Edited by: Jakub S. Gasior, Kazimierz Pułaski University of Technology and Humanities in Radom, Poland
Reviewed by: Gang Yao, University of Missouri, United States; Rachael A. Muscatello, Vanderbilt University, United States; Estate M. Sokhadze, University of South Carolina, United States
This article was submitted to Clinical and Translational Physiology, a section of the journal Frontiers in Physiology
†These authors have contributed equally to this work.
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Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental conditions featuring impairments in social communication, as well as restricted or stereotyped interests and behaviors (American Psychiatric Association,
Due to the wide clinical and etiological heterogeneity of ASD, the early identification of a set of informative risk markers represents a key-challenge to enable early detection and early diagnosis of ASD (Sacrey et al.,
Several studies suggest that Autonomic Nervous System (ANS) activity is linked to social functioning in individuals with ASD, suggesting a role of this system in regulating social interactions in this condition (Dawson and Lewy,
Several approaches could be employed to assess SNS and PNS, including minimally obtrusive methods based on electrodermal activity (GSR, Galvanic Skin Response) and electrocardiography (ECG). Concerning GSR, it is thought that a reduced electrodermal activity is associated with decreased SNS influence (Hubert et al.,
Joint attention (JA), which is defined as the ability to coordinate visual attention with another person and then shift the gaze toward an object or event (Mundy and Gomes,
Wearable systems and wireless technologies, allowing monitoring patients in an unobtrusive way, are particularly suitable for the recording of physiological parameters in very young children with neuropsychiatric conditions such as ASD during social tasks. Few previous investigations measured HRV in very young children with ASD, with some (Zantinge et al.,
Given that literature in this field is still poor, the aims of this work were to test the feasibility of using a wearable chest belt for the monitoring of ECG signal in toddlers with ASD, and to measure the ANS response in a group of toddlers with ASD and neurotypical age- and gender-matched controls during a JA eye-tracking task.
In the present study, we initially enrolled 46 subjects, equally divided into two groups (ASD and TD children). The ASD group was recruited in three different Institutions: the Autism Unit of IRCCS Stella Maris Foundation of Pisa, the Division of Child Neuropsychiatry of the University Hospital of Messina and the Hospital of Matera. The clinical diagnosis of ASD was established according to the Diagnostic and Statistical Manual of mental disorders-5 criteria (American Psychiatric Association,
The participants with TD were recruited from daycares in the Pisa, Messina and Matera metropolitan areas. All children (ASD and TD) received a nonverbal developmental evaluation through the administration of the performance subscale of the Griffiths Mental Developmental Scales (Griffiths,
Distribution of Griffith Performance for ASD and TD groups.
The inclusion criteria for TD children were an age between 18 and 36 months and the Child Behavior Checklist 1½−5 (CBCL; Achenbach and Rescorla,
Study population (significance:
20 | 20 | 1.00 | |
Age (months, mean ± SD) | 26.1 ± 3.3 | 26.2 ± 3.7 | 0.48 |
Gender (M/F) | 14/6 | 15/5 | 0.80 |
Griffith Performance (mean ± SD) | 81.9 ± 21.8 | 108.9 ± 19.6 | 0.001 |
ADOS-G, module 1– |
14.9 ± 4.5 | – | – |
ADOS-G, module 1– |
4.9 ± 2 | – | – |
ADOS-G, module 1– |
9.8 ± 3.4 | – | – |
ADOS-G, module 1– |
7.3 ± 2.1 | – | – |
An informed consent was obtained from all parents of the children enrolled, after receiving an exhaustive explanation of the study. The experimental procedures and the informed consent were approved by the ethics committee of the IRCCS Stella Maris Foundation (Calambrone, Pisa, Italy). The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki.
The ADOS-G (Lord et al.,
The M-CHAT (Robins et al.,
The Griffiths Mental Developmental Scales (Griffiths,
The CBCL (Achenbach and Rescorla,
Electrocardiography (ECG) signals were recorded during an eye-tracking JA task previously described by our group (Billeci et al.,
Attention to the stimuli was assured by the simultaneous acquisition of eye-tracking and ECG data. Trials for which the children did not look at the screen were excluded from the analysis and children with more than 50% of excluded trials were eliminated from the final sample. Moreover, all the sessions were recorded with a webcam connected to the stimulus PC.
