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A longitudinal study of pupillary light reflex in 6- to 24-month children

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Pupillary light reflex (PLR) is an involuntary response where the pupil size changes with luminance. Studies have shown that PLR response was altered in children with autism spectrum disorders (ASDs) and other neurological disorders. However, PLR in infants and toddlers is still understudied. We conducted a longitudinal study to investigate PLR in children of 6–24 months using a remote pupillography device. The participants are categorized into two groups. The ‘high risk’ (HR) group includes children with one or more siblings diagnosed with ASDs; whereas the ‘low risk’ (LR) group includes children without an ASD diagnosis in the family history. The participants’ PLR was measured every six months until the age of 24 months. The results indicated a significant age effect in multiple PLR parameters including resting pupil radius, minimal pupil radius, relative constriction, latency, and response time. In addition, the HR group had a significantly larger resting and minimal pupil size than the LR group. The experimental data acquired in this study revealed not only general age-related PLR changes in infants and toddlers, but also different PLRs in children with a higher risk of ASD.
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A longitudinal study of pupillary
light reex in 6- to 24-month
children
Clare Kercher1, Leila Azinfar1, Dinalankara M. R. Dinalankara1,2, T. Nicole Takahashi3,
Judith H. Miles3 & Gang Yao1*
Pupillary light reex (PLR) is an involuntary response where the pupil size changes with luminance.
Studies have shown that PLR response was altered in children with autism spectrum disorders
(ASDs) and other neurological disorders. However, PLR in infants and toddlers is still understudied.
We conducted a longitudinal study to investigate PLR in children of 6–24 months using a remote
pupillography device. The participants are categorized into two groups. The ‘high risk’ (HR) group
includes children with one or more siblings diagnosed with ASDs; whereas the ‘low risk’ (LR) group
includes children without an ASD diagnosis in the family history. The participants’ PLR was measured
every six months until the age of 24 months. The results indicated a signicant age eect in multiple
PLR parameters including resting pupil radius, minimal pupil radius, relative constriction, latency, and
response time. In addition, the HR group had a signicantly larger resting and minimal pupil size than
the LR group. The experimental data acquired in this study revealed not only general age-related PLR
changes in infants and toddlers, but also dierent PLRs in children with a higher risk of ASD.
Autism Spectrum Disorders (ASDs) are complicated disorders that are marked by persistent decits in social
communication and interactions and by restricted, repetitive patterns of behavior, interests or activities1. Initially
chronicled 75 years ago2, ASDs now aect about 2.47% children and adolescents in USA alone3. Although the
etiology of ASD is still not fully understood, our understanding of this disorder has since been signicantly
improved owing to a large amount of physiological, psychological, and neurological studies. Evidence sup-
ports that the outcome in children with ASDs can be greatly improved by using early behavioral intervention4,5.
Unfortunately, most children do not receive an ASD diagnosis until aer the age of four6, although early signs
may appear as young as 12 months of age7. erefore, there is a great interest in nding eective biological mark-
ers for early screening of risk of autism and assessing responses to interventions.
Pupillary light reex (PLR) is the involuntary and nearly instantaneous pupil size change that occurs as a
response to the luminous intensity of light that falls on the retina. e pupil size is controlled by the dilator
and sphincter muscles innervated primarily by the sympathetic and parasympathetic branches of the autonomic
nervous system (ANS), respectively8. In 1961, Rubin observed that the pupils in 7 to 12 years old children with
ASD constricted slower in responses to light adaption compared to typically developing children9. Using a com-
puterized pupillography system, Fan et al. discovered that pupils of children with ASD took a greater amount
of time to respond to short (0.1 s) light stimuli and constricted less and more slowly than those with typical
development10. Similar atypical PLR responses were also reported in subsequent studies in children with ASD of
dierent ages using pupillography and eye-tracking devices1113. In addition, studies have shown that quantitative
PLR responses were associated with sensory behaviors and autism traits14,15. e PLR’s potential for early identi-
cation of risk of autism was recently demonstrated by Nyström et al.16. ey reported that the pupil constricted
more in 9- to 10-month old infants who later received an ASD diagnosis and the amount of PLR constriction was
correlated with the severity of ASD symptoms.
Resting pupil size and PLR parameters are known to change with age. Existing literature indicates that resting
pupil size increases from infants to teenagers1719 and then decreases with age thereaer2022. In comparison with
resting or static pupil size, there are limited studies on age eect on PLR. Still, current evidence indicates that
1Department of Biomedical, Biological & Chemical Engineering, University of Missouri, Columbia, MO, 65211, USA.
2Department of Computer Engineering, University of Sri Jayewardenepura, Nugegoda, Sri Lanka. 3Thompson Center
for Autism and Neurodevelopmental Disorders, University of Missouri, Columbia, MO, 65211, USA. *email: YaoG@
missouri.edu
OPEN
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PLR parameters can change with age19. Studies suggested that the age trend might be altered in association with
ASD. For example, PLR latency (the delay between stimulation onset and the beginning of pupil constriction)
decreased from 6 to 8 years in children of typical development; this trend was not apparent in age-matched chil-
dren with ASD11, suggesting that the PLR dierences between individuals with and without ASD may change
with age. Interestingly, Nyström et al. later reported that the PLR latency was shorter in young children with
high-risk of ASD23, in contrast with reports that older children with ASD had longer latency than typically devel-
oping children10,11. Exiting experimental evidence12 suggested that dierent age trends may explain apparent
inconsistencies in ASD associated atypical resting pupil sizes reported in the literature2426.
Despite the importance of age eect on PLR, no age-dependent longitudinal study was reported in literature.
In particular, age-dependent PLR data in infants and toddlers are scarce due to the challenges in measuring PLR
in young children. is study used the recently developed remote PLR (rPLR) system to investigate the PLR
changes in children from 6 to 24 months old. is novel rPLR system is capable of imaging pupil size changes at a
high spatial resolution without the need of any restrain during the test, which makes it ideal to test PLR in young
children27. e participants were categorized into two groups based on their susceptibility to ASD. e risk of
younger siblings developing an ASD is signicantly higher if an older sibling has an ASD diagnosis28. erefore,
the ‘high risk’ (HR) group includes children with one or more siblings diagnosed with ASD. On the other hand,
the ‘low risk’ (LR) group includes children not associated with ASD in the family history. We intended to answer
the following questions: (1) whether the PLR parameters are age-dependent in the 6–24 months of age range, and
(2) whether any atypical parameters exist in the ‘high risk’ group of children.
Results
e Pervasive Developmental Disorders Screening Test-II (PDDST-II) scores were recorded as a simple screen-
ing for neurodevelopmental disorders in the participants from 12- to 24-month old. Figure1 shows the distri-
bution of the PDDST-II scores at 12-, 18-, and 24-month. In the HR group, the PDDST-II score changed from
2.05 ± 2.22 at 12-month, to 2.00 ± 2.60 at 18-month, and 1.86 ± 2.80 at 24-month. A few children (4 at 12-month,
3 at 18-month, 2 at 24-month) in the HR group had a score of 5 or above, suggesting potential developmen-
tal disorders29. In the LR group, the PDDST-II score appeared to increase slightly with age from 0.43 ± 0.65 at
12-month, to 0.68 ± 0.95 at 18-month, and 0.81 ± 0.83 at 24-month. However, none of the participants in the LR
group scored more than three.
