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Relationship between oculometry/pupillometry and disorders of consciousness

Relationship between oculometry/pupillometry and disorders of consciousness


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I recently got to wondering whether certain eye movements or pupillary responses were correlated with disorders of consciousness (coma, VS, MCS, or even locked-in syndrome). I know that the pupillary response to a direct light-source is part of the usual coma evaluation scale, but my question deals more with resting-state oculomotor activity or stimulus-evoked responses.

An example of such research might be (for example) a paper in which changes in pupil response co-varies with conscious-state changes in an MCS patient.

Does anybody know of any such research?


I have listed several articles below for your reference:

(Search terms: "oculometry pupillometry disorders of consciousness" in Google Scholar, nothing special):

Grandchamp et al, 2014, DOI: 10.3389/fpsyg.2014.00031 - A primary research article related to consciousness (mind wandering) where the authors investigated various pupillometric responses, among other measures. Good journal.

(Search terms: "pupillometry consciousness" in Google scholar):

Laeng et al., 2012, DOI 10.1177/1745691611427305 - Nice looking review on pupillometry and consciousness that should be a good source of additional references;

Smallwood et al., 2011, DOI: 10.1371/journal.pone.0018298 - A primary research article on attention and perception in Plos One. It is open source, but not my favorite journal


Most of the tasks that our brain orchestrates in our body are performed outside of our conscious awareness. In addition to breathing, maintaining our balance and keeping our heart beating, these tasks include increasing or decreasing the size of our pupils as our environment becomes darker or lighter. This allows us to see in both darkened rooms and on bright sunny days. Although the area of a pupil can change by as much as a factor of ten (Winn et al., 1994), we are not aware of the small movements in the ocular muscles that open and close our pupils.

Recent studies have painted a complex and fascinating picture of what drives these changes in pupil size. Despite representing the lowest level of visual function, these pupil dynamics can reflect sophisticated processes that are closely linked to our everyday experience (including attention, decision making, and even aesthetic experiences Binda and Murray, 2015 Hartmann and Fischer, 2014). Therefore, measurements of the pupils, also known as pupillometry, may be used as an indicator for cognitive and perceptual states. This method is also objective and non-invasive, and can, therefore be applied in a wide range of clinical contexts. For example, it enables us to communicate with patients suffering from ‘locked-in’ syndrome – a condition characterized by the paralysis of every muscle except the eye (Stoll et al., 2013).

Now, in eLife, Marco Turi, David Burr and Paola Binda – who are based at research institutes in Pisa, Florence and Sydney – report that fluctuations in pupil size may provide insight into clinical disorders (Turi et al., 2018). The researchers used a well-known optical cylinder illusion that consists of two overlapping sets of black and white dots moving in opposite directions on a 2D plane (Figure 1A). Due to the different speeds of the dots, anyone looking at the dots usually sees a rotating 3D cylinder (Andersen and Bradley, 1998 Video 1 in Turi et al., 2018). However, since the resulting illusion makes it difficult to detect which color is at the front (and, thus, the direction of rotation), our visual system selects one interpretation at any given time. As a result, the perceived rotation switches direction every few seconds (Figure 1B). Such approaches, where the physical stimulus remains constant but our subjective perception changes, have been widely used to study the mechanisms underlying visual awareness (Kim and Blake, 2005).

Changes in pupil size depend on subjective visual perception.

(A) Turi et al. used a visual stimulus in which two sets of dots (white and black) moved in opposite directions (thin arrows). (B) To an observer this stimulus appears as a 3D cylinder that rotates in a clockwise direction with the black dots at the front (top), or in a counter-clockwise direction with the white dots at the front (bottom). This perceived direction of rotation switches every few seconds. (C) Turi et al. found that pupil size increased when the black dots appeared to be at the front, and decreased when the white dots appeared to be at the front. The size of this change was correlated with Autism-Spectrum Quotient score.

To track any subjective change in perception, the participants reported how they experienced the rotation of the cylinder, while the researchers measured the pupil size. Although the overall intensity of light (the primary factor influencing pupil size) remained constant, the size of the pupils changed: the pupils became larger when the black dots seemed to be in the front, but became smaller, when the white dots appeared to be in front (Figure 1C).

In some participants, the size of the pupils changed substantially between the black and white phases, while in others the changes were only minimal. Turi et al. revealed that these differences strongly correlated with the Autism-Spectrum Quotient (AQ) scores of the participants (Baron-Cohen et al., 2001). The higher the number of autistic traits reported by an individual in the AQ questionnaire, the stronger the changes in pupil size became. This effect accounted for about half of the variance in AQ scores, which is considerably higher than the sizes of the effects reported for other sensory measures (e.g., Ujiie et al., 2015).

Turi et al. argue that the changes in pupil size reflect how individuals process visual information differently. Some people tend to focus on small, defined areas such as the surface at the front, while others concentrate on the entire cylinder. Consequently, people with a more detailed focus should have more fluctuations in pupil size, because their focus alternates between black and white dots. This observation is tightly linked with the long-standing hypothesis that individuals with autism spectrum disorders tend to focus more on the detail rather than the bigger picture (Happé and Frith, 2006).

The findings of Turi et al. provide compelling evidence that the size of a pupil reflects subjective differences in a way that is highly correlated to the reported autistic traits of the participants. The study provides numerous possibilities to investigate higher-level cognitive processes. This can be particularly valuable when studying individuals who may find it difficult to engage in complex behavioral tasks, such as individuals with minimal language skills or deficits in other cognitive abilities. Pupillometry is clearly a powerful tool for characterizing the individual differences in processing visual information and, potentially, for advancing our understanding of autism spectrum disorders.


2 EXPERIMENT 2

  1. Changes in TEPR will either be: (a) positively related to changes in effort and tiredness from listening ratings (traditional hypothesis) or (b) negatively related to changes in tiredness from listening (resource depletion hypothesis).
  2. Subjective effort ratings will be positively related to subjective tiredness from listening ratings, supporting Hockey's ( 2013 ) motivation control theory of fatigue prediction that fatigue influences one's own evaluation of demands on capacity.
  3. Tiredness from listening ratings will be negatively related to speech recognition performance, supporting the idea that fatigue has a detrimental impact on task performance (DeLuca, 2005 Hockey, 2013 ).

2.1 Method

Sample size, experimental design, hypotheses, outcome measures, and analysis plan for Experiment 2 were all preregistered on the Open Science Framework (https://osf.io/nya2g). Raw data, stimuli, and R scripts for analysis and plots can be found at https://osf.io/6mbk7/.

2.1.1 Participants

Twenty healthy young adults (two male) aged 18 to 30 years took part in this study. Only participants who had not taken part in Experiment 1 were eligible to take part in Experiment 2. Hopstaken et al. ( 2015 ) reported a Pearson's r correlation of −.33 between TEPR and subjective fatigue in their study. Based on power estimates for detecting a medium effect size when using the repeated-measures correlation (rmcorr) technique with k = 6 (see Figure 4 Bakdash & Marusich, 2017 ), we calculated that a sample size of 20 participants should provide >80% power to detect an association between these variables if one is present at the standard .05 alpha error probability. All participants had hearing thresholds of ≤20 dB at 0.5–4 kHz in each ear. Otherwise, the same eligibility criteria and recruitment methods were used as in Experiment 1.

2.1.2 Materials, design, and procedure

The equipment used, eye tracker setup, materials, design, and procedure were the same as those of Experiment 1, with the following exceptions. Participants performed the task in one condition (hard) only. This listening task included a total of 120 trials and lasted approximately 35–40 min. Participants performed the task continuously (i.e., without a break).6 6 However, please note that the competing talker stimulus was not played continuously in the background. As in Experiment 1, the masker stimulus started at the beginning of each trial and ended just before the speech repetition prompt. Two stimulus lists were created, and participants were randomly assigned to one of the two lists. List 1 consisted of the same 120 IEEE sentences used in Experiment 1. List 2 consisted of 120 IEEE sentences not used in Experiment 1. Based on pilot testing the new experiment among members of the lab, we decided to reduce screen brightness from 100 to 70 cd/m 2 to mitigate against the potential for participant discomfort. Two practice trials were administered, using the same two IEEE sentences as in Experiment 1's hard practice trials. All three subjective rating scales were administered after the second practice trial to establish baselines. The mean adapted SNR value for the main (hard) condition was −8.6 dB (SD = 1.88).

2.1.3 Analysis

Minor differences in how outcome measures were administered and/or scored in Experiment 2 were as follows. Subjective ratings of effort and performance evaluation were administered every five trials, resulting in a total of 24 ratings on each scale. Mean effort and performance evaluation rating scores were therefore calculated by averaging over every four (rather than two) responses. For example, block 1 effort and performance evaluation ratings reflected the average effort and performance evaluation ratings as indicated after trials 5, 10, 15, and 20. Mean TEPR scores reflected TEPRs averaged over every 20 trials. Subjective ratings of tiredness from listening were administered every 20 trials (six ratings in total). A tiredness from listening subjective rating scale was administered at the very beginning of the listening task (i.e., before trial one), and this score was used as a baseline in the analysis. To summarize, each of the six blocks in Experiment 2 reflected scores averaged over 20 (rather than 10) trials. The same pupil data preprocessing techniques were used as in Experiment 1. However, on this occasion, data from one subject (s17) were removed due to having 72/120 trials with >25% missing data. Of the remaining data set, a total of 46 trials (2% of all trials in the data set) were removed from the analysis due to >25% missing sample values. One-way repeated-measures ANOVAs were conducted for each of the dependent variables to examine linear trend over time.

2.2 Results

2.2.1 Speech recognition performance

Figure 4 (left panel) illustrates the general pattern of change in speech recognition performance accuracy as a function of block. There was no significant main effect of block on the linear term, (F(1,19) = 0.004, p = .95, partial η 2 < 0.001), with mean speech recognition performance showing no linear change over time.

