A weighted cranial diffusion-weighted imaging scale for Wilson’s disease

ObjectivesCranial magnetic resonance imaging (MRI) could be a crucial tool for the assessment for neurological symptoms in patients with Wilson’s disease (WD). Diffusion-weighted imaging (DWI) hyperintensity reflects the acute brain injuries, which mainly occur in specific brain regions. Therefore, this study aimed to develop a weighted cranial DWI scale for patients with WD, with special focus on specific brain regions.Materials and methodsIn total, 123 patients with WD were enrolled, 118 of whom underwent 1.5 T-MRI on admission. The imaging score was calculated as described previously and depended on the following sequences: one point was acquired when abnormal intensity occurred in the T1, T2, and fluid-attenuation inversion recovery sequences, and two points were acquired when DWI hyperintensity were found. Consensus weighting was conducted based on the symptoms and response to treatment.ResultsIntra-rater agreement were good (r = 0.855 [0.798–0.897], p < 0.0001). DWI hyperintensity in the putamen was a high-risk factor for deterioration during de-copper therapy (OR = 8.656, p < 0.05). The high-risk factors for readmission for intravenous de-copper therapies were DWI hyperintensity in the midbrain (OR = 3.818, p < 0.05) and the corpus callosum (OR = 2.654, p < 0.05). Both scoring systems had positive correlation with UWDRS scale (original semi-quantitative scoring system, r = 0.35, p < 0.001; consensus semi-quantitative scoring system, r = 0.351, p < 0.001.). Compared to the original scoring system, the consensus scoring system had higher correlations with the occurrence of deterioration (OR = 1.052, 95%CI [1.003, 1.0103], p < 0.05) and readmission for intravenous de-copper therapy (OR = 1.043, 95%CI [1.001, 1.086], p < 0.05).ConclusionThe predictive performance of the consensus semi-quantitative scoring system for cranial MRI was improved to guide medication, healthcare management, and prognosis prediction in patients with WD. For every point increase in the neuroimaging score, the risk of exacerbations during treatment increased by 5.2%, and the risk of readmission to the hospital within 6 months increased by 4.3%

The clinical impact of multidetector SPET technology

Introduction: Single photon emission tomography (SPET) is an established technique in Nuclear Medicine. Recent advances in SPET technology have now permitted the development of multidetector gamma cameras. This thesis evaluates some of these new gamma cameras and their impact on clinical practice. Aim: (a) To assess four new multidetector SPET gamma cameras (IGE Neurocam, Toshiba GCA-9300A, IGE Optima and Sopha DST). (b) To establish appropriate acquisition and analytical clinical protocols. Methodology: For each instrument, the tomographic spatial resolution, contrast and sensitivity were measured. The capability of a new slant hole collimator (IGE Optima) to perform radionuclide ventriculography (RNV) was assessed. To evaluate the utility of these systems, a total of 1215 patient studies were performed (1007 cardiac, 85 skeletal, 73 renal and 50 brain studies). The effect of 8, 16 and 32 minutes data acquisition on image quality and clinical relevance was evaluated. In addition, a new cardiac SPET protocol for rest/stress myocardial perfusion scintigraphy (thallium-201/Tc-99m tetrofosmin) was tested. Results: Tomographic spatial resolution of the order of 10 mm FWHM was achieved by all four systems. System sensitivity was related to the number of detectors and ranged between 9.2–11.2 Kcps/(MBq/ml)/cm per detector. The slant hole collimator with cephalic tilt gave highly reproducible results (r=0.98,SEE=+2) for ejection fraction measurements in 75 patients. There was no significant difference in the clinical information obtained using 8 min, 16 min and 32 min acquisitions. Based on patient studies and experience with these multidetector SPET systems, optimum acquisition and analysis protocols for commonly performed SPET studies were documented for routine clinical use. Artefacts due to patient movement during Tl-201 myocardial SPET studies were less frequent on a dual-detector system compared with a single detector system (0.7% and 4% respectively); while artefacts due to poor positioning or shift in centre of rotation were more. The rest/stress thallium-201/Tc-99m tetrofosmin study protocol (acquisition and analysis) was completed in 90 min. This protocol gave a sensitivity of 80% and specificity of 70% for the detection of coronary artery disease. Conclusion: For the first time a comprehensive comparison of multidetector SPET systems has been documented. Optimum acquisition and analysis protocols have been identified. The study also shows that the new generation of multidetector SPET systems offer adequate resolution and sensitivity for routine clinical imaging. Increased sensitivity can be translated into an increased patient throughput. This can increase the cost-effectiveness of this new technology

A cumulative index to the 1976 issues of a continuing bibliography on Aerospace Medicine and Biology

