Effects of Acute Intracranial Pressure Changes on Optic Nerve Head Morphology in Humnans and Pig Model

The optic nerve head (ONH) is located at the interface of intracranial and intraocular compartments. It is comprised of lamina cribrosa (LC), a fenestrated connective tissue tethered to the posterior sclera across the scleral canal. Since LC is exposed to intraocular pressure (IOP) anteriorly and intracranial pressure (ICP) posteriorly, it is an ideal site for noninvasively detecting intracranial pressure (ICP) fluctuation. We hypothesized that the pressure differential between IOP and ICP across LC, will determine LC position and meridional diameter of scleral canal (also called Bruch’s membrane opening- BMOD). We tested our hypothesis in 19 human subjects undergoing medically necessary lumbar puncture (LP) to lower ICP and 6 anesthetized pigs, whose ICP were increased in 5mm Hg increments using lumbar drain. We imaged the ONH using Optical Coherence Tomography (OCT) and measured IOP and ICP at baseline and after each intervention. We measured the following ONH morphological parameters: BMOD, anterior LC depth (ALCD) and retinal thickness from the OCT images. We modeled the effects of acute ICP changes on ONH morphological parameters using AVOVA for human study and generalized linear model with fixed intercepts for the pig study. We found that there was no significant effect of acute ICP changes on ONH morphological parameters in both humans and pigs. We conclude that the LC is resistant to displacement across large changes of ICP. Proposed mechanisms include compensatory change in IOP, and non-linear or non-monotonic effects of IOP and ICP across the LC

Relative Contributions of Intracranial Pressure and Intraocular Pressure on Lamina Cribrosa Behavior

Purpose. To characterize the relative contributions of intraocular pressure (IOP) and intracranial pressure (ICP) on lamina cribrosa (LC) behavior, specifically LC depth (LCD) and LC peak strain. Methods. An axially symmetric finite element model of the posterior eye was constructed with an elongated optic nerve and retro-orbital subarachnoid space ensheathed by pia and dura mater. -e mechanical environment in LC was evaluated with ICP ranging from 5 to 15mmHg and IOP from 10 to 45 mmHg. LCD and LC peak strains at various ICP and IOP levels were estimated using full factorial experiments. Multiple linear regression analyses were then applied to estimate LCD and LC peak strain using ICP and IOP as independent variables. Results. Both increased ICP and decreased IOP led to a smaller LCD and LC peak strain. -e regression correlation coefficient for LCD was −1.047 for ICP and 1.049 for IOP, and the ratio of the two regression coefficients was −1.0. -e regression correlation coefficient for LC peak strain was −0.025 for ICP and 0.106 for IOP, and the ratio of the two regression coefficients was −0.24. A stiffer sclera increased LCD but decreased LC peak strain; besides, it increased the relative contribution of ICP on the LCD but decreased that on the LC peak strain. Conclusions. ICP and IOP have opposing effects on LCD and LC peak strain. While their effects on LCD are equivalent, the effect of IOP on LC peak strain is 3 times larger than that of ICP. -e influences of these pressure are dependent on sclera material properties, which might explain the pathogenesis of ocular hypertension and normal-tension glaucoma

Relative Contributions of Intracranial Pressure and Intraocular Pressure on Lamina Cribrosa Behavior