Figure2 illustrates example pupilograms obtained from two participants in the LR and HR groups who com-
pleted all 4 tests at dierent ages from 6-month to 24-month. e pupilogram curves shown were averaged results
Figure 1. Distribution of the PDDST-II scores obtained at 12-, 18-, and 24-month in both LR and HR groups.
Figure 2. Example mean PLR curves obtained from a subject in the LR group and a subject in the HR group at
dierent ages. e errors bars indicate the standard error. e dashed lines indicated the time of the stimulation
ashes.
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from all successful trials obtained during a single PLR test. e PLR followed the typical PLR curve as those
observed in the older children. e pupil size was relatively stable before the stimulation (marked as dashed lines).
e pupil then started to constrict aer a delay (the latency period), reached a minimum, and then started to
recover back to the baseline. Overall, the amount of constriction in the HR group appeared to be slightly smaller
than the LR constriction.
Figure3 shows all extracted PLR parameters at different ages in both the HR and LR groups (see also
Supplementary Fig.S1). As a group, the base pupil radius, minimal pupil radius, and relative constriction all
increased with age; whereas the latency, response time, and constriction time showed a decreasing trend with age.
However, there were considerable variations amongsubjects, which justied the use of a random intercept in the
linear mixed-eects model(LMM) analysis. In addition, the HR group appeared to have a larger pupil than the
LR group. Both the base and minimal pupil radii appeared to be larger in the males. No clear group or sex eect
was observed in the other four parameters. e data from the two HR participants who received diagnoses at
the end of this study were labeled using symbols in Fig.3. e triangle symbol represented the one with an ASD
diagnosis and the circle represented the other with a diagnosis of global developmental delay.
e above observations were examined using the LMM analysis. Table1 shows the estimations of xed eects
and the corresponding 95% condence intervals (CI). e group (HR vs LR) had a signicant eect in three PLR
parameters: base radius (F = 10.02, p = 0.003), minimal radius (F = 11.62, p = 0.001), and relative constriction
(F = 5.82, p = 0.020). In comparison with the LR group, the pupils in the HR group were bigger before stimu-
lation (t = 3.17, p = 0.003), remained bigger at the maximal constriction (t = 3.41, p = 0.001); but the relative
Figure 3. e extracted PLR parameters (base radius, minimal radius, relative constriction, latency,
constriction time, and response time) in all participants at dierent age groups. e data were separated into
the high-risk (HR) and low-risk (LR) groups and males (M) and females (F) in each group. e symbols
indicated data from the two HR participants who received diagnoses at the end of this study with the triangles
representing the one diagnosed with an ASD and the circles representing the other with a diagnosis of global
developmental delay.
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constriction was smaller (t = 2.41, p = 0.020). All timing parameters (latency, response time, and constriction
time) were similar in the HR and LR groups.
e LMM analysis revealed that the sex eect was signicant only in the two pupil-size related parameters:
base radius (F = 8.41, p = 0.006) and minimal radius (F = 6.01, p = 0.019). As shown in Table1, in comparison
with the boys, the girls had smaller based pupil radius (t = 2.90, p = 0.006) and minimal pupil radius (t = 2.45,
p = 0.019).
e LMM analysis indicated that age had a signicant eect on base radius (F = 14.00, p < 0.001), minimal
radius (F = 10.52, p < 0.001), relative constriction (F = 3.74, p = 0.014), latency (F = 7.13, p < 0.001), and response
time (F = 6.34, p = 0.001). In constriction time, the LMM showed a marginal age eect (F = 2.626, p = 0.055).
e follow-up pairwise comparisons conrmed that the base radius increased signicantly with age (p < 0.05)
between any two age-group pairs except between 12-mo and 18-mo. Similarly, the minimal pupil radius increased
with age (p < 0.05) between any two age-group pairs except between 12- and 18-mo and 6- and 12-mo. e rela-
tive constriction showed an overall increasing trend with age. However, the dierence reached signicance only
between 6- and 12-mo, and between 6-mo and 24-mo. e decreasing tend in latency was signicant (p < 0.05)
between 6- and 24-mo, 12- and 24-mo, and between 18- and 24-mo. e pairwise comparison revealed that the
decreasing trend in response time was signicant between 12- and 18-mo, and between 12- and 24-mo.
Discussion
is study revealed signicant age trends in the pupil size, constriction, latency, and response time in 6- to
24-month children. e trend seen in the base pupil radius was consistent with previous reports that pupil size
increased from birth until teenage years in typically developing children17,18. e observation that male children
had slightly bigger pupil size was also in agreement with previous studies17. A close examination of the corre-
lations among the six PLR parameters (Fig.4) indicated that base pupil radius and minimal pupil radius were
highly correlated (Pearson correlation r = 0.945). erefore, the similar eects of age, sex, and group on base
radius and minimal radius can be expected.
e increase in relative constriction with age, in particular from 6-mo to 12-mo and to 24-mo, appeared to be
consistent with a previously observed trend in 2-year to 3-year old children12. e age trends in base and minimal
pupil radii were opposite to that of the constriction. Such opposite trends cannot be simply explained based on
correlation. As shown in Fig.4, the relative constriction only had a week negative correlation with base radius
(r = 0.265) and a moderate negative correlation with minimal radius (r = 0.523).
e observation of latency decreasing with age was consistent with previous results reported in 2- to 6-year
and 6- to 18-year old children of typical development11,12. Taken together, these data suggested that PLR latency
Parameter Estimate Std. Error df t Sig. 95% CI [Lower, Upper]
Based radius (mm)
Intercept 2.11 0.06 53.05 33.74 0 [1.98, 2.23]
[Group = HR]a0.21 0.07 41.36 3.17 0.003 [0.08, 0.35]
[Sex = F]b0.19 0.07 41.30 2.90 0.006 [0.33, 0.06]
[Age = 6]c0.26 0.05 90.54 5.46 0 [0.36, 0.17]
[Age = 12] 0.13 0.03 84.50 3.75 0 [0.20, 0.06]
[Age = 18] 0.02 0.03 83.80 0.46 0.649 [0.08, 0.05]
Min radius (mm)
Intercept 1.77 0.06 50.26 27.90 0 [1.65, 1.90]
[Group = HR]a0.23 0.07 40.48 3.41 0.001 [0.10, 0.37]
[Sex = F]b0.17 0.07 40.52 2.45 0.019 [0.31, 0.03]
[Age = 6]c0.21 0.05 87.43 4.48 0 [0.30, 0.12]
[Age = 12]c0.09 0.03 82.66 2.90 0.005 [0.16, 0.03]
[Age = 18]c0.01 0.03 81.84 0.18 0.858 [0.06, 0.07]
Constriction (%)
Intercept 29.32 1.69 51.43 17.40 0.000 [25.94, 32.70]
[Group = HR]a4.38 1.81 41.40 2.41 0.020 [8.04, 0.71]
[Age = 6]c4.10 1.24 88.39 3.32 0.001 [6.55, 1.64]
[Age = 12]c0.91 0.87 83.61 1.05 0.299 [2.65, 0.82]
[Age = 18]c1.12 0.87 82.80 1.30 0.199 [2.85, 0.60]
Latency (ms)
Intercept 240.62 5.17 66.50 46.51 0 [230.29, 250.95]
[Age = 6]c20.68 5.36 99.11 3.86 0 [10.05, 31.31]
[Age = 12]c15.59 3.92 86.23 3.97 0 [7.79, 23.39]
[Age = 18]c11.51 3.90 85.36 2.95 0.004 [3.75, 19.27]
Response time (ms)
Intercept 618.36 13.36 56.24 46.30 0 [591.60, 645.11]
[Age = 6]c28.06 12.09 89.35 2.32 0.023 [4.03, 52.09]
[Age = 12]c36.28 8.74 80.33 4.15 0 [18.89, 53.66]
[Age = 18]c13.58 8.60 79.33 1.58 0.118 [3.53, 30.68]
Table 1. Estimates of xed eects obtained using the linear mixed-eects model (LMM). ae LMM model
used results from LR group as the reference. be LMM model used results from the male group as the
reference. ce LMM model used results from the 24-month group as the reference.
