Association of autism tendency and hemodynamic changes in the prefrontal cortex during facial expression stimuli measured by multi‐channel near‐infrared spectroscopy

Hosokawa, Mai; Nakadoi, Yoshihiro; Watanabe, Yukina; Sumitani, Satsuki; Ohmori, Tetsuro

doi:10.1111/pcn.12240

Cited by 16 publications

(21 citation statements)

References 22 publications

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“…This is in line with a functional magnetic resonance imaging (fMRI) study showing that higher AQ scores were associated with decreased white matter volume and deactivation of the STS (Von dem Hagen et al., ). In a more direct comparison of a clinical and nonclinical group, Hosokawa, Nakadoi, Watanabe, Sumitani, and Ohmori () applied a research paradigm to TD individuals that had previously been used on individuals with ASD. Their results showed that the degree of autistic traits people exhibited negatively correlated with the hemodynamic change the PFC showed in response to negative facial expressions.…”

Section: Introductionmentioning

confidence: 99%

Spontaneous resting‐state gamma oscillations are not predictive of autistic traits in the general population

Groot

Strien

2018

Eur J of Neuroscience

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The autism spectrum hypothesis states that not only diagnosed individuals but also individuals from the general population exhibit a certain amount of autistic traits. While this idea is supported by neuroimaging studies, there have been few electrophysiological studies. In particular, there have been no spontaneous resting‐state studies yet. In order to examine the autism spectrum hypothesis, the present study tried to predict the level of autistic traits typically developing young adults (n = 93) exhibit from spontaneous resting‐state gamma power, a measure that has been linked to social functioning impairments seen in autism. The influence of age and gender was controlled for by employing regression. It was expected that enhanced gamma activity would be predictive of self‐reported autistic traits. The model with only age and gender included reached significance, with higher age within this student population being related to more autistic traits. However, no relationship between either low (30–50 Hz) or high (50–70 Hz) gamma power and autistic traits was found. Models with eyes closed low gamma asymmetry and eyes closed high gamma asymmetry included did reach significance, but these findings were not robust, and the gamma asymmetry explained very little additional variance above age and gender. In addition, exploratory correlation analyses showed no relationship between the other power spectra (delta, theta, alpha and beta) on the one hand and autistic traits on the other hand, suggesting that any relationship between spontaneous resting‐state brain electrophysiology and autistic traits might not be strong enough to be detected in the general population.

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Section: Introductionmentioning

confidence: 99%

Spontaneous resting‐state gamma oscillations are not predictive of autistic traits in the general population

Groot

Strien

2018

Eur J of Neuroscience

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“…The expression recognition task was developed by referencing the facial expressions that Hosokawa et al used eight facial expressions (anger, sadness, happiness, contempt, disgust, surprise, fear and neutral) [22]. Static images of Japanese models were selected from the ATR (ATR International Human Information Science Laboratories) face database containing eight facial expressions [28].…”

Section: Expression Recognition Taskmentioning

confidence: 99%

“…On the other hand, Hosokawa et al examined the relationship between autistic tendencies and the frontal cortex blood mobilization in expression recognition tasks in healthy people [22]. Results showed that when a healthy person exhibited thoughts and behavioral patterns characteristic of ASD, it was difficult for them to recognize expressions, and they had decreased prefrontal cortex activity.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Expression Recognition Function in Autism Spectrum Disorder Using Near-Infrared Spectroscopy

Furukawa¹,

Mori²,

Mori³

et al. 2018

OJPsych

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One of the characteristics of Autism Spectrum Disorder (ASD) is social disorder. The specificity of facial and expression recognition for people with ASD is gathering attention as a factor of this social disorder. The study examined the hemodynamic activities in the prefrontal cortex using near-infrared spectroscopy (NIRS) when a person with ASD performed an expression recognition task. The subjects were twenty males (18 -22 years old) with ASD and without intellectual disabilities. Forty-five healthy males matched for age and sex were included as a control group. In both groups, the degree of autistic tendencies was evaluated using the Autism-Spectrum Quotient (AQ). Using eight standard emotional expressions of Japanese people, two expression recognition tasks were set. An NIRS was used to measure the prefrontal cortex blood mobilization during the expression-processing process. The AQ was significantly higher in the ASD group, while the rate of overall correct expression response was significantly lower (p < 0.001). A negative correlation was found when the relationship between the AQ and the rate of overall correct expression response was evaluated by combining the results of the control group with those of the ASD group (ρ = −0.40 p < 0.001). In the automatic expression-processing task, no activation in the prefrontal cortex was found in either the ASD or the control group. In the conscious expression-processing task, the activation of the left and right lateral prefrontal cortex was weaker in the ASD group compared to the control group. Unlike in the control group, a mild activation of posterior prefrontal cortex was found in the ASD group. The expression-processing process of the ASD group was found to be different from that of the control group. NIRS was effective in detecting a brain function disorder in people with ASD during an expression-processing process.