2.2.2 TEPR

Figure 4 (right panel) illustrates the general pattern of change in mean TEPR as a function of block. There was a significant main effect of block on the linear term, (F(1,18) = 35.54, p < .001, partial η 2 = 0.66). Mean TEPR showed a general linear decrease over time.

2.2.3 Subjective ratings

Figure 5 displays the general pattern of results in each of the three subjective rating scores (effort, tiredness from listening, and performance evaluation) as a function of block. For mean effort ratings, there was no significant main effect of block on the linear term, (F(1,19) = 0.65, p = .43, partial η 2 = 0.03), with mean effort ratings showing no linear change over time. For mean tiredness from listening ratings, there was a significant main effect of block on the linear term, (F(1,19) = 77.61, p < .001, partial η 2 = 0.80). Mean tiredness from listening ratings showed a general linear increase over time. For mean performance evaluation ratings, there was no significant main effect of block on the linear term, (F(1,19) = 0.41, p = .53, partial η 2 = 0.02), with mean performance evaluation ratings showing no linear change over time.

2.2.4 Correlations

Rmcorr

Rmcorr analyses were conducted to examine associations between the dependent variables at the intraindividual level. We examined all possible pairwise correlations between the five dependent variables (effort ratings, tiredness from listening ratings, performance evaluation rating, speech recognition performance, and TEPR), resulting in a total of 10 tests. A Bonferroni-corrected alpha criterion significance level of .005 (.05/10) was applied.

Figure 6 shows the rmcorr scatterplots pertaining to the main correlation tests of interest. Table 2 shows rmcorr coefficients for within-subject correlation tests between all outcome measures. First, changes in mean TEPR showed a significant negative correlation with changes in mean tiredness from listening ratings. Smaller TEPRs coincided with increased tiredness from listening ratings. However, changes in mean TEPR did not correlate with changes in mean effort ratings. Similarly, no significant relationship was found between changes in mean effort ratings and changes in mean tiredness from listening ratings, nor between mean TEPR and speech recognition performance. Finally, changes in tiredness from listening were not associated with either mean speech recognition performance or mean performance evaluation ratings.

1 2 3 4
1. TEPR
2. Effort rating .03 [−.18, .23]
3. Tiredness from listening rating −.48 [−.63, −.31] .17 [−.02, .36]
4. Performance evaluation rating .11 [−.09, .31] −.71 [−.80, −.60] −.21 [−.39, −.01]
5. Speech recognition performance .05 [−.16, .25] −.49 [−.62, −.32] .11 [−.30, .09] .59 [.44, .70]

2.3 Discussion

Experiment 2 aimed to more closely examine intraindividual associations between TEPR, subjective ratings of effort and tiredness from listening, and performance evaluation. First, we found evidence in favor of the “resource depletion” account of the relationship between TEPR and tiredness from listening TEPRs became smaller as individuals reported increased tiredness from listening. Once again, no association was found between changes in TEPR and subjective effort. Unlike Experiment 1, no significant within-subject association was found between subjective ratings of effort and tiredness from listening (possible reasons are discussed in the General Discussion). We found no significant within-subject association between tiredness from listening and speech recognition performance, suggesting that tiredness from listening did not have a detrimental impact on task performance (Hockey, 2013 ). Finally, evidence for an association between tiredness from listening and performance evaluation ratings was weaker (and nonsignificant) in this experiment (r = −.21) compared with Experiment 1 (r = −.42). Potential reasons for these discrepant results are also discussed in the General Discussion.


Results

Behavioral Data

Bidelman et al. (2019b) fully describes the behavioral results. Figure 1A shows spectrograms of the individual speech tokens and Figure 1B shows behavioral identification functions across the SNRs. An analysis of slopes (β1) revealed a main effect of SNR [F2,28 = 35.25, p < 0.0001] (Figure 1C). Post hoc contrasts confirmed that while 0 dB SNR did not alter psychometric slopes relative to unmasked speech (p = 0.33), the psychometric function became shallower with 𢄥 dB SNR relative to 0 dB SNR (p < 0.0001). Additionally, SNR marginally but significantly shifted the perceptual boundary [F2,28 = 5.62, p = 0.0089] (Figure 1D). Relative to unmasked speech, 𢄥 dB SNR speech shifted the perceptual boundary rightward (p = 0.011), suggesting a small but measurable bias to report “u” (i.e., more frequent vw1-2 responses) when noise exceeds the signal. Collectively, these results suggest that categorical representations are largely resistant to acoustic interference until signal strength of noise exceeds that of speech.

Figure 1. Spectrograms and behavioral speech categorization at three levels of signal-to-noise ratio (SNR). (A) Spectrograms of individual speech tokens. (B) Perceptual psychometric functions. Note the curves are mirror symmetric reflecting the percentage of “u” (left curve) and 𠇊” identification (right curve), respectively. (C) Slopes and (D) locations of the perceptual boundary show that speech categorizing is robust even down to 0 dB SNR. (E) Speech classification speeds (RTs) show a categorical slowing in labeling (Pisoni and Tash, 1974 Bidelman and Walker, 2017) for ambiguous tokens (midpoint) relative to unambiguous ones (endpoints) in unmasked and 0 dB SNR conditions. Categorization accuracy and speed deteriorate with noise interference by remains possible until severely degraded SNRs. Data reproduced from Bidelman et al. (2019b). Spectrogram reproduced from Bidelman et al. (2014), with permission from John Wiley & Sons. errorbars = ± SEM.

Behavioral response times (RTs) show the speed of categorization (Figure 1E). RTs varied with SNR [F2,200 = 11.90, p < 0.0001] and token [F4,200 = 5.36, p = 0.0004]. RTs were similar for unmasked and 0 dB SNR speech (p = 1.0) but slower for 𢄥 dB SNR (p < 0.0001). A priori contrasts revealed this slowing was most prominent for more categorical tokens (vw1-2 and vw4-5). Ambiguous tokens (vw3) elicited similar RTs across noise conditions (ps > 0.69), suggesting that noise effects on RT were largely restricted to accessing categorical representations, not general slowing of decision speed across the board. We examined whether conditions elicited customary slowing in RTs near the midpoint of the continuum (Pisoni and Tash, 1974 Poeppel et al., 2004 Bidelman et al., 2013). Planned contrasts revealed this CP hallmark for unmasked [mean(vw1,2,4,5) vs. vw3 p = 0.0003] and 0 dB SNR (p = 0.0061) conditions, but not at 𢄥 dB SNR (p = 0.59).

Pupillometry Data

Figure 2 shows grand average pupil waveforms for each speech token and SNR as well as the responses specifically contrasting unambiguous [mean (vw1,vw5)] vs. ambiguous (vw3) tokens. Visually, the data indicated that both SNR and the categorical status of speech modulated pupil responses. To quantify these effects, we pooled the peak (maximum) pupil diameter and latency of unambiguous tokens (vw1 and vw5) (those with stronger category identities) and compared them with the ambiguous vw3 token (Liebenthal et al., 2010 Bidelman, 2015 Bidelman and Walker, 2017). Figure 3 shows the mean peak pupil diameters and latencies by SNR and behavioral RTs.

Figure 2. Grand average waveforms for pupil responses. Average responses to each token condition at each SNR level: (A) unmasked, (B) 0 dB SNR, (C) 𠄵 dB SNR conditions. Peak pupil diameter and latency between the 300 and 700 ms search window are extracted for further analysis. Grand average waveforms for pupil responses contrasting categorical [mean (vw1,vw5)] vs. ambiguous (vw3) tokens at each SNR level. (D) Unmasked, (E) 0 dB SNR, (F) 𠄵 dB SNR conditions. Pupil responses are modulated by SNR and token identity. shading = 1 SEM.

Figure 3. Mean peak pupil diameters and latencies by SNR. (A) Larger pupil size is observed at 0 dB SNR relative to unmasked and 𠄵 dB SNR. (B) Peak pupil diameter is elevated at 0 dB SNR relative to the other two conditions. (C,D) In general, 𠄵 dB speech shows the longest peak latencies of the three conditions. Pupil responses are delayed for 0 dB SNR speech and for categorically ambiguous speech (i.e., vw3 > vw1/5). errorbars = 1 SEM.

An ANOVA revealed a sole main effect of SNR on peak pupil size [F2,196 = 6.69, p = 0.0015] with no token [F4,196 = 0.53, p = 0.7157] nor token ∗ SNR interaction effect [F8,196 = 0.16, p = 0.9959] (Figure 3A). Planned contrasts of pupil size between pairwise SNRs showed that only unmasked speech differed from intermediate SNR speech. Specifically, pupil diameter increased when classifying speech in moderate interference (i.e., 0 dB > unmasked p = 0.0007) but did not differ with further increases in noise level (i.e., 0 dB = 𢄥 dB p = 0.0794) (Figure 3B).

An ANOVA on pupil latency revealed that SNR strongly modulated pupil response timing [F2,196 = 4.60, p = 0.0112], as did whether the token was unambiguous [F4,196 = 3.25, p = 0.0130] (Figures 3C,D). There was not a token ∗ SNR interaction effect [F8,196 = 0.94, p = 0.4827]. Follow-up contrasts revealed similar latencies for unmasked and 0 dB speech (p = 0.5379), but longer latencies at 𢄥 dB relative to 0 dB speech (p = 0.0061). Paralleling the RT data, a priori contrasts revealed an “inverted V-shaped” pattern analogous to the behavioral data𠅊 slowing in response timing for ambiguous relative to unambiguous tokens in the 0 dB SNR [mean(vw1,2,4,5) vs. vw3 p = 0.0244]. Unmasked and 𢄥 dB speech did not exhibit this pattern (ps > 0.27).

