Background: To compare objective electrophysiological contrast sensitivity function (CSF) in patients implanted with either multifocal intraocular lenses (MIOLs) or monofocal intraocular lenses (IOLs) by pattern reversal visual evoked potentials (prVEP) measurements.
Methods: Fourty-five cataract patients were randomly allocated to receive bilaterally: apodized diffractive-refractive Alcon Acrysof MIOL (A), full diffractive AMO Tecnis MIOL (B) or monofocal Alcon Acrysof IOL (C). Primary outcomes: 1-year differences in objective binocular CSF measured by prVEP with sinusoid grating stimuli of 6 decreasing contrast levels at 6 spatial frequencies. Secondary outcomes: psychophysical CSF measured with VCTS-6500, photopic uncorrected distance (UDVA), and mesopic and photopic uncorrected near and intermediate visual acuities (UNVA and UIVA respectively).
Results: Electrophysiological CSF curve had an inverted U-shaped morphology in all groups, with a biphasic pattern in Group B. Group A showed a lower CSF than group B at 4 and 8 cpd, and a lower value than group C at 8 cpd. Psychophysical CSF in group A exhibited a lower value at 12 cpd than group B. Mean photopic and mesopic UNVA and UIVA were worse in monofocal group compared to the multifocal groups. Mesopic UNVA and UIVA were better in group B.
Conclusions: Electrophysiological CSF behaves differently depending on the types of multifocal or monofocal IOLs. This may be related to the visual acuity under certain conditions or to IOL characteristics. This objective method might be a potential new tool to investigate on MIOL differences and on subjective device-related quality of vision.
Background: Visual cortex neurons often respond to stimuli very differently on repeated trials. This trial-by-trial variability is known to be correlated among nearby neurons. Our long-term goal is to quantitatively estimate neuronal response variability, using multi-channel local field potential (LFP) data from single trials.
Methods: Acute experiments were performed with anesthetized (Remifentanil, Propofol, nitrous oxide) and paralyzed (Gallamine Triethiodide) cats. Computer-controlled visual stimuli were displayed on a gamma-corrected CRT monitor. For the principal experiment, two kinds of visual stimuli were used: drifting sine-wave gratings, and a uniform mean-luminance gray screen. These two stimuli were each delivered monocularly for 100 sec in a random order, for 10 trials. Multi-unit activity (MUA) and LFP signals were extracted from broadband raw data acquired from Area 17 and 18 using A1X32 linear arrays (NeuroNexus) and the OpenEphys recording system. LFP signal processing was performed using Chronux, an open-source MATLAB toolbox. Current source density (CSD) analysis was performed on responses to briefly flashed full-field stimuli using the MATLAB toolbox, CSDplotter. The common response variability (global noise) of MUA was estimated using the model proposed by Scholvinck et al. [2015].
Results: On different trials, a given neuron responded with different firing to the same visual stimuli. Within one trial, a neuron’s firing rate also fluctuated across successive cycles of a drifting grating. When the animal was given extra anesthesia, neurons fired in a desynchronized pattern; with lighter levels of anesthesia, neuronal firing because more synchronized. By examining the cross-correlations of LFP signals recorded from different cortical layers, we found LFP signals could be divided to two groups: those recorded in layer IV and above, and those from layers V and VI. Within each group, LFP signals recorded by different channels are highly correlated. These two groups were observed in lighter and deeper anesthetized animals, also in sine-wave and uniform gray stimulus conditions. We also investigated correlations between LFP signals and global noise. Power in the LFP beta band was highly correlated with global noise, when animals were in deeper anesthesia.
Conclusions: Brain states contribute to variations in neuronal responses. Raw LFP correlation results suggest that we should analyze LFP data according to their laminar organization. Correlation of low-frequency LFP under deeper anesthesia with global noise gives us some insight to predict noise from single-trial data, and we hope to extend this analysis to lighter anesthesia in the future.