Briefly, the task consisted in watching some short videos reproducing three JA conditions: a response to JA (RJA) and two initiation of JA (IJA) conditions. The RJA condition consisted of a woman placed between two identical objects, in turn placed in front and on either side of her; she smiled and turned her head toward one of the two objects. On the other hand, in the two IJA tasks, the woman maintained direct gaze but in one case one of the objects activated unexpectedly, while in the other one the object appeared from one end of the frame and crossed the scene. The video sequence lasted about 8 s and was repeated several times, so that the total duration of the task was about 5 min (Billeci et al.,
The acquisition protocol also included a “baseline” phase of 5 min before the beginning of the task, in which the child was sitting on a chair near to the therapist, without any particular annoyance due to the clinical scenario.
Electrocardiography (ECG) signals were recorded with a smart sensor of the CE certified Shimmer® platform (Burns et al.,
The ECG sensor was modified by adding two pins in order to allow its interfacing with the common Polar™ cardio-fitness chest strap, featuring two dry electrodes in the inner side of the strap, interfaced to the skin surface of the subject for a single-lead acquisition. The chest strap employed is extremely customizable, allowing for monitoring physiological signals in children with different anatomical characteristics, such as the chest diameter. The signals were acquired with a sampling rate of 200 Hz and an A/D resolution of 12 bits. Home-made Matlab™ scripts were then used for ECG pre-processing and feature extraction.
A stepwise filtering was initially applied to remove artifacts and interferences. Body movements and respiration were removed with a cubic spline 3rd order interpolation between the fiducial isoelectric points of the ECG. A notch filter was also applied to remove the power line interference at 50 Hz and an IIR low pass filter at 40 Hz was applied to eliminate muscular noise. In addition, an interpolation using the Fourier method was applied to the signal to improve RR fiducial points recognition (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology,
RR series were further analyzed using home-made Matlab™ scripts to extract several time- and frequency-domain features both from the baseline and the task phases.
The following time-domain features were extracted and used for analysis:
– Heart rate (HR), expressed as beats per minute (bpm).
– Standard deviation of NN intervals (SDNN), which is a measure of both sympathetic and parasympathetic activity and therefore provides an index of total HRV (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology,
– The coefficient of variation (CV) of the time interval between two consecutive R-waves (RRI) calculated by dividing the standard deviation of RRI (SDNN) by the mean of RRI. We corrected the SDNN with respect to the mean of RRI as HRV and HR are mathematically associated (Sacha and Pluta,
– pNN10 defined as the percentage of successive normal IBI > × > 10 ms and assessing parasympathetic activity (Mietus et al.,
The power spectral density was estimated by the Welch method (Welch,
– Low Frequency (LF) which is the absolute value of the Low Frequency (LF) power (0.04–0.24 Hz) and emphasizes changes in sympathetic regulation.
– High Frequency (HF) which is the absolute value of the High Frequency (HF) power (0.24–1.04 Hz). This frequency band corresponds to band of the spontaneous breathing frequency of children (i.e., from 0.24 to 1.04 Hz or approximately 15–60 breaths per minute) (Bar-Haim et al.,
– Normalized Low Frequency (nLF), which is the ratio between LF and the sum of LF and HF.
– Normalized High Frequency (nHF), defined as the ratio between HF and the sum of LF and HF.
– LF/HF Ratio, representing the ratio between the power of LF and HF bands. Its measure indicates the overall balance between sympathetic and parasympathetic systems.
The frequency band limits of LF and HF were selected according to the recommendations for reporting HRV in children and infants (Quintana et al.,
Statistics was performed using SPSS 23 software (SPSS Inc., Chicago, IL, USA). The Shapiro-Wilk test was applied to evaluate whether the variables considered were normally distributed. A repeated-measures ANCOVA was performed with “phase” (i.e., baseline or JA task) as a “within-group” and “group” (i.e., ASD or TD) as a “between groups” factor. When the variables had a non-normal distribution, variables and covariate were transformed in ranks and the analysis of covariance on ranks was performed. Given that the two groups were different in terms of Griffiths performance, this measure was used as covariate in the ANCOVA. In case of a significant phase or phase x group effect (
After the exclusion criteria application, 3 TD were excluded because the CBCL Total score was over the threshold. Additional 3 ASD children were excluded, one of which due to premature birth, and the other two due to the use of psychotropic drugs.
Thus, the final sample included into the statistical analysis, was constituted of 40 children, 20 TD and 20 ASD.