To further test whether behavior modulated eye behavior, we analyzed each listener’s single-trial vw3 pupil responses based on (i) a median split of their behavioral RTs into fast and slow responses (Figures 4A𠄾) and (ii) the vowel category they reported (e.g., 𠇊” vs. “u”) (Figures 4F–J). This resulted in � trials for each subaverage. Despite having been elicited by an identical (though perceptually bistable) acoustic stimulus, vw3 pupil latencies were strongly dependent on the speed of listeners’ decision [F1,70 = 6.74, p = 0.0115]. Slow RTs were associated with slower pupil responses to the ambiguous token (Figure 4E). Pupil size was not dependent on RTs [SNR, speed, and SNR × speed effects: ps ≥ 0.0585] (Figure 4D). Split by listeners’ identification (i.e., vw3 reported as “u” vs. 𠇊”), we found a sole main effect of SNR on pupil response magnitudes [F2,70 = 3.78, p = 0.0275]. Pupil responses were again largest for 0 dB SNR speech compared to the other noise conditions (Figure 4I). These data reveal that under similar states of speech ambiguity, pupil responses are modulated according to the speed of listeners’ behavioral categorization. Note, this contrasts EEG findings for the same stimuli, which show that electrical brain activity differentiates the ambiguous speech depending on listeners’ subjective report (i.e., vw3 heard as “u” vs. 𠇊”) (Bidelman et al., 2013).

Figure 4. Pupil response latency but not size depends on speed of listeners’ decision. Grand average waveforms for pupil responses to vw3 based on (A𠄾) a median split of behavioral RTs and (F–J) the reported vowel category (e.g., 𠇊” vs. “u”). (E) Pupil latencies strongly depend on speed of listeners’ decision. Slow RTs are associated with slower pupil responses to ambiguous token. (D) Pupil size is not dependent on RTs. (I) SNR has a sole effect on pupil response magnitudes when split by listeners’ identification (i.e., reporting vw3 as “u” vs. 𠇊”). Pupil responses are again largest for 0 dB SNR speech compared to other noise conditions.


Conclusions

By analysing pupil diameters, we revealed that ADHD is associated with large pupil diameter and low complexity and symmetricity of dynamic pupil diameter behaviours. Moreover, the combination of these factors by machine learning enhances the accuracy of ADHD identification. Applying our proposed evaluation method and our findings may facilitate the development of tools to aid in ADHD diagnosis based on pupil diameter. Since they can indicate deficits in brain function and psychiatric disorders, our methods may be used for other pathologies.


Contents

In humans and many animals (but few fish), the size of the pupil is controlled by involuntary constriction and dilation of the iris in order to regulate the intensity of light entering the eye. This is known as the pupillary reflex. In normal room light, a healthy human pupil has a diameter of about 3-4 millimeters, in bright light, the pupil has a diameter of about 1.5 millimeters, and in dim light the diameter is enlarged to about 8 millimeters. The narrowing of the pupil results in a greater focal range. (see aperture for a more detailed explanation)

The shape of the pupil varies betweens species. Common shapes are circular or slit-shaped, although more convoluted shapes can be found in aquatic species. The reasons for the variation in shapes are complex the shape is closely related to the optical characteristics of the lens, the shape and sensitivity of the retina, and the visual requirements of the species.

Slit-shaped pupils are found in species which are active in a wide range of light levels. In strong light, the pupil constricts and is small, but still allows light to be cast over a large part of the retina.

The orientation of the slit may be related to the direction of motions the eye is required to notice most sensitively (so a vertical pupil would increase the sensitivity of the eyes of a small cat to the horizontal scurrying of mice). The narrower the pupil, the more accurate the depth perception of peripheral vision is, so narrowing it in one direction would increase depth perception in that plane. ΐ] Animals like goats and sheep may have evolved horizontal pupils because better vision in the vertical plane may be beneficial in mountainous environments. Α]

Many snakes, such as boas, pythons and vipers, have vertical, slit-shaped pupils that help them to hunt prey under a wide range of light conditions. Small cats and foxes also have slit shaped pupils while lions and wolves have round pupils even though they are in the same respective families. Some hypothesize that this is because slit pupils are more beneficial for animals that hunt small prey rather than large prey. Β]


Materials and methods

Subjects

We recruited a total of 62 subjects (42 women age (mean ± SD): 25.53 ± 4.04), in three groups (25 in the first, 26 in the second and 11 in the last). All were students from the University of Pisa or Florence, in at least their third year. All reported normal or corrected-to-normal vision, and had no diagnosed neurological condition. The number of participants recruited for the study was selected to provide a large effect size as indicated by a priori power analysis (effect size: 0.50, α = 0.05, two-tail) that reveals that in order to reach a power (1−β) of 0.8 a sample size of 26 subjects was needed.

Experimental procedures were approved by the regional ethics committee [Comitato Etico Pediatrico RegionaleAzienda Ospedaliero-Universitaria Meyer—Firenze (FI)] and are in line with the declaration of Helsinki participants gave their written informed consent.

AQ score

All participants completed the Autistic-traits Quotient questionnaire, self-administered with the validated Italian version (Baron-Cohen et al., 2001 Ruta et al., 2012). This contains 50 items, grouped in five subscales: Attention Switching, Attention to Detail, Imagination, Communication and Social Skills. For each question, participants read a statement and selected the degree to which the statement best described them: ‘‘strongly agree’’, ‘‘slightly agree’’, ‘‘slightly disagree’’, and ‘‘strongly disagree’’ (in Italian). Items were scored in the standard manner as described in the original paper(Baron-Cohen et al., 2001): 1 when the participant’s response was characteristic of ASD (slightly or strongly), 0 otherwise. Total scores ranged between 0 and 50, with higher scores indicating higher degrees of autistic traits. All tested subjects scored below 32, which is the threshold above which a clinical assessment is recommended (Baron-Cohen et al., 2001). The mean (SD) of the scores was 14.85 (6.73) scores were normally distributed (see Figure 3D), as measured by the Jarque-Bera goodness-of-fit test of composite normality (JB = 1.42 p=0.37).

Apparatus

The experiment was performed in a quiet room with artificial illumination of 100 lux. Subjects sat in front of a monitor screen, subtending 41 × 30° at 57 cm distance, with their heads stabilized by chin rest. Viewing was binocular. Stimuli were generated with the PsychoPhysics Toolbox routines (Brainard, 1997 Pelli, 1997) for MATLAB (MATLAB r2010a, The MathWorks) and presented on a 22-inch CRT colour monitor (120 Hz, 800 × 600 pixels Barco Calibrator), driven by a Macbook Pro Retina (OS X Yosemite, 10.10.5). Two-dimensional eye position and pupil diameter were monitored either with a CRS LiveTrack system (Cambridge Research Systems) at 60 Hz, or with an Eyelink1000 Plus (SR Research) at 1000 Hz. We verified that although the two systems have different precision and accuracy, they yielded comparable results in our experiments. Both systems use an infrared camera mounted below the screen. Pupil diameter measures were transformed from pixels to millimeters after calibrating the tracker with an artificial 4 mm pupil, positioned at the approximate location of the subjects’ left eye. Eye position recordings were linearized by means of a standard 9-point calibration routine performed at the beginning of each session.

Stimuli and procedure

Different subsets of participants took part in the four experiments (main, swapped motion directions, feature-based attention, double-task). For the ‘main’ experiment, we recruited a total of 51 participants of which one was excluded (see below). We recruited and tested them in two groups (subjects 1–25 and 26–51), intended as self-replications each with 25 participants (after the exclusion of one participant based on criteria detailed below). Trials began with subjects fixating a red dot (0.15° diameter) shown at the centre of a grey background (12.4 cd/m 2 ). The stimulus comprised a centrally positioned 8 × 14° rectangle which appeared to be a cylinder rotating about its vertical axis (Figure 1A). The 3D illusion was generated by presenting a total of 300 randomly positioned dots (each 0.30° diameter) moving around a virtual vertical axis with an angular velocity of 60 deg/s (10 rotations per minute): the linear velocity followed a cosine function, 3.9°/s at screen centre. Dots were black (0.05 cd/m2) when they moved rightwards (half at any one time) and white (55 cd/m2) when they moved leftwards. The resulting stimulus was compatible with two perceptual interpretations: a cylinder rotating anticlockwise (when viewed from above) with black surface in the front and white surface at rear or clockwise, with white surface in the front, black surface at rear. The two perceptual interpretations alternated spontaneously in all participants, who continuously reported their percept (clockwise or anticlockwise rotation of the cylinder), either by holding down one of two keyboard arrow keys or by joystick. There was no response button for uncertain or mixed percepts: subjects were instructed to report which of the two percepts was dominant if in doubt. The stimulus was played for 10 trials of 59 s each, during which participants continuously reported whether the rotation was clockwise or anticlockwise. Participants were instructed to minimize blinks and maintain their gaze on the fixation spot at all times, except during a 1 s inter-trial pause, marked by a change of colour of the fixation spot (which turned from red to black). Each participant completed a minimum of three runs, in a single session.

A subsample of 27 participants (19 of the first group of participants, 4 of the second group, 4 of the last group, one excluded as explained below) were also tested in the ‘swapped motion direction’ experiment – same as in the ‘main’ experiment, except that black dots moved leftward, and white dots moved rightward.

A small subset of participants (N = 10) were re-tested with the same stimuli and procedures as in the ‘main experiment’, but different instructions. These were meant to explicitly encourage a global or a local distribution of attention. In two separate sessions (randomized order), participants were either told to ‘try to attend to both surfaces and see the cylinder rotate as a single unit’ (encouraging global viewing), or to ‘focus attention on the front surface alone’ (encouraging local viewing). Each session lasted about 20 min and included two runs of ten trials each.

