Infants in the NF1 group were recruited through local medical and genetic centres, and the remaining infants were recruited as part of a longitudinal prospective study (Studying Autism and Attention Deficit Hyperactivity Disorder Risks programme; STAARS) [see [43] for more details]. All infants were born full-term (gestational age 36–42 weeks). At the time of enrolment, none of the infants (aside from those in the NF1 group) had a known medical or developmental condition. Informed written consent was provided by the parent(s) prior to the commencement of the study. The study was approved by the National Research Ethics Service and the Research Ethics Committees of Birkbeck, University of London and King’s College London. All NF1 infants had their diagnosis confirmed via molecular testing of cord blood samples or clinical diagnosis based on NIH consensus criteria [44] and had no other developmental concerns at the time of the visits. The remaining infants were assigned group membership for familial likelihood of autism and ADHD based on information on clinical diagnoses and scores on various screening measures (see Additional file 1 for more information). Infants in the EL-autism group had at least one first-degree relative with a community clinical diagnosis of autism, infants in the EL-ADHD group had at least one first-degree relative with a community clinical diagnosis or probable research diagnosis of ADHD, and infants in the TL group had at least one older sibling with typical development and no known autism or ADHD in first-degree family members (as confirmed through parent interviews regarding family medical history). The final sample included for current analyses includes data from 20 infants with NF1, 67 infants with an elevated likelihood of autism (EL-autism), 24 infants with an elevated likelihood of ADHD (EL-ADHD), 19 infants with an elevated likelihood of both ASD and ADHD (EL-autism + ADHD) and 24 infants with a typical likelihood for autism and/or ADHD (TL). See Table 1 for summary statistics of the included sample and Fig. 2 for a breakdown of participant retention at each time point [adapted from [45]].

Breakdown of Participant Dropout at 10-Month Visit. EL-ADHD = elevated likelihood for ADHD, EL-autism + ADHD = elevated likelihood for autism and ADHD, EL-autism = elevated likelihood for autism, NF1 = neurofibromatosis type 1, TL = typical likelihood

Executive attention was measured at 24 months with the Early Childhood Behavioral Questionnaire—Short Version [46], designed to assess temperament in children aged one to three years old. Parents rate how often their child exhibited each behaviour in the previous two weeks scored from 1 (Never) to 7 (Always). For current analyses, we generated a score for executive attention by summing scores on the Inhibitory Control, Attention Shifting and Attention Focusing subscales, each consisting of 12 items, which make up the wider Effortful Control subscale. We excluded the Low-Intensity Pleasure and Cuddliness subscales to ensure measurement was not biased by atypical expressions of affective states [as these may be more prevalent in autistic children; [47, 48]]. Confirmatory factor analysis in the full 24-month sample (N = 114) suggested scores from the Inhibitory Control, Attention Shifting and Attention Focusing subscales all significantly loaded on the hypothesised executive attention factor (loadings = 0.69–0.79, all ps < 0.001).

Autism traits were measured at 36 months using the Preschool form of the Social Responsiveness Scale—2 [49]. The SRS is designed to measure autistic traits in the general population and consists of 65 items, each rated on a 4-point scale ranging from 1 (Not True) to 4 (Almost Always True).

ADHD traits were measured at 36 months using the Preschool Child Behavior Checklist DSM Attention Deficit/Hyperactivity Problems subscale [50], which comprises six items that measure inattentive and hyperactive behaviours over the past two months. Parents are asked to indicate how well each statement describes their child’s behaviour ranging from 0 (Not True) to 2 (Very True or Often True).

Infants were shown naturalistic social (women singing) or non-social (toys moving) dynamic videos designed to produce calm attention [51]. Social videos consisted of the face, torso and hands of two women singing nursery rhymes with corresponding gestures. The nursery rhymes were: ‘Hi Baby, Where Are My Eyes?’, ‘Itsy Bitsy Spider’, ‘The Wheels on the Bus’, ‘Twinkle Twinkle Little Star’ and ‘Pat-a-cake’ (played in this fixed order). In the Non-Social video, infant appropriate toys were shown to be moving (e.g. spinning toys in motion, balls popping within a clear plastic toy, balls moving down a chute). There was no social content to these videos. The order of the videos was counterbalanced across infants, and other visual tasks (not reported in this paper) were presented between each block of videos. The videos were presented on a screen with a diagonal size of 23″ (58.42 cm × 28.6 cm, 52° × 26.8°, aspect ratio of 16:9). To ensure that all participants saw the same sized stimuli (in case of technical issues with/changes in the monitor screen over the course of this longitudinal study), we presented the stimuli within a ‘virtual window’ at the following size: a diagonal size of 17″ (34.5 cm × 25.9 cm, 32.1° × 24.4°, with a native resolution of 1280 × 1024 pixels and an aspect ratio of 5:4) and with black borders around the edge of the screen. Stimuli were therefore drawn with an effective display resolution of 37.1 pixels per cm. In order to maintain the source aspect ratio of 16:9 when presented within the ‘virtual window’, all videos were scaled to 32.6 cm × 31 cm (30.4° × 29°, 1210 × 1150 pixels) on screen. Videos were 1 min in length and presented up to 3 times, for a total of 3 min each, interspersed through a longer EEG session.

EEG was recorded using an EGI (Philips Neuro, Oregon, USA) 128-electrode Hydrocel Sensor Net, online referenced to Cz at 500 Hz. Infants were seated on their caregiver’s lap, 60 cm from a screen. All testing took place in a sound attenuated and electrically shielded room. A video camera situated below the screen used for stimulus presentation recorded the infants’ bodily and facial behaviour.

