SPECIAL ISSUE ORIGINAL ARTICLE
Development of the pupillary light reflex from 9 to 24
months: association with common autism spectrum
disorder (ASD) genetic liability and 3-year ASD
diagnosis
Laurel A. Fish,1 P€ar Nystr€om,2 Teodora Gliga,3 Anna Gui,4
Jannath Begum Ali,4 Luke Mason,4 Shruti Garg,5 Jonathan Green,5
Mark H. Johnson,4,6 Tony Charman,7 Rebecca Harrison,4 Emma Meaburn,4
Terje Falck-Ytter,8,9,10 Emily J. H. Jones,4 and the BASIS/STAARS* team
1Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK; 2Uppsala Child &
Babylab, Department of Psychology, Uppsala University, Uppsala, Sweden; 3School of Psychology, University of East
Anglia, Norwich, UK; 4Department of Psychological Sciences, Birkbeck College, University of London, London, UK;
5Neuroscience & Experimental Psychology, Manchester Academic Health Science Centre, University of Manchester,
Manchester, UK; 6Department of Psychology, Cambridge University, Cambridge, UK; 7Department of Psychology,
Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK; 8Development and
Neurodiversity Lab (DIVE), Department of Psychology, Uppsala University, Uppsala, Sweden; 9Center of
Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women’s and Children’s
Health, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden; 10Swedish
Collegium for Advanced Study (SCAS), Uppsala, Sweden
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 (b = 
.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: b = .54, 95% CI = 0.08, 0.99; other: b = .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 (b = .08, 95%
CI = 0.01, 0.14) and RRB (b = .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. Keywords: Autism spectrum disorder;
neurodevelopment; infancy; pupillary light reflex.
Introduction
Autism spectrum disorder (ASD) is a neurodevelop-
mental condition characterised by social communi-
cation impairments, repetitive/restrictedbehaviours,
and sensory anomalies (American Psychiatric Asso-
ciation, 2013). ASD is not typically diagnosed until
behavioural symptoms become clear at 2–3 years
(Ozonoff et al., 2015). Underpinning the gradual
emergence of recognisable ASD symptoms are envi-
ronmental and genetic factors (Bai et al., 2019),
hypothesised to funnel onto a set of common mech-
anisms critical in early development (Jones, Gliga,
Bedford, Charman, & Johnson, 2014). Measuring
early intermediate phenotypes using longitudinal
prospective studies with infants with a family history
of ASD (who have a 10%–20% likelihood of developing
ASD; Ozonoff et al., 2011) may reveal these early
mechanisms.
One candidate intermediate phenotype is the
pupillary light reflex (PLR), the reflexual pupil con-
striction in response to increased optical luminance
(Daluwatte, Miles, Sun, & Yao, 2015). A simple four-
neuron ocular-motor circuit mediates this reflex
(McDougal & Gamlin, 2015) and is innervated by a
range of brain regions [e.g. prefrontal cortex (Becket
Ebitz & Moore, 2017), cerebellum (Ijichi, Kiyohara,
Hosoba, & Tsukahara, 1977), and locus coeruleusConflict of interest statement: No conflicts declared.
© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent
Mental Health
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
Journal of Child Psychology and Psychiatry **:* (2021), pp **–** doi:10.1111/jcpp.13518
PFI_12mmX178mm.pdf + eps format
(Bast, Poustka, & Freitag, 2018; Lynch, 2018)]. Two
measurable components of the PLR can be linked to
distinct mechanisms. PLR latency (constriction
onset time) is likely regulated by neural signal
transduction efficiency (Dinalankara, Miles, Taka-
hashi, & Yao, 2017), whereas PLR amplitude is
highly, but not exclusively, dependent on cholinergic
and norepinephrine neurotransmission efficiency
(Heller, Perry, Jewett, & Levine, 1990; Loewenfeld,
1999; Lynch, 2018; de Vries, Fouquaet, Boets,
Naulaers, & Steyaert, 2021) – neural processes
implicated in early ASD (Abreu-Villac
a, Filgueiras,
& Manh~aes, 2011; Ben-Bashat et al., 2007). 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 differ-
ences 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). One step forward is to
study developmental trajectories rather than static
cross-sectional points, as has proved informative for
other putative ASD markers (e.g. 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 ampli-
tude increased (becomes stronger); ASD family his-
tory did not alter these developmental trajectories.
However, Nystr€om et al. (2018) demonstrated ampli-
tude increased from 9 to 14 months in infants with
typical development but decreased in those with
later ASD – no analysis was reported on latency
development. These findings highlight PLR develop-
ment as potentially fruitful in understanding early
ASD development.
High heritability estimates indicate strong genetic
influences on ASD (Tick, Bolton, Happe, Rutter, &
Rijsdijk, 2016), which includes the additive loading
of many common small effect variants (Bai et al.,
2019). Investigating associations between develop-
ing candidate intermediate phenotypes like infant
PLR and individual polygenic scores for ASD
(PGSASD) – weighted sums of ASD-associated com-
mon genetic variants (Euesden, Lewis, & O’Reilly,
2015) – could highlight developmental pathways
through which common genetic liability manifests
into later symptomology. To date, the relation
between infant PLR and individual PGSASD has not
been investigated (de Vries et al., 2021).
