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Neuroimaging in encephalitis: analysis of imaging findings and interobserver agreement.
AIM: To assess the role of imaging in the early management of encephalitis and the agreement on findings in a well-defined cohort of suspected encephalitis cases enrolled in the Prospective Aetiological Study of Encephalitis conducted by the Health Protection Agency (now incorporated into Public Health England). MATERIALS AND METHODS: Eighty-five CT examinations from 68 patients and 101 MRI examinations from 80 patients with suspected encephalitis were independently rated by three neuroradiologists blinded to patient and clinical details. The level of agreement on the interpretation of images was measured using the kappa statistic. The sensitivity, specificity, and negative and positive predictive values of CT and MRI for herpes simplex virus (HSV) encephalitis and acute disseminated encephalomyelitis (ADEM) were estimated. RESULTS: The kappa value for interobserver agreement on rating the scans as normal or abnormal was good (0.65) for CT and moderate (0.59) for MRI. Agreement for HSV encephalitis was very good for CT (0.87) and MRI (0.82), but only fair for ADEM (0.32 CT; 0.31 MRI). Similarly, the overall sensitivity of imaging for HSV encephalitis was ∼80% for both CT and MRI, whereas for ADEM it was 0% for CT and 20% for MRI. MRI specificity for HSV encephalitis between 3-10 days after symptom onset was 100%. CONCLUSION: There is a subjective component to scan interpretation that can have important implications for the clinical management of encephalitis cases. Neuroradiologists were good at diagnosing HSV encephalitis; however, agreement was worse for ADEM and other alternative aetiologies. Findings highlight the importance of a comprehensive and multidisciplinary approach to diagnosing the cause of encephalitis that takes into account individual clinical, microbiological, and radiological features of each patient.
Diagnostic strategy used to establish etiologies of encephalitis in a prospective cohort of patients in England.
The laboratory diagnostic strategy used to determine the etiology of encephalitis in 203 patients is reported. An etiological diagnosis was made by first-line laboratory testing for 111 (55%) patients. Subsequent testing, based on individual case reviews, resulted in 17 (8%) further diagnoses, of which 12 (71%) were immune-mediated and 5 (29%) were due to infection. Seventy-five cases were of unknown etiology. Sixteen (8%) of 203 samples were found to be associated with either N-methyl-d-aspartate receptor or voltage-gated potassium channel complex antibodies. The most common viral causes identified were herpes simplex virus (HSV) (19%) and varicella-zoster virus (5%), while the most important bacterial cause was Mycobacterium tuberculosis (5%). The diagnostic value of testing cerebrospinal fluid (CSF) for antibody was assessed using 139 samples from 99 patients, and antibody was detected in 46 samples from 37 patients. Samples collected at 14 to 28 days were more likely to be positive than samples taken 0 to 6 days postadmission. Three PCR-negative HSV cases were diagnosed by the presence of virus-specific antibody in the central nervous system (CNS). It was not possible to make an etiological diagnosis for one-third of the cases; these were therefore considered to be due to unknown causes. Delayed sampling did not contribute to these cases. Twenty percent of the patients with infections with an unknown etiology showed evidence of localized immune activation within the CNS, but no novel viral DNA or RNA sequences were found. We conclude that a good standard of clinical investigation and thorough first-line laboratory testing allows the diagnosis of most cases of infectious encephalitis; testing for CSF antibodies allows further cases to be diagnosed. It is important that testing for immune-mediated causes also be included in a diagnostic algorithm.
Acute symptomatic seizures secondary to autoimmune encephalitis and autoimmune-associated epilepsy: Conceptual definitions.
Seizures are a well-recognized and often prominent manifestation of autoimmune encephalitic syndromes. Progress in detection of pathogenic neural autoantibodies has led to increased awareness of autoimmune causes of seizures. Clinical studies of patients with these autoantibodies have improved our understanding of the seizure characteristics, treatments, and seizure prognosis in these disorders. The International League Against Epilepsy (ILAE) Autoimmunity and Inflammation Taskforce proposes conceptual definitions for two main diagnostic entities: (a) acute symptomatic seizures secondary to autoimmune encephalitis, and (b) autoimmune-associated epilepsy, the latter of which suggests an enduring predisposition to seizures. Such a distinction is relevant when discussing the pathophysiology, treatment, prognosis, and social consequences of these disorders. We discuss the role of biomarkers in the application of these conceptual definitions and illustrate their use in patients cared for by members of the task force.
An introduction to MEG connectivity measurements
© Springer Nature Switzerland AG 2019. All rights are reserved. Researchers are beginning to appreciate the brain as more than a mere collection of loosely connected, highly specialized components. While there is clear specialization among regions of the cortex, the true power of the brain appears to arise from the ability of those regions to work together across a range of spatial scales as a richly interconnected and complex network. On all levels, the study of brain connectivity seeks to understand how different regions of the cortex communicate, what the emerging networks signify functionally, and why these are important for normal behavior. The use ofMEG in this endeavor is an attempt to understand these processes on the broad, interregional scale, and in that respect MEG is an ideal tool. It has a good deal of spatial resolution, enough to distinguish between brain areas ∼1 cm apart, and exquisite temporal resolution, enough to record even the fastest electrical oscillations the brain can generate. This chapter begins with a brief overview of the history of electrophysiological measures and their application to the study of brain connectivity. We then describe some of the core theory underlying the measurement of magnetic fields generated by the brain and practical considerations of measuring correlated activity with MEG. Some notable applications of MEG to the study of brain networks will then be described, and a comparison will be made between MEG to other methods such as ECoG. The chapter will also explore some of the principal mathematical techniques used by researchers to probe different aspects of connectivity ranging from simple correlational approaches to more involved concepts such as multivariate autoregressive models (MARs). Finally, we will discuss limitations of using MEG to study connectivity and also give some insight into the exciting prospects the future might hold for MEG connectivity research.