Eye Tracking Technology in Medicine: History
Please note this is an old version of this entry, which may differ significantly from the current revision.

Eye tracking provides a quantitative measure of eye movements during different activities. We report the results from a bibliometric analysis to investigate trends in eye tracking research applied to the study of different medical conditions. We conducted a search on the Web of Science Core Collection (WoS) database and analyzed the dataset of 2456 retrieved articles using VOSviewer and the Bibliometrix R package. The most represented area was psychiatry (503, 20.5%) followed by neuroscience (465, 18.9%) and psychology developmental (337, 13.7%). The annual scientific production growth was 11.14% and showed exponential growth with three main peaks in 2011, 2015 and 2017. Extensive collaboration networks were identified between the three countries with the highest scientific production, the USA (35.3%), the UK (9.5%) and Germany (7.3%). Based on term co-occurrence maps and analyses of sources of articles, we identified autism spectrum disorders as the most investigated condition and conducted specific analyses on 638 articles related to this topic which showed an annual scientific production growth of 16.52%. The majority of studies focused on autism used eye tracking to investigate gaze patterns with regards to stimuli related to social interaction. Our analysis highlights the widespread and increasing use of eye tracking in the study of different neurological and psychiatric conditions. 

  • eye tracking
  • gaze tracking
  • autism spectrum disorders
  • psychiatric disorders
  • eye movements

1. Introduction

Eye tracking is a technique used to measure and study the range of eye movements of participants while they are engaged in different activities (e.g., during reading, assessment of a visual stimulus and so on), in a non-invasive way and with a high degree of precision. The assessment of eye movements is made through the eye tracker, a device that sends out a beam of invisible near-infrared light that is reflected in the cornea. After the reflection is collected by the eye tracker’s sensors, it is possible to apply algorithms to calculate where a person is looking. An eye tracker can capture the position of the eyes several times per second. It is, therefore, possible to produce a visual map to measure how and for how long the person looked at different visual stimuli. In addition to being useful for several commercial applications (e.g., to assess the impact of different aspects of packaging and/or to improve their visual presentation [1][2], evaluate the usability of websites [3] and so on), eye tracking has been increasingly applied to the study of different medical conditions, such as neurological and psychiatric disorders, based on the observation that eye movement can provide insights into cognitive processing [4].
Considerable evidence suggests that patients with different psychiatric and neurological conditions show abnormalities in eye movement. For instance, autism spectrum disorders (a group of neurodevelopmental disorders characterized by repetitive behaviors and alterations in social interaction/communication, language and nonverbal communication [5]) are associated with alterations in gaze patterns when exposed to different types of stimuli [6]. Moreover, patients with schizophrenia or bipolar disorder show distinct patterns of eye movement during smooth pursuit and visual search [7]. While studies on eye movement characteristics in these disorders have been conducted since the early 1900s, the increasing accessibility of eye tracking technology has made it possible to provide precise and quantitative measurements of these impairments. In the last few years, an increasing number of studies have investigated gaze behavior in individuals with different psychiatric disorders besides those already mentioned (for instance, eating disorders [8]) or with neurological disorders. For instance, eye tracking has been applied to the study of stroke [9], brain injury [10] or neurodegenerative disorders such as Alzheimer’s disorder or Parkinson’s disorder [11][12].
The eye tracking technique produces objective data, which are not influenced by the opinions of the subjects carrying out the study or by those who analyze the results. Moreover, the eye tracker can also be used in combination with other neurophysiological or brain imaging techniques, such as electroencephalography (EEG) or magnetic resonance imaging (MRI), to increase the understanding of the underlying neurobiological processes. Overall, eye tracking represents a non-invasive technique to collect objective and precise data allowing for the study of complex phenotypes such as cognition, emotion and social interaction.
In this entry, we provide a comprehensive and up-to-date bibliometric analysis of studies that applied eye tracking technology to different medical fields, aiming to describe trends regarding the most investigated conditions and to identify countries and institutions with the highest scientific production and map collaboration networks. Our analysis fills a gap in the literature by providing novel insights on the current trends as well as emerging and declining themes in the application of eye tracking to the study of different medical conditions.