In the present study, we measured PLR longitudi-
nally at 9, 14, and 24 months from 264 individuals,
139 of which overlapped with Nystr€om and col-
leagues’ (2018) sample. We aimed to expand on
previous work by exploring the early developmental
trajectory of the PLR amplitude and latency across
the first and second postnatal years in infants with
and without a family history of ASD. Specifically, we
tested to what degree the change in mean (a) latency
and (b) amplitude, from 9 to 14 months and 14 to
24 months, was associated with: (1) 3-year ASD
outcome, (2) PGSASD, and (3) 3-year dimensional (a)
social affect and (b) repetitive behaviours. We pre-
dicted amplitude development would significantly
associate with categorical ASD outcome, polygenic
liability, and dimensional ASD traits; we could not
make specific predictions regarding latency develop-
mental changes pertaining to 3-year ASD outcome,
PGSASD, and 3-year dimensional ASD traits (social
affect and repetitive behaviours) due to limited
previous work.
Methodology
Sample
Participants (N = 264) were recruited into the British
Autism Study of Infant Siblings (BASIS, N = 143) or
Studying Autism and Attention Deficit Hyperactivity
Disorder Risks programme (STAARS, N = 121).
Infants had either an older full sibling with a
community ASD diagnosis (N = 195) or no ASD
family history (N = 69); 139 of these infants
(54.72%) were included in Nystr€om et al. (2018).
Infants were born full-term (36+ weeks) and had no
ASD-associated genetic syndrome nor visual, audi-
tory or other disability. NHS Research Ethics Com-
mittees granted ethical approval (06/MRE02/73,
08/H0718/76 [BASIS] and 13/LO/0751 [STAARS],
15/LO/0468 [DNA collection, extraction and analy-
sis]). Informed written consent was provided by the
parent(s).
Measures and procedure
Pupillary light reflex (PLR). The PLR was induced
by one of two stimuli that transitioned from a black
to white slide (see Figure 1A–C). For the BASIS
cohort, stimulus one (Gliga et al., 2015) was pre-
sented 32 times at 9/14 months and 16 times at
24 months; a Tobii T120 eye tracker (sampling rate =
60 Hz; Tobii Technology AB, Danderyd, Sweden)
measured pupil diameter (millimetres to 2 decimal
places). Following previous work (Nystr€om et al.,
2018), to control for differing PLR inducing light
levels across the white slide in stimulus one, only
trials where the first look was within the centre 5% of
the white slide (see Figure 1B) were included. Stim-
ulus two was presented to the STAARS cohort 12
times at all timepoints; a Tobii TX300 eye tracker
(sampling rate = 120 Hz; Tobii Technology AB,
Danderyd, Sweden) measured pupil size (millimetres
to 14 decimal places). Stimuli were interspersed
throughout a longer eye-tracking battery. Ambient
room lighting and testing setup were held constant
within each protocol. Infants passively watched the
© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health
2 Laurel A. Fish et al.
stimuli monitor while sat on their caregiver’s lap at
an average distance of 60.61 cm (59.67 cm–
61.81 cm). Differences across protocols were
accounted for in the statistical models via random
effects (see below).
We preprocessed raw pupil data using a process-
ing pipeline similar to Nystr€om and colleagues (2018;
see Appendix S1 for further details). This removed
artefacts and extract PLR latency (constriction onset
time; ms) and amplitude (maximum constriction
amplitude relative to average pupil size before
latency; %). Latency was defined as the minimum
acceleration from 110–570ms after white slide onset.
Amplitude was calculated using the Fan, Miles,
Takahashi, and Yao (2009) formula (Figure 1C) such
that higher numbers represent greater constriction.
All trials were visually inspected for validity. Infants
required three or more valid trials per timepoint for
inclusion; percentage of missing PLR trials (Missing-
ness; see Appendix S1: Table S1 and Figure S1) was
included as a covariate in all analyses. The median
latency and amplitude per individual per timepoint
were calculated and included in models as depen-
dent variables.
Clinical diagnosis at 3 years. Experienced clinical
researchers ascertained consensus 3-year DSM-5
ASD diagnosis (American Psychiatric Association,
2013; see Appendix S2 for more details). Those who
did not reach criterion for ASD diagnosis but had
above threshold Autism Diagnostic Observational
Schedule (ADOS; Gotham, Pickles, & Lord, 2009;
Lord et al., 2000, 2012) and/or The Autism Diag-
nostic Interview-Revised (ADI-R; Lord, Rutter, Le
Couteur, & Free Hospital, 1994) scores or low (<
1.5
SD) scores on one or more of the Mullen Scales of
Early Learning (MSEL; Mullen, 1995) subscales were
categorised as ‘Other’ at 3 years.
Dimensional traits at 3 years. ADOS-G (BASIS;
Lord et al., 2000) and ADOS-2 (STAARS; Lord et al.,
2012) Calibrated Severity Score (ADOS-CSS;
Gotham et al., 2009) Social Affect (SA) and Repeti-
tive/Restricted Behaviour (RRB) scores opera-
tionalised 3-year dimensional domain-specific ASD
traits (Appendix S2 and Table S2 for summary).
