A mitochondria-specific NIR fluorescence probe for dual-detection of sulfur dioxide and viscosity in living cells and mice.

The level of sulfur dioxide (SO2) and viscosity in mitochondria play vital roles in various physiological and pathological processes. Abnormalities in mitochondrial SO2 and viscosity are closely associated with numerous biological diseases. It is of great significance to develop novel fluorescence probes for simultaneous detection of SO2 and viscosity within mitochondria. Herein, we have developed a water-soluble, mitochondrial-targeted and near-infrared fluorescent probe, CMBT, for the simultaneous detection of SO2 and viscosity. The probe CMBT incorporates benzothiazolium salt as a mitochondrial targeting moiety and 7-diethylaminocoumarin as a rotor for viscosity detection, respectively. Based on the prompt reaction between nucleophilic HSO3-/SO32- and the backbone of the benzothiazolium salt derivative, probe CMBT displayed high sensitivity and selectivity toward SO2 with a limit of detection as low as 0.17 µM. As viscosity increased, the twisted intramolecular charge transfer (TICT) process was restricted, resulting in fluorescence emission enhancement at 690 nm. Moreover, probe CMBT demonstrated exceptional mitochondrial targeting ability and was successfully employed to image variations of SO2 and viscosity in living cells and mice. The work highlights the great potential of the probe as a convenient tool for revealing the relationship between SO2 and viscosity in biological systems.

Transfer of visual perceptual learning over a task-irrelevant feature through feature-invariant representations: Behavioral experiments and model simulations.

A large body of literature has examined specificity and transfer of perceptual learning, suggesting a complex picture. Here, we distinguish between transfer over variations in a "task-relevant" feature (e.g., transfer of a learned orientation task to a different reference orientation) and transfer over a "task-irrelevant" feature (e.g., transfer of a learned orientation task to a different retinal location or different spatial frequency), and we focus on the mechanism for the latter. Experimentally, we assessed whether learning a judgment of one feature (such as orientation) using one value of an irrelevant feature (e.g., spatial frequency) transfers to another value of the irrelevant feature. Experiment 1 examined whether learning in eight-alternative orientation identification with one or multiple spatial frequencies transfers to stimuli at five different spatial frequencies. Experiment 2 paralleled Experiment 1, examining whether learning in eight-alternative spatial-frequency identification at one or multiple orientations transfers to stimuli with five different orientations. Training the orientation task with a single spatial frequency transferred widely to all other spatial frequencies, with a tendency to specificity when training with the highest spatial frequency. Training the spatial frequency task fully transferred across all orientations. Computationally, we extended the identification integrated reweighting theory (I-IRT) to account for the transfer data (Dosher, Liu, & Lu, 2023; Liu, Dosher, & Lu, 2023). Just as location-invariant representations in the original IRT explain transfer over retinal locations, incorporating feature-invariant representations effectively accounted for the observed transfer. Taken together, we suggest that feature-invariant representations can account for transfer of learning over a "task-irrelevant" feature.

Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study.

BACKGROUND: Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, the effects of lighting conditions on age-related vision changes and associated driving performance remain unclear. OBJECTIVE: This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime with glare. METHODS: Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF were also obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF) and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed, SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime with glare conditions. Pearson correlations determined the associations between visual function and driving performance across the 3 lighting conditions. RESULTS: Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlated with greater SDLP (r=-0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=-0.24; P=.007), larger SDspeed (r=-0.19; P=.04), greater SDLP (r=-0.22; P=.007), and longer reaction times (r=-0.22; P=.04) while driving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed (r=-0.32; P<.001), greater SDLP (r=-0.26; P=.001) and longer reaction times (r=-0.2; P=.04) while driving at night with glare. No other significant correlations were observed between visual function and driving performance under the same lighting conditions. CONCLUSIONS: Visual functions differentially affect driving performance in different lighting conditions among older drivers, with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes is needed to confirm these results.

Novel Quantitative Contrast Sensitivity Function Enhances the Prediction of Treatment Outcome and Recurrence in Amblyopia.

