal topography and signal magnitude both in relation to a highly resolved reference model and to the model generated in the previous refinement step. We evaluated the results of these simulations using a variety of visualization methods, allowing us to gain a general overview of effect strength, of the most important source parameters triggering these effects, and of the most affected brain regions. Thereby, starting from the 3-compartment approach, we identified the most important additional refinement steps in head volume conductor modeling. We were able to show that the inclusion of the highly conductive CSF compartment, whose conductivity value is well known, has the strongest influence on both signal topography and magnitude in both modalities. We found the effect of gray/white matter distinction to be nearly as big as that of the CSF inclusion, and for both of these steps we identified a clear pattern in the spatial distribution of effects. In comparison to these two steps, the introduction of white matter anisotropy led to a clearly weaker, but still strong, effect. Finally, the distinction between skull spongiosa and compacta caused the weakest effects in both modalities when using an optimized conductivity value for the homogenized compartment. We conclude that it is highly recommendable to include the CSF and distinguish between gray and white matter in head volume conductor modeling. Especially for the MEG, the modeling of skull spongiosa and compacta might be neglected due to the weak effects; the simplification of not modeling white matter anisotropy is admissible considering the complexity and current limitations of the underlying modeling approach.