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19 pages, 944 KiB  
Article
The Acute Effects of a Fast-Food Meal Versus a Mediterranean Food Meal on the Autonomic Nervous System, Lung Function, and Airway Inflammation: A Randomized Crossover Trial
by Diana Silva, Francisca Castro Mendes, Vânia Stanzani, Rita Moreira, Mariana Pinto, Marília Beltrão, Oksana Sokhatska, Milton Severo, Patrícia Padrão, Vanessa Garcia-Larsen, Luís Delgado, André Moreira and Pedro Moreira
Nutrients 2025, 17(4), 614; https://doi.org/10.3390/nu17040614 - 8 Feb 2025
Viewed by 743
Abstract
Background/Objectives: This study aimed to assess the acute effects of two isoenergetic but micronutrient-diverse meals—a Mediterranean-like meal (MdM) and a fast food-like meal (FFM)—on the autonomic nervous system (ANS), lung function, and airway inflammation response. Methods: Forty-six participants were enrolled in a randomized [...] Read more.
Background/Objectives: This study aimed to assess the acute effects of two isoenergetic but micronutrient-diverse meals—a Mediterranean-like meal (MdM) and a fast food-like meal (FFM)—on the autonomic nervous system (ANS), lung function, and airway inflammation response. Methods: Forty-six participants were enrolled in a randomized crossover clinical trial, consuming two isoenergetic meals: FFM (burger, fries, and sugar-sweetened drink) and MdM (vegetable soup, whole-wheat pasta, salad, olive oil, sardines, fruit, and water). Pupillometry assessed parasympathetic (MaxD, MinD, Con, ACV, MCV) and sympathetic (ADV, T75) nervous system outcomes. Lung function and airway inflammation were measured before and after each meal through spirometry and fractional exhaled nitric oxide (FeNO), respectively. Results: Mixed-effects model analysis showed that the MdM was associated with a hegemony of parasympathetic responses, with a significant increase of MaxD associated with a faster constriction velocity (ACV and MCV); on the other side, the FFM was associated with changes in the sympathetic response, showing a quicker redilation velocity (a decrease in T75). After adjusting for confounders, the mixed-effects models revealed that the FFM significantly decreased T75. Regarding lung function, a meal negatively impacted FVC (ae = −0.079, p < 0.001) and FEV1 (ae = −0.04, p = 0.017); however, FeNO increased, although after adjusting, no difference between meals was seen. Conclusions: Our study showed that the FFM counteracted the parasympathetic activity of a meal, while a meal, irrespective of the type, decreased lung function and increased airway inflammation. Full article
(This article belongs to the Section Nutritional Immunology)
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<p>Pupillometry parameter changes before and after each meal and comparison between meals. MdM—Mediterranean Meal; FFM—Fast Food Meal. MaxD, maximal diameter; MinD, minimum diameter; ADV, dilation velocity, T75, time at which pupil has re-dilated 75% of the reflex amplitude are presented as mean and standard deviation; for %Con, percent of the constriction; Latency, time of the onset of the constriction; ACV, average constriction velocities; MCV, maximum constriction velocity data is presented as median and interquartile ratio. *: Paired samples <span class="html-italic">t</span>-test; <sup>#</sup>: Wilcoxon Signed-Rank Test.</p>
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18 pages, 1468 KiB  
Article
Evaluation of Replacement Hearing Aids in Cochlear Implant Candidates Using the Hearing in Noise Test (HINT) and Pupillometry
by Yeliz Jakobsen, Kathleen Faulkner, Lindsey Van Yper and Jesper Hvass Schmidt
Audiol. Res. 2025, 15(1), 13; https://doi.org/10.3390/audiolres15010013 - 28 Jan 2025
Viewed by 534
Abstract
Background/Objectives: Advances in cochlear implant (CI) technology have led to the expansion of the implantation criteria. As a result, more CI candidates may have greater residual hearing in one or two ears. Many of these candidates will perform better with a CI in [...] Read more.
Background/Objectives: Advances in cochlear implant (CI) technology have led to the expansion of the implantation criteria. As a result, more CI candidates may have greater residual hearing in one or two ears. Many of these candidates will perform better with a CI in one ear and a hearing aid (HA) in the other ear, the so-called bimodal solution. The bimodal solution often requires patients to switch to HAs that are compatible with the CI. However, this can be a challenging decision, not least because it remains unclear whether this impacts hearing performance. Our aim is to determine whether speech perception in noise remains unchanged or improves with new replacement HAs compared to original HAs in CI candidates with residual hearing. Methods: Fifty bilateral HA users (mean age 63.4; range 23–82) referred for CI were recruited. All participants received new replacement HAs. The new HAs were optimally fitted and verified using Real Ear Measurement (REM). Participants were tested with the Hearing in Noise Test (HINT), which aimed at determining the signal-to-noise ratio (SNR) required for a 70% correct word recognition score at a speech sound pressure level (SPL) of 65 dB. HINT testing was performed with both their original and new replacement HAs. During HINT, pupillometry was used to control for task engagement. Results: Replacing the original HAs with new replacement HAs after one month was not statistically significant with a mean change of SRT70 by −1.90 (95% CI: −4.69;0.89, p = 0.182) dB SNR. Conclusions: New replacement HAs do not impact speech perception scores in CI candidates prior to the decision of cochlear implantation. Full article
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<p>Hearing in Noise Test (HINT) setup with pupillometry. The noise onset appears before the signal and continues after signal offset. The signal is a sentence with 5 words in Danish. The participant repeats the sentence after the noise ends.</p>
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<p>(<b>a</b>) The median and interquartile range (IQR) of speech reception threshold (SRT)70 (70% correct word recognition) (dB signal-to-noise ratio (SNR)). Comparing the original hearing aids (HAs) with the new HAs after one month of use (all participants). (<b>b</b>) The median and IQR of SRT70 dB SNR. Comparing the original HAs with the new HAs after one and three months of use (control group only).</p>
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<p>(<b>a</b>) The median and interquartile range (IQR) of peak pupil dilation (PPD) (mm). Comparisons were made between the original hearing aids (HAs) and the new HAs after one and three months of use (all participants). (<b>b</b>) The median and IQR of PPD (mm). Comparisons were made between the original HAs and the new HAs after one and three months of use (control group only). The data points outside the boxes represent outliers.</p>
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<p>(<b>a</b>) The median and interquartile range (IQR) of peak pupil latency (PPL) seconds (s). Comparing the original hearing aids (HAs) with the new HAs after one month of use (all participants). (<b>b</b>) The median and IQR of PPL (s). Comparing the original HAs with the new HAs after one and three months of use (control group only). The data points outside the boxes represent outliers.</p>
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18 pages, 1468 KiB  
Article
Eyes on the Pupil Size: Pupillary Response During Sentence Processing in Aphasia
by Christina Sen, Noelle Abbott, Niloofar Akhavan, Carolyn Baker and Tracy Love
Brain Sci. 2025, 15(2), 107; https://doi.org/10.3390/brainsci15020107 - 23 Jan 2025
Viewed by 554
Abstract
Background/Objectives: Individuals with chronic agrammatic aphasia demonstrate real-time sentence processing difficulties at the lexical and structural levels. Research using time-sensitive measures, such as priming and eye-tracking, have associated these difficulties with temporal delays in accessing semantic representations that are needed in real time [...] Read more.
