Pupil adaptation corresponds to quantitative measures of autism traits in children

DiCriscio, Antoinette Sabatino; Troiani, Vanessa

doi:10.1038/s41598-017-06829-1

Cited by 32 publications

(30 citation statements)

References 51 publications

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“…However, there are reports (77) that link tumors in the pineal gland and peduncular hallucinosis (78) and florid psychotic symptoms (79). In connection to ASD, some autistic behavior such as synesthesia (80), sensory sensitivity, hyper attention to visual details (81), attraction to geometric shape (82), hallucinations (83), and extended pupils (84, 85) [note that this suppresses melatonin synthesis (86)] might be explained as a mild DMT psychedelic effect. Though, some of these symptoms such as synesthesia, might be the result of hyperconnectivity due to abnormal maturity of the brain (87), which as shown by Ly et al (27) could also be a possible effect of DMT.…”

Section: The Endogenous Dmt From the Pineal Glandmentioning

confidence: 99%

Updated View on the Relation of the Pineal Gland to Autism Spectrum Disorders

Shomrat

Nesher

2019

Front. Endocrinol.

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Identification of the biological features of autism is essential for designing an efficient treatment and for prevention of the disorder. Though the subject of extensive research, the neurophysiological features of autism remain unclear. One of the proposed biological causes of autism is malfunction of the pineal gland and deficiency of its principal hormone, melatonin. The main function of melatonin is to link and synchronize the body's homeostasis processes to the circadian and seasonal rhythms, and to regulate the sleep-wake cycle. Therefore, pineal dysfunction has been implicated based on the common observation of low melatonin levels and sleep disorders associated with autism. In this perspective, we highlight several recent findings that support the hypothesis of pineal gland/melatonin involvement in autism. Another common symptom of autism is abnormal neuroplasticity, such as cortical overgrowth and dendritic spine dysgenesis. Here, we synthesize recent information and speculate on the possibility that this abnormal neuroplasticity is caused by hyperactivity of endogenous N,N-dimethyltryptamine (DMT). The pineal gland was proposed as the source of DMT in the brain and therefore, our assumption is that besides melatonin deficiency, pineal dysfunction might also play a part in the development of autism through abnormal metabolism of DMT. We hope that this manuscript will encourage future research of the DMT hypothesis and reexamination of several observations that were previously attributed to other factors, to see if they could be related to pineal gland/melatonin malfunction. Such research could contribute to the development of autism treatment by exogenous melatonin and monitored light exposure.

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Section: The Endogenous Dmt From the Pineal Glandmentioning

confidence: 99%

Updated View on the Relation of the Pineal Gland to Autism Spectrum Disorders

Shomrat

Nesher

2019

Front. Endocrinol.

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“…Laeng, Ørbo, Holmlund, & Miozzo, 2011; Mathôt, Dalmaijer, Grainger, & Van der Stigchel, 2014; Mathôt et al, 2013) and is linked to activity in a network of brain regions and brainstem nuclei (Beatty & Lucero-Wagoner, 2000; Corbett, Mendoza, Abdullah, Wegelin, & Levine, 2006; Corbett, Mendoza, Wegelin, Carmean, & Levine, 2008; Hou, Langley, Szabadi, & Bradshaw, 2007; Stenberg, 2007). Previous work showing greater baseline PD (Anderson & Colombo, 2009; Anderson et al, 2013) and meaningful variability in reflexive pupil response (DiCriscio & Troiani, 2017; Fan et al, 2009a) in ASD has suggested atypical or discordant autonomic arousal may drive these differences; however, it remains unclear how pupil measurements may be associated with individual differences in RST activity.…”

Section: Introductionmentioning

confidence: 97%

“…Instead of a brief and transient stimulus, sustained monochromatic stimuli are presented and functional response metrics including the amplitude peak pupil response are extracted. DiCriscio and Troiani (2017), using a simple eye tracking paradigm, assessed characteristic patterns of pupil dilation and constriction in response to alternating dark and light stimuli across a clinically heterogeneous cohort of children (i.e. with and without ASD and with a broad range of cognitive abilities).…”

