Diagnosis and management of multiple sclerosis: MRI in clinical practice.

BACKGROUND: Recent changes in the understanding and management of multiple sclerosis (MS) have increased the role of MRI in supporting diagnosis and disease monitoring. However, published guidelines on the use of MRI in MS do not translate easily into different clinical settings and considerable variation in practice remains. Here, informed by published guidelines for the use of MRI in MS, we identified a clinically informative MRI protocol applicable in a variety of clinical settings, from district general hospitals to tertiary centres. METHODS: MS specialists geographically representing the UK National Health Service and with expertise in MRI examined existing guidelines on the use of MRI in MS and identification of challenges in their applications in various clinical settings informed the formulation of a feasible MRI protocol. RESULTS: We identified a minimum set of MRI information, based on clinical relevance, as well as on applicability to various clinical settings. This informed the selection of MRI acquisitions for scanning protocols, differentiated on the basis of their purpose and stage of the disease, and indication of timing for scans. Advice on standardisation of MRI requests and reporting, and proposed timing and frequency of MRI scans were generated. CONCLUSIONS: The proposed MRI protocol can adapt to a range of clinical settings, aiding the impetus towards standardisation of practice and offering an example of research-informed service improvement to support optimisation of resources. Other neurological conditions, where a gap still exists between published guidelines and their clinical implementation, may benefit from this same approach

Fractal analysis of resting state fMRI signals in adults with ADHD

The fractal concept developed by Mandelbrot provides a useful tool for examining a variety of naturally occurring phenomena. Fractals are signals that display scale-invariant or self-similar behaviour. They can be found everywhere in nature including fractional Gaussian noise (fGn). Resting state fMRI signals can be modelled as fGn which makes them appropriate for fractal analysis. The Hurst exponent, H, is a measure of fractal processes and has values ranging between 0 and 1. Fractional Gaussian noise with 0<H<0.5 demonstrates negatively autocorrelated or antipersistent behaviour; fGn with 0.5<H<1 demonstrates a positively correlated, relatively persistent, predictable, long memory behaviour; and fGn with H = 0.5 corresponds to classical Gaussian white noise. In the present study, we aim to estimate the fractal behaviour of adult ADHD patients when compared to age-matched healthy controls using dispersional analysis. We hypothesize that ADHD patients will demonstrate more predictable (higher H values) fractal behaviour. Ten ADHD patients (5 female, mean age (32.60±10.46)) and ten controls (7 female, mean age (30.10±8.49)) were brain imaged by 3T MRI scanner. All patients and control participants completed the Conners’ Adult ADHD Rating Scales (ADHD scores). Our analysis shows that the ADHD patients demonstrate more positively correlated, relatively persistent, predictable and longer memory fractal behaviour in regards to healthy controls. The discriminated brain regions are part of the frontal-striatal-cerebellar circuits and are consistent with the hypothesis of abnormal frontal-striatal-cerebellar circuits in ADHD. We have shown that the analysis of fractal behaviour may be a useful tool in revealing abnormalities in ADHD brain dynamics

Resting state fMRI entropy probes complexity of brain activity in adults with ADHD

In patients with attention deficit hyperactivity disorder (ADHD), quantitative neuroimaging techniques have revealed abnormalities in various brain regions, including the frontal cortex, striatum, cerebellum, and occipital cortex. Nonlinear signal processing techniques such as sample entropy have been used to probe the regularity of brain magnetoencephalography signals in patients with ADHD. In the present study, we extend this technique to analyse the complex output patterns of the 4 dimensional resting state functional magnetic resonance imaging signals in adult patients with ADHD. After adjusting for the effect of age, we found whole brain entropy differences (P=0.002) between groups and negative correlation (r=-0.45) between symptom scores and mean whole brain entropy values, indicating lower complexity in patients. In the regional analysis, patients showed reduced entropy in frontal and occipital regions bilaterally and a significant negative correlation between the symptom scores and the entropy maps at a family-wise error corrected cluster level of P<0.05 (P=0.001, initial threshold). Our findings support the hypothesis of abnormal frontal-striatal-cerebellar circuits in ADHD and the suggestion that sample entropy is a useful tool in revealing abnormalities in the brain dynamics of patients with psychiatric disorders