Background/Objectives: Individuals with chronic agrammatic aphasia demonstrate real-time sentence processing difficulties at the lexical and structural levels. Research using time-sensitive measures, such as priming and eye-tracking, have associated these difficulties with temporal delays in accessing semantic representations that are needed in real time during sentence structure building. In this study, we examined the real-time processing effort linked to sentence processing in individuals with aphasia and neurotypical, age-matched control participants as measured through pupil reactivity (i.e., pupillometry). Specifically, we investigated whether a semantically biased lexical cue (i.e., adjective) influences the processing effort while listening to complex noncanonical sentences. Methods: In this eye-tracking while listening study (within-subjects design), participants listened to sentences that either contained biased or unbiased adjectives (e.g., venomous snake vs. voracious snake) while viewing four images, three related to nouns in the sentence and one unrelated, but a plausible match for the unbiased adjective. Pupillary responses were collected every 17 ms throughout the entire sentence. Results: While age-matched controls demonstrated increased pupil response throughout the course of the sentence, individuals with aphasia showed a plateau in pupil response early on in the sentence. Nevertheless, both controls and individuals with aphasia demonstrated reduced processing effort in the biased adjective condition. Conclusions: Individuals with aphasia are sensitive to lexical–semantic cues despite impairments in real-time lexical activation during sentence processing. Full article
(This article belongs to the Collection Collection on Neurobiology of Language)
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<p>Example of the visual world display and a sample experimental sentence.</p>
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<p>An example of the experimental procedure. Each trial would begin with a fixation cross, followed by a blank screen, and then the 2 × 2 image display. Following the onset of the visual display, sentences were presented over headphones while participant eye gaze and pupillary data were collected. At the end of each trial, participants were asked a comprehension question to ensure they were attending to the auditory information.</p>
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<p>Time windows of interest. Time Window 1 includes the whole sentence. Time Window 2 is from the beginning of the sentence until the offset of NP2.</p>
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<p>Time Window 1. Pupil responses throughout the whole sentence for AMC (blue) and IWA (red). Data are indicated by the shaded ribbons and growth curve models are indicated by the solid line.</p>
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<p>Time Window 2. Pupil responses from the beginning of the sentence to the onset of the second noun. AMC data are graphed on the left, IWA data on the right. The biased adjective condition for each group is shown in green, and the unbiased adjective condition is shown in grey. Data are indicated by the shaded ribbons and growth curve models are indicated by the solid line. Dotted lines were manually inserted to demonstrate visual differences in linear fit.</p>
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13 pages, 1156 KiB  
Article
The Effect of Audible Joint Manipulation Sounds in the Upper Cervical Spine on Brain Wave and Autonomic Nervous System Activity
by Dalton Whitman, Rob Sillevis and Matthew Frommelt
Life 2025, 15(1), 103; https://doi.org/10.3390/life15010103 - 15 Jan 2025
Viewed by 1454
Abstract
Background: High-velocity, low-amplitude (HVLA) manipulation is a common manual therapy technique used for treating pain and musculoskeletal dysfunction. An audible manipulation sound is commonly experienced by patients who undergo HVLA manipulation; however, there is little known about the effects and clinical relevance of [...] Read more.
Background: High-velocity, low-amplitude (HVLA) manipulation is a common manual therapy technique used for treating pain and musculoskeletal dysfunction. An audible manipulation sound is commonly experienced by patients who undergo HVLA manipulation; however, there is little known about the effects and clinical relevance of the audible manipulation sound on cortical output and the autonomic nervous system. This study aimed to identify the immediate impact of the audible manipulation sound on brainwave activity and pupil diameter in asymptomatic subjects following an HVLA cervical manipulation. Methods: 40 subjects completed this quasi-experimental repeated measure study design. Subjects were connected to electroencephalography and pupillometry simultaneously, and an HVLA cervical distraction manipulation was performed. The testing environment was controlled to optimize brainwave and pupillometry data acquisition. Pre-manipulation, immediately after manipulation, and post-manipulation data were collected. The presence of an audible manipulation sound was noted. Results: Twenty subjects experienced an audible manipulation sound. Brainwave activity changes were significant (p < 0.05) in both the audible manipulation sound and non-manipulation sound groups. Pupil diameter changes (p < 0.05) occurred in both eyes of the non-manipulation sound group and in the left eye of the audible-manipulation sound group. Brainwave activity patterns were similar in both groups. Conclusions: The presence of an audible manipulation sound is not required to produce central nervous system changes following an HVLA cervical manipulation; however, the audible manipulation sound does prolong the effects of brainwave activity, indicating a prolonged relaxation effect. Full article
(This article belongs to the Special Issue Advances in Non-Invasive Brain Stimulation)
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<p>Subjects seated with both the Emotiv EPOC+ electroencephalography and Micromedical Vorteq pupillometer.</p>
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<p>Setup for the atlantoaxial long-axis thrust manipulation with the subject wearing the EEG and pupillometry device. <b>Upper left</b>: The practitioner positions the subject in a left rotation and right side-bend with a chin tuck. <b>Upper right</b>: The practitioner places the first metacarpophalangeal joint of the manipulating hand on the transverse process of the C1 vertebrate. <b>Lower</b>: Atlantoaxial long-axis thrust manipulation pre-intervention position. A high-velocity, low-amplitude thrust manipulation was performed with a direction of force parallel to the practitioner manipulating the forearm, as seen in the lower image.</p>
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<p>Electrode name and location for electroencephalography. Visualization of significant (<span class="html-italic">p</span> &lt; 0.05) Friedman test findings for electroencephalography of NAS (<b>above</b>) and AS group (<b>below</b>). Ring colors correspond to brainwave bands, as shown on the right.</p>
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9 pages, 1038 KiB  
Study Protocol
There Is Significant Within-Subject Variation in the Time from Light Stimulus to Maximum Pupil Constriction Among Healthy Controls
by Abdulkadir Kamal, Yohan Kim, Amber Salter, Shripal Gunna, Emerson B. Nairon and DaiWai M. Olson
J. Clin. Med. 2024, 13(23), 7451; https://doi.org/10.3390/jcm13237451 - 6 Dec 2024
Viewed by 858
Abstract
Background: Handheld quantitative pupilometers (QPs) measure each phase of the pupillary light reflex (PLR) and provide a summary score based on these values. One phase of the PLR is the period of time from the onset of light exposure to the maximum [...] Read more.