Purpose: Although effective amblyopia treatments are available, treatment outcome is unpredictable, and the condition recurs in up to 25% of the patients. We aimed to evaluate whether a large-scale quantitative contrast sensitivity function (CSF) data source, coupled with machine learning (ML) algorithms, can predict amblyopia treatment response and recurrence in individuals. Methods: Visual function measures from traditional chart vision acuity (VA) and novel CSF assessments were used as the main predictive variables in the models. Information from 58 potential predictors was extracted to predict treatment response and recurrence. Six ML methods were applied to construct models. The SHapley Additive exPlanations was used to explain the predictions. Results: A total of 2559 consecutive records of 643 patients with amblyopia were eligible for modeling. Combining variables from VA and CSF assessments gave the highest accuracy for treatment response prediction, with the area under the receiver operating characteristic curve (AUC) of 0.863 and 0.815 for outcome predictions after 3 and 6 months, respectively. Variables from the VA assessment alone predicted the treatment response, with AUC values of 0.723 and 0.675 after 3 and 6 months, respectively. Variables from the CSF assessment gave rise to an AUC of 0.909 for recurrence prediction compared to 0.539 for VA assessment alone, and adding VA variables did not improve predictive performance. The interocular differences in CSF features are significant contributors to recurrence risk. Conclusions: Our models showed CSF data could enhance treatment response prediction and accurately predict amblyopia recurrence, which has the potential to guide amblyopia management by enabling patient-tailored decision making.

Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling.

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.

Codelivery of CPT and siPHB1 with GSH/ROS Dual-Responsive Hybrid Nanoparticles Based on a [12]aneN3-Derived Lipid for Synergistic Lung Cancer Therapy.

The combination of small-interfering RNA (siRNA)-mediated gene silencing and chemotherapeutic agents for lung cancer treatment has attracted widespread attention in terms of a greater therapeutic effect, minimization of systemic toxicity, and inhibition of multiple drug resistance (MDR). In this work, three amphiphiles, CBN1-CBN3, were first designed and synthesized as a camptothecin (CPT) conjugate and gene condensation agents by the combination of CPT prodrugs and di(triazole-[12]aneN3) through the ROS-responsive phenylborate ester and different lengths of alkyl chains (with 6, 9, 12 carbon chains for CBN1-CBN3, respectively). CBN1-CBN3 were able to be self-assembled into liposomes with an average diameter in the range of 320-240 nm, showing the ability to effectively condense siRNA. Among them, CBN2, with a nine-carbon alkyl chain, displayed the best anticancer efficiency in A549 cells. In order to give nanomedicines a stealth property and PEGylation/dePEGylation transition, a GSH-responsive PEGylated TPE derivative containing a disulfide linkage (TSP) was further designed and prepared. A combination of CBN2/siRNA complexes and DOPE with TSP resulted in GSH/ROS dual-responsive lipid-polymer hybrid nanoparticles (CBN2-DP/siRNA NPs). In present GSH and H2O2, CBN2-DP/siRNA NPs were decomposed, resulting in the controlled release of CPT drug and siRNA. In vitro, CBN2-DP/siPHB1 NPs showed the best anticancer activity for suppression of about 75% of A549 cell proliferation in a serum medium. The stability of CBN2-DP/siRNA NPs was significantly prolonged in blood circulation, and they showed effective accumulation in the A549 tumor site through an enhanced permeability and retention (EPR) effect. In vivo, CBN2-DP/siPHB1 NPs demonstrated enhanced synergistic cancer therapy efficacy and tumor inhibition as high as 71.2%. This work provided a strategy for preparing lipid-polymer hybrid NPs with GSH/ROS dual-responsive properties and an intriguing method for lung cancer therapy.

Binocular Summation With Quantitative Contrast Sensitivity Function: A Novel Parameter to Evaluate Binocular Function in Intermittent Exotropia.

Purpose: Intermittent exotropia (IXT) is the most common form of strabismus. Surgery can potentially improve binocular function in patients with IXT. We aimed to evaluate binocular function using a novel parameter-binocular summation ratio (BSR), measured using quantitative contrast sensitivity function (CSF) in patients with IXT before and after surgery. Methods: Prospective study of 63 patients with IXT and 41 healthy controls were consecutively enrolled and underwent quantitative CSF testing binocularly and monocularly. BSR was calculated by dividing the CSF of the binocular value by the better monocular value. Forty-eight patients with IXT underwent strabismus surgery. BSR, stereoacuity, fusion ability, and strabismus questionnaires were assessed pre-operatively and 2 months postoperatively. Results: Sixty-three patients with IXT (median age = 9 years) compared with 41 healthy controls showed a worse mean BSR based on all CSF metrics at baseline (the area under the log CSF [AULCSF], spatial frequency [SF] cutoff, and contrast sensitivity at 1.0-18.0 cpd SF). All 48 patients with IXT showed successful alignment after surgery, and there were significant improvements in BSR based on the AULCSF, SF cutoff, and contrast sensitivity at 6.0, 12.0, and 18.0 cpd SF, respectively. The distance stereoacuity and fusion ability also improved after surgery, and a better BSR was associated with better stereoacuity and fusion. For strabismus questionnaires, the psychosocial subscale scores improved postoperatively, whereas the functional subscale scores did not change. Conclusions: BSR based on quantitative CSF can characterize binocular function across a range of spatial frequencies and can be used as a supplemental measurement for monitoring binocularity in patients with IXT in clinical settings.