This publication is a cumulative index to the abstracts contained in Supplements 151 through 162 of Aerospace Medicine and Biology: A continuing bibliography. It includes three indexes - subject, personal author, and corporate source

Approaches For Capturing Time-Varying Functional Network Connectivity With Application to Normative Development and Mental Illness

Since the beginning of medical science, the human brain has remained an unsolved puzzle; an illusive organ that controls everything- from breathing to heartbeats, from emotion to anger, and more. With the power of advanced neuroimaging techniques, scientists have now started to solve this nearly impossible puzzle, piece by piece. Over the past decade, various in vivo techniques, including functional magnetic resonance imaging (fMRI), have been increasingly used to understand brain functions. fMRI is extensively being used to facilitate the identification of various neuropsychological disorders such as schizophrenia (SZ), bipolar disorder (BP) and autism spectrum disorder (ASD). These disorders are currently diagnosed based on patients’ self-reported experiences, and observed symptoms and behaviors over the course of the illnesses. Therefore, efficient identification of biological-based markers (biomarkers) can lead to early diagnosis of these mental disorders, and provide a trajectory for disease progression. By applying advanced machine learning techniques on fMRI data, significant differences in brain function among patients with mental disorders and healthy controls can be identified. Moreover, by jointly estimating information from multiple modalities, such as, functional brain data and genetic factors, we can now investigate the relationship between brain function and genes. Functional connectivity (FC) has become a very common measure to characterize brain functions, where FC is defined as the temporal covariance of neural signals between multiple spatially distinct brain regions. Recently, researchers are studying the FC among functionally specialized brain networks which can be defined as a higher level of FC, and is termed as functional network connectivity (FNC, defined as the correlation value that summarizes the overall connection between brain ‘networks’ over time). Most functional connectivity studies have made the limiting assumption that connectivity is stationary over multiple minutes, and ignore to identify the time-varying and reoccurring patterns of FNC among brain regions (known as time-varying FNC). In this dissertation, we demonstrate the use of time-varying FNC features as potential biomarkers to differentiate between patients with mental disorders and healthy subjects. The developmental characteristics of time-varying FNC in children with typically developing brain and ASD have been extensively studies in a cross-sectional framework, and age-, sex- and disease-related FNC profiles have been proposed. Also, time-varying FNC is characterized in healthy adults and patients with severe mental disorders (SZ and BP). Moreover, an efficient classification algorithm is designed to identify patients and controls at individual level. Finally, a new framework is proposed to jointly utilize information from brain’s functional network connectivity and genetic features to find the associations between them. The frameworks that we presented here can help us understand the important role played by time-varying FNC to identify potential biomarkers for the diagnosis of severe mental disorders

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 157, August 1976

This bibliography lists 228 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1976

Three-dimensional Characterization of Interorganelle Contact Sites in Hepatocytes using Serial Section Electron Microscopy

Transmission electron microscopy has been long considered to be the gold standard for the visualization of cellular ultrastructure. However, analysis is often limited to two dimensions, hampering the ability to fully describe the three-dimensional (3D) ultrastructure and functional relationship between organelles. Volume electron microscopy (vEM) describes a collection of techniques that enable the interrogation of cellular ultrastructure in 3D at mesoscale, microscale, and nanoscale resolutions. This protocol provides an accessible and robust method to acquire vEM data using serial section transmission EM (TEM) and covers the technical aspects of sample processing through to digital 3D reconstruction in a single, straightforward workflow. To demonstrate the usefulness of this technique, the 3D ultrastructural relationship between the endoplasmic reticulum and mitochondria and their contact sites in liver hepatocytes is presented. Interorganelle contacts serve vital roles in the transfer of ions, lipids, nutrients, and other small molecules between organelles. However, despite their initial discovery in hepatocytes, there is still much to learn about their physical features, dynamics, and functions. Interorganelle contacts can display a range of morphologies, varying in the proximity of the two organelles to one another (typically ~10-30 nm) and the extent of the contact site (from punctate contacts to larger 3D cisternal-like contacts). The examination of close contacts requires high-resolution imaging, and serial section TEM is well suited to visualize the 3D ultrastructural of interorganelle contacts during hepatocyte differentiation, as well as alterations in hepatocyte architecture associated with metabolic diseases

Development and evaluation of biomarkers in Huntington’s Disease: furthering our understanding of the disease and preparing for clinical trials

