V-Proportion: a method based on the Voronoi diagram to study spatial relations in neuronal mosaics of the retina

The visual system plays a predominant role in the human perception. Although all components of the eye are important to perceive visual information, the retina is a fundamental part of the visual system. In this work we study the spatial relations between neuronal mosaics in the retina. These relations have shown its importance to investigate possible constraints or connectivities between different spatially colocalized populations of neurons, and to explain how visual information spreads along the layers before being sent to the brain. We introduce the V-Proportion, a method based on the Voronoi diagram to study possible spatial interactions between two neuronal mosaics. Results in simulations as well as in real data demonstrate the effectiveness of this method to detect spatial relations between neurons in different layers

Stress detection using wearable physiological and sociometric sensors

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and k-nearest neighbour. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real time stress detection. Finally, we present an study of the most discriminative features for stress detection

Diagnostic ability of inner macular layers to discriminate early glaucomatous eyes using vertical and horizontal B-scan posterior pole protocols.

PURPOSE:To evaluate the diagnostic ability of macular ganglion cell (mGCL) and macular retinal nerve fiber (mRNFL) layers, to detect early glaucomatous eyes, using the new segmentation software of Spectralis optical coherence tomography (OCT) device (Heidelberg Engineering). METHODS:A total of 83 eyes from 83 subjects were included in this observational, prospective cross-sectional study: 43 healthy controls and 40 early primary open-angle glaucoma (POAG) patients. All participants were examined using the Horizontal and Vertical Posterior Pole protocols, and the peripapillary RNFL (pRNFL) protocol of Spectralis OCT device. The new automated retinal segmentation software was applied to horizontal and vertical macular B-scans to determine mGCL and mRNFL thicknesses in each one of the 9 sectors of the Early Treatment Diagnostic Retinopathy Study circle. Thickness of each layer was compared between groups, and the sectors with better area under the receiver operating characteristic curve (AUC) were identified. RESULTS:mGCL was significantly thinner in the POAG group, especially in outer and inner temporal sectors (p<0.001); and mRNFL was significantly thinner in the POAG group in the outer inferior and the outer superior sector (p<0.001). Diagnostic accuracy of inner macular layers was good, and in general mGCL was superior to mRNFL. pRNFL obtained the best diagnostic capability (AUC, 0.886). Horizontal and vertical Posterior Pole protocols performed similarly. CONCLUSIONS:Inner macular layers using either horizontal or vertical B-scans, especially temporal sectors of mGCL, have good diagnostic capability to differentiate early glaucomatous eyes from control eyes; however, pRNFL has the highest diagnostic sensitivity for glaucoma detection

Diagnostic ability of inner macular layers to discriminate early glaucomatous eyes using vertical and horizontal B-scan posterior pole protocols - Fig 4

<p><b>Receiver operating characteristic (ROC) curves for the macular (A) and peripapillary (B) parameters with the greater discriminating ability.</b> Abbreviations: mRNFL, macular retinal nerve fiber layer; mGCL, macular ganglion cell layer; pRNFL, peripapillary retinal nerve fiber layer.</p

Descriptive and clinical data and mean and standard deviation of peripapillary retinal nerve fiber layer thickness, for 40 early open-angle glaucoma eyes and 43 healthy control eyes, and comparison between groups.

<p>Descriptive and clinical data and mean and standard deviation of peripapillary retinal nerve fiber layer thickness, for 40 early open-angle glaucoma eyes and 43 healthy control eyes, and comparison between groups.</p

Early treatment diabetic retinopathy study (ETDRS) subfields.

<p>Macular ganglion cell (mGCL) and retinal nerve fiber (mRNFL) layers were measured in each of the nine macular areas defined by the ETDRS circle. Abbreviation: C0, central fovea; IS, inner superior; IN, inner nasal; II, inner inferior; IT, inner temporal; OS, outer superior; ON, outer nasal; OI, outer inferior; and OT, outer temporal; RE, right eye; LE, left eye.</p

Diagnostic ability of inner macular layers to discriminate early glaucomatous eyes using vertical and horizontal B-scan posterior pole protocols - Fig 1

<p><b>Representative optical coherence tomography (OCT) horizontal scan section of the macula of a normal (A and B) and early glaucomatous (C and D) left eye</b>. Macular ganglion cell layer (mGCL) is marked with two asterisks, and retinal nerve fiber (mRNFL) layer is marked with one asterisk, in Fig 1B (normal eye) and 1D (glaucomatous eye). The automated segmentation performed by the OCT Spectralis software between mRNFL and mGCL is shown with a light blue line, and between mGCL and inner plexiform layer is shown with a purple line. We can appreciate a slight thinning of mGCL in the glaucomatous eye, especially temporal to fovea. Optic nerve head (ONH) position and fovea (Fo) are indicated. The infrared image obtained with the Horizontal Posterior Pole protocol of Spectralis OCT is shown in the corner of each B-scan. The green lines of the infrared image delimit the square scanning area at the posterior pole.</p