Perceptual learning changes the amplitude not the shape of the temporal window of visual processing.

Perceptual learning (PL) can significantly improve human performance in perceptual tasks primarily through template reweighting. Previous studies have documented how PL changes perceptual template in stimulus feature space. We investigated how PL reweights visual information in time. With a dynamic external noise paradigm and the elaborated perceptual template model (ePTM) analysis, we found that training with an orientation identification task in the zero external noise condition reduced contrast thresholds in both zero and high external noise conditions, whereas training in the high external noise condition only reduced contrast thresholds in high external noise conditions. The ePTM analysis showed that training in both zero and high external noise changed the overall amplitude, but not the shape of the temporal window of the perceptual template to exclude external noise across time, and training in zero external noise additionally reduced additive internal noise. Our results provided additional constraints for models of PL. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Correlation of contrast sensitivity with ganglion cell/inner plexiform layer thickness and damage location in glaucoma with varying severity.

OBJECTIVES: To investigate the correlation of contrast sensitivity with macular region ganglion cell/inner plexiform layer (GC/IPL) thickness and damage location in open-angle glaucoma (OAG) of varying severity. METHODS: Cross-sectional study with 106 patients (203 eyes) who had OAG. Contrast sensitivity of each eye evaluated by quick contrast sensitivity function test based on intelligent algorithm. The GC/IPL thickness measured with optical coherence tomography; six sectors were delineated for localization of damage area. All eyes were grouped by the healthy macular sector and divided into pre-perimetric, early, moderate, and advanced stages, according to severity of visual field impairment. RESULTS: Mean GC/IPL thickness in the entire macular region and each sector were correlated with parameters that reflected contrast sensitivity (p < 0.01). The structure-function correlations were stronger nasally compared with temporally, and superiorly compared with inferiorly. Eyes with normal structure in inferior temporal sector had less visual field (p' = 0.024) and macular damage (p' = 0.034) compared with eyes that had healthy superior nasal sector; there was no difference in contrast sensitivity (p = 0.898). The structure-function correlations were significant in early, moderate, and advanced glaucoma (p < 0.05) but not in pre-perimetric glaucoma (p = 0.116). CONCLUSIONS: GC/IPL thinning in all sectors of the macular region in OAG was correlated with contrast sensitivity impairment, whereas the inferior temporal sector was least affected. Contrast sensitivity was supported as a severity evaluation indicator of early, moderate, and advanced glaucoma, but not of pre-perimetric glaucoma.

Adaptive smoothing of retinotopic maps based on Teichmüller parametrization.

Retinotopic mapping, the mapping between visual inputs on the retina and neural responses on the cortical surface, is one of the fundamental topics in visual neuroscience. In human studies, retinotopic maps are conventionally constructed and processed by decoding blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) responses to designed visual stimuli on the cortical surface. However, these methods frequently generate retinotopic maps that do not preserve topology, contradicting a fundamental property of retinotopic maps observed in neurophysiology. To address this problem, we propose an integrated approach to simultaneously refine the flattening from the 3D cortical surface to the 2D parametric space and adaptively smooth retinotopic perception centers in the visual space to make the retinotopic maps topological. One key element of the approach is the enhanced error tolerant Teichmüller mapping, which refines the parametrization by minimizing angle distortions and maximizing alignment to noisy landmarks. We validated our overall approach with synthetic and real retinotopic mapping datasets and applied it to compute cortical magnification factor (CMF). The results showed that the proposed approach was superior to other conventional retinotopic mapping methods in predicting BOLD fMRI time series and preserving topology.

Estimating the Trial-by-Trial Learning Curve in Perceptual Learning with Hierarchical Bayesian Modeling.

