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https://youtu.be/13dSRdn4lao

strengths in autistic outlook through being driven by passion rather than motivations that satisfy societal pressures

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The Strengths of Autism are Intimidating

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https://youtu.be/M5w8s2PKwxI

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New Brain Implant Begins Human Trials - Neuralink Update!

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https://youtu.be/L3XxbxfgzoQ

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IT HAPPENED! Elon Musk FINALLY Tested Neuralink On Humans!

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https://www.researchgate.net/profile/Aaiz-Ahmed/publication/313247979_Self-Diagnosis_in_Psychology_Students/links/58940e5192851c545748d268/Self-Diagnosis-in-Psychology-Students.pdf

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rise of self diagnosis and the ethics of that?
looking at it from both angles- the rise of social media pop psychology generalisation through tik Tok, and the flaws in diagnostic processes.
https://www.sciencedirect.com/science/article/pii/S0883941716300061?casa_token=brqqPJBzeRAAAAAA:pjXxMAy-QzHpTXBraKgj8cURSdDFqvB-6ghp4XOFii_nWx3lO7_Hp4PJQnoTzGLAW-MhnsHc

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Abstract

One in 68 Americans has autism spectrum disorder (ASD), and diagnosis is often delayed into adulthood in individuals without comorbid intellectual disability. Many undiagnosed adults resort to self-diagnosis. The purpose of this descriptive phenomenology was to explore the experience of realizing a self-diagnosis of ASD among 37 individuals who were not formally diagnosed. Results revealed five themes: feeling "othered," managing self doubt, sense of belonging, understanding myself, and questioning the need for formal diagnosis. Healthcare professionals must have an understanding of self-diagnosis to help individuals transition to formal diagnosis and to adequately educate, support, and screen this population for comorbidities.

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Exploring the Experience of Self-Diagnosis of Autism Spectrum Disorder in Adults

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One in 68 Americans has autism spectrum disorder (ASD), and diagnosis is often delayed into adulthood in individuals without comorbid intellectual disability. Many undiagnosed adults resort to self-diagnosis. The purpose of this descriptive phenomenology was to explore the experience of realizing a self-diagnosis of ASD among 37 individuals who were not formally diagnosed.

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https://www.liebertpub.com/doi/10.1089/aut.2022.0017 autism therapy should be autism lead- same sentiment I’ve seen on autism forums also

https://www.spectrumnews.org/features/deep-dive/theory-of-mind-in-autism-a-research-field-reborn/ autism and theory of mind- autism inst a lack of social interest but a difference in social understanding

https://journals.sagepub.com/doi/full/10.1177/1745691620958010

“Aligned with progressive theoretical frameworks, emerging empirical research indicates that mismatches in communication styles can contribute to autistic social difficulties and the important role that nonautistic difficulties play.”

https://www.frontiersin.org/articles/10.3389/fpsyg.2021.635690/full self advocacy in autism changes our perspective on it as a “disability” to a “difference”

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https://scitechdaily.com/gene-therapy-reverses-effects-of-autism-linked-mutation-in-human-brain-organoids/?utm_source=TrendMD&utm_medium=cpc&utm_campaign=SciTechDaily_TrendMD_0

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https://scitechdaily.com/what-causes-autism-new-research-uncovers-a-key-factor-in-brain-development/

The new knowledge on the basic principles regulating the shape of the neocortex will also help the design of in vitro brain culture systems that more accurately reflect the developmental processes of interest and improve the prospects for neurological drug development.- poses question of genetic engineering and autism- can be linked to neurolink

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https://www.science.org/content/article/autism-rates-are-it-really-rise poses the question of whether autism is on the rise or are we just understanding it more fully and diagnosing more people- could see an angle with religious and ethical commission of the BBC- is it right to understand people with certain diagnoses as disabled or in time will we learn that everyone fits into a mental diagnosis of some kind.