Huntington’s Disease (HD) is a devastating hereditary neurodegenerative disease for which there are currently only symptomatic treatments. Several potentially curative pharmaceutical and genetic therapies are however in varying stages of development and therefore an increasing number of large-scale clinical trials of disease-modifying therapies are imminent. There is consequently a need for biomarkers which are sensitive to beneficial attenuation of disease-related changes. Functional, neuroimaging and biochemical biomarkers have been developed in HD (Andre et al. 2014;Weir et al. 2011). Neuroimaging biomarkers are strong candidates based on their clear relevance to the neuropathology of disease, proven precision and superior sensitivity compared with some standard functional measures (Tabrizi et al. 2011;Tabrizi et al. 2012). Their use in early-stage clinical trials, as surrogate end-points providing initial evidence of biological effect, is becoming increasingly common. Comparison of biomarkers in HD will help to clarify which measures, over varying time intervals, are most sensitive to disease progression. Additionally, the identification of robust fully-automated methods, comparable to manual and semi-automated gold-standards, would facilitate large-scale volumetric analysis. These methods however require validation in observational studies of neurodegenerative disease before they can be applied to sensitive clinical trial data. This thesis will develop and evaluate biomarkers for use in HD; both furthering our understanding of the disease and in preparation for use as end-points in clinical trials. A direct comparison of the sensitivity of diffusion and volumetric imaging biomarkers to HD-related change will be reported for the first time. Several exploratory imaging investigations are also described which enhance current knowledge of the relationship between neuroimaging metrics, brain functioning and behaviour, additionally strengthening the argument for the clinical relevance of neuroimaging measures as surrogate end-points in HD. The thesis will conclude with a comprehensive biomarker evaluation in early-stage HD, along with suggested strategies for selection of primary and secondary trial end-points based on effect sizes and corresponding sample size requirements

Advanced Application of Diffusion Kurtosis Imaging

Diffusion tensor imaging (DTI) has become a standard procedure in clinical routine as well as research as it enables the reconstruction and visualization of fiber tracts in the human brain. Due to the simplified assumption the tensor model – a Gaussian distribution of the diffusion – it typically fails to provide neither accurate spatial mapping nor quantification of crossing or kissing fibers. A clinically feasible development might be diffusion kurtosis imaging (DKI), an extension of DTI also integrating non-Gaussian distribution diffusion processes and thereby shall overcome some of its limitations. The potential DKI will be evaluated in case of the detection of the interhemispheric asymmetry of the white matter in healthy volunteers (n = 20), as well as the analysis of tumor-related impairments of fiber tracts and their correlation with neurological deficits in patients (n = 13) diagnosed with glioma. In order to analyze interhemispheric asymmetry across the whole brain, especially of nine large fiber tracts, tract-based spatial statistics (TBSS) analysis was performed using DTI- and DKI-based parameters, a laterality index was calculated for asymmetries and DTI- and DKI-based results were compared. With regard to fractional anisotropy as marker of integrity, asymmetry was found for all nine fiber tracts based on DTI and seven tracts based on DKI. For mean diffusivity, asymmetries were found for three (DTI) and two (DKI) fiber tracts. Regarding mean kurtosis, asymmetry was found in one tract. The interhemispheric asymmetry thereby varied in anatomical location as well as in cluster size. Only small parts of the tracts were affected. A comparison of DTI and DKI showed significantly higher fractional anisotropy and mean diffusivity based on DKI compared to DTI. Gender and handedness did not seem to have any influence. For the assessment of tumor-related changes of fiber tracts in patients diagnosed with glioma, especially in relation to pre-existing and postoperative neurological deficits (hemiparesis, aphasia), templates for the corticospinal tract and the arcuate fasciculus were created based on DTI- and DKI-derived parameters, respectively. The corticospinal tract and the arcuate fasciculus were reconstructed for each patient and the associated parametric maps were projected onto the templates. Based on this, alterations along the tracts could be identified and quantified. Alterations were found on fiber tracts regardless of the spatial proximity to the lesion. There was a correlation between alterations based on fractional anisotropy, mean diffusivity and mean kurtosis. Increased mean diffusivity was associated with alteration in mean kurtosis, a decreased fractional anisotropy was found concurrent with a likewise decreased mean kurtosis. In the case of pre-existing neurological deficits (hemiparesis, aphasia) with regard to the changes along the fiber tracts (corticospinal tract, left arcuate fasciculus), most often increased mean diffusivity and altered mean kurtosis was found. Applying this pattern for prediction of corresponding postoperative neurological deficits a sensitivity of 75.0% and a specificity of 87.5% was achieved. DKI seems to more precisely estimated and depict the underlying microstructure in comparison to DTI. Thereby, in pathological cases especially the mean kurtosis seems to be of special interest. A combination of DTI- and DKI based parameters, particularly with regard to its clinical usability and value, offers great potential in clinical routine