Higher scores indicated heightened trait levels.
Polygenic scores for ASD (PGSASD). A total of 190
infants had PGSASD and PLR data for at least one
timepoint. PGS (an aggregate of trait-related effect
sizes of single nucleotide polymorphisms [SNPs]
distributed throughout the genome derived from
independent genome-wide association studies) was
constructed for infants and other family members
using PRSice-2 (Choi & O’Reilly, 2019). The full
sample and methods are fully described in Harrison
et al. (under review; see Appendix S3). SNP linkage
(A) (C)
(B)
(D)
Figure 1 (A) Stimulus one presented to the BASIS cohort (Gliga et al., 2015) alongside, (B) stimulus one’s PLR inducing stimuli slide
demonstrating the approximate location of the centre area of interest (centre ~5% of white slide). (C) Stimulus two presented to the
STAARS cohort. (D) Pupil size (mm) and acceleration (a.u.) traces demonstrating relative constriction formula components (Hellmer &
Nystr€om’s, 2017). A0 = average pupil diameter in the window 100 ms before constriction onset (latency). Am = minimum pupil diameter
in 170–1450 ms relative to A0. ms = milliseconds
© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health
Infant pupil reflex development in ASD 3
disequilibrium pruning based on p-value informed
clumping (pairwise LD r2 < .1 within 250 kb of the
index SNP) was performed on the target dataset. Two
principal components calculated with the PC-AiR
function of the ‘GENESIS’ R-package (Gogarten
et al., 2019) were included as covariates to account
for relatedness in the sample (kinship coefficient
threshold for relatedness = 0.125). For this study,
we calculated a high-resolution best-fit PGSASD
using an independent sample’s GWAS summary
statistics (Grove et al., 2019; 18,381 cases, 27,969
controls, SNP heritability = 0.118) and target pheno-
type of later ASD/No ASD. The best-fit PGSASD was
obtained at a GWAS p-value threshold of .021
(Nagelkerke’s R2 = 0.023, p = .0042, 8,294 SNPs)
and standardised (M = 0, SD = 1). See Figure S2 for
final best-fit PGSASD across 3-year outcome group.
Statistical analysis
In total, we conducted 8 separate linear multilevel
models to examine mean changes in PLR amplitude
and latency between the model reference timepoint
(14 months) and earlier/later timepoints (9/
24 months) depending on (1) 3-year ASD outcome
[ASD, Typical Development (TD) or Other], (2)
PGSASD, and (3) 3-year ASD domain trait level
[ADOS-CSS (a) SA and (b) RRB]. We followed the
experiment-wise procedure of multiple testing cor-
rection whereby all tests leading to the same con-
clusion should be corrected for multiplicity (Bender
& Lange, 2001). Therefore, we did not apply multiple
comparison corrections to the models as each model
asked a specific question in which a specific conclu-
sion would be drawn (results alongside Bonferroni
corrected confidence intervals, and p-values for all
models are reported in relevant appendices refer-
enced below).
We included random intercepts of participant (to
account for within-subject correlated variance) and
protocol (to account for different stimuli, eye tracker
and sampling rate used across cohorts). The per-
centage of missing trials (Missingness, see
Appendix S1) and baseline pupil size [the average
pupil diameter in the 100 ms window before PLR
latency (i.e. Constriction onset time)] were covariates
in all models. Mean distance from the screen was
considered as a covariate, though did not alter
results (referenced in relevant appendices). Final
model specifications and the sample size for each
model are reported in Table 1. Models were imple-
mented using scaled continuous variables for com-
parable coefficients across models in R (R
Development Core Team, 2011); model construction:
‘lmerTest’ R-package (Kuznetsova, Brockhoff, &
Christensen, 2017); confidence intervals: ‘lme4’ R-
package (Bates, M€achler, Bolker, & Walker, 2015);
continuous variable scaling: ‘base’ R-package scale()
function (R Development Core Team, 2011).
Restricted estimated maximum likelihood was used
to handle missing data, and unstructured variance-
covariance matrix was specified. Assumptions were
checked through visual evaluation of residuals.
Results
Sample
A sample breakdown and characteristics are dis-
played in Table 2 (and Table S3). See Appendix S4
(including Tables S4 and S5) for comparisons of
sample characteristics across 3-year outcome.
ASD outcome
Separate linear multilevel models (LMM) with ran-
dom intercepts (participant and protocol) were con-
ducted to explore associations between 3-year ASD
outcome and 9- to 14-/14- to 24-month changes in
amplitude (N = 247) and latency (N = 252). Figure 2
displays unstandardised estimated marginal means
(EMMs) over visit timepoints. Full reports of fixed
and random effect estimates and pairwise compar-
isons are presented in Appendix S5: Tables S6–S14.