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decreases from 6 months old until 9~10 years old in the typically developing children. A similar decreasing trend
was observed in the PLR response time, although the response time had only moderate correlation with latency
(Pearson correlation r = 0.446). e PLR constriction time was not correlated with latency and showed no sig-
nicant age trend.
e observation that base pupil size was larger in the HR group than in the LR group appeared similar to
the dierence previously reported between 2-year old children with ASD and those of typical development12.
e observation of a smaller relative constriction in the HR group was similar to that observed in older children
aected by ASD using a desktop PLR device10,11. Following previous studies11,23, the relative or normalized con-
striction was used as a way to compensate the variations caused by dierent baseline pupil sizes. Interestingly,
a careful examination indicated such a dierence in relative constriction was due to the larger resting pupil
radius which was the denominator in calculating the relative constriction C% = (Ro2 Rm2)/Ro2. No signicant
group eect was observed when the simple pupil size change Ro Rm was analyzed using the LMManalysis.
Nevertheless, this observation was inconsistent with a previous study by Nyström et al. who reported that PLR
constriction was larger in 9–10-month-old with high risk of ASD16,23. Such inconsistency may be attributed to dif-
ferent methodology and testing conditions used. ere were signicant variations in the room lighting conditions
and optical stimulations among previously reported studies due to dierent testing systemsused. Changes in
lightadaptation and optical stimulation can greatly aect the PLR response and may lead to altered age trends22.
e PLR dierences observed between HR and LR children are similar to those reported in older children
between those aected by autism and those of typical development. Presumably, only very few in the HR group
may be eventually diagnosed with an ASD. Such observation could be attributed to genetic or possible environ-
mental factors; but further studies are necessary to understand this. e lack of a strong correlation between based
radius, relative constriction, latency, and constriction time may suggest these parameters are modulated under
dierent neurological mechanisms. A bigger pupil size and a smaller constriction may be consistent undera
stronger sympathetic modulation8. e constriction speed is controlled by iris muscle contraction and thus is
more under the inuence of parasympathetic modulation. On the other hand, the latency represents essentially
the neural signal transduction and processing speed, which could be aected by synaptic function, white matter
maturation, or network connectivity, all implicated in ASD3033.
Two children in the HR groups received diagnoses at the end of this study: one was diagnosed with ASD and
the other with global developmental delay. e one diagnosed with ASD had PDDST-II score of 7 at both 12- and
Figure 4. Correlations among the six PLR parameters. e numbers in plots indicate Pearson’s correlation
coecients. **Correlation is signicant at the 0.01 level (2-tailed). * Correlation is signicant at the 0.05 level
(2-tailed).
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18-mo; whereas the other had a score of 4 at 12-mo and 6 at 18-mo. When examining the PLR results from these
two participants against the entire data set, no obvious distinct patterns were observed for the child diagnosed
with global developmental delay. However, the one who received an ASD diagnosis showed some interesting pat-
terns (Fig.3). First, this participant had a latency of 206.6 ms at 12-mo, the smallest among all participants, which
increased to 233.3 ms at 18-mo. Meanwhile, the constriction time decreased greatly from 455.6 ms at 12-mo
to 284.7 ms at 18-mo, which was the largest reduction among all participants. It is interesting to note that the
observation of a small PLR latency at 12-mo in the one diagnosed with an ASD appeared to be consistent with
previous speculation that ASD may be associated with shortened latency in infants, but longer latency in older
children11,12,23. Such age dependent dierence suggested the possible use of PLR as an indicator of atypical devel-
opmental trajectory in children.
In summary, we conducted a longitudinal study of PLR behaviors in 6-mo to 24-mo children with and without
high risk of developing ASD. e results indicated signicant age trends in base pupil radius, minimal pupil size,
and latency. Specically, this study showed that the base and minimal pupil size increased with age signicantly,
while the latency decreased signicantly during this period. Furthermore, atypical PLR parameters seen in previ-
ous studies of older children with ASD were also observed in younger children age 6–24-months. We have found
that the pupils of the children with higher risk of ASD were, on average, larger at both resting state and the time
of maximal constriction. e one participant who was diagnosed with ASD at the end of the study showed some
distinct patterns in PLR latency and constriction speed. Additional studies in a large population are necessary to
further evaluate these observations. In future studies, it will be valuable to also assess the eect of developmental
age in addition to chronological age. More advanced data analysis methodology such as Bayesian factor analysis
may also be employed to explore further the interactions among dierent factors.
Methods
Participants. Forty-two children participated in this study. All participants were recruited through the
ompson Center for Autism and Neurodevelopmental Disorders at the University of Missouri (MU). Twenty-
three participants made up the high-risk group (HR), which consists of children who have at least one sibling
diagnosed with ASD. e low-risk (LR) group had 19 children who have no family history of autism or other neu-
rodevelopmental disorders. is study was approved by the Institutional Review Board (IRB) of the University of
Missouri. All methods were performed in accordance with the relevant IRB guidelines and regulations. Written
informed consents were obtained from the parents/guardians prior to the PLR test.
Table2 shows the number of participants at each of the four nominal testing ages of 6-, 12-, 18-, and 24-month.
e actual age distributions were also shown in the table. e test data from one girl at 6-month and one girl at
12-month, both from the HR group, were not successful because they either could not look at the screen or their
excessive movement did not allow clear pupil images to be recorded. e nal dataset consists of PLR measures
from nine children (6 in HR and 3 in LR) who successfully completed test at all four ages, 24 children (9 in HR
and 15 in LR) who successfully completed tests at three ages, and nine children (8 in HR and 1 in LR) who only
completed tests at two or one age.
One participant in the HR group reported vision problems due to Usher syndrome34. is child’s PLR results
were included in the overall data analysis because they did not show any obvious dierences from other children’s
data in the group. All other participants reported neither vision problems nor any family history of eye disorders.
Participants were requested to withhold medications 48 hours prior to the test, unless it was necessary. Two sub-
jects received vaccinations within 24 hours of testing of their 6- and 12-month tests. ree subjects reported to
have taken antibiotics before their tests (two at 6-months and one at 24-months). Children in the LR group are
typically developing during the study period based on the family’s report of their most recent well-baby checkups.
In addition, the Pervasive Developmental Disorders Screening Test-II (PDDST-II, Pearson Clinical Assessment)29
scores were recorded as a simple screening for neurodevelopmental disorders in the participants before 24 month
of age. By the end of this study, two HR participants received diagnoses at the MU ompson Center: one with
ASD and one with non-ASD global development delay. Another participant was reported to have minor speech
delay. No developmental problems were reported by the parents of other participants on their recent well-baby
checkups.
