Interoceptive accuracy and interoceptive sensibility in individuals with alcohol use disorder-Different phenomena with different clinical correlations?

BACKGROUND: Interoception may contribute to substance use disorder as it relates to the body's experience of substance use or withdrawal. However, only a few studies have directly investigated associations between interoception and alcohol use. The objective of this study was to compare individuals with alcohol use disorder (AUD) and healthy controls on interoceptive sensibility and accuracy. METHODS: The sample was comprised of two groups: individuals meeting criteria for AUD (N = 114) and healthy controls (N = 110) not meeting criteria for AUD. Interoceptive sensibility was assessed with a self-report measure (the Private Body Consciousness subscale) and interoceptive accuracy - with a behavioral measure (the Schandry test). In addition, associations between interoception and other well-recognized correlates of AUD (sleep problems, depressive and anxiety symptoms, impulsivity) were tested. Barratt's Impulsiveness Scale, Brief Symptom Inventory, and Athens Insomnia Scale were utilized to assess psychopathological symptoms as covariates. RESULTS: When controlling for level of anxiety, sleep problems, age, sex and education, individuals with AUD scored significantly higher on self-reported interoceptive sensibility and lower on interoceptive accuracy in comparison to healthy controls. Higher interoceptive sensibility was associated with more severe sleep problems and anxiety symptoms. CONCLUSIONS: These results have to be treated as preliminary and need to be replicated; however, findings indicate that interoception may present a novel therapeutic target for treatment of AUD.

Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions.

The black void behind the pupil was optically impenetrable before the invention of the ophthalmoscope by von Helmholtz over 150 years ago. Advances in retinal imaging and image processing, especially over the past decade, have opened a route to another unexplored landscape, the retinal neurovascular architecture and the retinal ganglion pathways linking to the central nervous system beyond. Exploiting these research opportunities requires multidisciplinary teams to explore the interface sitting at the border between ophthalmology, neurology and computing science. It is from the detail and depth of retinal phenotyping that novel metrics and candidate biomarkers are likely to emerge. Confirmation that in vivo retinal neurovascular measures are predictive of microvascular change in the brain and other organs is likely to be a major area of research activity over the next decade. Unlocking this hidden potential within the retina requires integration of structural and functional data sets, that is, multimodal mapping and longitudinal studies spanning the natural history of the disease process. And with further advances in imaging, it is likely that this area of retinal research will remain active and clinically relevant for many years to come. Accordingly, this review looks at state-of-the-art retinal imaging and its application to diagnosis, characterization and prognosis of chronic illness or long-term conditions.

Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE.

It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support Vector Machine (LS-SVM) classifier for the first time (to the best of our knowledge) to label automatically arterioles and venules. We use only 4 image features and consider vessels inside zone B (802 vessels from 70 fundus camera images) and in an extended zone (1,207 vessels, 70 fundus camera images). We achieve an accuracy of 94.88% and 93.96% in zone B and the extended zone, respectively, with a training set of 10 images and a testing set of 60 images. With a smaller training set of only 5 images and the same testing set we achieve an accuracy of 94.16% and 93.95%, respectively. This experiment was repeated five times by randomly choosing 10 and 5 images for the training set. Mean classification accuracy are close to the above mentioned result. We conclude that the performance of our system is very promising and outperforms most recently reported systems. Our approach requires smaller training data sets compared to others but still results in a similar or higher classification rate.

Towards automatic detection of abnormal retinal capillaries in ultra-widefield-of-view retinal angiographic exams.

Retinal capillary abnormalities include small, leaky, severely tortuous blood vessels that are associated with a variety of retinal pathologies. We present a prototype image-processing system for detecting abnormal retinal capillary regions in ultra-widefield-of-view (UWFOV) fluorescein angiography exams of the human retina. The algorithm takes as input an UWFOV FA frame and returns the candidate regions identified. An SVM classifier is trained on regions traced by expert ophthalmologists. Tests with a variety of feature sets indicate that edge features and allied properties differentiate best between normal and abnormal retinal capillary regions. Experiments with an initial set of images from patients showing branch retinal vein occlusion (BRVO) indicate promising area under the ROC curve of 0.950 and a weighted Cohen's Kappa value of 0.822.