Table 1 Model specifications for each model displaying model
predictor of interest, dependent variable, model syntax and
sample size (overall and divided by 3-year outcome group)
Model
predictor
Dependent
variable Model syntax
N (3-year
outcome)
Outcome Amplitude PLR ~ ASD
Outcomea + Visitb
+ ASD
Outcomea*Visitb
+ Likelihoodc** +
Missingness +
Baseline Pupil
Size + (1|
Participant) + (1|
Protocol)
247
(ASD = 27,
Other =
49, Typical
= 171)
Latency 252
(ASD = 27,
Other =
50, Typical
=175)
PGSASD Amplitude PLR ~ PGSASD +
Visitb +
PGSASD*Visit
b +
Missingness +
Baseline Pupil
Size + (1|
Participant) + (1|
Protocol)
187
(ASD = 19)
Latency 190
(ASD = 19)
Dimensional
Traits
(ADOS SA
or RRB)
Amplitude PLR ~ADOS +
Visitb + ADOS-2-
CSS*Visitb +
Missingness +
Baseline Pupil
Size + (1|
Participant) + (1|
Protocol)
243
(ASD =
26***)
Latency 248
(ASD =
26***)
(1|Variable) = random intercept. Superscript letters denote the
reference category in the model for the relevant fixed effect
variable: aASD; b14 month; cElevated Likelihood.
*Interaction.
**Likelihood added as covariate.
***See Appendix S2 Note A.
© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health
4 Laurel A. Fish et al.
Amplitude. ThoughEMMs indicate thedirectionof9-
to 14-month amplitude change to differ across out-
come (9- and 14-month unstandardised EMMs,
respectfully, for: ASD = 37.52%, 35.53%; typical =
35.95%,37.64%;other=35.34%,35.73%), interaction
estimates revealed no significant difference in these
changes between those with ASD and other (b = 
.27,
SE = 0.25, 95% CI = 
0.77, 0.22) or typical (b = 
.41,
SE = 0.22, 95% CI = 
0.85, 0.02) development.
Across the cohort, EMMs indicate a decreasing 14- to
24-month amplitude change (14- and 24-month
unstandardised EMMs, respectfully, for: ASD =
35.53%, 28.11%; typical = 37.64%, 31.89%; other =
35.73%, 30.50%). Between 14 and 24 months, ampli-
tude significantly decreased for those with ASD
(b = 
.85,SE = 0.21,95%CI=
1.25,
0.44), a change
that did not significantly differ from those with other
(b = .25,SE =0.25,95%CI=
0.25,0.75) or typical (b =
.17, SE = 0.22, 95% CI = 
0.26, 0.61) development.
Latency. Across the cohort, EMMs indicate a small
decreasing pattern of latency change between 9 and
14 months (9- and 14-month unstandardised EMMs,
respectfully, for: ASD =339.69 ms, 334.35 ms; typical
= 331.52 ms, 323.70 ms; other = 337.81 ms,
325.47 ms). For those with ASD, this decrease was
not significant (b =.15, SE = 0.26, 95% CI = 
0.21,
0.78) and was not significantly different from the 9- to
14-month change in theOther (b =.20, SE = 0.26, 95%
CI=
0.32,0.71), or Typical (b = .07,SE =0.23, 95%CI
= 
0.38, 0.52) development groups.
Across the cohort, EMMs indicate a decreasing
pattern of latency change between 14 and 24 months
whichwasmuch larger for thosewithASD (14-and24-
month unstandardised EMMs, respectfully, for: ASD =
334.35 ms, 312.30 ms; typical = 323.70 ms,
320.78 ms; other = 325.47 ms, 322.29 ms). Between
14 and 24 months, latency decreased significantly
more in infants with later ASD (b = 
.62, SE = 0.22,
95%CI =
1.04,
0.19) compared to thosewith typical
(b = .54, SE = 0.23, 95% CI = 0.08, 0.99) or other (b =
.53, SE = 0.26, 95% CI = 0.02, 1.04) development.
Pairwise comparisons revealed no significant differ-
ences in latency between 14 and 24 months for typical
(b = 
.11, 97.5% CI = 
0.33, 0.11) or other (b = 
.15,
97.5% CI = 
0.56, 0.25) development.
ASD polygenic score
Separate LMMs (with participant and protocol ran-
dom intercepts) were conducted to explore associa-
tions between PGSASD and 9- to 14-/14- 24-month
amplitude (N = 187) and latency (N = 190) develop-
ment. Figure 3 displays LMM interactions plots. Full
fixed and random effect estimates lists are reported
in Appendix S5: Table S15–S20.
Amplitude. No significant associations were reported
between PGSASD and amplitude change (9 toT
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© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health
Infant pupil reflex development in ASD 5
14 months:b = .02,SE =0.08,95%CI=
0.14,0.18;14
to24 months:b = .07,SE =0.08,95%CI=
0.09,0.23).
Latency. Within the subset with PGS scores avail-
able, latency significantly decreased between 9 and
14 months (b = .27, SE = 0.08, 95% CI = 0.11, 0.43).
Higher PGSASD was associated with a smaller
decrease in latency between 9 and 14 months
(b = 
.16, SE = 0.08, 95% CI = 
0.31, 
0.002).