EEG was bandpass filtered (0.1–100 Hz), and 1-s segmented. Data were manually cleaned in NetStation 4.5 [52]; segments with excessive artefact (e.g. gross motor movement, eye blinks), where infants were not looking at the video, or with > 25 noisy channels were manually excluded. Infants with < 10 artefact-free trials in either condition were excluded (see Additional file 1: Fig. S1). Noisy channels were interpolated from neighbouring channels using spline interpolation. 1-s non-overlapping segments were referenced to the average reference, imported into MATLAB, detrended and subjected to a fast Fourier transform (FFT). Power values were calculated and averaged across artefact-free segments in 1 Hz bins.

The fitting oscillations and one over f (FOOOF) algorithm was used to obtain individual aperiodic exponent values [9]. When the power spectrum is plotted on a log–log axis (i.e. power on the y-axis, frequency on the x-axis), the aperiodic exponent is equivalent to the coefficient of a regression line characterising the slope. Aperiodic exponents were estimated for social and non-social videos separately and then averaged. Following previous work with infant samples [11], we parameterised spectra in the frequency range 1–10 Hz to avoid contamination by higher frequency artefacts, and only exponents from model fits with R2 ≥ 0.95 were kept for further analysis. Other settings were as follows: peak width limits = 2, 8, maximum number of peaks = 4, peak threshold = 0.1, aperiodic mode = ‘fixed’. Aperiodic exponent values were extracted from frontal, central and posterior regions (frontal = electrodes 2, 3, 4, 11, 19, 23, 26, 9, 10, 18, 22, 15, 16, central = electrodes 36, 104, 30, 7, 106, 105, 31, 37, 80, 87, 55, and posterior = electrodes 52, 62, 92, 61, 77, 78, 53, 86, 60, 67, 72, 85). Comparison of estimated aperiodic components overlaid against input EEG data was visually inspected for each participant. Based on the R2 threshold, data from two further EL-autism infants were excluded.

Statistical analyses were conducted in Stata 16 [53]. To minimise the impact of outliers whilst retaining data, aperiodic exponent values were winsorised such that the 5% of the lowest/highest values were recoded to the value of the 5th/95th percentile. As there were no differences in aperiodic exponent values between the social and non-social videos (p = 0.41), we collapsed values across conditions to maximise the signal-to-noise ratio. First, to validate our metric of E/I balance, we tested for differences in 10-month aperiodic exponent between NF1 and TL infants. We ran a mixed effects model, with region (Fz, Cz, Pz) as a within-subjects factor, 10-month aperiodic exponent as the outcome and the following predictors: NF1 status (present/absent), age in years at 10-month visit, number of EEG trials (averaged between social and non-social videos) and sex. As we did not have any a priori hypotheses for the topography of group differences, NF1 status*region interaction terms were only run if NF1 status effects were seen first. Next, we compared differences in aperiodic exponents between the EL and TL groups, splitting the EL group dependent on the type of familial likelihood status (EL-autism, EL-ADHD). We ran a comparable mixed effects model, but with EL-autism status (present/absent), EL-ADHD status (present/absent), region, age in years at 10-month visit, number of EEG trials (averaged between social and non-social videos) and sex as predictors of 10-month aperiodic exponent values. Secondary models included an interaction between EL-autism and EL-ADHD status, which allowed us to test if there were any additive/protective effects of the combined group. As before, likelihood status*region interaction terms were only run if likelihood status effects were seen first. We re-ran the EL-TL contrast models excluding infants with < 20 EEG trials (n = 4) and the pattern of results remained the same. (There were no infants with < 20 EEG trials in the NF1-TL contrast models.) Both the NF1-TL and the EL-TL comparison models were run with restricted maximum likelihood. We use the method outlined in [54, 55] to generate Cohen’s f2 from mixed effect models and report these for any significant group comparisons. Here, effects are considered small at values around 0.02, medium at values around 0.15 and large at values around 0.35 [56].

To test whether 24-month executive attention moderated associations between early E/I imbalance and later autism and ADHD traits in the combined EL and TL sample, we ran two linear regressions, with 36-month SRS total and CBCL DSM ADHD subscale total score as the outcome, respectively. First, we tested the main effect of 10-month aperiodic exponent (averaged across all three regions). After running main effects, we added 24-month executive attention and an interaction between aperiodic exponent and executive attention as predictors. In all models, we included age in months at 10-month visit, number of EEG trials, sex and likelihood group (EL-autism, EL-ADHD and the interaction of the two) as covariates. The SRS and CBCL were non-normally distributed and therefore square root transformed. As the Breusch–Pagan/Cook–Weisberg test for heteroskedasticity of residuals was significant for the SRS (p < 0.01), all analyses with SRS as the outcome were run with robust standard errors. As further robustness checks, we re-ran longitudinal models 1) excluding infants with < 20 trials (n = 4), 2) including 10-month head movement (as described in [57]) as an additional covariate to check that global trait-like differences in activity level not captured by individual differences in the number of trials were not contributing to results. This movement variable captures the amount of head movement as measured by an eye tracker during a separate battery of eye-tracking tasks that was administered during the same visit as the EEG assessment and 3) using a modified SRS total which has been proposed to be a more precise measurement of autistic traits as it excludes items which could relate to other co-occurring conditions [58]. We report both unstandardised (b) and standardised (β) coefficients.