Another subsample of 25 participants (6 from the first group, 9 from the second group, 10 from the last, chosen for the disposability for a second testing session) took part in the ‘double-task’ experiment, with the same trial structure as the ‘main’ experiment. The primary task was unaltered, with the participant reporting whether they perceived clockwise or anticlockwise rotation of the cylinder (using a joystick rather than the keyboard to minimize interference with the secondary task). Meanwhile, subtle speed increments (1 frame duration, 600 deg/s angular velocity increment) occurred for either the black or the white dots (forming the front or the rear surface depending on the participant’s perception), every 3 s on average (with 2 s minimum separation between speed increments). Participants were asked to press the space bar as soon as they detected a speed increment (on either surface). Any bar press within 2 s from a speed increment was counted as a hit any bar press that happened more than 2 s away from any speed change was counted as a false alarm. D-prime values were computed from z-transformed hits and false alarms, separately for speed increments occurring on the front and rear surface. For each of these conditions, a minimum of two runs of 10 trials each were acquired (approximately 20 min).

Finally, 50 participants (18 from the first group, 22 from the second group, 10 from the last one) were tested in the ‘feature-based attention’ experiment. Trials were only 10 s long. During a pre-stimulus 2 s interval, no dots were shown and a letter (0.5° wide, either ‘B’ or ‘N’, for bianco or nero, Italian for white or black) was shown at fixation and cued subjects to attend selectively to only white or black dots. Next came the two groups of dots, moving with the same direction and speed as in the main experiment, but lasting only 6 s. During this time, between 0 and 3 speed increments could occur on the cued and uncued surface. Upon extinction of the dots, the participant had 2 s to report by keypress how many speed increments occurred on the cued surface, ignoring speed increments on the uncued surface. In this case the participants did not report the perceived direction of rotation of the cylinder, which may or may not have perceived as a 3D object rather than two independent clouds of dots. Participants performed well above chance, with an average d-prime of 2.36 ± 0.09. We tracked pupil diameter during the 6 s stimulus interval, separating trials where the white and the black dots were cued. This experiment was performed in two runs of 50 trials each (approximately 15 min).

Analysis

An off-line analysis examined the eye-tracking output to exclude time-points with unrealistic pupil-size recordings (smaller than 1 mm, likely due to blinks, or larger than 7 mm, likely due to eyelash interference). We further excluded perceptual phases lasting less than 1 s (often finger errors) and longer than 15 s (marking trials with too few oscillations to measure bistability). These criteria led to the exclusion of one participant for the ‘main’ experiment, and one for the ‘swapped motion direction’ experiment (who had less than 10 usable phases), leaving 50 participants for the ‘main’ experiment, for which the percentage of excluded phases is 27.39 ± 2.18%, and 26 participants for the ‘swapped motion direction’ and ‘double-task’ experiments, for which the percentages of excluded phases were similar (23.32 ± 2.98% and 22.58 ± 3.21% respectively). No trials and no participants were excluded for the ‘feature-based attention’ experiment.

In all three bistable experiments (the ‘main’ experiment, the ‘swapped motion direction’ and ‘double-task’ experiments), dark and white dots were equally likely seen as foreground (percentage of time of the dark foreground percept, respectively: 51.85 ± 0.99%, 51.13 ± 1.43%, 51.34 ± 0.99%, never significantly different from 50%).

Pupil traces were parsed into epochs locked to each perceptual switch (when the subject changed reported perception). We aligned traces to the switch (zero in Figure 1B), and labeled the phases according to the perceived direction of rotation. For the ‘feature-based attention’ experiment, pupil traces were time-locked to stimulus onset and separated based on whether the black or white surface was cued (Figure 2B). For all experiments, we subtracted from each trace a baseline measure of pupil size, defined as the mean pupil size in the 150 ms immediately preceding or following the switch (for negative and positive traces, respectively), or stimulus onset for the ‘feature based attention’ experiment. The resulting traces were averaged across trials and participants, separately for the two perceptual phases (or attention cues for the feature-based attention experiment), to give, Figure 1B and Figure 2B. From these, and also for the individual traces, we defined two summary statistics: the difference of pupil traces between the two types of epochs, and overall mean pupil trace across all epochs. For the ‘main’, the ‘swapped motion direction’ and the ‘double-task’ experiments, these indices were computed after averaging pupil measurements over the first (or first and last) 1 s of each epoch, the minimum phase duration, ensuring that all phases contribute equally to the mean. However, we also verified our main result (correlations with AQ scores) with different epoch definitions.

For the ‘feature-based attention’ experiment, the difference of pupil traces between trials where the white and black dots were cued was computed in the interval between 1 and 3 s from stimulus onset (where the effect of attention is expected to peak [Binda et al., 2014]).


Pupillometry and P3 index the locus coeruleus–noradrenergic arousal function in humans

The authors declare no conflicts of interest in conducting the research presented here. This research was supported by an Irish Research Council for Science, Engineering and Technology (IRCSET) “Embark Initiative” grant, awarded to P.R.M., an IRCSET Enterprise Partnership Scheme Fellowship to J.H.B., and an IRCSET Empower Fellowship to R.G.O'C. The authors also acknowledge funding support via the HEA PRTLI Cycle 3 program of the EU Structural Funds and the Irish Government's National Development Plan 2002–2006. We thank Elisa Tatti for her assistance with data collection, Robert Whelan for assistance with stimulus coding, and Mark Bellgrove for his valuable comments on an early draft of the manuscript.

Abstract

The adaptive gain theory highlights the pivotal role of the locus coeruleus–noradrenergic (LC-NE) system in regulating task engagement. In humans, however, LC-NE functional dynamics remain largely unknown. We evaluated the utility of two candidate psychophysiological markers of LC-NE activity: the P3 event-related potential and pupil diameter. Electroencephalogram and pupillometry data were collected from 24 participants who performed a 37-min auditory oddball task. As predicted by the adaptive gain theory, prestimulus pupil diameter exhibited an inverted U-shaped relationship to P3 and task performance such that largest P3 amplitudes and optimal performance occurred at the same intermediate level of pupil diameter. Large phasic pupil dilations, by contrast, were elicited during periods of poor performance and were followed by reengagement in the task and increased P3 amplitudes. These results support recent proposals that pupil diameter and the P3 are sensitive to LC-NE mode.


Introduction

Headache attributed to traumatic injury to the head [1] also known as post-traumatic headache (PTH) is a common condition following injury to the head and/or neck. The prognosis is generally favorable with most cases resolving within 3–6 months of the inciting injury [2]. However, it is reported that 18–22% of PTH last for more than 1 year [3].

PTH is a poorly understood entity. According to the International Classification of Headache Disorders-3 (ICHD-3): It is defined as any headache related to a traumatic injury to the head and/or neck with headache being reported within 7 days [1]. Little is known about the pathophysiology of PTH: A number of factors have been suggested including microglial activation in the brain parenchyma, dural inflammation related to mast cell degranulation with sensitization of pain pathways, injury to the extracranial tissues and direct damage to neuronal and brain structures [4].

The diagnosis of acute versus persistent PTH is based on an arbitrary cutoff selection of 3 months of headache duration, greater than 3 months for persistent PTH and lesser than 3 months for acute PTH [1]. Limited evidence has looked into the factors associated with the transformation of acute to persistent PTH. A prior population-based study identified that history of traumatic brain injury, being injured under the influence of alcohol, and history of acute PTH were predictors for persistent PTH [5]. PTH is also associated with somatic, cognitive and psychological symptoms [6]. It is known that there is a bidirectional association between headache and psychiatric disorders [7, 8]. Anxiety, depression, affective temperamental dysregulation and suicidal behavior may be seen in patients with chronic headache disorders [6, 8, 9]. In the diagnosis of PTH, the possibility of co-occurring medication overuse headache (MOH) is an important consideration as well [10]. Based on this information, we wanted to test the hypothesis that exposure to clinical predictors, such as medication overuse and psychological symptoms are associated with persistent PTH compared to acute PTH. In addition, we hypothesized that there exists naturally occurring heterogeneous clusters within the persistent PTH group of patients.

In this hospital-based study, we explored the clinical predictors that may be more likely associated with persistent versus acute PTH. Identifying potential clinical predictors may have treatment implications and provide a plausible explanation as to why some patients develop persistent headaches after an injury to the head and/or neck. In addition, we conducted clustering analysis to identify naturally-occurring subgroups of PTH and compare them with ICHD-3 classification of acute versus persistent.


Different Types of Network States

Alternating states of excitability in the waking brain have first been observed in intracellular and local field potential (LFP) recordings of awake rodents. They show large low-frequency fluctuations during periods of quiet resting (Crochet and Petersen, 2006 Poulet et al., 2012 McGinley et al., 2015a). The initiation of whisking or locomotion suppresses the recurrent, slow (<10 Hz) component of the LFP and increases the power of higher frequency oscillations (Poulet et al., 2012 Eggermann et al., 2014 McGinley et al., 2015a). Based on these observations, a classification distinguishing states within waking periods emerged: synchronized states show bimodal, slow rhythmic network activity, and desynchronized states show unimodal persistent, fast network activity. Since network states are continuous and transient phenomena, rather than discrete, categorical conditions, assigning such a dichotomous classification may appear overly simplistic. Nevertheless, classifying data for these substates of waking could explain neuronal response variability and behaviorally relevant correlates in rodents (McGinley et al., 2015a), highlighting its applicability. While the bimodal, synchronized activity was usually related to resting (but see Hall et al., 2014), inattentive behavior, and slower neuronal responses, the persistent desynchronized activity was demonstrated to occur during locomotion or task engagement, showing faster and temporally more precise neuronal responses (Crochet and Petersen, 2006 Pachitariu et al., 2014 McGinley et al., 2015a Schwalm et al., 2017). In humans, intracortical electrode recordings in the hippocampus of awake subjects undergoing surgical treatment for refractory epilepsy showed similar results: during resting states, slow ripples appeared coordinated in hippocampal areas, whereas in active states during cognitively demanding tasks, high frequency activity emerged in hippocampus and parahippocampal cortex (Billeke et al., 2017). Macroscopic measures similarly demonstrated fluctuating substates in awake human brain activity, during rest (Scheinost et al., 2016 Custo et al., 2017) or during task engagement (Coon et al., 2016 Godwin et al., 2015).