Within this subset of infants, latency did not signif-
icantly change between 14 and 24 months (b = 
.08,
SE = 0.08, 95% CI = 
0.23, 0.08); PGSASD was not
significantly associated with individual differences in
change profile (though effect sizes were similar to
those at 9- to 14-month PGS-associated change,
b = 
.14, SE = 0.08, 95% CI = 
0.29, 0.01).
ASD dimensional domain traits
Separate LMMs (with participant and protocol ran-
dom intercepts) were conducted to explore associa-
tions between 3-year ADOS-CSS (social affect [SA]
and repetitive behaviours [RRB]) and 9- to 24-month
amplitude (N = 243) and latency (N = 248) develop-
ment. Figure 4 displays LMM interactions plots. Full
fixed and random effect estimates lists are reported
in Appendix S5: Table S21–S26.
Amplitude. Overall, amplitude became larger from
9 to 14 months (SA: b = 
.32, SE = 0.11, 95% CI =

0.54, 
0.10; RRB: b = 
.32, SE = 0.13, 95% CI =

0.57, 
0.07); less change significantly associated
with higher ADOS-CSS scores (SA: b = .08, SE =
0.03, 95% CI = 0.01, 0.14; RRB: b = .05, SE = 0.03,
95% CI = 0.004, 0.11). Over the cohort, amplitude
significantly decreased from 14 to 24 months (SA:
b = 
.81, SE = 0.11, 95% CI = 
1.03, 
0.58; RRB:
b = 
.61, SE = 0.13, 95% CI = 
0.86, 
0.35); ADOS-
CSS scores did not associate with this change (SA:
b = .60, SE = 0.03, 95% CI = 
0.01, 0.12; RRB:
b = 
.01, SE = 0.03, 95% CI = 
0.07, 0.04).
Latency. Latency did not change between 9 and
14 months (SA: b = .19, SE = 0.11, 95% CI = 
0.03,
(A) (B)
Figure 2 Association of 3-year ASD outcome with mean PLR (A) amplitude (%) and (B) latency (ms) across visit timepoints. ASD, ASD
outcome; ms, milliseconds; O, Other Development; TD, Typical Development. Error bars = SE
(A) (B)
Figure 3 Mean difference scores from 9 to 14 months and 14 to 24 months against PGSASD for PLR (A) amplitude (%) and (B) PLR latency
(ms). Lines = unstandardised interaction estimates obtained with LMM
© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health
6 Laurel A. Fish et al.
0.42; RRB: b = .15, SE = 0.13, 95% CI = 
0.10,
0.40), and individual variation was not associated
with ADOS-CSS scores (SA: b = .03, SE = 0.03, 95%
CI = 
0.04, 0.10; RRB: b = .03, SE = 0.03, 95% CI =

0.02, 0.08). Latency decreased between 14 and
24 months (SA: b = 
.23, SE = 0.11, 95% CI =

0.45, 
0.01; RRB: b = 
.12, SE = 0.13, 95% CI =

0.37, 0.13); again, this was not linked to ADOS-
CSS scores (SA: b = .04, SE = 0.03, 95% CI = 
0.03,
0.10; RRB: b = 
.003, SE = 0.03, 95% CI = 
0.06,
0.05).
Discussion
The pupillary light reflex (PLR), a candidate ASD
intermediate phenotype, could illuminate develop-
mental pathways linking genes to behaviour. We
investigated associations between PLR (latency and
amplitude) changes in the first and second years of
life and ASD outcome, domain trait levels and ASD
polygenetic liability (Findings are summarised in
Appendix S6: Table S27–S28). Overall, PLR constric-
tion magnitude (amplitude) increased from 9 to
14 months and decreased thereafter; latency (Con-
striction onset time) decreased with time. From 9 to
14 months, higher PRSASD associated with slower
latency decreases; 14- to 24-month latency then
decreased rapidly for those with later ASD. Latency
change was not associated with 3-year dimensional
trait measures; relatively smaller 9- to 14-month
amplitude increases associated with Social Affect
(SA) and Repetitive/Restrictive Behaviours (RRB),
but not categorical ASD or PGSASD. Overall, in the
first year, changes in amplitude associate with
dimensional ASD traits at 3 years while stagnating
latency development associated with polygenic lia-
bility, followed by a more rapid decrease in latency in
the second year that was associated with categorical
ASD outcome. Given our growing understanding of
the PLR’s neural underpinnings, examining its
development may yield insights into early mecha-
nistic pathways to emerging ASD.
Amplitude
Across the cohort, pupillary constriction amplitude
between 9 and 14 months varied between outcome,
though not significantly. Within this time window,
relatively smaller changes in amplitude associated
with higher SA and RRBs. These development pat-
terns corroborate previously reported increasing
amplitude in the first year in typically developing
-30
-20
-10
0
10
20
2.5 5.0 7.5 10.0
ADOS SA
-30
-20
-10
0
10
20
2.5 5.0 7.5
ADOS RRB
-100
-50
0
50
100
2.5 5.0 7.5 10.0
ADOS SA
-100
-50
0
50
100
2.5 5.0 7.5
ADOS RRB
(A) (B)
(C) (D)
9 to 14months 14 to 24months
Figure 4 Mean PLR differences from 9 to 14 months and 14 to 24 months against 3-year ADOS severity scores. Mean PLR amplitude
difference (%) against (A) social affect and (B) repetitive/restricted behaviours. Mean PLR latency difference (ms) against (C) Social Affect
and (D) Repetitive/Restricted Behaviours. RRB, Repetitive/Restricted Behaviours; SA, Social Affect. Lines = unstandardised interaction
estimates obtained with LMM
© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health
Infant pupil reflex development in ASD 7
infants (Kercher et al., 2020; Nystr€om et al., 2018).