2. Current Insights

In this entry, we conducted a bibliometric analysis of studies investigating eye tracking in different medical conditions. We observed a substantial growth of the use of this technology in the last few years and identified strong collaboration networks among the countries with the highest scientific production (USA, UK and Germany). This substantial growth might be explained by the increased availability of eye tracking systems at different price ranges as well as by open-source data acquisition software. Based on the most frequent keywords, autism spectrum disorders represented the most investigated condition, followed by other psychiatric disorders (schizophrenia and depression) and neurodegenerative disorders (Parkinson’s disease and Alzheimer’s disease). Different causes might explain the observed relevance of eye tracking technology for research on autism spectrum disorders. First, autism represents a neurodevelopmental disorder and eye tracking is a non-invasive technique that can be applied to studies including infants and children. Second, different studies support the hypothesis that patients with this condition focus their attention on different types of stimuli compared to typically developing individuals, especially in the case of stimuli related to social interactions. Indeed, an analysis of the most cited articles retrieved in our search and related to autism identified social interactions and face processing as the most investigated field. The majority of these articles used eye tracking to investigate differences in gaze patterns during vision exposure to social stimuli, such as faces or social events, either in patients with autism [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], in their siblings [28][29] or participants at risk of autism [30] compared with typically developing controls. Some of these studies combined eye tracking with additional techniques such as MRI in order to investigate whether the identified differences in gaze patterns were associated with neurobiological abnormalities [28][13][31]. In the most cited article, Dalton and colleagues showed that patients with autism spent less time looking at the eye region when presented with photographs of human faces compared to typically developing controls [13]. The authors showed that the amount of time spent observing the eye region positively correlated with the activation of a specific brain area (fusiform gyrus), measured with MRI. In a subsequent study, the authors also showed similar findings in unaffected siblings of patients with autism spectrum disorders compared with controls [28]. Abnormalities identified using eye tracking might also be associated with the increased severity of illness as shown by Jones and colleagues in a study including 2-year-old children with autism [14]. In this study, children with autism spent less time looking at the eyes and more time looking at the mouths of people pictured in videos compared with typically developing children or children with a developmental delay different from autism. Importantly, children with autism showing a low number of fixations located on eyes also showed higher levels of social disability [14].
Our analysis of trend topics supported the relevance of autism spectrum disorders as a central theme and identified “deep learning” as the niche theme that is mostly reported in recent articles. This finding might be explained by the fact that, in the last few years, a number of studies have started to explore the utility of eye tracking markers to improve the diagnosis of autism spectrum disorders. While different pipelines to analyze eye tracking data are available, increasing attention is now focused on the development of visual attention models using machine learning methods [32][33][34][35][36]. Since the diagnosis of autism is challenging and no biomarker is available [37], the development of computational models based on early abnormalities such as the differences in gaze processing might be of substantial help to improve and anticipate the diagnosis, thus, making it possible to initiate treatment at an earlier stage, when it is most effective [38]. Eye tracking measurements that might prove to be useful as early biomarkers include dysregulations in pupil dilation [39][40][41], changes in saccadic behavior, differences in gaze patterns during vision exposure to social stimuli [42][43][44] and analysis of scan paths or gaze patterns [45][46][47][48][49][50]. Some studies combined eye tracking data with other measurements such as resting-state EEG data [51][52][53]. Using this approach, Kang and colleagues showed a classification accuracy of up to 85.44% using a support vector machine (SVM) classifier to discriminate children aged from 3 to 6 with autism from controls [51]. Even higher performances were shown by another study conducted by Li and colleagues, using the three-layer Long Short-Term Memory (LSTM) network to discriminate 136 children with autism compared with 136 typically developing children based on gaze patterns during observations of 272 videos [54]. In this study, accuracy improved from 86.4% to 92.6% using LSTM compared to SVM [54]. Overall, results from these studies appear to be promising, although further research will be needed to evaluate their potential utility in the clinical setting and better assess the role of potential confounding factors that might affect gaze patterns (such as age, disease severity and duration, cognitive functioning and so on). Similarly, eye tracking measurements might be useful as prognostic markers [55] or to evaluate the efficacy of different types of interventions [56][57][58][59].
While we conducted a comprehensive analysis with no date and language restrictions, findings from our article must be interpreted in light of some limitations. First, our search might have missed some articles in which the terms included in our search strategy were mentioned in the main text but not in the title, abstract or keywords. In addition, our search might include some articles that mentioned the terms included in our search strategy but in which eye tracking experiments were not conducted (for instance reviews incorrectly classified by WOS as research articles, or articles in which new analytical methods were proposed). Despite these limitations, our analysis of large numbers of documents provides an updated and comprehensive picture of the use of eye tracking to study different medical conditions and highlights relevant trends with regards to investigated disorders and specific applications.