Test procedure. All participants were tested using a remote PLR (rPLR) instrument. e details of the system
and testing arrangement have been described in detail previously27. e rPLR utilizes a tracking system to follow
the position of the subject’s right eye. e data of the eyes position is then used to focus and change the direction
of the PLR imaging camera to the subject’s right pupil. is design enables PLR measurements in children with-
out the need of a strict physical restraint. e PLR tests were conducted in a bright room with illuminance level
measured at ~ 120 lux. e participants were placed in front of the rPLR system in a car seat or seated on a parent’s
6-mo 12-mo 18-mo 24-mo
Low-risk Number 2 F/3 M 9 F/10 M 9 F/10 M 8 F/8 M
Actua l age 6.0 ± 0.7 mo 11.3 ± 1.3 mo 17.4 ± 0.5 mo 23.7 ± 0.5
High-risk Number 5 F/6 M 9 F/13 M 8 F/12 M 6 F/8 M mo
Actua l age 6.3 ± 0.8 mo 12.2 ± 1.1 mo 17.6 ± 0.6 mo 23.4 ± 0.5 mo
Table 2. Number of participants and their age distributions at the four nominal testing ages (F: female; M:
male).
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lap. Cartoon videos were shown on a projection screen on the wall ~ 200 cm from the participants to maintain
their attention during the test. e videos that displayed on the projection screen had a size of 81.3 cm × 55.9 cm
(width × height). PLR was elicited by ashing the projection screen using a ceiling-mount green LED (530 nm
wavelength). We focused on studying the transient constriction phase of the PLR induced by a brief 100 ms ash
as in previous studies1012. e transient PLR responses are mainly mediated by the cone and rod photoreceptors
under photopic conditions3537. e stimulation wavelength used in this study is approximately midway between
the wavelengths at the peak sensitivities (V-lambda) of rods and cones. e stimulus light intensity at the position
of the eye was calibrated as 4.1 μW/cm2 (13.0 log photons cm2 s1). e rPLR system used 850-nm near infrared
(NIR) LED array to illuminate the subject’s right pupil for imaging.
Each PLR test lasted less than 10 minutes with about 20–25 PLR trials recorded from the participants as they
watched the cartoons. ere was a minimum of a 20 s interval between two consecutive trials. To begin with, the
participant was given a few minutes to get comfortable and acclimate to the testing room environment. Within
each PLR trial, the pupil images were recorded for 2 s starting 0.25 s prior to the 100 ms optical stimulation.
e PLR data recorded from all trials in each test were processed o-line to create the pupilogram curve to
quantify the change of pupil size with time. Similar to previous studies10,11, the following six PLR parameters were
extracted from the resulting pupilograms to characterize the constriction phase of the pupil responses (Fig.5).
1. e baseline pupil radius Ro was calculated as the average pupil radius prior to the light stimulation onset.
2. e minimal pupil radius Rm was calculated as the smallest pupil radius during constriction.
3. e relative constriction C% of the pupil was calculated as C% = (Ro2 Rm2)/Ro2. is PLR measure nor-
malized changes in pupil area against the baseline pupil area.
4. e PLR latency tL was calculated as the time interval between the beginning of the stimulation and the
onset of the pupillary constriction. e constriction onset was determined as the rst deection data point
when pupil started to constrict consistently.
5. e constriction time tC was calculated as the time interval between the onset of the constriction and the
minimum pupil radius size.
6. e response time tR was calculated as the time interval between the stimulation onset and when pupil
reaches the minimal size, which was equivalent to tL + tC.
Multiple PLR trials were acquired during a single test. All PLR trials that could not be used to construct the
pupilogram were discarded. ese failed trials were generally caused by excessive eye/head movement. e PLR
parameters were calculated from all remaining successful PLR trials. Not all PLR parameters could be obtained
from all trials due to eye closing, movement, or blinking within the 2 s acquisition window. For each PLR param-
eter, trial results were considered as outliers if the values were more than 3 times of the Scaled Median Absolute
Deviation (MAD) away from the median of all successful trial results measured in the same test. Aer removing
all outliers, the mean PLR parameters from all remaining trials were calculated and used in the nal data analysis.
Figure 5. An illustration of the quantitative PLR parameters extracted from a measured pupilogram.
RoRmC%tLtRtC
Outliers mean ± std 0.3 ± 0.7 0.3 ± 0.7 0.4 ± 0.7 0.3 ± 0.6 0.4 ± 0.7 0.3 ± 0.7
% 1 93.7% 92.1% 89.7% 92.1% 88.9% 91.3%
Good trials mean ± std 16.5 ± 5.3 12.8 ± 6.0 12.4 ± 5.9 12.2 ± 5.6 12.1 ± 5.9 11.0 ± 5.5
% 5 99.2% 89.7% 88.1% 89.7% 84.1% 82.5%
Table 3. e distributions of number of outliers removed and the remaining good PLR trials for each PLR
parameter.
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e average and standard variation of numbers of good PLR trails for each parameter are shown below in Table3.
e majority of tests yielded at least ve good PLR trials that were included in the nal data analysis.
Statistical analysis. A linear mixed-eects model (LMM) was applied with maximum likelihood method to
determine the main eects of participant group (HR or LR), age, and sex on PLR parameters. A random intercept
model was applied. e eect of age as represented in four age groups (6-, 12-, 18-, and 24-month) was treated as
repeated measure with “Scaled Identity” as the repeated covariance type. Neither the group × age interaction, nor
the sex × age interaction, nor the group × sex interaction was found signicant during the model selection pro-
cess. erefore, no interaction term was included in the nal LMM analysis. Follow-up pairwise comparison with
Bonferroni condence interval adjustment was used to compare mean PLR parameters between dierent groups.
Alpha was set at 0.05 for all statistical tests. All statistical analysis was conducted in IBM SPSS Statistics V25.
Data availability
e datasets generated and/or analyzed during the current study are available from the corresponding author on
reasonable request.
Received: 7 August 2019; Accepted: 13 January 2020;
Published: xx xx xxxx
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Acknowledgements
is study was supported by a grant from the National Science Foundation (CBET-1507066). We thank all the
participating families for helping this project. We also thank Melissa Mahurin and Becky Gerdes for recruiting
participants.
Author contributions
J.H.M. and G.Y. designed the study. T.N.T. and J.H.M. identified, recruited the participants, collected and
examined medical diagnoses. L.A., D.M.R.D., C.K., and G.Y. conducted the PLR tests. C.K. and G.Y. processed,
analyzed the data, and wrote the main manuscript. All authors reviewed and revised the manuscript.
Competing interests
D.M.R.D., J.H.M. and G.Y. hold two US patents (US9050035B2, US9314157B2) “Device to measure pupillary
light reex in infants and toddlers” related to the remote PLR device used in this study. All other authors declare
no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41598-020-58254-6.
Correspondence and requests for materials should be addressed to G.Y.
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... Indeed, preliminary evidence indicates altered PLR during infancy associates with later ASD, although results are mixed. As a group, infants with ASD family history may show smaller constriction but no differences in latency (Kercher et al., 2020); others have reported faster latency relative to controls at 9 months (Nystr€ om, Gredeb€ ack, B€ olte, & Falck-Ytter, 2015). Prospectively, PLR amplitude at 9 months is larger in infants with versus without later ASD and positively correlates with symptom severity (Nystr€ om et al., 2018). ...
... Elsabbagh et al., 2013;Jones & Klin, 2013). Both PLR latency and amplitude change over early development, with Kercher et al. (2020) reporting latency decreased (becomes faster) from 6 to 24 months while amplitude increased (becomes stronger); ASD family history did not alter these developmental trajectories. However, Nystr€ om et al. (2018) demonstrated amplitude increased from 9 to 14 months in infants with typical development but decreased in those with later ASDno analysis was reported on latency development. ...