Background: Handheld quantitative pupilometers (QPs) measure each phase of the pupillary light reflex (PLR) and provide a summary score based on these values. One phase of the PLR is the period of time from the onset of light exposure to the maximum constriction of the pupil, also known as time to maximum constriction (tMC). Although tMC has been found to vary significantly among patients with neurological injury, there are no studies reporting tMC in healthy controls. This study addresses this gap. Methods: Subjects in this prospective observational study were healthy controls who provided paired (left and right eye) QP readings during four separate observations over the course of 2 days. The tMC was derived by determining the smallest observed pupil size during videos filmed at 30 frames per second, and we assessed within-subject variability using the coefficient of variance and intraclass correlation coefficient (ICC). Results: Fifty subjects provided 380 QP readings (190 left eye and 190 right eye). Subjects primarily identified as female (80%), non-Hispanic (86%), white (62%), and <40 years old (74%). The mean tMC was 1.0 (0.14) seconds (s) for the left eye and 1.0 (0.17) s for the right eye; the coefficient of variance ranged from 11.6% to 18.8% and the ICC ranged from 0.25 to 0.40. For the between-subject comparisons across the four observation periods, the left and right eye mean differences ranged from 0.001 to 0.063 and the ICC ranged from 0.12 to 0.52. Conclusions: The tMC values vary significantly in healthy controls. Changes in pupil function as a clinical biomarker of intracranial pathology are not fully understood. Identifying clinical correlations of tMC variation may provide insight for the prognostication and treatment of neurocritically ill patients. Full article
(This article belongs to the Section Clinical Neurology)
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<p>Highlighting the time to maximum constriction.</p>
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<p>Data collection plan for QP readings during the 4 observation periods.</p>
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<p>Showing the mean (µ) coefficient of variation (CoVar) time to maximum constriction (tMC) values during each observation period, as well as the mean difference (∆) and intraclass correlation coefficient (ICC) values of each comparison, showing left eye values (<b>a</b>) and right eye values (<b>b</b>) separately.</p>
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12 pages, 1638 KiB  
Article
Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion
by Anthony J. Maxin, Bridget M. Whelan, Michael R. Levitt, Lynn B. McGrath and Kimberly G. Harmon
Diagnostics 2024, 14(23), 2723; https://doi.org/10.3390/diagnostics14232723 - 3 Dec 2024
Viewed by 1072
Abstract
Background: Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). Objective: To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. Methods: Division I college football players had baseline pupillometry including [...] Read more.
Background: Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). Objective: To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. Methods: Division I college football players had baseline pupillometry including pupillary light reflex (PLR) parameters of maximum resting diameter, minimum diameter after light stimulus, percent change in pupil diameter, latency of pupil constriction onset, mean constriction velocity, maximum constriction velocity, and mean dilation velocity using a smartphone-based app. When an SRC occurred, athletes had the smartphone pupillometry repeated as part of their concussion testing. All combinations of the seven PLR parameters were tested in machine learning binary classification models to determine the optimal combination for differentiating between non-concussed and concussed athletes. Results: 93 football athletes underwent baseline pupillometry testing. Among these athletes, 11 suffered future SRC and had pupillometry recordings repeated at the time of diagnosis. In the machine learning pupillometry analysis that used the synthetic minority oversampling technique to account for the significant class imbalance in our dataset, the best-performing model was a random forest algorithm with the combination of latency, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity PLR parameters as feature inputs. This model produced 91% overall accuracy, 98% sensitivity, 84.2% specificity, area under the curve (AUC) of 0.91, and an F1 score of 91.6% in differentiating between baseline and SRC recordings. In the machine learning analysis prior to oversampling of our imbalanced dataset, the best-performing model was k-nearest neighbors using latency, maximum diameter, maximum constriction velocity, and mean dilation velocity to produce 82% accuracy, 40% sensitivity, 87% specificity, AUC of 0.64, and F1 score of 24%. Conclusions: Smartphone pupillometry in combination with machine learning may provide fast and objective SRC diagnosis in football athletes. Full article
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<p>Demonstration of use of the box apparatus. The smartphone inserts into the box from the side (Mariakakis et al. [<a href="#B30-diagnostics-14-02723" class="html-bibr">30</a>]).</p>
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<p>Double histograms of raw data for each pupillary light reflex parameter.</p>
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<p>Three-D scatter plots comparing raw data from different combinations of three out of the four PLR parameters in the best-performing model without SMOTE. Views have been adjusted to give the best appearance of potential areas of differentiation between concussed and baseline recordings in our dataset.</p>
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11 pages, 622 KiB  
Article
Measuring the Pupillary Light Reflex Using Portable Instruments in Applied Settings
by Nicola S. Gray, Menna Price, Jennifer Pink, Chris O’Connor, Ana Antunes and Robert J. Snowden
Vision 2024, 8(4), 60; https://doi.org/10.3390/vision8040060 - 1 Oct 2024
Viewed by 1827
Abstract
The early components of the pupillary light reflex (PLR) are governed by the parasympathetic nervous system. The use of cheap, portable pupillometry devices may allow for the testing of parasympathetic-system health in field settings. We examined the reliability of two portable instruments for [...] Read more.
