Brain and Muscle Metabolic Changes by FDG-PET in Stiff Person Syndrome Spectrum Disorders

Objective: To report clinical characteristics and fluorodeoxyglucose positron emission tomography (FDG-PET) findings in the brain and muscles of individuals with stiff person syndrome (SPS) spectrum disorders (SPSSDs).Methods: Retrospective cohort study from 1997 to 2018 at Johns Hopkins Hospital identified 170 individuals with SPS or cerebellar ataxia (CA) associated with anti-glutamic acid decarboxylase (anti-GAD)-65 antibodies. Fifty-one underwent FDG-PET, with 50 involving the body and 30 with dedicated brain acquisition. The clinical and immunological profiles were extracted via medical record review. The brain scans were analyzed quantitatively using the NeuroQ software, with comparison with an averaged normal database. The body scans were reviewed qualitatively by a blinded nuclear medicine radiologist.Results: Mean age of symptom onset was 41.5 years (range 12–75 years). Majority were female (68%) and White (64%). Of the patients, 82% had SPS (majority being classic phenotype), and 18% had CA. Three had a paraneoplastic process. Forty-seven had serum anti-GAD, two with anti-amphiphysin, and one with anti-glycine receptor antibodies. Brain metabolic abnormalities were seen in both SPS and CA, with significant differences between the groups noted in the right superior frontal cortex, right sensorimotor cortex, left inferior parietal cortex, bilateral thalami, vermis, and left cerebellum. Of the patients, 62% demonstrated muscle hypermetabolism, most commonly bilateral, involving the upper extremities or axial muscles. Neither brain nor muscle metabolism was correlated with functional outcomes nor treatments.Conclusions: Metabolic changes as seen by FDG-PET are present in the brain and muscle in many individuals with SPSSD. Future studies are needed to assess whether FDG-PET can help aid in the diagnosis and/or monitoring of individuals with SPSSD.

Hybrid storage engine for geospatial data using NoSQL and SQL paradigms

The design and implementation of services to handle geospatial data involves thinking about storage engine performance and optimization for the desired use. NoSQL and relational databases bring their own advantages; therefore, it is necessary to choose one of these options according to the requirements of the solution. These requirements can change, or some operations may be performed in a more efficient way on another database engine, so using just one engine means being tied to its features and work model. This paper presents a hybrid approach (NoSQL-SQL) to store geospatial data on MongoDB, which are replicated and mapped on a PostgreSQL database, using an open source tool called ToroDB Stampede; solutions then can take advantage from either NoSQL or SQL features, to satisfy most of the requirements associated to the storage engine performance. A descriptive analysis to explain the workflow of the replication and synchronization in both engines precedes the quantitative analysis by which it was possible to determine that a normal database in PostgreSQL has a shorter response time than to perform the query in PostgreSQL with the hybrid database. In addition, the type of geometry increases the update response time of a materialized view.

Capitate and Lunate Morphology in Normal Wrist Radiographs-A Pilot Study

Background: Morphology may provide the basis for the understanding of wrist mechanics. Methods: We used classification systems based on cadaver dissection of lunate and capitate types to evaluate a normal database of 70 wrist radiographs in 35 subjects looking for associations between bone shapes. Kappa statistics and a log-linear mixed -effects model with a random intercept were used. Results: There were 39 type-1, 31 type- 2 lunates, 50 spherical, 10 flat and 10 V-shaped capitates. There was a significant difference in lunate and capitate shape between the hands of the same individual p <0.001. This may be due to different loads on the dominant vs. nondominant hands in the same individual. Conclusion: Further study to better understand the development of radiographic parameters of the midcarpal joint may aid in our understanding of the morphology and mechanics of the wrist.

Harmonized Z-scores calculated from the large scale normal MRI database to evaluate brain atrophy for neurodegenerative disorder

Alzheimer’s disease (AD), the most common type of dementia in elderly individuals, slowly and progressively diminishes cognitive function. Mild cognitive impairment is also a significant risk factor to the onset of AD. Magnetic resonance imaging (MRI) images have become widely used to detection and understand the natural progression not only AD but also neurodegenerative disorders. For this purpose, construct a reliable cognitive normal database is important. However, difference in magnetic field strength, sex and age between normal database and evaluation data-set can be affect the accuracy of detection and evaluation of AD and other neurodegenerative disorders.To solve this problem, we suggest a harmonized Z-score considering differences filed strength, sex and age derived from large cognitive normal subjects dataset ((1235 subjects)) including 1.5 T and 3T T1 brain MRI. And we evaluate our harmonized Z-score of discriminative power of AD, and classification accuracy between stable MCI and progressive MCI.The harmonized Z-score of hippocampus achieved high accuracy (AUC=0.96) for detection AD and moderate accuracy (AUC=0.70) for classification stable MCI and progressive MCI. Theses results shows that our method not only can detect AD with high accuracy and high generalization capability, but also can be valid to classify stable MCI and progressive MCI.

18F-FDG-PET Imaging Patterns in Autoimmune Encephalitis: Impact of Image Analysis on the Results

Brain positron emission tomography imaging with 18Fluorine-fluorodeoxyglucose (FDG-PET) has demonstrated utility in suspected autoimmune encephalitis. Visual and/or assisted image reading is not well established to evaluate hypometabolism/hypermetabolism. We retrospectively evaluated patients with autoimmune encephalitis between 2003 and 2018. Patients underwent EEG, brain magnetic resonance imaging (MRI), cerebrospinal fluid (CSF) sampling and autoantibodies testing. Individual FDG-PET images were evaluated by standard visual reading and assisted by voxel-based analyses, compared to a normal database. For the latter, three different methods were performed: two based on statistical surface projections (Siemens syngo.via Database Comparison, and 3D-SSP Neurostat) and one based on statistical parametric mapping (SPM12). Hypometabolic and hypermetabolic findings were grouped to identify specific patterns. We found six cases with definite diagnosis of autoimmune encephalitis. Two cases had anti-LGI1, one had anti-NMDA-R and two anti-CASPR2 antibodies, and one was seronegative. 18F-FDG-PET metabolic abnormalities were present in all cases, regardless of the method of analysis. Medial–temporal and extra-limbic hypermetabolism were more clearly depicted by voxel-based analyses. We found autoantibody-specific patterns in line with the literature. Statistical surface projection (SSP) methods (Neurostat and syngo.via Database Comparison) were more sensitive and localized larger hypermetabolic areas. As it may lead to comparable and accurate results, visual analysis of FDG-PET studies for the diagnosis of autoimmune encephalitis benefits from voxel-based analysis, beyond the approach based on MRI, CSF sample and EEG.