The first analysis aimed at assessing feasibility of the approach proposed in monitoring toddlers with ASD and TD controls. The tolerability and comfort of toddlers were judged by a psychologist who was present during the sessions and was further confirmed by the inspection of the videos recorded by the webcam. These observations showed that the system did not cause any kind of annoyance and all 40 children successfully accomplished the experimental protocol proposed without showing sensory-motor and/or behavioral issues in wearing the devices and without any difficulties or constraints. Excessive movements were limited also because in the protocol the toddlers were seated in a chair while watching the stimuli and thus relatively restrained in their physical activity. Both the ASD and the TD group attended the videos at the same way for an acceptable time. Indeed, repeated-measures analysis of variance revealed that there was no significant effect of task or group × task on the number of usable trials (Billeci et al.,
The second analysis aimed at comparing the two study cohorts according to the features extracted from the ECG signal.
According to the Shapiro-Wilk test, LF and HF (both during baseline and task) had a non-normal distribution while the other variables were distributed normally. Thus, we performed parametric or non-parametric tests according to the variables distribution.
There was a significant effect of phase x group for LF (
LF changes between phases (baseline and task) in ASD (blue) and TD (red) groups. Median and interquartile are reported.
Comparing the two groups, we observed that SDNN, CV, and LF were significantly higher in the ASD group compared to the TD group at baseline. In addition, CV was also higher in in the ASD group during the task. All the other time- and frequency-domain features did not differ between-groups, nor within-group. In Table
Comparison between the
HR (bpm, mean ± SD) | 104.24 ± 36.41 | 112.49 ± 22.82 | 0.41 |
SDNN (ms, mean ± SD) | 0.45 ± 0.63 | 0.14 ± 0.16 | 0.04 |
CV (n.u., mean ± SD) | 0.78 ± 0.80 | 0.28 ± 0.32 | 0.021 |
pNN10 (%, mean ± SD) | 74.21 ± 23.53 | 68.57 ± 24.57 | 0.49 |
LFn (n.u., mean ± SD) | 0.43 ± 0.19 | 0.38 ± 0.16 | 0.59 |
HFn (n.u., mean ± SD) | 0.57 ± 0.10 | 0.62 ± 0.08 | 0.95 |
LF/HF Ratio (mean ± SD) | 3.81 ± 1.91 | 4.67 ± 2.04 | 0.26 |
LF [ms2, median (IQR)] | 696.00 (1669.00) | 316.00 (1162.00) | 0.047 |
HF [ms2, median (IQR)] | 1550.00 (3746.00) | 344.50 (2587.00) | 0.34 |
HR (bpm, mean ± SD) | 119.06 ± 43.53 | 120.56 ± 25.43 | 0.90 |
SDNN (ms, mean ± SD) | 0.26 ± 0.32 | 0.15 ± 0.22 | 0.03 |
CV (n.u., mean ± SD) | 0.53 ± 0.44 | 0.30 ± 0.35 | 0.09 |
pNNx (%, mean ± SD) | 75.24 ± 19.55 | 68.01 ± 19.66 | 0.28 |
LFn (n.u., mean ± SD) | 0.32 ± 0.21 | 0.41 ± 0.19 | 0.24 |
HFn (n.u., mean ± SD) | 0.68 ± 0.44 | 0.59 ± 0.19 | 0.24 |
LF/HF Ratio (mean ± SD) | 2.86 ± 2.12 | 3.33 ± 1.91 | 0.50 |
LF [ms2, median (IQR)] | 318.00 (1669.0) | 537.00 (1199.30) | 0.50 |
HF [ms2, median (IQR)] | 1300.00 (2738.00) | 472.50 (3110.50) | 0.91 |
Concerning the correlation analysis, in the ASD group, CV at baseline was positively correlated with the item “initiation joint attention” of the ADOS-G (
In the TD group, a significant negative correlation between LF at task and Internalizing item of the CBCL Scale (
The first aim of our study was to test the feasibility of using wireless and wearable technology in toddlers with ASD and TD to record ECG signals, since these technologies are scarcely applied in these populations. The results of our study provided evidence of the feasibility of using a wearable unobtrusive chest strap for assessing ANS activation through ECG signal recording and analysis in such a sample. This could be particularly important in young age as it could provide important physiological markers of social functioning in toddlers with ASD, thus contributing to the diagnosis and the identification of the best therapeutic approach. The system proposed was already successfully applied by our group in schoolers with ASD (Billeci et al.,
The second aim of the study was to evaluate specific differences in ANS response between toddlers with ASD and typical peers during baseline and, in particular, in response to a JA task. Despite the relatively small sample size of the population enrolled in this research, autonomic dysregulation can be seen among ASD subjects. Indeed, both SDNN and CV (normalized SDNN) were higher in ASD at baseline, indicating an increased HRV at rest, before the start of the task. In fact, SDNN is an index of HRV, indicating the variability of the HR during the whole duration of recording. Indeed, SDNN is mathematically equal to total power of spectral analysis, and so reflects all the cyclic components responsible for variability in the period of recording (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology,
Literature regarding the activation of the ANS in ASD is somewhat inconsistent with some studies indicated an abnormally higher sympathetic, parasympathetic activity or finding no differences with TD (Klusek et al.,
Focusing on the LF power, it is worth noting that the trend from baseline to task is significantly different between the two groups. Indeed, while in ASD subjects the LF power decreased from baseline to task, TD children displayed the opposite trend, with an increased value of LF during the task. This fact suggests a significantly increased activation of the SNS among TD children during JA, normally concerned with mental effort in attention-demanding tasks (see, for example, Beauchaine et al.,
As a result, during the task, there is a higher LF in TD than in ASD although it does not reach significance. The positive role of increasing LF during the task, is confirmed by the negative correlation between LF during this phase and Internalizing item of the CBCL Scale I TD group.