Advances in Pediatric Neurovirology

Viral infections of the pediatric central nervous system (CNS) encompass a broad spectrum of both perinatally and postnatally acquired diseases with potentially devastating effects on the developing brain. In children, viral infections have been associated with chronic encephalopathy, encephalitis, demyelinating disease, tumors, and epilepsy. Older diagnostic techniques of biopsy, viral culture, electron microscopy, gel-based polymerase chain reaction (PCR), and viral titer quantification are being replaced with more rapid, sensitive, and specific real-time and microarray-based PCR technologies. Advances in neuroimaging technologies have provided for earlier recognition of CNS injury without elucidation of specific viral etiology. Although the mainstay therapy of many pediatric neurovirologic diseases, aside from HIV, includes intravenous acyclovir, much work is being done to develop novel antiviral immunotherapies aimed at both treating and preventing pediatric CNS viral disease

Antiangiogenic agents in the treatment of recurrent or newly diagnosed glioblastoma: Analysis of single-agent and combined modality approaches

Surgical resection followed by radiotherapy and temozolomide in newly diagnosed glioblastoma can prolong survival, but it is not curative. For patients with disease progression after frontline therapy, there is no standard of care, although further surgery, chemotherapy, and radiotherapy may be used. Antiangiogenic therapies may be appropriate for treating glioblastomas because angiogenesis is critical to tumor growth. In a large, noncomparative phase II trial, bevacizumab was evaluated alone and with irinotecan in patients with recurrent glioblastoma; combination treatment was associated with an estimated 6-month progression-free survival (PFS) rate of 50.3%, a median overall survival of 8.9 months, and a response rate of 37.8%. Single-agent bevacizumab also exceeded the predetermined threshold of activity for salvage chemotherapy (6-month PFS rate, 15%), achieving a 6-month PFS rate of 42.6% (p < 0.0001). On the basis of these results and those from another phase II trial, the US Food and Drug Administration granted accelerated approval of single-agent bevacizumab for the treatment of glioblastoma that has progressed following prior therapy. Potential antiangiogenic agents-such as cilengitide and XL184-also show evidence of single-agent activity in recurrent glioblastoma. Moreover, the use of antiangiogenic agents with radiation at disease progression may improve the therapeutic ratio of single-modality approaches. Overall, these agents appear to be well tolerated, with adverse event profiles similar to those reported in studies of other solid tumors. Further research is needed to determine the role of antiangiogenic therapy in frontline treatment and to identify the optimal schedule and partnering agents for use in combination therapy

Case Report - MRI findings in Kallmann syndrome

Kallmann syndrome (KS) is a neuronal migration disorder characterised by hypogonadotrophic hypogonadism and anosmia or hyposmia. Five patients with clinical findings suggestive of KS were evaluated with MRI. All patients had abnormalities of olfactory system. Olfactory bulbs were absent in all patients. Olfactory sulci were absent in 3 patients and hypoplastic in 2 patients. Anterior pituitary was hypoplastic in two patients. The MRI findings in KS are characteristic and MRI is a useful adjunct to the diagnosis of KS

Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.

AIM To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma. MATERIALS AND METHODS A retrospective analysis was undertaken of 76 patients (46 tPD, 30 psPD) with early enhancing disease following chemoradiotherapy for glioblastoma. Outcome was determined on follow-up until 6 months post-chemoradiotherapy. Models comprised clinical characteristics, O-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and 307 quantitative imaging features extracted from enhancing disease and perilesional oedema masks on early post-chemoradiotherapy contrast-enhanced T1-weighted imaging, T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) maps. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm. Naive Bayes five-fold cross-validation was used to validate the final model. RESULTS Top selected features included age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask, three radiomic features from the enhancing disease mask on ADC, and one radiomic feature from the perilesional oedema mask on T2WI. The final model had an area under the receiver operating characteristics curve (AUC) of 0.80, sensitivity 78.2%, specificity 66.7%, and accuracy of 73.7%. CONCLUSION Incorporating a machine learning-based approach using quantitative radiomic features from standard-of-care magnetic resonance imaging (MRI), in combination with clinical characteristics and MGMT promoter methylation status has a complementary effect and improves model performance for early prediction of glioblastoma treatment response

MRI features of tuberculosis of peripheral joints

The aim of this article is to present the magnetic resonance imaging (MRI) features of peripheral tubercular arthritis. The clinical presentation of peripheral tubercular arthritis is variable and simulates other chronic inflammatory arthritic disorders. MRI is a highly sensitive technique which demonstrates fine anatomical details and identifies the early changes of arthritis, which are not visible on radiographs. The MRI features of tubercular arthritis include synovitis, effusion, central and peripheral erosions, active and chronic pannus, abscess, bone chips and hypo-intense synovium. These imaging features in an appropriate clinical setting may help in the diagnosis of tubercular arthritis. Early diagnosis and treatment can effectively eliminate the long-term morbidity of joints affected by tuberculosis