The learning curve serves as a crucial metric for assessing human performance in perceptual learning. It may encompass various component processes, including general learning, between-session forgetting or consolidation, and within-session rapid relearning and adaptation or deterioration. Typically, empirical learning curves are constructed by aggregating tens or hundreds of trials of data in blocks or sessions. Here, we devised three inference procedures for estimating the trial-by-trial learning curve based on the multi-component functional form identified in Zhao et al. (submitted): general learning, between-session forgetting, and within-session rapid relearning and adaptation. These procedures include a Bayesian inference procedure (BIP) estimating the posterior distribution of parameters for each learner independently, and two hierarchical Bayesian models (HBMv and HBMc) computing the joint posterior distribution of parameters and hyperparameters at the population, subject, and test levels. The HBMv and HBMc incorporate variance and covariance hyperparameters, respectively, between and within subjects. We applied these procedures to data from two studies investigating the interaction between feedback and training accuracy in Gabor orientation identification across about 2000 trials spanning six sessions (Liu et al., 2010, 2012) and estimated the trial-by-trial learning curves at both the subject and population levels. The HBMc generated best fits to the data and the smallest half width of 68.2% credible interval of the learning curves compared to the BIP and HBMv. The parametric HBMc with the multi-component functional form provides a general framework for trial-by-trial analysis of the component processes in perceptual learning and for predicting the learning curve in unmeasured time points.

Lu-177-PSMA dosimetry for kidneys and tumors based on SPECT images at two imaging time points.

Background: Personalized dosimetry for Lu-177-PSMA treatment requires multiple-time-point SPECT/CT scans to calculate time-integrated activity (TIA). This study evaluates two-time-point (TTP) methods for TIA calculation for kidneys and tumors. Methods: A total of 18 patients treated with 3.7-7.4 GBq Lu-177 PSMA-617 were analyzed retrospectively, including 18 sets of left and right kidneys, as well as 45 tumors. Four quantitative SPECT/CT (4TP) were acquired at 2 h, 20 h, 40 h, 60 h (n = 11), or 200 h (n = 7) after treatment, and they were fit bi-exponentially as reference. The TTP method was fitted by a mono-exponential washout function using two selected imaging time points for kidneys. For tumors, one uptake and one washout phase were modeled, assuming linear (type I) and same (type II) uptake phase between 0 h to the first time point and mono-exponential washout thereafter. Two single-time-point (STP) methods were also implemented for comparison. TIA calculated by TTP and STP methods were compared with reference to the 4TP TIA. Results: For the kidneys, the TTP methods using 20 h-60 h and 40 h-200 h had smaller mean absolute errors of 8.05 ± 6.05% and 4.95 ± 3.98%, respectively, as compared to other combinations of time points and STP methods. For tumors, the type I and type II TTP methods using 20h-60 h and 40-200 h had smaller mean absolute errors of 6.14 ± 5.19% and 12.22 ± 4.44%, and 8.31 ± 7.16% and 4.48 ± 7.10%, respectively, as compared to other TTP and STP methods. Conclusion: The TTP methods based on later imaging time demonstrated fewer errors than the STP methods in kidney and tumor TIA. Imaging at 20 h-60 h and 40 h-200 h could simplify the dosimetry procedures with fewer TIA estimation errors.

Quantification of expected information gain in visual acuity and contrast sensitivity tests.

We make use of expected information gain to quantify the amount of knowledge obtained from measurements in a population. In the first application, we compared the expected information gain in the Snellen, ETDRS, and qVA visual acuity (VA) tests, as well as in the Pelli-Robson, CSV-1000, and qCSF contrast sensitivity (CS) tests. For the VA tests, ETDRS generated more expected information gain than Snellen. Additionally, the qVA test with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS, whether scored with VA threshold alone or with both VA threshold and VA range. Regarding the CS tests, CSV-1000 generated more expected information gain than Pelli-Robson, and the qCSF test with 25 trials generated more expected information gain than CSV-1000, whether scored with AULCSF or with CSF at six spatial frequencies. The active learning-based qVA and qCSF tests have the potential to generate more expected information gain than traditional paper chart tests. Although we have specifically applied it to compare VA and CS tests, expected information gain is a general concept that can be used to compare measurements in any domain.

[12]aneN3-modified camptothecin and PEGylated AIEgens co-assembly into core-shell nanoparticles with ROS/NTR dual-response for enhanced cancer therapy.