Purpose. To characterize the relative contributions of intraocular pressure (IOP) and intracranial pressure (ICP) on lamina cribrosa (LC) behavior, specifically LC depth (LCD) and LC peak strain. Methods. An axially symmetric finite element model of the posterior eye was constructed with an elongated optic nerve and retro-orbital subarachnoid space ensheathed by pia and dura mater. -e mechanical environment in LC was evaluated with ICP ranging from 5 to 15mmHg and IOP from 10 to 45 mmHg. LCD and LC peak strains at various ICP and IOP levels were estimated using full factorial experiments. Multiple linear regression analyses were then applied to estimate LCD and LC peak strain using ICP and IOP as independent variables. Results. Both increased ICP and decreased IOP led to a smaller LCD and LC peak strain. -e regression correlation coefficient for LCD was −1.047 for ICP and 1.049 for IOP, and the ratio of the two regression coefficients was −1.0. -e regression correlation coefficient for LC peak strain was −0.025 for ICP and 0.106 for IOP, and the ratio of the two regression coefficients was −0.24. A stiffer sclera increased LCD but decreased LC peak strain; besides, it increased the relative contribution of ICP on the LCD but decreased that on the LC peak strain. Conclusions. ICP and IOP have opposing effects on LCD and LC peak strain. While their effects on LCD are equivalent, the effect of IOP on LC peak strain is 3 times larger than that of ICP. -e influences of these pressure are dependent on sclera material properties, which might explain the pathogenesis of ocular hypertension and normal-tension glaucoma

Three-dimensional shape analysis of peripapillary retinal pigment epithelium-basement membrane layer based on OCT radial images

The peripapillary retinal pigment epithelium-basement membrane (ppRPE/BM) layer angle was recently proposed as a potential index for estimating intracranial pressure noninvasively. However, the ppRPE/BM layer angle, measured from the optical coherence tomography (OCT) scans, varied across the radial directions of the optic disc. This made the ppRPE/BM layer angle difficult to be utilized in its full potential. In this study, we developed a mathematical model to quantify the ppRPE/BM layer angles across radial scans in relation to the ppRPE/BM 3D morphology in terms of its 3D angle and scanning tilt angles. Results showed that the variations of the ppRPE/BM layer angle across radial scans were well explained by its 3D angle and scanning tilt angles. The ppRPE/BM layer 3D angle was reversely fitted from the measured ppRPE/BM layer angles across radial directions with application to six eyes from four patients, who underwent medically necessary lumbar puncture. The fitted curve from our mathematical model matched well with the experimental measurements (R2 \u3e 0.9 in most cases). This further validated our mathematical model. The proposed model in this study has elucidated the variations of ppRPE/BM layer angle across 2D radial scans from the perspective of the ppRPE/BM layer 3D morphology. It is expected that the ppRPE/BM layer 3D angle developed in this study could be further exploited as a new biomarker for the optic disc

A New Method for Estimating Effects of Visual Field Loss in a Panoramic Driving Environment

Glaucoma is a key cause of peripheral visual field loss and increases risk of a vehicle crash. Patients may be unaware of their visual loss and of hazards in the driving panorama. Standard clinical automated perimetry, the “gold standard” for monitoring glaucoma progression, lacks external validity to evaluate functional effect of visual field loss in driving environments. We developed and piloted a new technique to study the effects of glaucoma in a panoramic (290 forward FOV) simulated driving environment. Preliminary results in 11 drivers (7 with glaucoma and 4 with suspected glaucoma): (1) demonstrate the relationship between standard clinical perimetry and driving simulator visual fields, (2) replicate clinical evidence of glaucoma-related peripheral visual field loss, and (3) show added visual field loss due to visual occlusion by in-cab geometry

A deep audiovisual approach for human confidence classification

Research on self-efficacy and confidence has spread across several subfields of psychology and neuroscience. The role of one’s confidence is very crucial in the formation of attitude and communication skills. The importance of differentiating the levels of confidence is quite visible in this domain. With the recent advances in extracting behavioral insight from a signal in multiple applications, detecting confidence is found to have great importance. One such prominent application is detecting confidence in interview conversations. We have collected an audiovisual data set of interview conversations with 34 candidates. Every response (from each of the candidate) of this data set is labeled with three levels of confidence: high, medium, and low. Furthermore, we have also developed algorithms to efficiently compute such behavioral confidence from speech and video. A deep learning architecture is proposed for detecting confidence levels (high, medium, and low) from an audiovisual clip recorded during an interview. The achieved unweighted average recall (UAR) reaches 85.9% on audio data and 73.6% on video data captured from an interview session