... Within this time window, relatively smaller changes in amplitude associated with higher SA and RRBs. These development patterns corroborate previously reported increasing amplitude in the first year in typically developing infants (Kercher et al., 2020;Nystr€ om et al., 2018). ...
Article
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Background: Although autism spectrum disorder (ASD) is heritable, the mechanisms through which genes contribute to symptom emergence remain unclear. Investigating candidate intermediate phenotypes such as the pupillary light reflex (PLR) prospectively from early in development could bridge genotype and behavioural phenotype. Methods: Using eye tracking, we longitudinally measured the PLR at 9, 14 and 24 months in a sample of infants (N = 264) enriched for a family history of ASD; 27 infants received an ASD diagnosis at 3 years. We examined the 9- to 24-month developmental trajectories of PLR constriction latency (onset; ms) and amplitude (%) and explored their relation to categorical 3-year ASD outcome, polygenic liability for ASD and dimensional 3-year social affect (SA) and repetitive/restrictive behaviour (RRB) traits. Polygenic scores for ASD (PGSASD ) were calculated for 190 infants. Results: While infants showed a decrease in latency between 9 and 14 months, higher PGSASD was associated with a smaller decrease in latency in the first year (β = -.16, 95% CI = -0.31, -0.002); infants with later ASD showed a significantly steeper decrease in latency (a putative 'catch-up') between 14 and 24 months relative to those with other outcomes (typical: β = .54, 95% CI = 0.08, 0.99; other: β = .53, 95% CI = 0.02, 1.04). Latency development did not associate with later dimensional variation in ASD-related traits. In contrast, change in amplitude was not related to categorical ASD or genetics, but decreasing 9- to 14-month amplitude was associated with higher SA (β = .08, 95% CI = 0.01, 0.14) and RRB (β = .05, 95% CI = 0.004, 0.11) traits. Conclusions: These findings corroborate PLR development as possible intermediate phenotypes being linked to both genetic liability and phenotypic outcomes. Future work should incorporate alternative measures (e.g. functionally informed structural and genetic measures) to test whether distinct neural mechanisms underpin PLR alterations.
... On the other hand, the literature reports multiple instances of differences in basic pupillary responses to light or dark in autism, including reports of enhanced pupillary responses to light in autistic individuals compared with controls (Nystrom et al., 2015(Nystrom et al., , 2018; our findings are in line with this pattern (Figure 3A) and might be linked to hypersensitivity phenomena that are often associated with autism (Williams, 1994;Robertson and Baron-Cohen, 2017). However, the literature presents discordant findings, with some studies reporting no differences with autism, or reporting differences in latency but not in amplitude (Dinalankara et al., 2017;Lynch et al., 2018); some studies even show the opposite pattern (Fan et al., 2009;Kercher et al., 2020). For example, Fan et al. (2009) reported that pupils of autistic children took longer to respond to short (0.1 s) light stimuli, and constricted less and more slowly than those with typical development. ...
... For example, Fan et al. (2009) reported that pupils of autistic children took longer to respond to short (0.1 s) light stimuli, and constricted less and more slowly than those with typical development. We also found a marginally larger prestimulus pupil diameter in the autistic group, in line with other studies (Anderson et al., 2006;Anderson and Colombo, 2009;Kercher et al., 2020). Also in this case, however, the literature includes conflicting reports, with some finding a weak (Martineau et al., 2011) or non-existent (Fan et al., 2009;Nystrom et al., 2015Nystrom et al., , 2018 steady-state pupil size difference between autistic individuals and controls. ...
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Recent Bayesian models suggest that perception is more “data-driven” and less dependent on contextual information in autistic individuals than others. However, experimental tests of this hypothesis have given mixed results, possibly due to the lack of objectivity of the self-report methods typically employed. Here we introduce an objective no-report paradigm based on pupillometry to assess the processing of contextual information in autistic children, together with a comparison clinical group. After validating in neurotypical adults a child-friendly pupillometric paradigm, in which we embedded test images within an animation movie that participants watched passively, we compared pupillary response to images of the sun and meaningless control images in children with autism vs. age- and IQ-matched children presenting developmental disorders unrelated to the autistic spectrum. Both clinical groups showed stronger pupillary constriction for the sun images compared with control images, like the neurotypical adults. However, there was no detectable difference between autistic children and the comparison group, despite a significant difference in pupillary light responses, which were enhanced in the autistic group. Our report introduces an objective technique for studying perception in clinical samples and children. The lack of statistically significant group differences in our tests suggests that autistic children and the comparison group do not show large differences in perception of these stimuli. This opens the way to further studies testing contextual processing at other levels of perception.
... mm larger pupils, see Table S5). These patterns of age and sex effects are in line with previous literature with infants and toddlers (Kercher et al., 2020). Number of valid trials and year of testing were statistically associated with baseline diameter (more valid trials yielded a smaller pupil size, b = À.04, and pupil size was smaller in the last year of data collection, b = À.14; both p < .01, ...
... This study is the first, to our knowledge, to quantify the influence of genetic and environmental factors on pupil size and the PLR using a classic twin design, and to report on the associations of these measures with common genetic variants for neurodevelopmental and psychiatric conditions. Due to the recent interest in these measures as potential antecedent markers of neurodevelopmental conditions (Hall & Chilcott, 2018;Kercher et al., 2020), we focused on the early infancy period. As hypothesized, for relative constriction amplitude and pupil size at baseline, our results suggested strong genetic effects. ...
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Background: Measures based on pupillometry, such as the pupillary light reflex (PLR) and baseline pupil size, reflect physiological responses linked to specific neural circuits that have been implicated as atypical in some psychiatric and neurodevelopmental conditions. Methods: We investigated the contribution of genetic and environmental factors to the baseline pupil size and the PLR in 510 infant twins assessed at 5 months of age (281 monozygotic and 229 dizygotic pairs), and its associations with common genetic variants associated with neurodevelopmental (autism spectrum disorder and attention deficit hyperactivity disorder) and mental health (bipolar disorder, major depressive disorder and schizophrenia) conditions using genome-wide polygenic scores (GPSs). Results: Univariate twin modelling showed high heritability at 5 months for both pupil size (h2 = .64) and constriction in response to light (h2 = .62), and bivariate twin modeling indicated substantial independence between the genetic factors influencing each (rG = .38). A statistically significant positive association between infant tonic pupil size and the GPS for schizophrenia was found (β = .15, p = .024), while there was no significant association with the GPS for autism or any other GPSs. Conclusions: This study shows that some pupil measures are highly heritable in early infancy, although substantially independent in their genetic etiologies, and associated with common genetic variants linked to schizophrenia. It illustrates how genetically informed studies of infants may help us understand early physiological responses associated with psychiatric disorders which emerge much later in life.
... On the other hand, the literature reports multiple instances of differences in basic pupillary responses to light or dark in autism, including reports of enhanced pupillary responses to light in autistic individuals compared to controls [68, 69]; our ndings are in line with this pattern (Fig. 3A) and they might be linked to hypersensitivity phenomena that are often associated with autism [70,71]. However, the literature presents discordant ndings, with some studies reporting no differences with autism, or reporting differences in latency but not in amplitude [72,73]; some studies even show the opposite pattern [74,75]. For example, Fan et al. reported that pupils of autistic children took longer to respond to short (0.1 s) light stimuli, and constricted less and more slowly than those with typical development [75]. ...
