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BACKGROUND: Acute retinal necrosis (ARN) is a progressive necrotizing retinitis caused by viral infection. Optimal management strategies have not been established for this detrimental disease. Previous literature published suggests that Varicella-zoster virus (VZV) and Herpes simplex virus-1 (HSV1) are the most common promoters of acute retinal necrosis (ARN). AIMS: The purpose of our study was to investigate the viral distribution, demographic, and treatment outcomes of ARN. METHODS: A retrospective chart review evaluated data from PCR-positive ARN patients diagnosed between 2009 and 2018. RESULTS: Analysis of fourteen eyes from 12 patients found CMV and VZV as the commonest causes of ARN. Patients on 1 g of valacyclovir three times a day (V1T) had worse vision between first and final visits (mean difference of 1.25 ± 0.65, n = 2) compared with patients treated with 2 g of valacyclovir three times a day (V2T), or 900 mg twice a day of valganciclovir (V9B) (mean difference of - 0.067 ± 0.13, n = 6, and 0.067 ± 0.067, n = 6, respectively). Both V1T patients developed retinal detachments (RD). Both CMV patients treated with intravitreal triamcinolone developed ARN, elevated IOP, and one developed multiple RD. CONCLUSIONS: Our review found increased incidence of CMV-positive ARN. Patients with zone 1 disease had worse initial visual acuity. Moreover, patients had more favorable outcomes with V2T and V9B compared to V1T. CMV-positive patients clinically worsened after intravitreal steroid injections, further underscoring the value of a PCR diagnosis to tailor the patients' treatment plan accordingly.

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PRECIS: The peripapillary choriocapillaris (CC) was observed to be significantly impaired in normal tension glaucoma (NTG) subjects compared with normal controls using optical coherence tomography angiography (OCTA). PURPOSE: The aim was to quantitatively evaluate the peripapillary CC in NTG, primary open-angle glaucoma (POAG), and control eyes using OCTA. MATERIALS AND METHODS: Ninety eyes (30 controls, 30 NTG, and 30 POAG) from 73 patients were imaged using the Zeiss Plex Elite 9000. Five repeat 3×3 mm OCTA scans were acquired both nasally and temporally to the optic disc and subsequently averaged. Four CC flow deficit (FD) measures were calculated using the fuzzy C-means approach: FD density (FDD), mean FD size (MFDS), FD number (FDN), and FD area (FDA). RESULTS: Temporal NTG CC parameters were associated with visual field index and mean deviation (P<0.05). The control group showed a significantly lower nasal FDD (nasal: 3.79±1.26%, temporal: 4.48±1.73%, P=0.03), FDN (nasal: 156.43±38.44, temporal: 178.40±45.68, P=0.02), and FDA (nasal: 0.22±0.08, temporal: 0.26±0.10, P=0.03) when compared with temporal optic disc. The NTG group showed a significantly higher FDD (NTG: 5.04±2.38%, control: 3.79±1.26%, P=0.03), FDN (NTG: 185.90±56.66, control: 156.43±38.44, P=0.04), and FDA (NTG: 0.30±0.14 mm2, control: 0.22±0.08 mm2, P=0.03) nasal to the optic disc compared with controls. CONCLUSIONS: Association between CC parameters and glaucoma severity in NTG, but not POAG subjects, suggests vascular abnormalities may be a potential factor in the multifactorial process of glaucoma damage in NTG patients.

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Optical coherence tomography (OCT) has become an essential tool in the evaluation of glaucoma, typically through analyzing retinal nerve fiber layer changes in circumpapillary scans. Three-dimensional OCT volumes enable a much more thorough analysis of the optic nerve head (ONH) region, which may be the site of initial glaucomatous optic nerve damage. Automated analysis of this region is of great interest, though large anatomical variations and the termination of layers make the requisite peripapillary layer and Bruch's membrane opening (BMO) segmentation a challenging task. Several machine learning-based segmentation methods have been proposed for retinal layer segmentation, and a few for the ONH region, but they typically depend on either heavily averaged or pre-processed B-scans or a large amount of annotated data, which is a tedious task and resource-intensive. We evaluated a semi-supervised adversarial deep learning method for segmenting peripapillary retinal layers in OCT B-scans to take advantage of unlabeled data. We show that the use of a generative adversarial network and unlabeled data can improve the performance of segmentation. Additionally, we use a Faster R-CNN architecture to automatically segment the BMO. The proposed methods are then used for the 3D morphometric analysis of both control and glaucomatous ONH volumes to demonstrate the potential for clinical utility.