In a partially overlapping sample, Nystr€om et al.
(2018) also demonstrated infants with later ASD
showed decreasing 9- to 14-month amplitude, which
significantly differed from the typically developing
group’s trajectory. Although effects are in the same
direction in the present report (Figure 2), results are
nonsignificant. We did not observe trait, outcome
group or polygenic effects in the second year con-
tributing novel findings to the field. Collating our
work with previous investigations, infants with later
traits show an initially stronger PLR constriction that
weakens over the first year, in contrast to typically
developing infants who show the opposite profile.
The association between early amplitude and later
ASDtraitsmay represent the emergenceofhigher-level
cognitiveprocesses like executiveattention.Amplitude
is modulated by acetylcholine (Loewenfeld, 1999) and
norepinephrine (Lynch, 2018); key neurotransmitters
involved in early attention regulation (Bast et al., 2018;
Colombo, 2001). Indeed, PLR constriction can be
induced semantically (Matho^t, Dalmaijer, Grainger, &
Van der Stigchel, 2014) and by stimulating prefrontal
brain regions regulating attention (Ebitz & Moore,
2017). Developmental changes in amplitude may
reflect developmental shifts from early arousal regu-
lated attention to more endogenous forms of attention
control (Colombo, 2001). Theweaker changes between
9 and 14 months in infants with later ASD (Nystr€om
et al., 2018) or those with higher trait levels (present
study) may reflect altered neurotransmitter systems
supporting executive attention. Indeed, altered devel-
opmental changes in attention between 9 and
14 months have previously been linked to later ASD
(Elsabbagh et al., 2013) and executive functioning
(Hendry et al., 2018).Ourfindings couldbe interpreted
through theAMENDmodel ofASDdevelopment (John-
son, Charman, Pickles, & Jones, 2021) whereby later-
developing processes (e.g. executive attention) modify
or compensate for perturbation in earlier-emerging
sensory/motor functions; a reduction in the action of
developmental modifiers is hypothesised to contribute
to ASD traits. Further investigations should be done to
test the relationship between attention and PLR devel-
opment and its putative reflection of a key neurocog-
nitive modifier system.
Latency
While the cohort overall showed decreasing latency
across the first two years, infants with higher
PGSASD showed relatively slower 9- to 14-month
decreases, while infants with later ASD had a steeper
14- to 24-month latency decrease. These findings
alongside the pattern in Figure 2B suggests an ASD-
associated stagnated latency development that is
potentially driven by earlier action of polygenic
factors. This stagnation is also demonstrated by
other candidate ASD intermediate phenotypes dur-
ing infancy [e.g. look duration at faces (Hendry et al.,
2018), and motor skills (Harris, 2017)]. Latency
depends on neural transduction efficiency, poten-
tially reflecting white matter maturation (Kercher
et al., 2020), a neural feature during early develop-
ment associated with later ASD (Wolff et al., 2012)
and other early features of ASD [e.g. visual orienting
(Elison et al., 2013) and motor development (Parikh
et al., 2020)]. This profile of early stagnation followed
by a later ‘catch-up’ to apparently typical latency at
2 years (see also Dinalankara et al., 2017) may point
to an adaptive or modified developmental pathway
triggered by earlier atypicalities (Johnson, 2017;
Johnson et al., 2021). Future work should determine
long-term stability of the ‘catching up’ profile and
consider potential modifiers.
While developmental changes in latency related to
categorical ASD outcome, they were not significantly
related to dimensional variation in SA and RRB. We
operationalised ASD traits using calibrated severity
scores obtained from the ADOS (an observational
assessment with an unfamiliar examiner). As such,
this measure may underestimate infrequent beha-
viours or overestimate social difficulties in shy
children. Consequently, diagnosis is made on addi-
tional observational assessments and developmental
history questionnaires (e.g. the ADI). Latency may
relate more strongly to these other aspects of diag-
nosis or measures capturing broader development.
Alternatively, latency changes may reflect differences
in basic sensory processing in early development
that trigger an adaptive process contributing to
behavioural symptoms of ASD (Johnson, 2017).
Under this view, there may be no linear map between
early latency and later dimensional behaviours
because the latter emerges through the response of
a complex dynamical system to early perturbation
(Turkheimer et al., 2021).