... For example, Fan et al. reported that pupils of autistic children took longer to respond to short (0.1 s) light stimuli, and constricted less and more slowly than those with typical development [75]. We also found a marginally larger pre-stimulus pupil diameter in the autistic group, in line with studies by Anderson et al. [74,76,77]. Also in this case, however, the literature includes con icting reports, some of which found a weak [78] or null [68, 69, 75] steady-state pupil size difference between autistic individuals and controls. ...
Preprint
Full-text available
Background. Recent Bayesian models suggest that perception is more “data-driven” and less dependent on contextual information in autistic individuals than others. However, experimental tests of this hypothesis have given mixed results, possibly due to the lack of objectivity of the self-report methods typically employed. Here we introduce an objective no-report paradigm based on pupillometry to assess the processing of contextual information in autistic children and a comparison clinical group. Methods. After validating (in a group of neurotypical adults) a child-friendly pupillometric paradigm, in which we embedded test images within an animation movie that participants watched passively, we compared pupillary response to images of the sun and meaningless control images in children with autism versus age- and IQ-matched children presenting developmental disorders unrelated to the autistic spectrum. Results. Both clinical groups showed stronger pupillary constriction for the sun images compared with control images, like the neurotypical adults. There was no detectable difference between autistic children and the comparison group (in spite of a significant difference in pupillary light responses, enhanced in the autistic group). Limitations: Having found no statistically significant differences between groups, we cannot exclude that group differences existed but were too small to be detected – a critique that applies to most negative findings. Additional limitations concern the heterogeneous composition of the comparison group and the types of stimuli tested, which only allowed for studying the effect of context on relatively complex perceptual processes. Conclusions: Our report introduces an objective technique for studying perception in clinical samples and children. The lack of statistically significant group differences in our tests suggests that autistic children and the comparison group do not show large differences in perception of these stimuli. This opens the way to further studies testing contextual processing at other levels of perception.
... Other researchers also found no differences in arousal between children with ASD and NT children [44]. This could be attributed to the early age of the participants [27,[44][45][46]. Dinalankara et al. [45] observed that the baseline pupil size increased with age, up to four years in NT children, but this pattern was not observed in children with ASD. ...
... These changes with age appear to be due to the increased acceleration of white matter maturation in ASD [45]. Another possible explanation for our results could be the level of possible autism in our participants because it was observed that toddlers with a high risk of ASD presented larger base pupil size in resting than toddlers with a low possibility of ASD [46]. ...
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Background: Children with autism spectrum disorder (ASD) show certain characteristics in visual attention. These may generate differences with non-autistic children in the integration of relevant social information to set the basis of communication. Reliable and objective measurement of these characteristics in a language learning context could contribute to a more accurate early diagnosis of ASD. Gaze following and pupil dilation are being studied as possible reliable measures of visual attention for the early detection of ASD. The eye-tracking methodology allows objective measurement of these biomarkers. The aim of this study is to determine whether measurements of gaze following and pupillary dilation in a linguistic interaction task are potential objective biomarkers for the early diagnosis of ASD. Method: A group of 20 children between 17 and 24 months of age, made up of 10 neurotypical children (NT) and 10 children with an increased likelihood of developing ASD were paired together according to chronological age. A human face on a monitor pronounced pseudowords associated with pseudo-objects. Gaze following and pupil dilation were registered during the task These measurements were captured using eye-tracking methodology. Results: Significant statistical differences were found in the time of gaze fixation on the human face and on the object, as well as in the number of gazes. Children with an increased possibility of developing ASD showed a slightly higher pupil dilation than NT children. However, this difference was not statistically significant. Nevertheless, their pupil dilation was uniform throughout the different periods of the task while NT participants showed greater dilation on hearing the pseudoword. Conclusions: The fixing and the duration of gaze, objectively measured by a Tobii eye-tracking system, could be considered as potential biomarkers for early detection of ASD. Additionally, pupil dilation measurement could reflect differential activation patterns during word processing in possible ASD toddlers and NT toddlers.
... These findings were attributed to possible upregulation of parasympathetic activity earlier in development. Significant age trends were observed in infants between 6 and 24 months, in which constriction time increased between 12 and 18 months, and smaller resting pupil diameter and longer constriction latency was observed by 24 months [37]. Given that the parasympathetic component of the PLR is modulated by acetylcholine, Nyström et al. [11] suggested that elevated constriction time in infancy within ASD may reflect disruption in the cholinergic system and represents a change in developmental trajectory beyond infancy, such that constriction time decreases over time as a function of age. ...
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Introduction: Automated hand-held pupillometry demonstrates precision accuracy, offering promise for augmenting ASD screening. Methods: Monocular pupillometry was examined in children and adolescents (36 ASD; 24 TD). Multiple logistic regression and receiver operating characteristic analysis assessed PLR metrics and diagnostic status. Results: Constriction time (Ct1) (ASD: M = 0.69, SD = 0.21; TD: M = 0.82, SD = 0.18; t(58 = 2.37; p = 0.02) and return to baseline (RTB T75) (ASD: M = 2.93, SD = 1.21; TD: M = 2.32, SD = 1.08; t(58) = - 2.03; p = 0.04) predicted ASD (β = - 1.31, OR = 0.27; RTB T75, β = 0.156, OR = 1.162). Sensitivity = 74.8%, when RTB ≥ 1.83 s and 69.4% when Ct1 = 0.785 s. Conclusion: Findings suggest monocular pupillometry captures differences detecting ASD.
... Interestingly, a delayed pupillary light reflex was observed. Abnormal pupillary light reflexes have also been reported in ASD-patients [226]. ...
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Background Autism spectrum disorder (ASD) is a neurodevelopmental condition, which is characterized by clinical heterogeneity and high heritability. Core symptoms of ASD include deficits in social communication and interaction, as well as restricted, repetitive patterns of behavior, interests, or activities. Many genes have been identified that are associated with an increased risk for ASD. Proteins encoded by these ASD risk genes are often involved in processes related to fetal brain development, chromatin modification and regulation of gene expression in general, as well as the structural and functional integrity of synapses. Genes of the SH3 and multiple ankyrin repeat domains ( SHANK ) family encode crucial scaffolding proteins (SHANK1-3) of excitatory synapses and other macromolecular complexes. SHANK gene mutations are highly associated with ASD and more specifically the Phelan-McDermid syndrome (PMDS), which is caused by heterozygous 22q13.3-deletion resulting in SHANK3 -haploinsufficiency, or by SHANK3 missense variants. SHANK3 deficiency and potential treatment options have been extensively studied in animal models, especially in mice, but also in rats and non-human primates. However, few of the proposed therapeutic strategies have translated into clinical practice yet. Main text This review summarizes the literature concerning SHANK3-deficient animal models. In particular, the structural, behavioral, and neurological abnormalities are described and compared, providing a broad and comprehensive overview. Additionally, the underlying pathophysiologies and possible treatments that have been investigated in these models are discussed and evaluated with respect to their effect on ASD- or PMDS-associated phenotypes. Conclusions Animal models of SHANK3 deficiency generated by various genetic strategies, which determine the composition of the residual SHANK3-isoforms and affected cell types, show phenotypes resembling ASD and PMDS. The phenotypic heterogeneity across multiple models and studies resembles the variation of clinical severity in human ASD and PMDS patients. Multiple therapeutic strategies have been proposed and tested in animal models, which might lead to translational implications for human patients with ASD and/or PMDS. Future studies should explore the effects of new therapeutic approaches that target genetic haploinsufficiency, like CRISPR-mediated activation of promotors.