The early components of the pupillary light reflex (PLR) are governed by the parasympathetic nervous system. The use of cheap, portable pupillometry devices may allow for the testing of parasympathetic-system health in field settings. We examined the reliability of two portable instruments for measuring the PLR and their sensitivity to individual differences known to modulate the PLR. Parameters of the PLR were measured in a community sample (N = 108) in a variety of field settings. Measurements were taken using a commercial pupillometer (NeuroLight, IDMED) and an iPhone using the Reflex Pro PLR analyser (Brightlamp). The parameters of baseline pupil diameter, constriction latency, amplitude and relative amplitude of constriction, and constriction velocity were measured. Individual differences related to age, levels of anxiety, and post-traumatic stress disorder (PTSD) symptomology were assessed. Some measures could not be attained using the iPhone under these field conditions. The reliability of the measures was high, save for the measurement of contraction latency which was particularly unreliable for the iPhone system. The parameters of the PLR showed the same internal relationships as those established in laboratory-based measurements. Age was negatively correlated with all the reliable PLR parameters for both systems. Effects of anxiety and PTSD symptomology were also apparent. The study demonstrated that a hand-held portable infrared pupillometer can be used successfully to measure the PLR parameters under field settings and can be used to examine individual differences. This may allow these devices to be used in workplaces, sports fields, roadsides, etc., to examine parasympathetic activity where needed. Full article
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<p>Illustration of the pupillary light reflex and measurement parameters.</p>
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16 pages, 6129 KiB  
Article
Development of an Innovative Pupillometer Able to Selectively Stimulate the Eye’s Fundus Photoreceptor Cells
by Giovanni Gibertoni, Anton Hromov, Filippo Piffaretti and Martial H. Geiser
Diagnostics 2024, 14(17), 1940; https://doi.org/10.3390/diagnostics14171940 - 2 Sep 2024
Viewed by 1216
Abstract
Recent advancements in clinical research have identified the need to combine pupillometry with a selective stimulation of the eye’s photoreceptor cell types to broaden retinal and neuroretinal health assessment opportunities. Our thorough analysis of the literature revealed the technological gaps that currently restrict [...] Read more.
Recent advancements in clinical research have identified the need to combine pupillometry with a selective stimulation of the eye’s photoreceptor cell types to broaden retinal and neuroretinal health assessment opportunities. Our thorough analysis of the literature revealed the technological gaps that currently restrict and hinder the effective utilization of a method acknowledged to hold great potential. The available devices do not adequately stimulate the photoreceptor types with enough contrast and do not guarantee seamless device function integration, which would enable advanced data analysis. RetinaWISE is an advanced silencing pupillometry device that addresses these deficiencies. It combines a Maxwellian optical arrangement with advanced retinal stimulation, allowing for calibrated standard measurements to generate advanced and consistent results across multiple sites. The device holds a Class 1 CE marking under EU regulation 2017/745, thus facilitating clinical research progress. Full article
(This article belongs to the Special Issue Structure–Function Relationship in Retinal Diseases, Second Edition)
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<p>retinaWISE, a binocular silenced pupillometer equipped with three cameras allowing for simultaneous and bilateral pupil monitoring and two controlled light engines to generate silent substitution stimulation of the patient’s eye fundus. An ophthalmic joystick and a chin rest are included to align the device with the patient’s eyes.</p>
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<p>Schematic view of the retinaWISE internal components including a control board, a light mixer (LM), a light-source aperture (AS), a diffuser (D), a field stop (FS), an ophthalmic lens (OL), and cameras. The external components included a computer (PC), which was connected to the control board, and a spectrometer.</p>
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<p>Schematic of the protocol generation and measurement process, exemplified by an mRGC silent stimulation procedure.</p>
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<p>Protocol timing: for each 3 s stimulation, a 20 s window was defined, with 5 s pre-stimulation serving as a baseline and 12 s post stimulation for pupil recovery.</p>
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<p>Stability test performed on each of the six LEDs with maximum output current (LR = 1). Each curve was normalized to the maximal peak value of the respective LED. The curves are color-coded to match the LEDs.</p>
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<p>On the left, for each LED, the radiant flux, in photocurrent (nA), was measured for a random increase in the light ratio (LR) from 0 to 0.1 with 25 steps of resolution. On the right, measurements were performed by setting the LR randomly between 0 and 1 with 25 steps for each LED. The curves are color-coded to match the LEDs.</p>
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<p>Representative exit pupil image at different FoVs: (<b>a</b>) 60°and (<b>b</b>) 10°.</p>
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<p>Variations in the light emission ratios between stimulation and background. Measurements were performed over 60 days without controlling the temperature of either the device or the measurement environment. All boxes except the last one were color-coded to match the stimulation LEDs.</p>
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<p>Calibration Report: spectral flux measurements obtained by combining spectral distribution collected with a C10988MA Mini-spectrometer (Hamamatsu Photonics, Shizuoka, Japan), and total radiant flux measured with the optical powermeter PM100D, (Thorlabs inc., Newton, NJ, USA). In the graph, the curves were collected by changing the intensity of each LED with 25 resolution steps in a random fashion.</p>
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<p>Normalized pupil size (diameter) variation in response to <span class="html-italic">blue</span> (470 nm), <span class="html-italic">red</span> (630 nm), and <span class="html-italic">melanopsin</span> stimulation, from top to bottom. The stimulation was applied to the right eye while both eyes were measured simultaneously. The thick line represents the mean of individual curves (thin lines). The yellow box indicates the three-second stimulation period.</p>
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<p>Level maps of the Michelson contrast for melanopsin-containing retinal ganglion cell silent stimulation over the CIE chromaticity diagram for the selected six primary peak wavelengths simulated with a Gaussian spectrum profile.</p>
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10 pages, 2388 KiB  
Article
Using Pupillometry to Evaluate Balance in Patients Implanted with a Cochleo-Vestibular Implant
by Joyce Tang, Ángel Ramos de Miguel, Juan Carlos Falcón González, Silvia Borkoski Barreiro, Isaura Rodriguez Montesdeoca and Ángel Ramos Macías
J. Clin. Med. 2024, 13(13), 3797; https://doi.org/10.3390/jcm13133797 - 28 Jun 2024
Viewed by 856
Abstract
Maintaining balance comes naturally to healthy people. In subjects with vestibulopathy, even when compensated, and especially if it is bilateral, maintaining balance requires cognitive effort. Pupillometry is an established method of quantifying cognitive effort. Background/Objectives: We hypothesized that pupillometry would be able [...] Read more.