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https://www.cmu.edu/news/stories/archives/2022/august/autism-roeder.html

Autism gene and link w schizophrenia- could be cool to interview someone with both these conditions

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Autism doc potential participants

Courtney love

Dylan Ross

https://youtu.be/M2DRPEbujrg

Rapper who claims his autism aids him in his experimental music

Mark

(my friend mark has Aspergers also. I am interested in his perspective on autism as he leads a pretty interesting life- a professional wrestler with a goth/demon persona. He also has a unique look on his identity and how he sees himself in the world, he is trans and also has bpd, and I’m curious if autism has affected those other aspects of himself, what it was like growing up and figuring out who he is and how other people are. Also there is a massive overlap between autism and bpd and autism and queer identity which is rarely discussed. So I think Mark’s story is important.)

Striborg
Black metal musician living in isolation to focus on his craft. Like Courtney and Dylan, as a teenager I really connected with the themes and ideas in his work and thought he could be autistic. I recently found out through social media that he has recently been diagnosed, I was right! I find his perspective on humanity, society and his decision to essentially opt out of society very interesting. He discusses his lifestyle in relation to his work in the documentary series below, but I am more interested in how autism made him to be the person he is today.
https://youtu.be/UQl6PzXU4cQ

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Black Metal's Unexplored Fringes - One Man Metal - Part 1/3

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https://youtu.be/lb5Ko_sTwlc

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880 Art ideas in 2022 | lightroom effects, hex color palette, flat color palette

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880 Art ideas in 2022 | lightroom effects, hex color palette, flat color palette

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Mar 10, 2022 - Explore Alice Greenland's board "Art" on Pinterest. See more ideas about lightroom effects, hex color palette, flat color palette.

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Having been a collector of various items over the years, (mostly Retro Mid century china ware & Vintage Books) as well as other trinkets. The time has come to say goodbye and downsize my collection my aim is to find them new loving homes with people who will love them just as much as I do! Feel free to browse my shop and don't forget to check back often as I am in the middle of sorting through my many collections and am listing new items every week

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You searched for: Vintagegirllondon! Discover the unique items that Vintagegirllondon creates. At Etsy, we pride ourselves on our global community of sellers. Each Etsy seller helps contribute to a global marketplace of creative goods. By supporting Vintagegirllondon, you're supporting a small business, and, in turn, Etsy!

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Production design references: Cobain unseen 

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Production design references: Cobain unseen 

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Possibly do film layers??

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2756601/

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Abstract

Studies of explicit processing of facial expressions by individuals with autism spectrum disorder (ASD) have found a variety of deficits and preserved abilities compared to their typically developing (TD) peers. However, little attention has been paid to their implicit processing abilities for emotional facial expressions. The question has also been raised whether preferential attention to the mouth region of a speaker's face by ASD individuals has resulted in a relative lipreading expertise. We present data on implicit processing of pseudo-dynamic facial emotions and visual speech in adolescents with autism. We compared 25 ASD and 25 TD participants on their ability to recreate the sequences of four dynamic emotional facial expressions (happy, sad, disgust, fear) as well as four spoken words (with, bath, thumb, watch) using six still images taken from a video sequence. Typical adolescents were significantly better at recreating the dynamic properties of emotional expressions than those of facial speech, while the autism group showed the reverse accuracy pattern. For Experiment 2 we obscured the eye region of the stimuli and found no significant difference between the 22 adolescents with ASD and 22 TD controls. Fearful faces achieved the highest accuracy results among the emotions in both groups.

Reading Faces for Information about Words and Emotions in Adolescents with Autism

Autism is a complex developmental disorder that includes social, pragmatic, and language deficits, which can have a devastating impact on a person's ability to conduct daily face-to-face conversation. The capacity to recognize and respond to nonverbal conversational cues, including facial expressions, is central to effective human communication. Typically developing (TD) infants develop the capacity to recognize faces and facial expressions very early. They preferentially attend to faces within a few hours of birth ( Goren, Sarty, & Wu, 1975; Johnson and Morton, 1991); at four months they can already discern emotional facial expressions ( Montague & Walker-Andrews, 2001). Although there is evidence that the ability to process facial expressions of emotions (FEEs) continues to improve throughout childhood ( Egan, Brown, Goonan, Goonan, & Celano, 1998), adult competence is reached by early adolescence ( Custrini & Feldman, 1989; Batty & Taylor, 2006). In contrast, individuals with autism spectrum disorders (ASD), including autism, Asperger syndrome, and pervasive developmental disorder-not otherwise specified (PPD-NOS), experience a variety of difficulties and use atypical strategies in both face recognition and the identification of emotional facial expressions that persist through adulthood ( Schultz, 2005, for review).