... Today, the pupil behavior has become an interdisciplinary field of research (La Morgia et al., 2018;Schneider et al., 2020;Joshi, 2021;Pinheiro and da Costa, 2021) in which the number of involved scientists rises, as the trend of the number of publications with the keywords "pupil diameter" or "pupillometry" reveals ( Figure 2). The renewed attention to the temporal pupil aperture (Binda and Gamlin, 2017), its application in clinical diagnostics (Granholm et al., 2017;Joyce et al., 2018;Chougule et al., 2019;Kercher et al., 2020;Tabashum et al., 2021) and increasing popularity of chromatic pupillometry (Rukmini et al., 2017;Crippa et al., 2018) topics requires additional efforts in terms of standardization and provision of consistent tools, contributing to comparability in measurement and pre-processing methodologies. For instance, one key point of standardization is the prevention of artificially induced changes to raw data by the used tools, as in cognitive or vision-related pupillary research small diameter margins are of interest. ...
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The human pupil behavior has gained increased attention due to the discovery of the intrinsically photosensitive retinal ganglion cells and the afferent pupil control path’s role as a biomarker for cognitive processes. Diameter changes in the range of 10 –2 mm are of interest, requiring reliable and characterized measurement equipment to accurately detect neurocognitive effects on the pupil. Mostly commercial solutions are used as measurement devices in pupillometry which is associated with high investments. Moreover, commercial systems rely on closed software, restricting conclusions about the used pupil-tracking algorithms. Here, we developed an open-source pupillometry platform consisting of hardware and software competitive with high-end commercial stereo eye-tracking systems. Our goal was to make a professional remote pupil measurement pipeline for laboratory conditions accessible for everyone. This work’s core outcome is an integrated cross-platform (macOS, Windows and Linux) pupillometry software called PupilEXT, featuring a user-friendly graphical interface covering the relevant requirements of professional pupil response research. We offer a selection of six state-of-the-art open-source pupil detection algorithms (Starburst, Swirski, ExCuSe, ElSe, PuRe and PuReST) to perform the pupil measurement. A developed 120-fps pupillometry demo system was able to achieve a calibration accuracy of 0.003 mm and an averaged temporal pupil measurement detection accuracy of 0.0059 mm in stereo mode. The PupilEXT software has extended features in pupil detection, measurement validation, image acquisition, data acquisition, offline pupil measurement, camera calibration, stereo vision, data visualization and system independence, all combined in a single open-source interface, available at https://github.com/openPupil/Open-PupilEXT .
... Fourthly, the study considered a single pupillometry measure. However, the pupillary responses are involuntary [41], and previous studies found that pupil responses were stable after repeated measurements during a single test session [42]. In addition, despite the fact that previous research showed that this test could be performed to identify an early autonomic dysfunction; there is no standardization and consensus of testing protocols [42]. ...
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Evidence about the impact of vegetable and fruit diversity consumption on the autonomic nervous system (ANS) functioning is scarce. In this cross-sectional study (513 participants, 49.9% girls aged 7 to 12 years), we evaluated the association between vegetable and fruit diversity consumption and the ANS in school-aged children. Dietary intake was collected using a single 24-h recall questionnaire. Fruit and vegetable diversity consumption was estimated by summing up all the different individual vegetables and fruits consumed in one day. Pupillometry was used to assess pupillary light response, which evaluated the ANS activity. Adjusted linear regressions estimated the association between vegetable and fruit diversity consumption with pupillary light response measures. There was a positive and significant association between vegetable diversity consumption and the average dilation velocity, a measure related to the sympathetic nervous system activity (β-coefficient = 0.03, 95%CI: 0.002; 0.07). Our findings show that vegetable diversity consumption is associated with the ANS response, a possible early link between diet and health in school-aged children. Open access: https://www.mdpi.com/2072-6643/13/5/1456
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Pupillometry, measuring pupil size and reactivity, has been proposed as a measure of autonomic nervous system functioning, the latter which might be altered in individuals with autism spectrum disorder (ASD). This study aims to evaluate if pupillary responses differ in individuals with and without ASD. After performing a systematic literature search, we conducted a meta-analysis and constructed a qualitative synthesis. The meta-analysis shows a longer latency of the pupil response in the ASD-group as a substantial group difference, with a Hedges’ g of 1.03 (95% CI 0.49–1.56, p = 0.008). Evidence on baseline pupil size and amplitude change is conflicting. We used the framework method to perform a qualitative evaluation of these differences. Explanations for the group differences vary between studies and are inconclusive, but many authors point to involvement of the autonomous nervous system and more specifically the locus coeruleus-norepinephrine system. Pupillometry reveals differences between people with and without ASD, but the exact meaning of these differences remains unknown. Future studies should align research designs and investigate a possible effect of maturation.
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Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting around 1% of the population. We previously discovered that infant siblings of children with ASD had stronger pupillary light reflexes compared to low-risk infants, a result which contrasts sharply with the weak pupillary light reflex typically seen in both children and adults with ASD. Here, we show that on average the relative constriction of the pupillary light reflex is larger in 9-10-month-old high risk infant siblings who receive an ASD diagnosis at 36 months, compared both to those who do not and to low-risk controls. We also found that the magnitude of the pupillary light reflex in infancy is associated with symptom severity at follow-up. This study indicates an important role of sensory atypicalities in the etiology of ASD, and suggests that pupillometry, if further developed and refined, could facilitate risk assessment in infants.
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Record review and abstraction occurs in a variety of data sources ranging from general pediatric health clinics to specialized programs serving children with developmental disabilities. In addition, most of the ADDM sites also review records for children who have received special education services in public schools. In the second phase of the study, all abstracted information is reviewed systematically by experienced clinicians to determine ASD case status. A child is considered to meet the surveillance case definition for ASD if he or she displays behaviors, as described on one or more comprehensive evaluations completed by community-based professional providers, consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnostic criteria for autistic disorder; pervasive developmental disorder-not otherwise specified (PDD-NOS, including atypical autism); or Asperger disorder. This report provides updated ASD prevalence estimates for children aged 8 years during the 2014 surveillance year, on the basis of DSM-IV-TR criteria, and describes characteristics of the population of children with ASD. In 2013, the American Psychiatric Association published the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which made considerable changes to ASD diagnostic criteria. The change in ASD diagnostic criteria might influence ADDM ASD prevalence estimates; therefore, most (85%) of the records used to determine prevalence estimates based on DSM-IV-TR criteria underwent additional review under a newly operationalized surveillance case definition for ASD consistent with the DSM-5 diagnostic criteria. Children meeting this new surveillance case definition could qualify on the basis of one or both of the following criteria, as documented in abstracted comprehensive evaluations: 1) behaviors consistent with the DSM-5 diagnostic features; and/or 2) an ASD diagnosis, whether based on DSM-IV-TR or DSM-5 diagnostic criteria. Stratified comparisons of the number of children meeting either of these two case definitions also are reported. Results: For 2014, the overall prevalence of ASD among the 11 ADDM sites was 16.8 per 1,000 (one in 59) children aged 8 years. Overall ASD prevalence estimates varied among sites, from 13.1-29.3 per 1,000 children aged 8 years. ASD prevalence estimates also varied by sex and race/ethnicity. Males were four times more likely than females to be identified with ASD. Prevalence estimates were higher for non-Hispanic white (henceforth, white) children compared with non-Hispanic black (henceforth, black) children, and both groups were more likely to be identified with ASD compared with Hispanic children. Among the nine sites with sufficient data on intellectual ability, 