In conclusion, the eye tracking technique is increasingly being used in the study of different medical conditions. In autism spectrum disorders, eye tracking is widely used to evaluate eye movement abnormalities during face processing or looking at other social stimuli. Studies developing machine learning models provided promising evidence to support the utility of eye tracking measurements as biomarkers to discriminate patients with autism spectrum disorders from typically developing controls.

This entry is adapted from the peer-reviewed paper 10.3390/vision5040056


  1. Bix, L.; Sundar, R.P.; Bello, N.M.; Peltier, C.; Weatherspoon, L.J.; Becker, M.W. To See or Not to See: Do Front of Pack Nutrition Labels Affect Attention to Overall Nutrition Information? PLoS ONE 2015, 10, e0139732.
  2. McNeill, A.; Gravely, S.; Hitchman, S.C.; Bauld, L.; Hammond, D.; Hartmann-Boyce, J. Tobacco packaging design for reducing tobacco use. Cochrane Database Syst. Rev. 2017, 4, CD011244.
  3. Zammarchi, G.; Frigau, L.; Mola, F. Markov chain to analyze web usability of a university website using eye tracking data. Stat. Anal. Data Min. 2021, 14, 331–341.
  4. Liversedge, S.P.; Findlay, J.M. Saccadic eye movements and cognition. Trends Cogn. Sci. 2000, 4, 6–14.
  5. Persico, A.M.; Ricciardello, A.; Cucinotta, F. The psychopharmacology of autism spectrum disorder and Rett syndrome. Handb. Clin. Neurol. 2019, 165, 391–414.
  6. Howard, P.L.; Zhang, L.; Benson, V. What Can Eye Movements Tell Us about Subtle Cognitive Processing Differences in Autism? Vision 2019, 3, 22.
  7. Morita, K.; Miura, K.; Kasai, K.; Hashimoto, R. Eye movement characteristics in schizophrenia: A recent update with clinical implications. Neuropsychopharmacol. Rep. 2020, 40, 2–9.
  8. Giel, K.E.; Friederich, H.C.; Teufel, M.; Hautzinger, M.; Enck, P.; Zipfel, S. Attentional processing of food pictures in individuals with anorexia nervosa--an eye-tracking study. Biol. Psychiatry 2011, 69, 661–667.
  9. Walle, K.M.; Nordvik, J.E.; Becker, F.; Espeseth, T.; Sneve, M.H.; Laeng, B. Unilateral neglect post stroke: Eye movement freqauencies indicate directional hypokinesia while fixation distributions suggest compensational mechanism. Brain Behav. 2019, 9, e01170.
  10. Hunfalvay, M.; Murray, N.P.; Roberts, C.M.; Tyagi, A.; Barclay, K.W.; Carrick, F.R. Oculomotor Behavior as a Biomarker for Differentiating Pediatric Patients with Mild Traumatic Brain Injury and Age Matched Controls. Front. Behav. Neurosci. 2020, 14, 581819.
  11. Archibald, N.K.; Hutton, S.B.; Clarke, M.P.; Mosimann, U.P.; Burn, D.J. Visual exploration in Parkinson’s disease and Parkinson’s disease dementia. Brain 2013, 136, 739–750.
  12. Boucart, M.; Bubbico, G.; Szaffarczyk, S.; Pasquier, F. Animal spotting in Alzheimer’s disease: An eye tracking study of object categorization. J. Alzheimer’s Dis. 2014, 39, 181–189.
  13. Dalton, K.M.; Nacewicz, B.M.; Johnstone, T.; Schaefer, H.S.; Gernsbacher, M.A.; Goldsmith, H.H.; Alexander, A.L.; Davidson, R.J. Gaze fixation and the neural circuitry of face processing in autism. Nat. Neurosci. 2005, 8, 519–526.
  14. Jones, W.; Carr, K.; Klin, A. Absence of preferential looking to the eyes of approaching adults predicts level of social disability in 2-year-old toddlers with autism spectrum disorder. Arch. Gen. Psychiatry 2008, 65, 946–954.