Limitations
The ASD outcome subgroup was relatively small
(N = 27), limiting capture of the full ASD spectrum
and reducing generalisability. Additionally, data amal-
gamation from different stimuli, eye trackers and
sampling rates [to which latency is susceptible (Berga-
min&Kardon,2003)] limit our study, though latency is
averaged across trials within individuals to improve
resolution [as suggested by previous investigations
(Bergamin & Kardon, 2003)] and statistically
accounted for using random intercepts and previously
reported (Nystr€om et al., 2018). Although we con-
ducted eight models, we did not apply multiple com-
parison corrections as we followed experiment-wise
procedure of multiple testing correction whereby all
tests leading to the same conclusion should be cor-
rected for multiplicity (Bender & Lange, 2001). Each
model in our analysis asked a specific question
regarding the relationship between PLR development
and ASD. We acknowledge some would consider this
too lenient and have provided corrected confidence
© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health
8 Laurel A. Fish et al.
intervals andp-values formultiple comparisonswithin
each PLR parameter in Appendix S5. Additionally, the
procedure for the development of the PGSASD is under
review and only accounted for 2% of the phenotypic
variance in our sample; therefore, findings may alter
with future iterations of the PGS and more robust
scores as larger GWAS are generated. Future investi-
gations should consider samples with ranging severi-
ties and co-occurring conditions aiming for
homogenous protocols with higher sampling frequen-
cies to improve latencysensitivity (Bergamin&Kardon,
2003) and future potentially more robust PGS. Our
stimuli only stimulated retinal rods and cones, limiting
inferences on the complete PLR circuit. Cyanblue light
sensitive and dopaminergic, intrinsically photosensi-
tive retinal ganglion cells (ipRGC) also induce the PLR
(Hattar, Liao, Takao, Berson, & Yau, 2002; Pickard &
Sollars, 2012). Future investigations should include
stimuli targeting ipRGC (Hellmer & Nystr€om, 2017) to
clarify specificities associating PLR and ASD.
Summary
The present study demonstrates PLR development
associates with later ASD (categorically and dimen-
sionally), with PLR latency development relating to
ASD polygenic liability. This supports PLR as a
candidate intermediate phenotype for investigating
pathways between genes and phenotype. PLR atyp-
icalities varied over developmental time, reiterating
the importance of longitudinal investigations in early
development. Future work should incorporate alter-
native measures (e.g. functionally informed struc-
tural and genetic measures) to test whether distinct
neural mechanisms underpin PLR alterations and
explore the degree to which findings might reflect
compensatory or adaptive processes that follow an
earlier period of disruption (Johnson et al., 2021).
Taken together, the potential for cross-
developmental and translational insights yielded by
simple measures like the PLR makes it an excellent
candidate for future large-scale investigation.
Supporting information
Additional supporting information may be found online
in the Supporting Information section at the end of the
article:
Appendix S1. PLR stimuli and processing.
Appendix S2. 3-year Clinical outcome.
Appendix S3. DNA Processes and Polygenic Score
Calculations.
Appendix S4. Sample characteristics.
Appendix S5. Model results.
Appendix S6. Overview of findings.
Table S1. Mean number of trials included and percent-
age of trials missing during processing for each PLR
parameter for each visit.
Table S2. 3-year sample characteristics of individuals
with PLR from at least one timepoint from 9 to 24
months.
Table S3. Results from ANOVA and Tukey pairwise
comparisons examining group-wise differences in age
across outcome at each timepoint.
Table S4. Results from ANOVA and Tukey pairwise
comparisons examining group-wise differences in M-
ELC across outcome at each timepoint.
Table S5. Sample size and characteristics describing,
age, Mullen Early Learning Composite score (M-ELC)
across 3-year outcome group for each visit timepoint for
individuals with PLR Amplitude data.
Table S6. Standardised and unstandardised fixed effect
estimates for association between outcome and PLR
Amplitude obtained using linear mixed effect models.
Table S7. Standardised and unstandardised random
Effect variance and standard deviation for association
between outcome and PLR Amplitude obtained using
linear mixed effect models.
Table S8. Estimated marginal means obtained using
LMM on the relation between PLR Amplitude and 3-year
ASD outcome over visits.
Table S9. Standardised and unstandardised fixed effect
estimates for association between outcome and PLR
Amplitude obtained using linear mixed effect models
including mean distance from screen as covariate.
Table S10. Standardised and unstandardised fixed
effect estimates for association between outcome and
PLR Latency obtained using linear mixed effect models.
Table S11. Standardised and unstandardised random
Effect variance and standard deviation for association
between outcome and PLR Latency obtained using
linear mixed effect models.
Table S12. Estimated marginal means obtained using
LMM on the relation between PLR Latency (ms) and 3-
year ASD outcome over visits.
Table S13. Standardised and unstandardised pairwise
comparisons of Latency within outcome group across
14 to 24 months.
Table S14. Standardised and unstandardised fixed
effect estimates for association between outcome and
PLR Latency obtained using linear mixed effect models
including mean distance from screen as covariate.
Table S15. Standardised and unstandardised fixed
effect estimates for association between ASD polygenic
score and PLR Amplitude obtained using linear mixed
effect models.
Table S16. Standardised and unstandardised random
effect variance and standard deviation for association
between ASD polygenic score and PLR Amplitude
obtained using linear mixed effect models.