Most of the tasks that our brain orchestrates in our body are performed outside of our conscious awareness. In addition to breathing, maintaining our balance and keeping our heart beating, these tasks include increasing or decreasing the size of our pupils as our environment becomes darker or lighter. This allows us to see in both darkened rooms and on bright sunny days. Although the area of a pupil can change by as much as a factor of ten (Winn et al., 1994), we are not aware of the small movements in the ocular muscles that open and close our pupils.

Recent studies have painted a complex and fascinating picture of what drives these changes in pupil size. Despite representing the lowest level of visual function, these pupil dynamics can reflect sophisticated processes that are closely linked to our everyday experience (including attention, decision making, and even aesthetic experiences Binda and Murray, 2015 Hartmann and Fischer, 2014). Therefore, measurements of the pupils, also known as pupillometry, may be used as an indicator for cognitive and perceptual states. This method is also objective and non-invasive, and can, therefore be applied in a wide range of clinical contexts. For example, it enables us to communicate with patients suffering from ‘locked-in’ syndrome – a condition characterized by the paralysis of every muscle except the eye (Stoll et al., 2013).

Now, in eLife, Marco Turi, David Burr and Paola Binda – who are based at research institutes in Pisa, Florence and Sydney – report that fluctuations in pupil size may provide insight into clinical disorders (Turi et al., 2018). The researchers used a well-known optical cylinder illusion that consists of two overlapping sets of black and white dots moving in opposite directions on a 2D plane (Figure 1A). Due to the different speeds of the dots, anyone looking at the dots usually sees a rotating 3D cylinder (Andersen and Bradley, 1998 Video 1 in Turi et al., 2018). However, since the resulting illusion makes it difficult to detect which color is at the front (and, thus, the direction of rotation), our visual system selects one interpretation at any given time. As a result, the perceived rotation switches direction every few seconds (Figure 1B). Such approaches, where the physical stimulus remains constant but our subjective perception changes, have been widely used to study the mechanisms underlying visual awareness (Kim and Blake, 2005).

Changes in pupil size depend on subjective visual perception.

(A) Turi et al. used a visual stimulus in which two sets of dots (white and black) moved in opposite directions (thin arrows). (B) To an observer this stimulus appears as a 3D cylinder that rotates in a clockwise direction with the black dots at the front (top), or in a counter-clockwise direction with the white dots at the front (bottom). This perceived direction of rotation switches every few seconds. (C) Turi et al. found that pupil size increased when the black dots appeared to be at the front, and decreased when the white dots appeared to be at the front. The size of this change was correlated with Autism-Spectrum Quotient score.

To track any subjective change in perception, the participants reported how they experienced the rotation of the cylinder, while the researchers measured the pupil size. Although the overall intensity of light (the primary factor influencing pupil size) remained constant, the size of the pupils changed: the pupils became larger when the black dots seemed to be in the front, but became smaller, when the white dots appeared to be in front (Figure 1C).

In some participants, the size of the pupils changed substantially between the black and white phases, while in others the changes were only minimal. Turi et al. revealed that these differences strongly correlated with the Autism-Spectrum Quotient (AQ) scores of the participants (Baron-Cohen et al., 2001). The higher the number of autistic traits reported by an individual in the AQ questionnaire, the stronger the changes in pupil size became. This effect accounted for about half of the variance in AQ scores, which is considerably higher than the sizes of the effects reported for other sensory measures (e.g., Ujiie et al., 2015).

Turi et al. argue that the changes in pupil size reflect how individuals process visual information differently. Some people tend to focus on small, defined areas such as the surface at the front, while others concentrate on the entire cylinder. Consequently, people with a more detailed focus should have more fluctuations in pupil size, because their focus alternates between black and white dots. This observation is tightly linked with the long-standing hypothesis that individuals with autism spectrum disorders tend to focus more on the detail rather than the bigger picture (Happé and Frith, 2006).

The findings of Turi et al. provide compelling evidence that the size of a pupil reflects subjective differences in a way that is highly correlated to the reported autistic traits of the participants. The study provides numerous possibilities to investigate higher-level cognitive processes. This can be particularly valuable when studying individuals who may find it difficult to engage in complex behavioral tasks, such as individuals with minimal language skills or deficits in other cognitive abilities. Pupillometry is clearly a powerful tool for characterizing the individual differences in processing visual information and, potentially, for advancing our understanding of autism spectrum disorders.


Different Types of Network States

Alternating states of excitability in the waking brain have first been observed in intracellular and local field potential (LFP) recordings of awake rodents. They show large low-frequency fluctuations during periods of quiet resting (Crochet and Petersen, 2006 Poulet et al., 2012 McGinley et al., 2015a). The initiation of whisking or locomotion suppresses the recurrent, slow (<10 Hz) component of the LFP and increases the power of higher frequency oscillations (Poulet et al., 2012 Eggermann et al., 2014 McGinley et al., 2015a). Based on these observations, a classification distinguishing states within waking periods emerged: synchronized states show bimodal, slow rhythmic network activity, and desynchronized states show unimodal persistent, fast network activity. Since network states are continuous and transient phenomena, rather than discrete, categorical conditions, assigning such a dichotomous classification may appear overly simplistic. Nevertheless, classifying data for these substates of waking could explain neuronal response variability and behaviorally relevant correlates in rodents (McGinley et al., 2015a), highlighting its applicability. While the bimodal, synchronized activity was usually related to resting (but see Hall et al., 2014), inattentive behavior, and slower neuronal responses, the persistent desynchronized activity was demonstrated to occur during locomotion or task engagement, showing faster and temporally more precise neuronal responses (Crochet and Petersen, 2006 Pachitariu et al., 2014 McGinley et al., 2015a Schwalm et al., 2017). In humans, intracortical electrode recordings in the hippocampus of awake subjects undergoing surgical treatment for refractory epilepsy showed similar results: during resting states, slow ripples appeared coordinated in hippocampal areas, whereas in active states during cognitively demanding tasks, high frequency activity emerged in hippocampus and parahippocampal cortex (Billeke et al., 2017). Macroscopic measures similarly demonstrated fluctuating substates in awake human brain activity, during rest (Scheinost et al., 2016 Custo et al., 2017) or during task engagement (Coon et al., 2016 Godwin et al., 2015).


2 EXPERIMENT 2

  1. Changes in TEPR will either be: (a) positively related to changes in effort and tiredness from listening ratings (traditional hypothesis) or (b) negatively related to changes in tiredness from listening (resource depletion hypothesis).
  2. Subjective effort ratings will be positively related to subjective tiredness from listening ratings, supporting Hockey's ( 2013 ) motivation control theory of fatigue prediction that fatigue influences one's own evaluation of demands on capacity.
  3. Tiredness from listening ratings will be negatively related to speech recognition performance, supporting the idea that fatigue has a detrimental impact on task performance (DeLuca, 2005 Hockey, 2013 ).

2.1 Method

Sample size, experimental design, hypotheses, outcome measures, and analysis plan for Experiment 2 were all preregistered on the Open Science Framework (https://osf.io/nya2g). Raw data, stimuli, and R scripts for analysis and plots can be found at https://osf.io/6mbk7/.

2.1.1 Participants

Twenty healthy young adults (two male) aged 18 to 30 years took part in this study. Only participants who had not taken part in Experiment 1 were eligible to take part in Experiment 2. Hopstaken et al. ( 2015 ) reported a Pearson's r correlation of −.33 between TEPR and subjective fatigue in their study. Based on power estimates for detecting a medium effect size when using the repeated-measures correlation (rmcorr) technique with k = 6 (see Figure 4 Bakdash & Marusich, 2017 ), we calculated that a sample size of 20 participants should provide >80% power to detect an association between these variables if one is present at the standard .05 alpha error probability. All participants had hearing thresholds of ≤20 dB at 0.5–4 kHz in each ear. Otherwise, the same eligibility criteria and recruitment methods were used as in Experiment 1.

2.1.2 Materials, design, and procedure

The equipment used, eye tracker setup, materials, design, and procedure were the same as those of Experiment 1, with the following exceptions. Participants performed the task in one condition (hard) only. This listening task included a total of 120 trials and lasted approximately 35–40 min. Participants performed the task continuously (i.e., without a break).6 6 However, please note that the competing talker stimulus was not played continuously in the background. As in Experiment 1, the masker stimulus started at the beginning of each trial and ended just before the speech repetition prompt. Two stimulus lists were created, and participants were randomly assigned to one of the two lists. List 1 consisted of the same 120 IEEE sentences used in Experiment 1. List 2 consisted of 120 IEEE sentences not used in Experiment 1. Based on pilot testing the new experiment among members of the lab, we decided to reduce screen brightness from 100 to 70 cd/m 2 to mitigate against the potential for participant discomfort. Two practice trials were administered, using the same two IEEE sentences as in Experiment 1's hard practice trials. All three subjective rating scales were administered after the second practice trial to establish baselines. The mean adapted SNR value for the main (hard) condition was −8.6 dB (SD = 1.88).