  15. Riby, D.M.; Hancock, P.J. Do faces capture the attention of individuals with Williams syndrome or autism? Evidence from tracking eye movements. J. Autism Dev. Disord. 2009, 39, 421–431.
  16. Speer, L.L.; Cook, A.E.; McMahon, W.M.; Clark, E. Face processing in children with autism: Effects of stimulus contents and type. Autism 2007, 11, 265–277.
  17. Young, G.S.; Merin, N.; Rogers, S.J.; Ozonoff, S. Gaze behavior and affect at 6 months: Predicting clinical outcomes and language development in typically developing infants and infants at risk for autism. Dev. Sci. 2009, 12, 798–814.
  18. Nacewicz, B.M.; Dalton, K.M.; Johnstone, T.; Long, M.T.; McAuliff, E.M.; Oakes, T.R.; Alexander, A.L.; Davidson, R.J. Amygdala volume and nonverbal social impairment in adolescent and adult males with autism. Arch. Gen. Psychiatry 2006, 63, 1417–1428.
  19. van der Geest, J.N.; Kemner, C.; Verbaten, M.N.; van Engeland, H. Gaze behavior of children with pervasive developmental disorder toward human faces: A fixation time study. J. Child Psychol. Psychiatry 2002, 43, 669–678.
  20. Neumann, D.; Spezio, M.L.; Piven, J.; Adolphs, R. Looking you in the mouth: Abnormal gaze in autism resulting from impaired top-down modulation of visual attention. Soc. Cogn. Affect. Neurosci. 2006, 1, 194–202.
  21. Nakano, T.; Tanaka, K.; Endo, Y.; Yamane, Y.; Yamamoto, T.; Nakano, Y.; Ohta, H.; Kato, N.; Kitazawa, S. Atypical gaze patterns in children and adults with autism spectrum disorders dissociated from developmental changes in gaze behaviour. Proc. Biol. Sci. 2010, 277, 2935–2943.
  22. Chawarska, K.; Shic, F. Looking but not seeing: Atypical visual scanning and recognition of faces in 2 and 4-year-old children with autism spectrum disorder. J. Autism Dev. Disord. 2009, 39, 1663–1672.
  23. Wang, S.; Jiang, M.; Duchesne, X.M.; Laugeson, E.A.; Kennedy, D.P.; Adolphs, R.; Zhao, Q. Atypical Visual Saliency in Autism Spectrum Disorder Quantified through Model-Based Eye Tracking. Neuron 2015, 88, 604–616.
  24. Riby, D.M.; Hancock, P.J. Viewing it differently: Social scene perception in Williams syndrome and autism. Neuropsychologia 2008, 46, 2855–2860.
  25. Fletcher-Watson, S.; Leekam, S.R.; Benson, V.; Frank, M.C.; Findlay, J.M. Eye-movements reveal attention to social information in autism spectrum disorder. Neuropsychologia 2009, 47, 248–257.
  26. Sasson, N.J.; Turner-Brown, L.M.; Holtzclaw, T.N.; Lam, K.S.; Bodfish, J.W. Children with autism demonstrate circumscribed attention during passive viewing of complex social and nonsocial picture arrays. Autism Res. 2008, 1, 31–42.
  27. Chawarska, K.; Macari, S.; Shic, F. Context modulates attention to social scenes in toddlers with autism. J. Child Psychol. Psychiatry 2012, 53, 903–913.
  28. Dalton, K.M.; Nacewicz, B.M.; Alexander, A.L.; Davidson, R.J. Gaze-fixation, brain activation, and amygdala volume in unaffected siblings of individuals with autism. Biol. Psychiatry 2007, 61, 512–520.
  29. Merin, N.; Young, G.S.; Ozonoff, S.; Rogers, S.J. Visual Fixation Patterns during Reciprocal Social Interaction Distinguish a Subgroup of 6-Month-Old Infants At-Risk for Autism from Comparison Infants. J. Autism Dev. Disord. 2007, 37, 108–121.
  30. Chawarska, K.; Macari, S.; Shic, F. Decreased spontaneous attention to social scenes in 6-month-old infants later diagnosed with autism spectrum disorders. Biol. Psychiatry 2013, 74, 195–203.