Maintaining balance comes naturally to healthy people. In subjects with vestibulopathy, even when compensated, and especially if it is bilateral, maintaining balance requires cognitive effort. Pupillometry is an established method of quantifying cognitive effort. Background/Objectives: We hypothesized that pupillometry would be able to capture the increased effort required to maintain posture in subjects with bilateral vestibulopathy in increasingly difficult conditions. Additionally, we hypothesized that the cognitive workload during balance tasks, indexed by pupil size, would decrease with the activation of the BionicVEST cochleo-vestibular implants. Methods: Subjects with a cochleo-vestibular implant as of March 2023 were recruited, excluding those with ophthalmological issues that precluded pupillometry. Pupillometry was performed using a validated modified videonystagmography system. Computed dynamic posturography and a Modified Clinical Test of Sensory Integration on Balance were performed while the pupil was recorded. Tests were first performed after 24 h of deactivating the vestibular component of the implant. Thereafter, it was reactivated, and after 1 h of rest, the tests were repeated. The pupil recording was processed using custom software and the mean relative pupil diameter (MRPD) was calculated. Results: There was an average of 10.7% to 24.2% reduction in MRPD when the vestibular implant was active, with a greater effect seen in tasks of moderate difficulty, and lesser effect when the task was easy or of great difficulty. Conclusions: Despite technical challenges, pupillometry appears to be a promising method of quantifying the cognitive effort required for maintaining posture in subjects with bilateral vestibulopathy before and after vestibular implantation. Full article
(This article belongs to the Section Otolaryngology)
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<p>Pupillometry size tracing from a sample subject.</p>
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<p>Expanded view of pupillometry size tracing (first shaded area of <a href="#jcm-13-03797-f001" class="html-fig">Figure 1</a>).</p>
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13 pages, 790 KiB  
Article
Automated Pupillometry Is Able to Discriminate Patients with Acute Stroke from Healthy Subjects: An Observational, Cross-Sectional Study
by Irene Scala, Massimo Miccoli, Pia Clara Pafundi, Pier Andrea Rizzo, Francesca Vitali, Simone Bellavia, Jacopo Di Giovanni, Francesca Colò, Giacomo Della Marca, Valeria Guglielmi, Valerio Brunetti, Aldobrando Broccolini, Riccardo Di Iorio, Mauro Monforte, Paolo Calabresi and Giovanni Frisullo
Brain Sci. 2024, 14(6), 616; https://doi.org/10.3390/brainsci14060616 - 20 Jun 2024
Viewed by 1375
Abstract
Background: Automated pupillometry (AP) is a handheld, non-invasive tool that is able to assess pupillary light reflex dynamics and is useful for the detection of intracranial hypertension. Limited evidence is available on acute ischemic stroke (AIS) patients. The primary objective was to evaluate [...] Read more.
Background: Automated pupillometry (AP) is a handheld, non-invasive tool that is able to assess pupillary light reflex dynamics and is useful for the detection of intracranial hypertension. Limited evidence is available on acute ischemic stroke (AIS) patients. The primary objective was to evaluate the ability of AP to discriminate AIS patients from healthy subjects (HS). Secondly, we aimed to compute a predictive score for AIS diagnosis based on clinical, demographic, and AP variables. Methods: We included 200 consecutive patients admitted to a comprehensive stroke center who underwent AP assessment through NPi-200 (NeurOptics®) within 72 h of stroke onset and 200 HS. The mean values of AP parameters and the absolute differences between the AP parameters of the two eyes were considered in the analyses. Predictors of stroke diagnosis were identified through univariate and multivariate logistic regressions; we then computed a nomogram based on each variable’s β coefficient. Finally, we developed a web app capable of displaying the probability of stroke diagnosis based on the predictive algorithm. Results: A high percentage of pupil constriction (CH, p < 0.001), a low constriction velocity (CV, p = 0.002), and high differences between these two parameters (p = 0.036 and p = 0.004, respectively) were independent predictors of AIS. The highest contribution in the predictive score was provided by CH, the Neurological Pupil Index, CV, and CV absolute difference, disclosing the important role of AP in the discrimination of stroke patients. Conclusions: The results of our study suggest that AP parameters, and in particular, those concerning pupillary constriction, may be useful for the early diagnosis of AIS. Full article
(This article belongs to the Special Issue Stroke and Acute Stroke Care: Looking Ahead)
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<p>Calibration plot of the final model using bootstrap internal validity resampling method, randomly sampling 100 returnable cases. The lateral axis shows the predicted probability of stroke for each patient, while the vertical axis shows the actual probability of stroke for each patient. It is ideal if the straight line exactly coincides with the dotted line.</p>
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<p>Nomogram displaying the probability of the occurrence of stroke. The upper points help assign the correct score to each variable, whilst the total points in the bottom part of the nomogram, alongside the predicted probability in the last line, allow the assignment of the predicted probability of stroke according to the total score. Abbreviations: NPi, Neurological Pupil Index; BPD, Baseline Pupil Diameter; CH, Percentage of Constriction; CV, Average Constriction Velocity.</p>
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13 pages, 754 KiB  
Review
Eyes on Memory: Pupillometry in Encoding and Retrieval
by Alex Kafkas
Vision 2024, 8(2), 37; https://doi.org/10.3390/vision8020037 - 14 Jun 2024
Viewed by 1673
Abstract
This review critically examines the contributions of pupillometry to memory research, primarily focusing on its enhancement of our understanding of memory encoding and retrieval mechanisms mainly investigated with the recognition memory paradigm. The evidence supports a close link between pupil response and memory [...] Read more.
This review critically examines the contributions of pupillometry to memory research, primarily focusing on its enhancement of our understanding of memory encoding and retrieval mechanisms mainly investigated with the recognition memory paradigm. The evidence supports a close link between pupil response and memory formation, notably influenced by the type of novelty detected. This proposal reconciles inconsistencies in the literature regarding pupil response patterns that may predict successful memory formation, and highlights important implications for encoding mechanisms. The review also discusses the pupil old/new effect and its significance in the context of recollection and in reflecting brain signals related to familiarity or novelty detection. Additionally, the capacity of pupil response to serve as a true memory signal and to distinguish between true and false memories is evaluated. The evidence provides insights into the nature of false memories and offers a novel understanding of the cognitive mechanisms involved in memory distortions. When integrated with rigorous experimental design, pupillometry can significantly refine theoretical models of memory encoding and retrieval. Furthermore, combining pupillometry with neuroimaging and pharmacological interventions is identified as a promising direction for future research. Full article
(This article belongs to the Special Issue Pupillometry)
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<p>Differential pupillary responses to novelty, their brain basis and the different behavioral outputs. (<b>a</b>) Contextual novelty, characterized by the unexpected appearance of new events, triggers dopaminergic and noradrenergic signals to the hippocampus and parahippocampal gyrus. Significant dopaminergic contributions to the medial temporal lobe come from the midbrain—specifically the locus coeruleus and the substantia nigra/ventral tegmental area [<a href="#B32-vision-08-00037" class="html-bibr">32</a>,<a href="#B47-vision-08-00037" class="html-bibr">47</a>]. Their roles are evident in the sympathetic control of pupil dilation, where greater dilation correlates with enhanced memory formation [<a href="#B41-vision-08-00037" class="html-bibr">41</a>]. (<b>b</b>) In contrast, absolute stimulus or expected novelty, engages cholinergic inputs to the medial temporal lobe originating from the pedunculopontine nucleus and basal forebrain. Their impact appears to drive the parasympathetically mediated pupil constriction patterns, while the extent of pupil constriction is predictive of stronger memory formation [<a href="#B41-vision-08-00037" class="html-bibr">41</a>]. M = missed/forgotten stimuli; F = familiar; R = recollected stimuli.</p>
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<p>Pupil response patterns at retrieval. (<b>a</b>) The pupil old/new effect with accurately recognized old stimuli accompanied by increased pupil dilation. The memory type (F = familiar; R = recollected) further differentiates the pupil dilation pattern in accurate recognition [<a href="#B38-vision-08-00037" class="html-bibr">38</a>]. (<b>b</b>,<b>c</b>) Hypothetical cause of the pupil old/new effect, (<b>b</b>) driven by subjective memory experience irrespective of true old/new status or (<b>c</b>) by objective old/new status irrespective of subjective memory response. (<b>d</b>,<b>e</b>) Pupil response discriminates true from false memories at different temporal stages during recognition memory decisions depending on the type of reported memory (data from [<a href="#B57-vision-08-00037" class="html-bibr">57</a>]).</p>
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11 pages, 1445 KiB  
Article
Characterization of Pupillary Light Response through Low-Cost Pupillometry and Machine Learning Techniques
by David A. Gutiérrez-Hernández, Miguel S. Gómez-Díaz, Francisco J. Casillas-Rodríguez and Emmanuel Ovalle-Magallanes
Eng 2024, 5(2), 1085-1095; https://doi.org/10.3390/eng5020059 - 7 Jun 2024
Viewed by 1270
Abstract
This article employed pupillometry as a non-invasive technique to analyze pupillary light reflex (PLR) using LED flash stimuli. Particularly, for the experiments, only the red LED with a wavelength of 600 nm served as the light stimulation source. To stabilize the initial pupil [...] Read more.