2.1.3 Analysis

Minor differences in how outcome measures were administered and/or scored in Experiment 2 were as follows. Subjective ratings of effort and performance evaluation were administered every five trials, resulting in a total of 24 ratings on each scale. Mean effort and performance evaluation rating scores were therefore calculated by averaging over every four (rather than two) responses. For example, block 1 effort and performance evaluation ratings reflected the average effort and performance evaluation ratings as indicated after trials 5, 10, 15, and 20. Mean TEPR scores reflected TEPRs averaged over every 20 trials. Subjective ratings of tiredness from listening were administered every 20 trials (six ratings in total). A tiredness from listening subjective rating scale was administered at the very beginning of the listening task (i.e., before trial one), and this score was used as a baseline in the analysis. To summarize, each of the six blocks in Experiment 2 reflected scores averaged over 20 (rather than 10) trials. The same pupil data preprocessing techniques were used as in Experiment 1. However, on this occasion, data from one subject (s17) were removed due to having 72/120 trials with >25% missing data. Of the remaining data set, a total of 46 trials (2% of all trials in the data set) were removed from the analysis due to >25% missing sample values. One-way repeated-measures ANOVAs were conducted for each of the dependent variables to examine linear trend over time.

2.2 Results

2.2.1 Speech recognition performance

Figure 4 (left panel) illustrates the general pattern of change in speech recognition performance accuracy as a function of block. There was no significant main effect of block on the linear term, (F(1,19) = 0.004, p = .95, partial η 2 < 0.001), with mean speech recognition performance showing no linear change over time.

2.2.2 TEPR

Figure 4 (right panel) illustrates the general pattern of change in mean TEPR as a function of block. There was a significant main effect of block on the linear term, (F(1,18) = 35.54, p < .001, partial η 2 = 0.66). Mean TEPR showed a general linear decrease over time.

2.2.3 Subjective ratings

Figure 5 displays the general pattern of results in each of the three subjective rating scores (effort, tiredness from listening, and performance evaluation) as a function of block. For mean effort ratings, there was no significant main effect of block on the linear term, (F(1,19) = 0.65, p = .43, partial η 2 = 0.03), with mean effort ratings showing no linear change over time. For mean tiredness from listening ratings, there was a significant main effect of block on the linear term, (F(1,19) = 77.61, p < .001, partial η 2 = 0.80). Mean tiredness from listening ratings showed a general linear increase over time. For mean performance evaluation ratings, there was no significant main effect of block on the linear term, (F(1,19) = 0.41, p = .53, partial η 2 = 0.02), with mean performance evaluation ratings showing no linear change over time.

2.2.4 Correlations

Rmcorr

Rmcorr analyses were conducted to examine associations between the dependent variables at the intraindividual level. We examined all possible pairwise correlations between the five dependent variables (effort ratings, tiredness from listening ratings, performance evaluation rating, speech recognition performance, and TEPR), resulting in a total of 10 tests. A Bonferroni-corrected alpha criterion significance level of .005 (.05/10) was applied.

Figure 6 shows the rmcorr scatterplots pertaining to the main correlation tests of interest. Table 2 shows rmcorr coefficients for within-subject correlation tests between all outcome measures. First, changes in mean TEPR showed a significant negative correlation with changes in mean tiredness from listening ratings. Smaller TEPRs coincided with increased tiredness from listening ratings. However, changes in mean TEPR did not correlate with changes in mean effort ratings. Similarly, no significant relationship was found between changes in mean effort ratings and changes in mean tiredness from listening ratings, nor between mean TEPR and speech recognition performance. Finally, changes in tiredness from listening were not associated with either mean speech recognition performance or mean performance evaluation ratings.

1 2 3 4
1. TEPR
2. Effort rating .03 [−.18, .23]
3. Tiredness from listening rating −.48 [−.63, −.31] .17 [−.02, .36]
4. Performance evaluation rating .11 [−.09, .31] −.71 [−.80, −.60] −.21 [−.39, −.01]
5. Speech recognition performance .05 [−.16, .25] −.49 [−.62, −.32] .11 [−.30, .09] .59 [.44, .70]

2.3 Discussion

Experiment 2 aimed to more closely examine intraindividual associations between TEPR, subjective ratings of effort and tiredness from listening, and performance evaluation. First, we found evidence in favor of the “resource depletion” account of the relationship between TEPR and tiredness from listening TEPRs became smaller as individuals reported increased tiredness from listening. Once again, no association was found between changes in TEPR and subjective effort. Unlike Experiment 1, no significant within-subject association was found between subjective ratings of effort and tiredness from listening (possible reasons are discussed in the General Discussion). We found no significant within-subject association between tiredness from listening and speech recognition performance, suggesting that tiredness from listening did not have a detrimental impact on task performance (Hockey, 2013 ). Finally, evidence for an association between tiredness from listening and performance evaluation ratings was weaker (and nonsignificant) in this experiment (r = −.21) compared with Experiment 1 (r = −.42). Potential reasons for these discrepant results are also discussed in the General Discussion.


Results

Behavioral Data

Bidelman et al. (2019b) fully describes the behavioral results. Figure 1A shows spectrograms of the individual speech tokens and Figure 1B shows behavioral identification functions across the SNRs. An analysis of slopes (β1) revealed a main effect of SNR [F2,28 = 35.25, p < 0.0001] (Figure 1C). Post hoc contrasts confirmed that while 0 dB SNR did not alter psychometric slopes relative to unmasked speech (p = 0.33), the psychometric function became shallower with 𢄥 dB SNR relative to 0 dB SNR (p < 0.0001). Additionally, SNR marginally but significantly shifted the perceptual boundary [F2,28 = 5.62, p = 0.0089] (Figure 1D). Relative to unmasked speech, 𢄥 dB SNR speech shifted the perceptual boundary rightward (p = 0.011), suggesting a small but measurable bias to report “u” (i.e., more frequent vw1-2 responses) when noise exceeds the signal. Collectively, these results suggest that categorical representations are largely resistant to acoustic interference until signal strength of noise exceeds that of speech.

Figure 1. Spectrograms and behavioral speech categorization at three levels of signal-to-noise ratio (SNR). (A) Spectrograms of individual speech tokens. (B) Perceptual psychometric functions. Note the curves are mirror symmetric reflecting the percentage of “u” (left curve) and 𠇊” identification (right curve), respectively. (C) Slopes and (D) locations of the perceptual boundary show that speech categorizing is robust even down to 0 dB SNR. (E) Speech classification speeds (RTs) show a categorical slowing in labeling (Pisoni and Tash, 1974 Bidelman and Walker, 2017) for ambiguous tokens (midpoint) relative to unambiguous ones (endpoints) in unmasked and 0 dB SNR conditions. Categorization accuracy and speed deteriorate with noise interference by remains possible until severely degraded SNRs. Data reproduced from Bidelman et al. (2019b). Spectrogram reproduced from Bidelman et al. (2014), with permission from John Wiley & Sons. errorbars = ± SEM.

Behavioral response times (RTs) show the speed of categorization (Figure 1E). RTs varied with SNR [F2,200 = 11.90, p < 0.0001] and token [F4,200 = 5.36, p = 0.0004]. RTs were similar for unmasked and 0 dB SNR speech (p = 1.0) but slower for 𢄥 dB SNR (p < 0.0001). A priori contrasts revealed this slowing was most prominent for more categorical tokens (vw1-2 and vw4-5). Ambiguous tokens (vw3) elicited similar RTs across noise conditions (ps > 0.69), suggesting that noise effects on RT were largely restricted to accessing categorical representations, not general slowing of decision speed across the board. We examined whether conditions elicited customary slowing in RTs near the midpoint of the continuum (Pisoni and Tash, 1974 Poeppel et al., 2004 Bidelman et al., 2013). Planned contrasts revealed this CP hallmark for unmasked [mean(vw1,2,4,5) vs. vw3 p = 0.0003] and 0 dB SNR (p = 0.0061) conditions, but not at 𢄥 dB SNR (p = 0.59).

Pupillometry Data

Figure 2 shows grand average pupil waveforms for each speech token and SNR as well as the responses specifically contrasting unambiguous [mean (vw1,vw5)] vs. ambiguous (vw3) tokens. Visually, the data indicated that both SNR and the categorical status of speech modulated pupil responses. To quantify these effects, we pooled the peak (maximum) pupil diameter and latency of unambiguous tokens (vw1 and vw5) (those with stronger category identities) and compared them with the ambiguous vw3 token (Liebenthal et al., 2010 Bidelman, 2015 Bidelman and Walker, 2017). Figure 3 shows the mean peak pupil diameters and latencies by SNR and behavioral RTs.

Figure 2. Grand average waveforms for pupil responses. Average responses to each token condition at each SNR level: (A) unmasked, (B) 0 dB SNR, (C) 𠄵 dB SNR conditions. Peak pupil diameter and latency between the 300 and 700 ms search window are extracted for further analysis. Grand average waveforms for pupil responses contrasting categorical [mean (vw1,vw5)] vs. ambiguous (vw3) tokens at each SNR level. (D) Unmasked, (E) 0 dB SNR, (F) 𠄵 dB SNR conditions. Pupil responses are modulated by SNR and token identity. shading = 1 SEM.

Figure 3. Mean peak pupil diameters and latencies by SNR. (A) Larger pupil size is observed at 0 dB SNR relative to unmasked and 𠄵 dB SNR. (B) Peak pupil diameter is elevated at 0 dB SNR relative to the other two conditions. (C,D) In general, 𠄵 dB speech shows the longest peak latencies of the three conditions. Pupil responses are delayed for 0 dB SNR speech and for categorically ambiguous speech (i.e., vw3 > vw1/5). errorbars = 1 SEM.