  31. Elison, J.T.; Paterson, S.J.; Wolff, J.J.; Reznick, J.S.; Sasson, N.J.; Gu, H.; Botteron, K.N.; Dager, S.R.; Estes, A.M.; Evans, A.C.; et al. White matter microstructure and atypical visual orienting in 7-month-olds at risk for autism. Am. J. Psychiatry 2013, 170, 899–908.
  32. Chang, Z.; Di Martino, J.M.; Aiello, R.; Baker, J.; Carpenter, K.; Compton, S.; Davis, N.; Eichner, B.; Espinosa, S.; Flowers, J.; et al. Computational Methods to Measure Patterns of Gaze in Toddlers with Autism Spectrum Disorder. JAMA Pediatr. 2021, 175, 827–836.
  33. Oliveira, J.S.; Franco, F.O.; Revers, M.C.; Silva, A.F.; Portolese, J.; Brentani, H.; Machado-Lima, A.; Nunes, F.L.S. Computer-aided autism diagnosis based on visual attention models using eye tracking. Sci. Rep. 2021, 11, 10131.
  34. He, Q.; Wang, Q.; Wu, Y.; Yi, L.; Wei, K. Automatic classification of children with autism spectrum disorder by using a computerized visual-orienting task. Psych J. 2021, 10, 550–565.
  35. Carette, R.; Cilia, F.; Dequen, G.; Bosche, J.; Guerin, J.L.; Vandromme, L.; Carette, R.; Cilia, F.; Dequen, G.; Bosche, J.; et al. Automatic Autism Spectrum Disorder Detection Thanks to Eye-Tracking and Neural Network-Based Approach. Internet Things (Iot) Technol. Healthc. 2017, 225, 75–81.
  36. Vabalas, A.; Gowen, E.; Poliakoff, E.; Casson, A.J. Applying Machine Learning to Kinematic and Eye Movement Features of a Movement Imitation Task to Predict Autism Diagnosis. Sci. Rep. 2020, 10, 8346.
  37. Hayes, J.; Ford, T.; Rafeeque, H.; Russell, G. Clinical practice guidelines for diagnosis of autism spectrum disorder in adults and children in the UK: A narrative review. BMC Psychiatry 2018, 18, 222.
  38. Frazier, T.W.; Coury, D.L.; Sohl, K.; Wagner, K.E.; Uhlig, R.; Hicks, S.D.; Middleton, F.A. Evidence-based use of scalable biomarkers to increase diagnostic efficiency and decrease the lifetime costs of autism. Autism Res. 2021, 14, 1271–1283.
  39. Anderson, C.J.; Colombo, J. Larger tonic pupil size in young children with autism spectrum disorder. Dev. Psychobiol. 2009, 51, 207–211.
  40. Anderson, C.J.; Colombo, J.; Unruh, K.E. Pupil and salivary indicators of autonomic dysfunction in autism spectrum disorder. Dev. Psychobiol. 2013, 55, 465–482.
  41. Camero, R.; Martinez, V.; Gallego, C. Gaze Following and Pupil Dilation as Early Diagnostic Markers of Autism in Toddlers. Children (Basel) 2021, 8, 113.
  42. Buffle, P.; Cavadini, T.; Posada, A.; Gentaz, E. A study on visual preference for social stimuli in typical Ecuadorian preschoolers as a contribution to the identification of autism risk factors. Sci. Rep. 2021, 11, 8461.
  43. Jiang, M.; Francis, S.M.; Srishyla, D.; Conelea, C.; Zhao, Q.; Jacob, S.; Jiang, M.; Francis, S.M.; Srishyla, D.; Conelea, C.; et al. Classifying Individuals with ASD through Facial Emotion Recognition and Eye-Tracking. In Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; pp. 6063–6068.
  44. Vargas-Cuentas, N.I.; Roman-Gonzalez, A.; Gilman, R.H.; Barrientos, F.; Ting, J.; Hidalgo, D.; Jensen, K.; Zimic, M. Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children. PLoS ONE 2017, 12, e0188826.
  45. Chatterjee, M.; Manyakov, N.V.; Bangerter, A.; Kaliukhovich, D.A.; Jagannatha, S.; Ness, S.; Pandina, G. Learning Scan Paths of Eye Movement in Autism Spectrum Disorder. Stud. Health Technol. Inform. 2020, 270, 287–291.