Retinal vessel classification: sorting arteries and veins.

For the discovery of biomarkers in the retinal vasculature it is essential to classify vessels into arteries and veins. We automatically classify retinal vessels as arteries or veins based on colour features using a Gaussian Mixture Model, an Expectation-Maximization (GMM-EM) unsupervised classifier, and a quadrant-pairwise approach. Classification is performed on illumination-corrected images. 406 vessels from 35 images were processed resulting in 92% correct classification (when unlabelled vessels are not taken into account) as compared to 87.6%, 90.08%, and 88.28% reported in [12] [14] and [15]. The classifier results were compared against two trained human graders to establish performance parameters to validate the success of classification method. The proposed system results in specificity of (0.8978, 0.9591) and precision (positive predicted value) of (0.9045, 0.9408) as compared to specificity of (0.8920, 0.7918) and precision of (0.8802, 0.8118) for (arteries, veins) respectively as reported in [13]. The classification accuracy was found to be 0.8719 and 0.8547 for veins and arteries, respectively.

The use of radial symmetry to localize retinal landmarks.

Locating the optic disc center and the fovea in digital fundus images is surprisingly difficult due to the variation range in color and contrast and the possible presence of pathologies creating bright spots or changing the appearance of retinal landmarks. These reasons make it difficult to find good templates of optic disc and fovea shape and color for pattern matching. In this paper we propose radial symmetry as the principal cue to locate both optic disc and macula centers. Centers of bright and dark circularly symmetrical regions with arbitrary radii, can be found robustly against changes in brightness and contrast by using the Fast Radial Symmetry transform. Detectors based on this transform coupled with a weak hypothesis on vessel density (optic disc intersects large vessels while the fovea lies in an avascular region), can provide a fast location of both OD and macula with accuracy similar or better than state-of-the-art methods. The approach has been chosen as the default technique for fast localization of the two landmarks in the VAMPIRE software suite.

Improving vessel segmentation in ultra-wide field-of-view retinal fluorescein angiograms.

Vessel segmentation on ultra-wide field-of-view fluorescein angiogram sequences of the retina is a challenging problem. Vessel appearance undergoes severe changes, as different portions of the vascular structure become perfused in different frames. This paper presents a method for segmenting vessels in such sequences using steerable filters and automatic thresholding. We introduce a penalization stage on regions with high vessel response in the filtered image, improving the detection of peripheral vessels and reducing false positives around the optic disc and in regions of choroidal vessels and lesions. Quantitative results are provided, in which the penalization stage improves the segmentation precision segmentation by 11.84%, the recall by 12.98% and the accuracy by 0.40%. To facilitate further evaluation, usage, and algorithm comparison, the algorithm, the data set used, the ground truth, and the results are made available on the internet.

VAMPIRE: Vessel assessment and measurement platform for images of the REtina.

We present VAMPIRE, a software application for efficient, semi-automatic quantification of retinal vessel properties with large collections of fundus camera images. VAMPIRE is also an international collaborative project of four image processing groups and five clinical centres. The system provides automatic detection of retinal landmarks (optic disc, vasculature), and quantifies key parameters used frequently in investigative studies: vessel width, vessel branching coefficients, and tortuosity. The ultimate vision is to make VAMPIRE available as a public tool, to support quantification and analysis of large collections of fundus camera images.

Contextual detection of ischemic regions in ultra-wide-field-of-view retinal fluorescein angiograms.

We report a novel prototype algorithm using contextual knowledge to locate ischemic regions in ultra-wide-field-of-view retinal fluorescein angiograms. We use high-resolution images acquired by an Optos ultra-wide-field-of-view (more than 200 degrees) scanning laser ophthalmoscope. We leverage the simultaneous occurrence of ischemia with a number of other signs, detected automatically, typical for the state of progress of the condition in a diabetic patient. The specific nature of ischemic and non-ischemic regions is determined with an AdaBoost learning algorithm. Preliminary results demonstrate above 80% pixel classification accuracy against manual annotations.