This article employed pupillometry as a non-invasive technique to analyze pupillary light reflex (PLR) using LED flash stimuli. Particularly, for the experiments, only the red LED with a wavelength of 600 nm served as the light stimulation source. To stabilize the initial pupil size, a pre-stimulus (PRE) period of 3 s was implemented, followed by a 1 s stimulation period (ON) and a 4 s post-stimulus period (POST). Moreover, an experimental, low-cost pupillometer prototype was designed to capture pupillary images of 13 participants. The prototype consists of a 2-megapixel web camera and a lighting system comprising infrared and RGB LEDs for image capture in low-light conditions and stimulus induction, respectively. The study reveals several characteristic features for classifying the phenomenon, notably the mobility of Hjórth parameters, achieving classification percentages ranging from 97% to 99%, and offering novel insights into pattern recognition in pupillary activity. Moreover, the proposed device successfully captured the PLR from all the participants with zero reported incidents or health affectations. Full article
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<p>Pupillometer diagram, highlighting the main components. (<b>a</b>) Front view and (<b>b</b>) lateral view.</p>
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<p>Graphical interface and communication protocol of the digital pupillometer.</p>
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<p>Illumination protocol including the PRE, ON, and POST period.</p>
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<p>Image acquisition during the (<b>a</b>) PRE, (<b>b</b>) ON, and (<b>c</b>) POST period.</p>
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<p>Experimental posture setup. The A symbol represents the pupillometer position, the B symbol the Interface Communication, and the C symbol the Graphical User Interface.</p>
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<p>General pipeline for the image pre-processing procedure.</p>
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<p>Image pre-processing, pupil detection, and diameter computation. (<b>a</b>) Participant with clear pupil, (<b>b</b>) participant with partially occluded pupil. The green circles represent the final detected diameter of the pupil.</p>
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<p>Average pupilarity light response. The average response of all participants of each trial is computed. The red line is the first trial response, the green line is the second trial response, and the blue line is the average of these two.</p>
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15 pages, 1558 KiB  
Article
The Severity of Diabetic Retinopathy Corresponds with Corneal Nerve Alterations and Ocular Discomfort of the Patient
by Anna Machalińska, Agnieszka Kuligowska, Alicja Ziontkowska-Wrzałek, Beata Stroynowska, Ewa Pius-Sadowska, Krzysztof Safranow, Jan Machaliński, Katarzyna Mozolewska-Piotrowska and Bogusław Machaliński
Int. J. Mol. Sci. 2024, 25(11), 6072; https://doi.org/10.3390/ijms25116072 - 31 May 2024
Cited by 3 | Viewed by 1296
Abstract
Diabetic retinopathy (DR) remains the leading cause of blindness in the working-age population. Its progression causes gradual damage to corneal nerves, resulting in decreased corneal sensitivity (CS) and disruption of anterior-eye-surface homeostasis, which is clinically manifested by increased ocular discomfort and dry eye [...] Read more.
Diabetic retinopathy (DR) remains the leading cause of blindness in the working-age population. Its progression causes gradual damage to corneal nerves, resulting in decreased corneal sensitivity (CS) and disruption of anterior-eye-surface homeostasis, which is clinically manifested by increased ocular discomfort and dry eye disease (DED). This study included 52 DR patients and 52 sex- and age-matched controls. Ocular Surface Disease Index (OSDI) survey, tear film-related parameters, CS, and in vivo corneal confocal microscopy (IVCM) of the subbasal plexus were performed. Furthermore, all patients underwent tear sampling for neurotrophin and cytokine analysis. OSDI scores were greater in DR patients than in controls (p = 0.00020). No differences in the Schirmer test score, noninvasive tear film-break-up time (NIBUT), tear meniscus or interferometry values, bulbar redness, severity of blepharitis or meibomian gland loss were found. In the DR group, both the CS (p < 0.001), and the scotopic pupil diameter (p = 0.00008) decreased. IVCM revealed reduced corneal nerve parameters in DR patients. The stage of DR was positively correlated with the OSDI (Rs = +0.51, 95% CI: + 0.35–+0.64, p < 0.001) and negatively correlated with IVCM corneal nerve parameters and scotopic pupillometry (Rs = −0.26, 95% CI: −0.44–−0.06, p = 0.0097). We found negative correlations between the OSDI and IVCM corneal innervation parameters. The DR group showed lower tear film-brain-derived neurotrophic factor (BDNF) levels (p = 0.0001) and no differences in nerve growth factor (NGF)-β, neurotrophin (NT)-4, vascular endothelial growth factor (VEGF), interleukin (IL)-1β, IL-4, IL-5, IL-6, or IL-12 concentrations. Tumor necrosis factor (TNF)-α, IL-2, IL-8, IL-10, granulocyte macrophage colony-stimulating factor (GM-CSF), and interferon (IFN)-γ levels were decreased among patients with DR. Corneal innervation defects have a direct impact on patients’ subjective feelings. The evolution of DR appears to be associated with corneal nerve alterations, emphasizing the importance of IVCM. Full article
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<p>(<b>A</b>): Ultra-wide-field fundus photography images (angular range: 200 degrees; resolution: 14 μm); (<b>B</b>): corresponding fluorescein angiography images (angular range: 200 degrees; resolution: 14 μm); (<b>C</b>): corresponding in vivo corneal confocal microscopy images of the subbasal nerve plexus (800× magnification; field of view: 400 × 400 μm); (<b>D</b>): corresponding in vivo corneal confocal microscopy images of the subbasal nerve plexus using ACCMetrics software (<a href="https://sites.manchester.ac.uk/ccm-image-analysis/" target="_blank">https://sites.manchester.ac.uk/ccm-image-analysis/</a>; marked red—main fibers, marked blue—branches, marked green—branching points) (800× magnification; field of view: 400 × 400 μm); <b>K</b>—a healthy control; <b>1</b>—Stage 1: mild nonproliferative diabetic retinopathy (NPDR); <b>2</b>—Stage 2: moderate NPDR; <b>3</b>—Stage 3: severe NPDR; <b>4</b>—Stage 4: proliferative retinopathy (PDR).</p>
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23 pages, 3571 KiB  
Article
Studying the Role of Visuospatial Attention in the Multi-Attribute Task Battery II
by Daniel Gugerell, Benedikt Gollan, Moritz Stolte and Ulrich Ansorge
Appl. Sci. 2024, 14(8), 3158; https://doi.org/10.3390/app14083158 - 9 Apr 2024
Cited by 3 | Viewed by 1134
Abstract
Task batteries mimicking user tasks are of high heuristic value. Supposedly, they measure individual human aptitude regarding the task in question. However, less is often known about the underlying mechanisms or functions that account for task performance in such complex batteries. This is [...] Read more.