  46. Cho, K.W.; Lin, F.; Song, C.; Xu, X.W.; Hartley-McAndrew, M.; Doody, K.R.; Xu, W.Y.; Cho, K.W.; Lin, F.; Song, C.; et al. Gaze-Wasserstein: A Quantitative Screening Approach to Autism Spectrum Disorders. In Proceedings of the 2016 IEEE Wireless Health (WH), Bethesda, MD, USA, 25–27 October 2016; pp. 14–21.
  47. Falck-Ytter, T.; Nystrom, P.; Gredeback, G.; Gliga, T.; Bolte, S.; The EASE Team. Reduced orienting to audiovisual synchrony in infancy predicts autism diagnosis at 3 years of age. J. Child Psychol. Psychiatry 2018, 59, 872–880.
  48. Fujioka, T.; Inohara, K.; Okamoto, Y.; Masuya, Y.; Ishitobi, M.; Saito, D.N.; Jung, M.; Arai, S.; Matsumura, Y.; Fujisawa, T.X.; et al. Gazefinder as a clinical supplementary tool for discriminating between autism spectrum disorder and typical development in male adolescents and adults. Mol. Autism 2016, 7, 19.
  49. Fukushima, S.; Takahashi, T.; Tsukamoto, K.; Matsumura, M.; Takigawa, R.; Sakai, Y.; Maniwa, S.; Murphy, L.; Taketani, T. The feasibility of Gazefinder under 12 months of age infants. Sci. Rep. 2021, 11, 10009.
  50. Tsuchiya, K.J.; Hakoshima, S.; Hara, T.; Ninomiya, M.; Saito, M.; Fujioka, T.; Kosaka, H.; Hirano, Y.; Matsuo, M.; Kikuchi, M.; et al. Diagnosing Autism Spectrum Disorder without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder. Front. Neurol. 2020, 11, 603085.
  51. Kang, J.; Han, X.; Song, J.; Niu, Z.; Li, X. The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data. Comput. Biol. Med. 2020, 120, 103722.
  52. Thapaliya, S.; Jayarathna, S.; Jaime, M.; Thapaliya, S.; Jayarathna, S.; Jaime, M. Evaluating the EEG and Eye Movements for Autism Spectrum Disorder. In Proceedings of the IEEE International Conference on Big Data (BIG DATA), Seattle, WA, USA, 10–13 December 2018; pp. 2328–2336.
  53. Zhang, S.; Chen, D.; Tang, Y.; Zhang, L. Children ASD Evaluation through Joint Analysis of EEG and Eye-Tracking Recordings with Graph Convolution Network. Front. Hum. Neurosci. 2021, 15, 651349.
  54. Li, J.; Zhong, Y.H.; Han, J.X.; Ouyang, G.X.; Li, X.L.; Liu, H.H.; Li, J.; Zhong, Y.; Han, J.; Ouyang, G.; et al. Classifying ASD children with LSTM based on raw videos. Neurocomputing 2020, 390, 226–238.
  55. Bacon, E.C.; Moore, A.; Lee, Q.; Carter Barnes, C.; Courchesne, E.; Pierce, K. Identifying prognostic markers in autism spectrum disorder using eye tracking. Autism 2020, 24, 658–669.
  56. Aresti-Bartolome, N.; Garcia-Zapirain, B. Cognitive rehabilitation system for children with autism spectrum disorder using serious games: A pilot study. Biomed. Mater. Eng. 2015, 26 (Suppl. 1), S811–S824.
  57. Bradshaw, J.; Shic, F.; Holden, A.N.; Horowitz, E.J.; Barrett, A.C.; German, T.C.; Vernon, T.W. The Use of Eye Tracking as a Biomarker of Treatment Outcome in a Pilot Randomized Clinical Trial for Young Children with Autism. Autism Res. 2019, 12, 779–793.
  58. Gepner, B.; Charrier, A.; Arciszewski, T.; Tardif, C. Slowness Therapy for Children with Autism Spectrum Disorder: A Blind Longitudinal Randomized Controlled Study. J. Autism Dev. Disord. 2021. online ahead of print.
  59. Strathearn, L.; Kim, S.; Bastian, D.A.; Jung, J.; Iyengar, U.; Martinez, S.; Goin-Kochel, R.P.; Fonagy, P. Visual systemizing preference in children with autism: A randomized controlled trial of intranasal oxytocin. Dev. Psychopathol. 2018, 30, 511–521.
This entry is offline, you can click here to edit this entry!
Video Production Service