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PRéCIS:: The Bruch membrane opening (BMO) was posteriorly bowed and the degree of nonplanarity increased in stable and progressive glaucoma subjects. BMO became more posterior relative to the Bruch membrane (BM) in control and both stable and progressive glaucoma subjects. PURPOSE: To investigate longitudinal changes in morphologic characteristics of the BMO in control and glaucomatous subjects. MATERIALS AND METHODS: A total of 53 myopic eyes (17 control, 6 suspect, 20 stable glaucoma, and 10 progressing glaucoma) were followed for an average of 4.2±1.4 years and imaged at the baseline and 2 follow-up appointments using a 1060 nm swept-source optical coherence tomography system. BM and BMO were segmented, and 4 morphometric BMO parameters (area, ellipse ratio, nonplanarity, and depth) were measured. RESULTS: There were no significant changes in BMO area or ellipse ratio for all groups. BMO nonplanarity was shown to increase in the glaucoma groups. BMO depth relative to BM increased in all groups except the suspects (control: 8.1 µm/y, P=0.0001; stable glaucoma: 3.5 µm/y, P=0.0001; progressing glaucoma: 14.0 µm/y, P=0.0026). In linear mixed-model analysis, axial length was positively associated with BMO area in all groups except for progressing glaucoma, and with BMO nonplanarity in stable glaucoma. It was not a significant factor to the slopes of the BMO parameters in the ANCOVA analysis of slopes. CONCLUSIONS: Longitudinally, BMO increased in nonplanarity in the glaucoma eyes, and its axial position relative to BM became more posterior in both control and glaucoma eyes.

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When analyzing large multicenter databases, the effects of multiple confounding covariates increase the variability in the data and may reduce the ability to detect changes due to the actual effect of interest, for example, changes due to disease. Efficient ways to evaluate the effect of covariates toward the data harmonization are therefore important. In this article, we showcase techniques to assess the "goodness of harmonization" of covariates. We analyze 7,656 MR images in the multisite, multiscanner Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We present a comparison of three methods for estimating total intracranial volume to assess their robustness and correct the brain structure volumes using the residual method and the proportional (normalization by division) method. We then evaluated the distribution of brain structure volumes over the entire ADNI database before and after accounting for multiple covariates such as total intracranial volume, scanner field strength, sex, and age using two techniques: (a) Zscapes, a panoramic visualization technique to analyze the entire database and (b) empirical cumulative distributions functions. The results from this study highlight the importance of assessing the goodness of data harmonization as a necessary preprocessing step when pooling large data set with multiple covariates, prior to further statistical data analysis.

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Automated measurements of the human cone mosaic requires the identification of individual cone photoreceptors. The current gold standard, manual labeling, is a tedious process and can not be done in a clinically useful timeframe. As such, we present an automated algorithm for identifying cone photoreceptors in adaptive optics optical coherence tomography (AO-OCT) images. Our approach fine-tunes a pre-trained convolutional neural network originally trained on AO scanning laser ophthalmoscope (AO-SLO) images, to work on previously unseen data from a different imaging modality. On average, the automated method correctly identified 94% of manually labeled cones when compared to manual raters, from twenty different AO-OCT images acquired from five normal subjects. Voronoi analysis confirmed the general hexagonal-packing structure of the cone mosaic as well as the general cone density variability across portions of the retina. The consistency of our measurements demonstrates the high reliability and practical utility of having an automated solution to this problem.

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Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N=2984) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively.

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Frontotemporal dementia (FTD) is a neurodegenerative disease with a strong genetic basis. Understanding the structural brain changes during pre-symptomatic stages may allow for earlier diagnosis of patients suffering from FTD; therefore, we investigated asymptomatic members of FTD families with mutations in C9orf72 and granulin (GRN) genes. Clinically asymptomatic subjects from families with C9orf72 mutation (15 mutation carriers, C9orf72+; and 23 non-carriers, C9orf72-) and GRN mutations (9 mutation carriers, GRN+; and 15 non-carriers, GRN-) underwent structural neuroimaging (MRI). Cortical thickness and subcortical gray matter volumes were calculated using FreeSurfer. Group differences were evaluated, correcting for age, sex and years to mean age of disease onset within the subject's family. Mean age of C9orf72+ and C9orf72- were 42.6 ±â€¯11.3 and 49.7 ±â€¯15.5 years, respectively; while GRN+ and GRN- groups were 50.1 ±â€¯8.7 and 53.2 ±â€¯11.2 years respectively. The C9orf72+ group exhibited cortical thinning in the temporal, parietal and frontal regions, as well as reduced volumes of bilateral thalamus and left caudate compared to the entire group of mutation non-carriers (NC: C9orf72- and GRN- combined). In contrast, the GRN+ group did not show any significant differences compared to NC. C9orf72 mutation carriers demonstrate a pattern of reduced gray matter on MRI prior to symptom onset compared to GRN mutation carriers. These findings suggest that the preclinical course of FTD differs depending on the genetic basis and that the choice of neuroimaging biomarkers for FTD may need to take into account the specific genes involved in causing the disease.

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