An ANOVA revealed a sole main effect of SNR on peak pupil size [F2,196 = 6.69, p = 0.0015] with no token [F4,196 = 0.53, p = 0.7157] nor token ∗ SNR interaction effect [F8,196 = 0.16, p = 0.9959] (Figure 3A). Planned contrasts of pupil size between pairwise SNRs showed that only unmasked speech differed from intermediate SNR speech. Specifically, pupil diameter increased when classifying speech in moderate interference (i.e., 0 dB > unmasked p = 0.0007) but did not differ with further increases in noise level (i.e., 0 dB = 𢄥 dB p = 0.0794) (Figure 3B).

An ANOVA on pupil latency revealed that SNR strongly modulated pupil response timing [F2,196 = 4.60, p = 0.0112], as did whether the token was unambiguous [F4,196 = 3.25, p = 0.0130] (Figures 3C,D). There was not a token ∗ SNR interaction effect [F8,196 = 0.94, p = 0.4827]. Follow-up contrasts revealed similar latencies for unmasked and 0 dB speech (p = 0.5379), but longer latencies at 𢄥 dB relative to 0 dB speech (p = 0.0061). Paralleling the RT data, a priori contrasts revealed an “inverted V-shaped” pattern analogous to the behavioral data𠅊 slowing in response timing for ambiguous relative to unambiguous tokens in the 0 dB SNR [mean(vw1,2,4,5) vs. vw3 p = 0.0244]. Unmasked and 𢄥 dB speech did not exhibit this pattern (ps > 0.27).

To further test whether behavior modulated eye behavior, we analyzed each listener’s single-trial vw3 pupil responses based on (i) a median split of their behavioral RTs into fast and slow responses (Figures 4A𠄾) and (ii) the vowel category they reported (e.g., 𠇊” vs. “u”) (Figures 4F–J). This resulted in � trials for each subaverage. Despite having been elicited by an identical (though perceptually bistable) acoustic stimulus, vw3 pupil latencies were strongly dependent on the speed of listeners’ decision [F1,70 = 6.74, p = 0.0115]. Slow RTs were associated with slower pupil responses to the ambiguous token (Figure 4E). Pupil size was not dependent on RTs [SNR, speed, and SNR × speed effects: ps ≥ 0.0585] (Figure 4D). Split by listeners’ identification (i.e., vw3 reported as “u” vs. 𠇊”), we found a sole main effect of SNR on pupil response magnitudes [F2,70 = 3.78, p = 0.0275]. Pupil responses were again largest for 0 dB SNR speech compared to the other noise conditions (Figure 4I). These data reveal that under similar states of speech ambiguity, pupil responses are modulated according to the speed of listeners’ behavioral categorization. Note, this contrasts EEG findings for the same stimuli, which show that electrical brain activity differentiates the ambiguous speech depending on listeners’ subjective report (i.e., vw3 heard as “u” vs. 𠇊”) (Bidelman et al., 2013).

Figure 4. Pupil response latency but not size depends on speed of listeners’ decision. Grand average waveforms for pupil responses to vw3 based on (A𠄾) a median split of behavioral RTs and (F–J) the reported vowel category (e.g., 𠇊” vs. “u”). (E) Pupil latencies strongly depend on speed of listeners’ decision. Slow RTs are associated with slower pupil responses to ambiguous token. (D) Pupil size is not dependent on RTs. (I) SNR has a sole effect on pupil response magnitudes when split by listeners’ identification (i.e., reporting vw3 as “u” vs. 𠇊”). Pupil responses are again largest for 0 dB SNR speech compared to other noise conditions.


Conclusions

By analysing pupil diameters, we revealed that ADHD is associated with large pupil diameter and low complexity and symmetricity of dynamic pupil diameter behaviours. Moreover, the combination of these factors by machine learning enhances the accuracy of ADHD identification. Applying our proposed evaluation method and our findings may facilitate the development of tools to aid in ADHD diagnosis based on pupil diameter. Since they can indicate deficits in brain function and psychiatric disorders, our methods may be used for other pathologies.


Contents

In humans and many animals (but few fish), the size of the pupil is controlled by involuntary constriction and dilation of the iris in order to regulate the intensity of light entering the eye. This is known as the pupillary reflex. In normal room light, a healthy human pupil has a diameter of about 3-4 millimeters, in bright light, the pupil has a diameter of about 1.5 millimeters, and in dim light the diameter is enlarged to about 8 millimeters. The narrowing of the pupil results in a greater focal range. (see aperture for a more detailed explanation)

The shape of the pupil varies betweens species. Common shapes are circular or slit-shaped, although more convoluted shapes can be found in aquatic species. The reasons for the variation in shapes are complex the shape is closely related to the optical characteristics of the lens, the shape and sensitivity of the retina, and the visual requirements of the species.

Slit-shaped pupils are found in species which are active in a wide range of light levels. In strong light, the pupil constricts and is small, but still allows light to be cast over a large part of the retina.

The orientation of the slit may be related to the direction of motions the eye is required to notice most sensitively (so a vertical pupil would increase the sensitivity of the eyes of a small cat to the horizontal scurrying of mice). The narrower the pupil, the more accurate the depth perception of peripheral vision is, so narrowing it in one direction would increase depth perception in that plane. ΐ] Animals like goats and sheep may have evolved horizontal pupils because better vision in the vertical plane may be beneficial in mountainous environments. Α]

Many snakes, such as boas, pythons and vipers, have vertical, slit-shaped pupils that help them to hunt prey under a wide range of light conditions. Small cats and foxes also have slit shaped pupils while lions and wolves have round pupils even though they are in the same respective families. Some hypothesize that this is because slit pupils are more beneficial for animals that hunt small prey rather than large prey. Β]


Introduction

Headache attributed to traumatic injury to the head [1] also known as post-traumatic headache (PTH) is a common condition following injury to the head and/or neck. The prognosis is generally favorable with most cases resolving within 3–6 months of the inciting injury [2]. However, it is reported that 18–22% of PTH last for more than 1 year [3].

PTH is a poorly understood entity. According to the International Classification of Headache Disorders-3 (ICHD-3): It is defined as any headache related to a traumatic injury to the head and/or neck with headache being reported within 7 days [1]. Little is known about the pathophysiology of PTH: A number of factors have been suggested including microglial activation in the brain parenchyma, dural inflammation related to mast cell degranulation with sensitization of pain pathways, injury to the extracranial tissues and direct damage to neuronal and brain structures [4].

The diagnosis of acute versus persistent PTH is based on an arbitrary cutoff selection of 3 months of headache duration, greater than 3 months for persistent PTH and lesser than 3 months for acute PTH [1]. Limited evidence has looked into the factors associated with the transformation of acute to persistent PTH. A prior population-based study identified that history of traumatic brain injury, being injured under the influence of alcohol, and history of acute PTH were predictors for persistent PTH [5]. PTH is also associated with somatic, cognitive and psychological symptoms [6]. It is known that there is a bidirectional association between headache and psychiatric disorders [7, 8]. Anxiety, depression, affective temperamental dysregulation and suicidal behavior may be seen in patients with chronic headache disorders [6, 8, 9]. In the diagnosis of PTH, the possibility of co-occurring medication overuse headache (MOH) is an important consideration as well [10]. Based on this information, we wanted to test the hypothesis that exposure to clinical predictors, such as medication overuse and psychological symptoms are associated with persistent PTH compared to acute PTH. In addition, we hypothesized that there exists naturally occurring heterogeneous clusters within the persistent PTH group of patients.

In this hospital-based study, we explored the clinical predictors that may be more likely associated with persistent versus acute PTH. Identifying potential clinical predictors may have treatment implications and provide a plausible explanation as to why some patients develop persistent headaches after an injury to the head and/or neck. In addition, we conducted clustering analysis to identify naturally-occurring subgroups of PTH and compare them with ICHD-3 classification of acute versus persistent.


Materials and methods

Subjects

We recruited a total of 62 subjects (42 women age (mean ± SD): 25.53 ± 4.04), in three groups (25 in the first, 26 in the second and 11 in the last). All were students from the University of Pisa or Florence, in at least their third year. All reported normal or corrected-to-normal vision, and had no diagnosed neurological condition. The number of participants recruited for the study was selected to provide a large effect size as indicated by a priori power analysis (effect size: 0.50, α = 0.05, two-tail) that reveals that in order to reach a power (1−β) of 0.8 a sample size of 26 subjects was needed.

Experimental procedures were approved by the regional ethics committee [Comitato Etico Pediatrico RegionaleAzienda Ospedaliero-Universitaria Meyer—Firenze (FI)] and are in line with the declaration of Helsinki participants gave their written informed consent.

AQ score

All participants completed the Autistic-traits Quotient questionnaire, self-administered with the validated Italian version (Baron-Cohen et al., 2001 Ruta et al., 2012). This contains 50 items, grouped in five subscales: Attention Switching, Attention to Detail, Imagination, Communication and Social Skills. For each question, participants read a statement and selected the degree to which the statement best described them: ‘‘strongly agree’’, ‘‘slightly agree’’, ‘‘slightly disagree’’, and ‘‘strongly disagree’’ (in Italian). Items were scored in the standard manner as described in the original paper(Baron-Cohen et al., 2001): 1 when the participant’s response was characteristic of ASD (slightly or strongly), 0 otherwise. Total scores ranged between 0 and 50, with higher scores indicating higher degrees of autistic traits. All tested subjects scored below 32, which is the threshold above which a clinical assessment is recommended (Baron-Cohen et al., 2001). The mean (SD) of the scores was 14.85 (6.73) scores were normally distributed (see Figure 3D), as measured by the Jarque-Bera goodness-of-fit test of composite normality (JB = 1.42 p=0.37).

