A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET.

With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.

A three-dimensional deep learning model for inter-site harmonization of structural MR images of the brain: Extensive validation with a multicenter dataset.

In multicenter MRI studies, pooling the imaging data can introduce site-related variabilities and can therefore bias the subsequent analyses. To harmonize the intensity distributions of brain MR images in a multicenter dataset, unsupervised deep learning methods can be employed. Here, we developed a model based on cycle-consistent adversarial networks for the harmonization of T1-weighted brain MR images. In contrast to previous works, it was designed to process three-dimensional whole-brain images in a stable manner while optimizing computation resources. Using six different MRI datasets for healthy adults (n=1525 in total) with different acquisition parameters, we tested the model in (i) three pairwise harmonizations with site effects of various sizes, (ii) an overall harmonization of the six datasets with different age distributions, and (iii) a traveling-subject dataset. Our results for intensity distributions, brain volumes, image quality metrics and radiomic features indicated that the MRI characteristics at the various sites had been effectively homogenized. Next, brain age prediction experiments and the observed correlation between the gray-matter volume and age showed that thanks to an appropriate training strategy and despite biological differences between the dataset populations, the model reinforced biological patterns. Furthermore, radiologic analyses of the harmonized images attested to the conservation of the radiologic information in the original images. The robustness of the harmonization model (as judged with various datasets and metrics) demonstrates its potential for application in retrospective multicenter studies.

Neuropsychiatric associations of apolipoprotein E alleles in subjects with combat-related posttraumatic stress disorder.

Previous studies have reported associations between apolipoprotein E (APOE) genotype, cognitive function, and psychopathology in psychiatric populations. The authors investigated the associations between APOE allele status, memory function, and posttraumatic stress disorder (PTSD) symptom severity in PTSD subjects. Presence of the APOE 2 allele was associated with significantly worse reexperiencing symptoms and impaired memory function in this population.

Psychosensory symptoms in combat veterans with posttraumatic stress disorder.

Psychosensory symptoms have relevance to the study of chronic posttraumatic stress disorder (PTSD), given that their presence is associated with limbic system dysfunction and that several features of chronic PTSD suggest that it, too, may be associated with limbic dysfunction. The Iowa Interview for Partial Seizure-like Symptoms (IIPSS), a measure of psychosensory symptoms, was administered to a PTSD group and a comparison group. The PTSD group generated significantly higher IIPSS scores than did the other group. Within the PTSD group, higher IIPSS scores were associated with significantly more severe PTSD symptoms, dissociative symptoms, aggression, and overall psychopathology.

A survey of gun collection and use among three groups of veteran patients admitted to veterans affairs hospital treatment programs.

BACKGROUND: An important risk factor for suicide is psychiatric illness, but only a limited amount of work has been directed at assessing the use of firearms and other weapons by select psychiatric populations at high risk for violent acts. METHOD: Patients with combat-related posttraumatic stress disorder (PTSD), patients with schizophrenia, and patients undergoing rehabilitation for substance abuse were asked to complete a weapons-use survey and measures of psychopathology. RESULTS: The PTSD patients surveyed related owning more than four times as many firearms as other subjects and reported significantly higher levels of potentially dangerous firearm-related behaviors than the other psychiatric subjects surveyed. CONCLUSION: High levels of aggression, impulsive and dangerous weapon use, and ready weapon availability may be significant factors in gun-related violence in the PTSD patient population. Additional prospective research is needed to determine whether gun ownership or certain types of weapon use in this population is associated with future acts of violence.