Brain SPECT imaging in Huntington's disease before and after therapy with olanzapine: case report

Olanzapine, an atypical antipsychotic drug, was administered to a patient with Huntington's disease (HD) with marked choreiform movements. Brain SPECT with 99mTc-HMPAO was performed before and after treatment. Brain SPECT imaging has been performed in patients with HD in order to determine the status of basal ganglia perfusion. The use of brain SPECT with 99mTc-HMPAO before and after treatment in patients with HD has not been yet reported. The marked hypoperfusion of the basal ganglia on brain SPECT performed before therapy with olanzapine improved significantly after treatment.

Molecular diagnosis of Huntington disease in Brazilian patients

Huntington disease (HD) is a progressive neurodegenerative disorder with autosomal dominant inheritance, characterized by choreiform movements and cognitive impairment. Onset of symptoms is around 40 years of age and progression to death occurs in approximately 10 to 15 years from the time of disease onset. HD is associated with an unstable CAG repeat expansion at the 5' and of the IT15 gene. We have genotyped the CAG repeat in the IT15 gene in 44 Brazilian individuals (42 patients and 2 unaffected family members) belonging to 34 unrelated families thought to segregate HD. We found one expanded CAG allele in 32 individuals (76%) belonging to 25 unrelated families. In these HD patients, expanded alleles varied from 43 to 73 CAG units and normal alleles varied from 18 to 26 CAGs. A significant negative correlation between age at onset of symptoms and size of the expanded CAG allele was found (r=0.6; p=0.0001); however, the size of the expanded CAG repeat could explain only about 40% of the variability in age at onset (r2=0.4). In addition, we genotyped 25 unrelated control individuals (total of 50 alleles) and found normal CAG repeats varying from 16 to 33 units. The percentage of heterozigocity of the normal allele in the control population was 88%. In conclusion, our results showed that not all patients with the HD phenotype carried the expansion at the IT15 gene. Furthermore, molecular diagnosis was possible in all individuals, since no alleles of intermediate size were found. Therefore, molecular confirmation of the clinical diagnosis in HD should be sought in all suspected patients, making it possible for adequate genetic counseling.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.