Section: Introductionmentioning

confidence: 99%

Resting and functional pupil response metrics indicate features of reward sensitivity and Autism Spectrum Disorder in children

DiCriscio

Troiani

2020

Preprint

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Altered motivational drives and aberrant reward system function may contribute to the social impairments observed in autism spectrum disorders (ASD). Pupil metrics have been highlighted as peripheral indicators of autonomic arousal and reward system function, specifically noradrenergic and dopaminergic activity that influence motivational drive states. However, research on individual differences in the neurobiological correlates of reward responsivity and clinically relevant features associated with ASD is sparse. The goal of the current study was to examine the relationship between measures of sensitivity to punishment and reward, ASD features, and resting as well as functional pupil response metrics across a clinically heterogeneous pediatric sample. We assessed whether quantitative features of reward sensitivity are linearly related to core clinical features of ASD. Pupil metrics were measured using a passive eye tracking task. Scores on a parent-report measure of punishment and reward sensitivity were found to be positively correlated with ASD features. Given these relationships, we assessed whether pupil measurements could be used as a neurobiological correlate of reward sensitivity and predictor of clinically significant ASD traits. In a logistic regression model, we find that the amplitude of pupil dilation, along with sex and full-scale IQ, could be used to correctly classify 84.9% of participants as having an ASD diagnosis versus not having an ASD diagnosis. This research highlights individual differences of reward sensitivity that scale with ASD features. Furthermore, reported results emphasize that functional pupil response metrics and other objective patient-level variables can be used together as predictors of ASD diagnostic status.

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“…Vargas-Cuentas et al (2017) designed an eye tracking algorithm to measure the gaze preferences of children with ASD and of a healthy control group when viewing social scenes and abstract scenes shown simultaneously on the left and the right sides of the screen, respectively, and achieved very high accuracy in the classification of children with ASD and healthy children. Dicriscio and Troiani (2017) implemented a paradigm designed to capture patterns of pupil adaptation in children with ASD during sustained periods in dark and light environments. It was found that pupil dilation features are related to individual differences measured by the Social Responsiveness Scale, a measurement for autism traits.…”

Section: Introductionmentioning

confidence: 99%

Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest

Mao

Liu

et al. 2020

Front. Neurosci.

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Medical research shows that eye movement disorders are related to many kinds of neurological diseases. Eye movement characteristics can be used as biomarkers of Parkinson's disease, Alzheimer's disease (AD), schizophrenia, and other diseases. However, due to the unknown medical mechanism of some diseases, it is difficult to establish an intuitive correspondence between eye movement characteristics and diseases. In this paper, we propose a disease classification method based on decision tree and random forest (RF). First, a variety of experimental schemes are designed to obtain eye movement images, and information such as pupil position and area is extracted as original features. Second, with the original features as training samples, the long short-term memory (LSTM) network is used to build classifiers, and the classification results of the samples are regarded as the evolutionary features. After that, multiple decision trees are built according to the C4.5 rules based on the evolutionary features. Finally, a RF is constructed with these decision trees, and the results of disease classification are determined by voting. Experiments show that the RF method has good robustness and its classification accuracy is significantly better than the performance of previous classifiers. This study shows that the application of advanced artificial intelligence (AI) technology in the pathological analysis of eye movement has obvious advantages and good prospects.

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Pupil adaptation corresponds to quantitative measures of autism traits in children

Cited by 32 publications

References 51 publications

Updated View on the Relation of the Pineal Gland to Autism Spectrum Disorders

Updated View on the Relation of the Pineal Gland to Autism Spectrum Disorders

Resting and functional pupil response metrics indicate features of reward sensitivity and Autism Spectrum Disorder in children

Disease Classification Based on Eye Movement Features With Decision Tree and Random Forest

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