31% of children with ASD were classified in the range of intellectual disability (intelligence quotient [IQ] <70), 25% were in the borderline range (IQ 71-85), and 44% had IQ scores in the average to above average range (i.e., IQ >85). The distribution of intellectual ability varied by sex and race/ethnicity. Although mention of developmental concerns by age 36 months was documented for 85% of children with ASD, only 42% had a comprehensive evaluation on record by age 36 months. The median age of earliest known ASD diagnosis was 52 months and did not differ significantly by sex or race/ethnicity. For the targeted comparison of DSM-IV-TR and DSM-5 results, the number and characteristics of children meeting the newly operationalized DSM-5 case definition for ASD were similar to those meeting the DSM-IV-TR case definition, with DSM-IV-TR case counts exceeding DSM-5 counts by less than 5% and approximately 86% overlap between the two case definitions (kappa = 0.85). Interpretation: Findings from the ADDM Network, on the basis of 2014 data reported from 11 sites, provide updated population-based estimates of the prevalence of ASD among children aged 8 years in multiple communities in the United States. The overall ASD prevalence estimate of 16.8 per 1,000 children aged 8 years in 2014 is higher than previously reported estimates from the ADDM Network. Because the ADDM sites do not provide a representative sample of the entire United States, the combined prevalence estimates presented in this report cannot be generalized to all children aged 8 years in the United States. Consistent with reports from previous ADDM surveillance years, findings from 2014 were marked by variation in ASD prevalence when stratified by geographic area, sex, and level of intellectual ability. Differences in prevalence estimates between black and white children have diminished in most sites, but remained notable for Hispanic children. For 2014, results from application of the DSM-IV-TR and DSM-5 case definitions were similar, overall and when stratified by sex, race/ethnicity, DSM-IV-TR diagnostic subtype, or level of intellectual ability. Public health action: Beginning with surveillance year 2016, the DSM-5 case definition will serve as the basis for ADDM estimates of ASD prevalence in future surveillance reports. Although the DSM-IV-TR case definition will eventually be phased out, it will be applied in a limited geographic area to offer additional data for comparison. Future analyses will examine trends in the continued use of DSM-IV-TR diagnoses, such as autistic disorder, PDD-NOS, and Asperger disorder in health and education records, documentation of symptoms consistent with DSM-5 terminology, and how these trends might influence estimates of ASD prevalence over time. The latest findings from the ADDM Network provide evidence that the prevalence of ASD is higher than previously reported estimates and continues to vary among certain racial/ethnic groups and communities. With prevalence of ASD ranging from 13.1 to 29.3 per 1,000 children aged 8 years in different communities throughout the United States, the need for behavioral, educational, residential, and occupational services remains high, as does the need for increased research on both genetic and nongenetic risk factors for ASD.
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Autism spectrum disorder (ASD) is a serious neurodevelopmental disorder resulting in a substantial burden for individuals, families, and society.¹ Previous surveys have reported a steady increase in ASD prevalence in US children over the past 2 decades.²- 4 However, the most recent estimate from the Autism and Developmental Disabilities Monitoring (ADDM) Network for the first time reported a plateau in ASD prevalence (1.46%) in 2012, after documenting a continuous increase from 0.67% in 2000 to 1.47% in 2010.² In this study, we analyzed nationally representative data to estimate current prevalence of ASD among US children and adolescents in 2014-2016.
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Lay summary: Milder forms of autism spectrum disorder (ASD) can be difficult to diagnose based on behavioral testing alone. This study used eye-tracking equipment and a hand-held penlight to measure the pupil reflex in adolescents with "high functioning" ASD and in adolescents without ASD. The ASD group showed a delay in pupil response. This is the first eye-tracking study to conduct this test as typically performed by a clinical provider, demonstrating differences in older individuals with a subtype of ASD.
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The pupil is known to reflect a range of psychological and physiological variables, including cognitive effort, arousal, attention, and even learning. Within autism spectrum disorder (ASD), some work has used pupil physiology to successfully classify patients with or without autism. As we have come to understand the heterogeneity of ASD and other neurodevelopmental disorders, the relationship between quantitative traits and physiological markers has become increasingly more important, as this may lead us closer to the underlying biological basis for atypical responses and behaviors. We implemented a novel paradigm designed to capture patterns of pupil adaptation during sustained periods of dark and light conditions in a pediatric sample that varied in intellectual ability and clinical features. We also investigate the relationship between pupil metrics derived from this novel task and quantitative behavioral traits associated with the autism phenotype. We show that pupil metrics of constriction and dilation are distinct from baseline metrics. Pupil dilation metrics correlate with individual differences measured by the Social Responsiveness Scale (SRS), a quantitative measure of autism traits. These results suggest that using a novel, yet simple, paradigm can result in meaningful pupil metrics that correlate with individual differences in autism traits, as measured by the SRS.
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To the Editor In the January 2, 2018, issue of JAMA, we published a Research Letter: “Prevalence of Autism Spectrum Disorder Among US Children and Adolescents, 2014-2016.”¹ Using data from the National Health Interview Survey (NHIS), we found that the weighted prevalence of autism spectrum disorder (ASD) was 2.41% (95% CI, 2.17%-2.65%) in 2014-2016. Sample weights are provided with the NHIS data for researchers to use during analysis to take into account unequal probabilities of selection and nonresponse. For the analysis of the 2016 data, we used the weights originally released by the Centers for Disease Control and Prevention (CDC) in June 2017. The CDC identified inaccuracies in the original sampling weights and updated them in November 2017, after acceptance of the Research Letter, but we did not become aware of this change until December 27, 2017. We reran all the analyses for our estimates using the updated weights. The conclusions were not changed, but there were some small changes in the results for the 2016 data and the pooled data from 2014-2016. For example, the overall prevalence of ASD changed from 2.41% to 2.47% (95% CI, 2.20%-2.73%), with the estimate being within the original 95% CI. The text and table of the Research Letter have been corrected online and a correction notice accompanies this letter.
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Rapid and stable control of pupil size in response to light is critical for vision, but the neural coding mechanisms remain unclear. Here, we investigated the neural basis of pupil control by monitoring pupil size across time while manipulating each photoreceptor input or neurotransmitter output of intrinsically photosensitive retinal ganglion cells (ipRGCs), a critical relay in the control of pupil size. We show that transient and sustained pupil responses are mediated by distinct photoreceptors and neurotransmitters. Transient responses utilize input from rod photoreceptors and output by the classical neurotransmitter glutamate, but adapt within minutes. In contrast, sustained responses are dominated by non-conventional signaling mechanisms: melanopsin phototransduction in ipRGCs and output by the neuropeptide PACAP, which provide stable pupil maintenance across the day. These results highlight a temporal switch in the coding mechanisms of a neural circuit to support proper behavioral dynamics.
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The purpose of this study was to investigate pupillary light reflex (PLR) in 2-6-years-old children with autism spectrum disorders (ASD). A total of 117 medication-free 2-6-year-old boys participated in this study. Sixty participants were diagnosed with ASD (the "ASD group") and the other 57 were in the control group of typical development (the "TD group"). A questionnaire was completed by the parent/guardian for assessing potential dysfunctions in the autonomic nervous system (ANS). The base pupil radius, PLR latency, and constriction time showed a significant age-related trend in both the ASD and TD groups. The base pupil size increased with age in the typically developing children, but not in the ASD group. The ASD group showed more symptoms related to ANS dysfunctions. An association between abnormal sweating with base pupil radius and PLR constriction was observed in the TD group but not the ASD group. The different association of PLR parameters with ANS dysfunction may suggest disrupted autonomic controls in children with ASD. Autism Res 2016. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.