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“…The high autism-trait group had poorer performance when recognising anger, disgust and sadness, as shown by more errors and a higher intensity needed to identify the emotions, compared to the low autismtrait group. Similarly, Hosokawa, Nakadoi, Watanabe, Sumitani and Ohmori (2015) found that higher AQ scores were negatively correlated with the ability to identify fearful facial expressions.…”

Section: The Broader Autism Phenotypementioning

confidence: 88%

“…Several studies have demonstrated associations between non-clinical autism-like traits and differences in emotion processing. Participants with higher levels of autism-like traits show less spontaneous imitation of facial expressions, and poorer recognition of negative facial expressions, compared to those with lower levels of autism-like traits (Hermans et al, 2009;Hosokawa et al, 2015;Poljac, Poljac & Wagemans, 2012). Additionally, those with higher levels of autism-like traits show poorer understanding of general social non-verbal behaviours and poorer recognition of facial emotion, compared to those with lower levels of autism-like traits (Ingersoll, 2010).…”

Section: Introductionmentioning

confidence: 92%

Emotion processing and the broader autism phenotype

West¹

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The processing of emotion is fundamental in various aspects of human experience. However, our understanding of emotional processes is incomplete, particularly in relation to language processing. Further, individuals such as those with an autism spectrum disorder (ASD) often show differences in their perception and understanding of emotion. Whilst many characteristics of ASD present to varying degrees in the wider population, the extent to which differences in emotion processing extend beyond those with a diagnosis of ASD remains largely unknown. The aims of this thesis were to determine the links between emotion and language processes in both adults and children, and to determine the relationship between emotion processing and non-clinical levels of autism-like traits, known as the broader autism phenotype (BAP). These aims were achieved by investigating (i) the recognition of facial and vocal emotional cues in relation to autism traits, and (ii) the impact of emotional cues on language learning in relation to autism traits. Chapter 2 aimed to determine how vocal expressions of emotion influenced subsequent recognition of facial emotions, and whether this influence differed according to levels of autism-like traits among typical adults. The results revealed that emotions, expressed via a combination of semantic meaning and prosody, enhanced subsequent facial emotion recognition when the vocal and facial cues were congruent, and impaired recognition when the emotional cues were incongruent. However, these effects were apparent only for those with low levels of autism-like traits and not for those with higher levels. Chapter 3 aimed to determine whether typically developing children were able to match various vocal expressions of emotion to facial expressions, and whether this ability differed between those with low or high levels of autism-like traits. The results showed that the children"s ability to match emotions differed according to emotion type, such that more errors were made when matching expressions of happiness and fear, compared to sadness and anger. However, matching ability did not differ between those with low or high levels of autism-like traits. Taken together, the results of the two studies outlined in Chapters 2 and 3 indicated that adults with higher levels of autism-like traits failed to integrate emotional information across modalities. However children with higher levels of autism-like traits were able to integrate crossmodal emotional information in a similar manner to those with lower levels of autism-like traits. These findings suggest that difficulties integrating emotional information across modalities may be characteristic of the BAP, but may become apparent between mid-childhood and adulthood. Chapter 4 aimed to determine whether vocal emotional cues impacted adults" ability to learn new words, and explored whether this ability to learn was influenced by levels of autism-like traits. The results revealed that fearful prosody interfered with the ability to learn new words for all partic...

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Association of autism tendency and hemodynamic changes in the prefrontal cortex during facial expression stimuli measured by multi‐channel near‐infrared spectroscopy

Cited by 16 publications

References 22 publications

Spontaneous resting‐state gamma oscillations are not predictive of autistic traits in the general population

Spontaneous resting‐state gamma oscillations are not predictive of autistic traits in the general population

Evaluation of Expression Recognition Function in Autism Spectrum Disorder Using Near-Infrared Spectroscopy

Emotion processing and the broader autism phenotype

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