A novel dual-responsive nanoparticle (NP) system was aimed to be developed for the co-delivery of camptothecin (CPT) and plasmid encoding TNF-related apoptosis-inducing ligand (pTRAIL) DNA in cancer therapy. The combination of the prodrug CPT and the nucleic acid condensing di-(triazole-[12]aneN3) unit with 4-nitrobenzyl ester through alkyl chains resulted in three nitroreductase (NTR) responsive amphiphiles, CNN1-CNN3 (with 5, 8, and 11 carbon chains, respectively). Among them, CNN2 was the most effective in inhibiting the proliferation of HeLa cells in the presence of fusogenic lipid DOPE. The NPs composed of CNN2, pDNA, and DOPE were further co-assembled with ROS-responsive thioketal-linked amphiphilic polymer (TTP) to afford the core-shell NPs (CNN2-DT/pDNA) with an average size of 118 nm, which exhibited high drug-loading capacity, excellent serum tolerance, and good biocompatibility. In the presence of ROS, NTR, and NADH, the core-shell NPs were decomposed, leading to the efficient release of 80% CPT and abundant pDNA. The self-assembly and delivery process of CNN2-DT NPs and DNA were clearly observed through the AIE fluorescent imaging. In vitro and in vivo results demonstrated that the CNN2-DT/pTRAIL NPs synergistically promoted 68% apoptosis of tumor cells and inhibited tumor growth with negligible toxic side effects. This study showed that the combination of prodrug and nucleic acid through dual-responsive core-shell NPs provide a spatially and temporally-controlled strategy for cancer therapy.

Informational feedback accelerates learning in multi-alternative perceptual judgements of orientation.

Experience or training can substantially improve perceptual performance through perceptual learning, and the extent and rate of these improvements may be affected by feedback. In this paper, we first developed a neural network model based on the integrated reweighting theory (Dosher et al., 2013) to account for perceptual learning and performance in n-alternative identification tasks and the dependence of learning on different forms of feedback. We then report an experiment comparing the effectiveness of response feedback (RF) versus accuracy feedback (AF) or no feedback (NF) (full versus partial versus no supervision) in learning a challenging eight-alternative visual orientation identification (8AFC) task. Although learning sometimes occurred in the absence of feedback (NF), RF had a clear advantage above AF or NF in this task. Using hybrid supervision learning rules, a new n-alternative identification integrated reweighting theory (I-IRT) explained both the differences in learning curves given different feedback and the dynamic changes in identification confusion data. This study shows that training with more informational feedback (RF) is more effective, though not necessary, in these challenging n-alternative tasks, a result that has implications for developing training paradigms in realistic tasks.

Biodistribution and Internal Dosimetry of 68 Ga-DOTA-IBA PET Imaging for Patients With Bone Metastases.

PURPOSE: We have developed a new pharmaceutical, ibandronic acid (IBA), and preliminarily demonstrated that it is an efficient bisphosphonate for the diagnosis and treatment of bone metastases. This study aims to examine the biodistribution and internal dosimetry of the diagnostic 68 Ga-DOTA-IBA in patients. PATIENTS AND METHODS: 68 Ga-DOTA-IBA was intravenously injected based on 1.81-2.57 MBq/Kg into 8 patients with bone metastases. Each patient underwent 4 sequential static whole-body PET scans at 0.1, 0.45, 0.8, and 1.8 hours after injection. The acquisition time for each scan was 20 minutes with 10 bed positions. Image registrations and volume of interest delineation were first performed on Hermes, whereas percentage injected activity (%IA), absorbed dose, and effective dose were measured for source organs, using OLINDA/EXM v2.0. Dosimetrics for the bladder was based on a bladder voiding model. RESULTS: No adverse effects were observed on all patients. After the injection, 68 Ga-DOTA-IBA rapidly accumulated in bone metastases and cleared from nonbone tissues, as indicated by visual analysis and %IA measured on the sequential scans. High activity uptake was presented in the expected target organs, that is, bone, red marrow, and the drug-excretion organs such as kidneys and bladder. The mean total body effective dose is 0.022 ± 0.002 mSv/MBq. CONCLUSIONS: 68 Ga-DOTA-IBA has high bone affinity and is promising in the diagnosis of bone metastases. Dosimetric results show that the absorbed doses for critical organs and total body are within the safety limit and with high bone retention. It also has the potential to be used in 177 Lu-therapy as a theranostic pair.