In this research, however, RSA does not appear to be different between the two groups, nor between the two phases. RSA has been theoretically linked to social engagement (Porges et al.,
According to Porges (
Few studies have investigated changes in HRV in response to social events and stimuli in young children with ASD. Corona et al. (
Considering our preliminary results and the literature evidences, we could hypothesize that, aside the autonomic dysregulation noticed already at baseline, the ASD subjects did not display the increased SNS activation during the task seen among TD children, demonstrating a lower degree of mental engagement during JA.
Taken together, these results suggest that the unobtrusive measurement of ANS response during JA task, which was seen to be feasible and well tolerated also by ASD toddlers, could represent an early marker of social dysfunction in ASD, contributing to a more objective diagnosis and to the definition of a more tailored treatment protocol. However, as above mentioned, the procedure here described could probably form the basis for future investigations on this specific population in this field.
Some limitations need to be considered when interpreting the results of this study. First, the relatively small sample size should also be acknowledged, limiting the significance of the results we found. However, we should mention that the restricted age range of toddlers with ASD does not ease the recruitment of a numerous cohort. Second, we did not include a control group with a developmental quotient similar to that of the ASD group. The comparison with a TD group could prevent our result to be considered specific of ASD, and could be questioned whether the group effects reflect differences specifically due to ASD. Nevertheless, this limitation is reduced by the use of the nonverbal development quotient as a covariate in all between-subject comparisons. Finally, we evaluated ANS in response to videos made up of JA tasks, providing a partial assessment of JA as compared with a real-life situation; therefore, also the physiological response could be altered.
In the future, the findings of the study need to be replicated in a larger sample to prove the efficacy of the approach and to consolidate the results obtained. Larger samples will also allow for the evaluation of how different could be the physiological response of subgroups of children with ASD, i.e., high and low functioning children. As JA is an early marker of impairments in ASD, in a larger sample and even in young children it could be important to test the predictive value of ANS assessment for clinical outcomes in these groups. In addition, future studies could include multiple measures of the ANS, including additional HRV features (as the estimation of phasic contribution), GSR and respiration, using synchronized wearable sensors to evaluate relationships within and between components, and their relationships to social response during JA tasks.
In conclusion, in this study, we demonstrated the feasibility of using a wearable non-invasive technology for characterizing ANS response in toddlers with ASD during a social attention task. Our results possibly suggest autonomic dysregulation in ASD already at baseline. In addition, TD children showed an increased LF during the JA task, with an opposite trend with respect to ASD children. This result possibly demonstrates a different attitude toward JA, with a significantly higher mental effort performed by neurotypical children.
Both ASD and TD subjects, however, fail to exhibit a variation in RSA during JA, proving the necessity of future studies on larger cohorts. The results of this study foster the application of the proposed approach for evaluating physiological correlates of JA response in very young children and toddlers with ASD.
LB helped in data collection, guided in data and statistical analyses, and wrote the final version of the manuscript; AT performed the data and statistical analysis and made a manuscript draft; ZM helped in the data and statistical analysis; MV contributed in the ECG signals analysis; LB, AN, and FM conceived the study; AN and CL participated in data collection; AN, CL, SC, and FF participated in the clinical assessment of the subjects; FM was responsible of recruitment and diagnosis of children; FM, MV, and CL contributed in the discussion and approval of the paper.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors want to thank the participating children and their parents for their voluntary participation. They would like also to thank the ALERT group (Giulia Campatelli, Ilaria Rossi, Agnese Ballarani, Giovanni Pioggia, Liliana Ruta, Giulia Crifaci, Rossella Raso, Rosamaria Siracusano, Antonella Gagliano, Alessandra Darini, Valentina Comminiello and Carlo Calzone) for helping in data collection and children evaluation.