Table S17. Standardised and unstandardised fixed
effect estimates for association between ASD polygenic
score and PLR Amplitude obtained using linear mixed
effect models including mean distance from screen as
covariate.
Table S18. Standardised and unstandardised fixed
effect estimates for association between ASD polygenic
score and PLR Latency obtained using linear mixed
effect models.
Table S19. Standardised and unstandardised random
Effect variance and standard deviation for association
© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health
Infant pupil reflex development in ASD 9
between ASD polygenic score and PLR Latency obtained
using linear mixed effect models.
Table S20. Standardised and unstandardised fixed
effect estimates for association between ASD polygenic
score and PLR Latency obtained using linear mixed
effect model models including mean distance from
screen as covariate.
Table S21. Standardised and unstandardised fixed
effect estimates for associations between ADOS-CSS
score (social affect or repetitive behaviours) and PLR
Amplitude obtained using linear mixed effect models.
Table S22. Random Effect variance and standard
deviation for associations between ADOS score (social
affect or repetitive behaviours) and PLR Amplitude
obtained using linear mixed effect models.
Table S23. Standardised and unstandardised fixed effect
estimates for association between ADOS and PLR Ampli-
tude obtained using linear mixed effect model models
includingmeandistance fromscreen as covariate.
Table S24. Standardised and unstandardised fixed
effect estimates for associations between ADOS score
(social affect or repetitive behaviours) and PLR Latency
obtained using linear mixed effect models.
Table S25. Random Effect variance and standard
deviation for associations between ADOS score (social
affect or repetitive behaviours) and PLR Latency
obtained using linear mixed effect models.
Table S26. Standardised and unstandardised fixed
effect estimates for association between ADOS and
PLR Latency obtained using linear mixed effect model
models including mean distance from screen as covari-
ate.
Table S27. Summary of findings for models on PLR
Amplitude.
Table S28. Summary of findings for models on PLR
Latency.
Figure S1. Distribution of individual’s remaining trials
contributing to the (a) PLR Latency and (b) PLR Ampli-
tude values split by visit.
Figure S2. Standardised best-fit PGSASD per 3-year
outcome.
Acknowledgements
This work was funded by the Simons Foundation
Autism Research Initiative (SFARI) Pilot Award grant
no. 511504; European Union’s Horizon 2020
research and innovation programme under the Marie
Skłodowska-Curie grant no.642996 (BRAINVIEW).
L.A.F. was funded by the SGDP Studentship award.
Data collection was funded by MRC Programme grant
nos. G0701484 and MR/K021389/1, the
BASIS funding consortium led by Autistica (www.ba
sisnetwork.org), EU-AIMS and AIMS-2-TRIALS pro-
grammes funded by the Innovative Medicines Initia-
tive (IMI) Joint Undertaking Grant Nos. 115300
(M.H.J., T.C.) and No. 777394 (M.H.J., E.J.H.J. and
T.C.; European Union’s FP7 and Horizon 2020,
respectively). This Joint Undertaking receives sup-
port from the European Union’s Horizon 2020
research and innovation programme, with in-kind
contributions from the European Federation of Phar-
maceutical Industries and Associations (EFPIA) com-
panies and funding from Autism Speaks, Autistica
and SFARI. High performance computing facilities at
KCL (ROSALIND) were funded with capital equipment
grants from the GSTT Charity (TR130505) and
Maudsley Charity (980). The authors acknowledge
the contribution of the NIHR BioResource Centre
Maudsley, National Institute for Health Research
Maudsley Biomedical Research Centre (BRC) at
South London and Maudsley NHS Foundation Trust
and Institute of Psychiatry, Psychology and Neuro-
science (IoPPN), King’s College London. The BASIS/
STAARS team consists of: Mary Agyapong, Tessel
Bazelmans, Leila Dafner, Mutluhan Ersoy, Amy
Goodwin, Rianne Haartsen, Alexandra Hendry,
Rebecca Holman, Sarah Kalwarowsky, Anna Koles-
nik, Greg Pasco, Chlo€e Taylor. The authors acknowl-
edge Matthew Danvers, Candice Moore and Laura
Lennuyeux-Comnene for support with the genetic
data collection and Charles Curtis and Hamel Patel
for DNA data preprocessing and genotyping. Finally,
the authors would like to thank all the participating
families. The authors have declared that they have
no competing or potential conflicts of interest.
Correspondence
Laurel A. Fish, Social, Genetic and Developmental
Psychiatry Centre, King’s College London, London,
UK; Email: laurel.fish@kcl.ac.uk
Key points
 Infant pupillary light reflex (PLR) is the reflexual pupil constriction in response to light.
 Decreasing 9- and 14-month PLR constriction amplitude associates with increased social difficulties and
repetitive behaviours.
 Stagnated 9- to 14-month PLR latency development associates with polygenic liability for ASD; steeper 14- to
24-month decreases associates with ASD outcome.
 Infant PLR development may be used to uncover neural mechanisms to emerging ASD.
© 2021 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for
Child and Adolescent Mental Health
10 Laurel A. Fish et al.
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Accepted for publication: 10 August 2021
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Child and Adolescent Mental Health
12 Laurel A. Fish et al.