Association between contrast sensitivity function and structural damage in primary open-angle glaucoma.

AIMS: To evaluate the association between contrast sensitivity function (CSF) and glaucomatous structural damage in primary open-angle glaucoma (POAG). METHODS: A cross-sectional study was performed with 103 patients (103 eyes) aged 25-50 years who had POAG without any other ocular disease. CSF measurements were obtained by the quick CSF method, a novel active learning algorithm that covers 19 spatial frequencies and 128 contrast levels. The peripapillary retinal nerve fibre layer (pRNFL), macular ganglion cell complex (mGCC), radial peripapillary capillary (RPC) and macular vasculature were measured by optical coherence tomography and angiography. Correlation and regression analyses were used to assess the association of area under log CSF (AULCSF), CSF acuity and contrast sensitivities at multiple spatial frequencies with structural parameters. RESULTS: AULCSF and CSF acuity were positively associated with pRNFL thickness, RPC density, mGCC thickness and superficial macular vessel density (p<0.05). Those parameters were also significantly associated with contrast sensitivity at 1, 1.5, 3, 6, 12, 18 cycles per degree spatial frequencies (p<0.05) and, the lower the spatial frequency, the higher the correlation coefficient. RPC density (p=0.035, p=0.023) and mGCC thickness (p=0.002, p=0.011) had significant predictive value for contrast sensitivity at 1 and 1.5 cycles per degree, with adjusted R 2 of 0.346 and 0.343, respectively. CONCLUSIONS: Full spatial frequency contrast sensitivity impairment, most notably at low spatial frequencies, is a characteristic change in POAG. Contrast sensitivity is a potential functional endpoint for the measurement of glaucoma severity.

Collective endpoint of visual acuity and contrast sensitivity function from hierarchical Bayesian joint modeling.

Clinical trials typically analyze multiple endpoints for signals of efficacy. To improve signal detection for treatment effects using the high-dimensional data collected in trials, we developed a hierarchical Bayesian joint model (HBJM) to compute a five-dimensional collective endpoint (CE5D) of contrast sensitivity function (CSF) and visual acuity (VA). The HBJM analyzes row-by-row CSF and VA data across multiple conditions, and describes visual functions across a hierarchy of population, individuals, and tests. It generates joint posterior distributions of CE5D that combines CSF (peak gain, peak frequency, and bandwidth) and VA (threshold and range) parameters. The HBJM was applied to an existing dataset of 14 eyes, each tested with the quantitative VA and quantitative CSF procedures in four Bangerter foil conditions. The HBJM recovered strong correlations among CE5D components at all levels. With 15 qVA and 25 qCSF rows, it reduced the variance of the estimated components by 72% on average. Combining signals from VA and CSF and reducing noises, CE5D exhibited significantly higher sensitivity and accuracy in discriminating performance differences between foil conditions at both the group and test levels than the original tests. The HBJM extracts valuable information about covariance of CSF and VA parameters, improves precision of the estimated parameters, and increases the statistical power in detecting vision changes. By combining signals and reducing noise from multiple tests for detecting vision changes, the HBJM framework exhibits potential to increase statistical power for combining multi-modality data in ophthalmic trials.

Quantification of Expected Information Gain in Visual Acuity and Contrast Sensitivity Tests.

We introduce expected information gain to quantify measurements and apply it to compare visual acuity (VA) and contrast sensitivity (CS) tests. We simulated observers with parameters covered by the visual acuity and contrast sensitivity tests and observers based on distributions of normal observers tested in three luminance and four Bangerter foil conditions. We first generated the probability distributions of test scores for each individual in each population in the Snellen, ETDRS and qVA visual acuity tests and the Pelli-Robson, CSV-1000 and qCSF contrast sensitivity tests and constructed the probability distributions of all possible test scores of the entire population. We then computed expected information gain by subtracting expected residual entropy from the total entropy of the population. For acuity tests, ETDRS generated more expected information gain than Snellen; scored with VA threshold only or with both VA threshold and VA range, qVA with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS. For contrast sensitivity tests, CSV-1000 generated more expected information gain than Pelli-Robson; scored with AULCSF or with CS at six spatial frequencies, qCSF with 25 trials generated more expected information gain than CSV-1000. The active learning based qVA and qCSF tests can generate more expected information than the traditional paper chart tests. Although we only applied it to compare visual acuity and contrast sensitivity tests, information gain is a general concept that can be used to compare measurements and data analytics in any domain.