Task batteries mimicking user tasks are of high heuristic value. Supposedly, they measure individual human aptitude regarding the task in question. However, less is often known about the underlying mechanisms or functions that account for task performance in such complex batteries. This is also true of the Multi-Attribute Task Battery (MATB-II). The MATB-II is a computer display task. It aims to measure human control operations on a flight console. Using the MATB-II and a visual-search task measure of spatial attention, we tested if capture of spatial attention in a bottom-up or top-down way predicted performance in the MATB-II. This is important to understand for questions such as how to implement warning signals on visual displays in human–computer interaction and for what to practice during training of operating with such displays. To measure visuospatial attention, we used both classical task-performance measures (i.e., reaction times and accuracy) as well as novel unobtrusive real-time pupillometry. The latter was done as pupil size covaries with task demands. A large number of analyses showed that: (1) Top-down attention measured before and after the MATB-II was positively correlated. (2) Test-retest reliability was also given for bottom-up attention, but to a smaller degree. As expected, the two spatial attention measures were also negatively correlated with one another. However, (3) neither of the visuospatial attention measures was significantly correlated with overall MATB-II performance, nor with (4) any of the MATB-II subtask performance measures. The latter was true even if the subtask required visuospatial attention (as in the system monitoring task of the MATB-II). (5) Neither did pupillometry predict MATB-II performance, nor performance in any of the MATB-II’s subtasks. Yet, (6) pupil size discriminated between different stages of subtask performance in system monitoring. This finding indicated that temporal segregation of pupil size measures is necessary for their correct interpretation, and that caution is advised regarding average pupil-size measures of task demands across tasks and time points within tasks. Finally, we observed surprising effects of workload (or cognitive load) manipulation on MATB-II performance itself, namely, better performance under high- rather than low-workload conditions. The latter findings imply that the MATB-II itself poses a number of questions about its underlying rationale, besides allowing occasional usage in more applied research. Full article
(This article belongs to the Special Issue Eye-Tracking Technologies: Theory, Methods and Applications)
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<p>Experimental setup.</p>
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<p>Example of a trial in the visual search task (Blocks 1 and 3).</p>
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<p>Multi-Attribute Task Battery (MATB-II).</p>
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<p>NASA Task-Load Index (NASA-TLX).</p>
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<p>Linear regressions of bottom-up scores after the Multi-Attribute Task Battery (MATB-II) on bottom-scores before the MATB-II, as well as between top-down scores after the MATB-II on top-down scores before the MATB-II. Blue dots and orange dots correspond to individual bottom-up and top-down scores in the visual search task, respectively.</p>
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<p>Linear regressions between bottom-up scores and top-down scores before the Multi-Attribute Task Battery (MATB-II), depicted in gray, as well as after the MATB-II, depicted in orange. Gray dots represent individual performance scores on the visual search task before the MATB-II; orange dots represent individual performance scores after the MATB-II.</p>
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<p>Linear regression of top-down scores on the difference between the number of fixations on target-similar minus on target-dissimilar distractors. The gray dots and the orange dots depict individual data from the visual search task before and after the Multi-Attribute Task Battery II (MATB-II), respectively.</p>
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<p>Changes in pupil size and cognitive load of a single participant in the high workload condition.</p>
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26 pages, 6309 KiB  
Article
Method to Quickly Map Multifocal Pupillary Response Fields (mPRF) Using Frequency Tagging
by Jean Lorenceau, Suzon Ajasse, Raphael Barbet, Muriel Boucart, Frédéric Chavane, Cédric Lamirel, Richard Legras, Frédéric Matonti, Maxence Rateaux, Jean-François Rouland, José-Alain Sahel, Laure Trinquet, Mark Wexler and Catherine Vignal-Clermont
Vision 2024, 8(2), 17; https://doi.org/10.3390/vision8020017 - 9 Apr 2024
Viewed by 1825
Abstract
We present a method for mapping multifocal Pupillary Response Fields in a short amount of time using a visual stimulus covering 40° of the visual angle divided into nine contiguous sectors simultaneously modulated in luminance at specific, incommensurate, temporal frequencies. We test this [...] Read more.