Apparatus

The experiment was performed in a quiet room with artificial illumination of 100 lux. Subjects sat in front of a monitor screen, subtending 41 × 30° at 57 cm distance, with their heads stabilized by chin rest. Viewing was binocular. Stimuli were generated with the PsychoPhysics Toolbox routines (Brainard, 1997 Pelli, 1997) for MATLAB (MATLAB r2010a, The MathWorks) and presented on a 22-inch CRT colour monitor (120 Hz, 800 × 600 pixels Barco Calibrator), driven by a Macbook Pro Retina (OS X Yosemite, 10.10.5). Two-dimensional eye position and pupil diameter were monitored either with a CRS LiveTrack system (Cambridge Research Systems) at 60 Hz, or with an Eyelink1000 Plus (SR Research) at 1000 Hz. We verified that although the two systems have different precision and accuracy, they yielded comparable results in our experiments. Both systems use an infrared camera mounted below the screen. Pupil diameter measures were transformed from pixels to millimeters after calibrating the tracker with an artificial 4 mm pupil, positioned at the approximate location of the subjects’ left eye. Eye position recordings were linearized by means of a standard 9-point calibration routine performed at the beginning of each session.

Stimuli and procedure

Different subsets of participants took part in the four experiments (main, swapped motion directions, feature-based attention, double-task). For the ‘main’ experiment, we recruited a total of 51 participants of which one was excluded (see below). We recruited and tested them in two groups (subjects 1–25 and 26–51), intended as self-replications each with 25 participants (after the exclusion of one participant based on criteria detailed below). Trials began with subjects fixating a red dot (0.15° diameter) shown at the centre of a grey background (12.4 cd/m 2 ). The stimulus comprised a centrally positioned 8 × 14° rectangle which appeared to be a cylinder rotating about its vertical axis (Figure 1A). The 3D illusion was generated by presenting a total of 300 randomly positioned dots (each 0.30° diameter) moving around a virtual vertical axis with an angular velocity of 60 deg/s (10 rotations per minute): the linear velocity followed a cosine function, 3.9°/s at screen centre. Dots were black (0.05 cd/m2) when they moved rightwards (half at any one time) and white (55 cd/m2) when they moved leftwards. The resulting stimulus was compatible with two perceptual interpretations: a cylinder rotating anticlockwise (when viewed from above) with black surface in the front and white surface at rear or clockwise, with white surface in the front, black surface at rear. The two perceptual interpretations alternated spontaneously in all participants, who continuously reported their percept (clockwise or anticlockwise rotation of the cylinder), either by holding down one of two keyboard arrow keys or by joystick. There was no response button for uncertain or mixed percepts: subjects were instructed to report which of the two percepts was dominant if in doubt. The stimulus was played for 10 trials of 59 s each, during which participants continuously reported whether the rotation was clockwise or anticlockwise. Participants were instructed to minimize blinks and maintain their gaze on the fixation spot at all times, except during a 1 s inter-trial pause, marked by a change of colour of the fixation spot (which turned from red to black). Each participant completed a minimum of three runs, in a single session.

A subsample of 27 participants (19 of the first group of participants, 4 of the second group, 4 of the last group, one excluded as explained below) were also tested in the ‘swapped motion direction’ experiment – same as in the ‘main’ experiment, except that black dots moved leftward, and white dots moved rightward.

A small subset of participants (N = 10) were re-tested with the same stimuli and procedures as in the ‘main experiment’, but different instructions. These were meant to explicitly encourage a global or a local distribution of attention. In two separate sessions (randomized order), participants were either told to ‘try to attend to both surfaces and see the cylinder rotate as a single unit’ (encouraging global viewing), or to ‘focus attention on the front surface alone’ (encouraging local viewing). Each session lasted about 20 min and included two runs of ten trials each.

Another subsample of 25 participants (6 from the first group, 9 from the second group, 10 from the last, chosen for the disposability for a second testing session) took part in the ‘double-task’ experiment, with the same trial structure as the ‘main’ experiment. The primary task was unaltered, with the participant reporting whether they perceived clockwise or anticlockwise rotation of the cylinder (using a joystick rather than the keyboard to minimize interference with the secondary task). Meanwhile, subtle speed increments (1 frame duration, 600 deg/s angular velocity increment) occurred for either the black or the white dots (forming the front or the rear surface depending on the participant’s perception), every 3 s on average (with 2 s minimum separation between speed increments). Participants were asked to press the space bar as soon as they detected a speed increment (on either surface). Any bar press within 2 s from a speed increment was counted as a hit any bar press that happened more than 2 s away from any speed change was counted as a false alarm. D-prime values were computed from z-transformed hits and false alarms, separately for speed increments occurring on the front and rear surface. For each of these conditions, a minimum of two runs of 10 trials each were acquired (approximately 20 min).

Finally, 50 participants (18 from the first group, 22 from the second group, 10 from the last one) were tested in the ‘feature-based attention’ experiment. Trials were only 10 s long. During a pre-stimulus 2 s interval, no dots were shown and a letter (0.5° wide, either ‘B’ or ‘N’, for bianco or nero, Italian for white or black) was shown at fixation and cued subjects to attend selectively to only white or black dots. Next came the two groups of dots, moving with the same direction and speed as in the main experiment, but lasting only 6 s. During this time, between 0 and 3 speed increments could occur on the cued and uncued surface. Upon extinction of the dots, the participant had 2 s to report by keypress how many speed increments occurred on the cued surface, ignoring speed increments on the uncued surface. In this case the participants did not report the perceived direction of rotation of the cylinder, which may or may not have perceived as a 3D object rather than two independent clouds of dots. Participants performed well above chance, with an average d-prime of 2.36 ± 0.09. We tracked pupil diameter during the 6 s stimulus interval, separating trials where the white and the black dots were cued. This experiment was performed in two runs of 50 trials each (approximately 15 min).

Analysis

An off-line analysis examined the eye-tracking output to exclude time-points with unrealistic pupil-size recordings (smaller than 1 mm, likely due to blinks, or larger than 7 mm, likely due to eyelash interference). We further excluded perceptual phases lasting less than 1 s (often finger errors) and longer than 15 s (marking trials with too few oscillations to measure bistability). These criteria led to the exclusion of one participant for the ‘main’ experiment, and one for the ‘swapped motion direction’ experiment (who had less than 10 usable phases), leaving 50 participants for the ‘main’ experiment, for which the percentage of excluded phases is 27.39 ± 2.18%, and 26 participants for the ‘swapped motion direction’ and ‘double-task’ experiments, for which the percentages of excluded phases were similar (23.32 ± 2.98% and 22.58 ± 3.21% respectively). No trials and no participants were excluded for the ‘feature-based attention’ experiment.

In all three bistable experiments (the ‘main’ experiment, the ‘swapped motion direction’ and ‘double-task’ experiments), dark and white dots were equally likely seen as foreground (percentage of time of the dark foreground percept, respectively: 51.85 ± 0.99%, 51.13 ± 1.43%, 51.34 ± 0.99%, never significantly different from 50%).

Pupil traces were parsed into epochs locked to each perceptual switch (when the subject changed reported perception). We aligned traces to the switch (zero in Figure 1B), and labeled the phases according to the perceived direction of rotation. For the ‘feature-based attention’ experiment, pupil traces were time-locked to stimulus onset and separated based on whether the black or white surface was cued (Figure 2B). For all experiments, we subtracted from each trace a baseline measure of pupil size, defined as the mean pupil size in the 150 ms immediately preceding or following the switch (for negative and positive traces, respectively), or stimulus onset for the ‘feature based attention’ experiment. The resulting traces were averaged across trials and participants, separately for the two perceptual phases (or attention cues for the feature-based attention experiment), to give, Figure 1B and Figure 2B. From these, and also for the individual traces, we defined two summary statistics: the difference of pupil traces between the two types of epochs, and overall mean pupil trace across all epochs. For the ‘main’, the ‘swapped motion direction’ and the ‘double-task’ experiments, these indices were computed after averaging pupil measurements over the first (or first and last) 1 s of each epoch, the minimum phase duration, ensuring that all phases contribute equally to the mean. However, we also verified our main result (correlations with AQ scores) with different epoch definitions.

For the ‘feature-based attention’ experiment, the difference of pupil traces between trials where the white and black dots were cued was computed in the interval between 1 and 3 s from stimulus onset (where the effect of attention is expected to peak [Binda et al., 2014]).


Pupillometry and P3 index the locus coeruleus–noradrenergic arousal function in humans

The authors declare no conflicts of interest in conducting the research presented here. This research was supported by an Irish Research Council for Science, Engineering and Technology (IRCSET) “Embark Initiative” grant, awarded to P.R.M., an IRCSET Enterprise Partnership Scheme Fellowship to J.H.B., and an IRCSET Empower Fellowship to R.G.O'C. The authors also acknowledge funding support via the HEA PRTLI Cycle 3 program of the EU Structural Funds and the Irish Government's National Development Plan 2002–2006. We thank Elisa Tatti for her assistance with data collection, Robert Whelan for assistance with stimulus coding, and Mark Bellgrove for his valuable comments on an early draft of the manuscript.

Abstract

The adaptive gain theory highlights the pivotal role of the locus coeruleus–noradrenergic (LC-NE) system in regulating task engagement. In humans, however, LC-NE functional dynamics remain largely unknown. We evaluated the utility of two candidate psychophysiological markers of LC-NE activity: the P3 event-related potential and pupil diameter. Electroencephalogram and pupillometry data were collected from 24 participants who performed a 37-min auditory oddball task. As predicted by the adaptive gain theory, prestimulus pupil diameter exhibited an inverted U-shaped relationship to P3 and task performance such that largest P3 amplitudes and optimal performance occurred at the same intermediate level of pupil diameter. Large phasic pupil dilations, by contrast, were elicited during periods of poor performance and were followed by reengagement in the task and increased P3 amplitudes. These results support recent proposals that pupil diameter and the P3 are sensitive to LC-NE mode.


Watch the video: Pupil Dilation and Contraction (June 2022).


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