We present a method for mapping multifocal Pupillary Response Fields in a short amount of time using a visual stimulus covering 40° of the visual angle divided into nine contiguous sectors simultaneously modulated in luminance at specific, incommensurate, temporal frequencies. We test this multifocal Pupillary Frequency Tagging (mPFT) approach with young healthy participants (N = 36) and show that the spectral power of the sustained pupillary response elicited by 45 s of fixation of this multipartite stimulus reflects the relative contribution of each sector/frequency to the overall pupillary response. We further analyze the phase lag for each temporal frequency as well as several global features related to pupil state. Test/retest performed on a subset of participants indicates good repeatability. We also investigate the existence of structural (RNFL)/functional (mPFT) relationships. We then summarize the results of clinical studies conducted with mPFT on patients with neuropathies and retinopathies and show that the features derived from pupillary signal analyses, the distribution of spectral power in particular, are homologous to disease characteristics and allow for sorting patients from healthy participants with excellent sensitivity and specificity. This method thus appears as a convenient, objective, and fast tool for assessing the integrity of retino-pupillary circuits as well as idiosyncrasies and permits to objectively assess and follow-up retinopathies or neuropathies in a short amount of time. Full article
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<p>(<b>A</b>): Distribution of the temporal modulation frequencies (TMFs) and the resulting overall luminance modulation. (<b>B</b>). Stimulus configuration of the 9 sectors, each coupled with a TMF denoted by its index. The stimulus subtends about 40° of visual angle at 57 cm (central disk 4.6°; paracentral sectors, 5–19.6°; peripheral sectors 20–40°). See <a href="#app1-vision-08-00017" class="html-app">Video S1</a>.</p>
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<p>Steps of analyses: (<b>A</b>). Visual inspection of raw eye movements, pupillary activity, and technical event tracks. (<b>B</b>). Analysis of the PLR after blink detection and correction from which 5 descriptive variables are derived (see <a href="#vision-08-00017-t001" class="html-table">Table 1</a> for the list of all features derived from analyses). (<b>C</b>). Analysis of eye movements—fixation (in)stability—during the stimulation, from which 6 variables are computed. Top left: position over time; Top right: position over space, whole screen; Bottom right: zoom on centered spatial eye positions. Bottom left: histogram of vertical and horizontal eye positions. (<b>D</b>). (<b>1</b>) Raw (red line) and pupillary signal corrected for blinks and transient data (green line) during mPFT stimulation, with computation of 7 descriptive variables and characterization of 5 global pupillary variables, including stimulus/signal cross-correlation lag. (<b>2</b>) FFT of the corrected signal, estimating the amplitude spectrum: full spectrum (blue lines); Raw power at FOIs (red bars); Normalized FOI power (Green bars). (<b>E</b>). Cross-correlation between the stimulus luminance oscillations and the pupillary response. Top stimulus oscillation (blue line) and pupillary response (red line). Bottom, cross-correlogram results indicating the lag and correlation distribution between the stimulus and the pupil response.</p>
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<p>Maps for the right eye of an individual Pupillary Response Field for raw power (<b>left</b>), normalized power (<b>middle</b>), and phase lag (<b>right</b>) according to the sectors of mPFT stimulationlabelled according to its projection onto the retina (ST: supero-temporal; SN: supero-nasal; IN: infero-nasal; IT: infero-temporal. C: central; e: eccentric p: paracentral) and has a color reflecting its value relative to the color scale (right of each figure).</p>
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<p>Group results showing the power distribution (boxplots) and Pupillary Response Fields (PRFs) of the right and left eyes: (<b>A</b>): FOI distribution (Hz) and retinal projections of sectors for the left eye. Labels for each sector are as in <a href="#vision-08-00017-f002" class="html-fig">Figure 2</a>. (<b>B</b>): Distribution of raw power for the 9 FOIs and associated PRFs for the right (upper panel) and left (bottom panel) eyes. (<b>C</b>): Distribution of normalized power for the 9 FOIs and associated PRFs. (<b>D</b>): Distribution of phase lags for the 9 FOIs and associated PRFs.</p>
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<p>(<b>A</b>): Example of an individual stimulus/pupillary signal cross-correlation using the Matlab <span class="html-italic">xcor</span> function. (<b>B</b>): Histogram of phase lags for all participants. Bottom left: right eye; bottom middle: left eye. (<b>C</b>): Correlation between cross-correlation lags of the right and left eyes of all participants. Red lines show the linear regression (r = 083, <span class="html-italic">p</span> &lt; 0.0001) together with 95% confidence intervals. Note that because pupillary signals are down-sampled to 60 Hz, the time resolution of lags is only 16.666 ms such that phase lags from different participants overlap. The high correlation shown here indicate that similar lags are observed for the right and left eyes of each participant.</p>
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<p>Maxima of the cross-correlations between the stimulus/signal (phase lags) and PLR latencies. (<b>A</b>). Lag vs. PLR start constriction latency: right (red disks) and left (green disks) eyes. (<b>B</b>). Lag vs. PLR maximum constriction latency: right (red disks) and left (green disks) eyes. Red lines show the linear regressions for the two eyes together with 95% confidence intervals Inserts indicate the values of Pearson’s coefficient correlation for each eye.</p>
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<p>Test/retest: distribution of Pearson’s r coefficient between Run 1 and Run 2 of 8 participants for right and left eyes. (<b>A</b>). Correlations for raw power; (<b>B</b>). Correlations for normalized power. (<b>C</b>). Correlations for phase lags. (<b>D</b>). Correlations for PLR variables.</p>
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<p>Test/Retest Pearson’s r coefficient at the group level (N = 8, pooled right and left eyes). Black dots show the PSP for all FOIs. (<b>A</b>). Correlations for raw power; (<b>B</b>). Bland–Altman plot for raw power. (<b>C</b>). Correlations for normalized power. Red lines show the linear regression together with 95% confidence intervals. (<b>D</b>). Bland–Altman plot of normalized power. Horizontal lines show the 95% confidence intervals.</p>
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<p>(<b>A</b>) Example of RNFL data and extraction of relevant values from a PDF file. (<b>B</b>) Distribution of the average mPFT power as a function of the average RNFL values for the right eye (red symbols) and the left eye (green symbols). Red lines show the linear regression together with 95% confidence intervals. No correlation is found between the two variables. (inserts show Pearson’s correlation coefficients, with different colors for the 2 eyes).</p>
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<p>(<b>A</b>). Averaged spectral power of each participant as a function of the percentage of blink-corrected data for the right (green symbols) and left (red symbols) eyes. (<b>B</b>). Averaged spectral power as a function of the number of data corrected for transients for the right (green symbols) or left (red symbols) eyes.</p>
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<p>Maps of differences of spectral power between healthy participants and patients in the Marseille and Paris studies for each sector of the stimulation of the right eye (see <a href="#app1-vision-08-00017" class="html-app">Supplementary Figure S9</a> for the left eye). The reference maps of healthy subjects are framed with a blue square. The remaining maps present the differences of power relative to healthy participants for each of the studied pathologies: Age-Related Macular Degeneration (AMD), Diabetic Retinopathy (RD) and Age-Related Maculopathy (ARM) for the Marseille study; Retinitis Pigmentosa (RP), Stargardt disease (SD) and Leber Hereditary Optic Neuropathy (LHON) for the Paris study. Stars within each sector indicate the significance level (<span class="html-italic">p</span> &lt; 0.05 = *; <span class="html-italic">p</span> &lt; 0.01 = **; <span class="html-italic">p</span> &lt; 0.001 = ***), written in white font. See text for details.</p>
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