A rapidly growing literature exists on the recognition of FEEs by children and adults with ASD. Some studies found that individuals with ASD are able to match emotions during slowed, ( Tardif, Laineé, Rodriguez, & Gepner, 2007), or even strobelike presentations ( Gepner, Deruell, & Grynfelt, 2001) and to recognize basic emotions, although not complex ones ( Castelli, 2005). Other studies, however, indicate that children with ASD show a deficit in labeling, categorizing, matching, or identifying static facial emotions ( Tantam et al. 1989, Gepner, et al. 1996, Gross, 2004, Celani, Battacchi, & Arcidiacono, 1999, see Jemel, Mottron, & Dawson, 2006 for review), dynamic expressions ( Lindner & Rosén 2006), and for categorical perception of emotional expressions ( Teunisse & de Gelder, 2001).

When trying to interpret an emotional facial expression, typical adults initially focus their gaze on the eye region of a face. However, when they are attending to a spoken word, their gaze focuses more directly on the lower half of the face since the mouth is more relevant for processing facial speech ( Buchan, Paré, & Munhall, 2007). In contrast, several studies of face processing in individuals with ASD suggest that they focus on the lower half of the face, particularly the mouth, in a variety of social or emotional contexts ( Joseph & Tanaka, 2003; Klin, Jones, Schultz, Volkmar, & Cohen 2002; Pelphrey et al., 2002, Dawson, Webb, Carver, Panagiotides & McPartland, 2004). The lack of attention toward the eye region of the face may account for some of the deficits identified in studies of face and emotion recognition.

In a classic study, Langdell (1978) found that children with ASD were better than their age-matched peers in face identification when only the lower part of the face was shown. Gross (2004) found that very young children with ASD were relatively less able than a TD cohort to match emotions of humans, dogs, and apes based on the upper face alone, and their error patterns suggested that primary attention was focused on the lower portion of the face. Baron-Cohen et al. (1997; 2001) found that typical adults were able to rely exclusively on the eye region of a face to extract information about an expressed complex emotion or mental state, whereas individuals with ASD were significantly impaired on such tasks. Spezio, Adolphs, Hurley, & Piven (2007), demonstrated not only that TD individuals visually attend to the eye region of a face, but that the eyes are crucial to their successful identification of emotional expressions. In contrast, participants with ASD used mostly information from the mouth to try and determine facial emotion. Some studies, however, indicate that individuals with ASD are able to attend to the eye region of faces ( Lahaie et al. 2006, van der Geest et al., 2002, Bar-Haim et al., 2006). Using a task similar to Baron-Cohen's (1997, 2001) Roeyers, Buysse, Ponnet, and Pichal, (2001) and Ponnet, et al. (2004) found that high functioning adults with ASD can identify emotions based on photographs of eyes alone. The difference between these results and those obtained by Baron-Cohen et al. may be explained by the fact that Roeyers et al. used posed expressions and a slightly different task design, as well as differences in autism symptomology among their respective ASD populations. The differences in these data raise the question whether individuals with ASD do in fact use information from a speaker's eye region without being specifically prompted to do so, especially when the eyes are presented within the context of an entire, natural face.

Another potential cause for the different face processing results obtained across studies may be the presence or absence of dynamic transitions in the facial emotion stimuli. Back, Ropar, and Mitchell (2007) used computer-manipulated stimuli that preserved the dynamic components of either the entire face or only the eye or mouth region and found that adolescents with ASD were less able to determine complex emotions than their TD peers in all conditions. Lindner and Rosén (2006) found deficits in the ASD population's ability to process static and dynamic expressions in a small sample of children and adolescents (aged 5-16), and Gepner et al. (2001) showed that children with ASD were able to recognize dynamic facial emotions only in slowed, strobe-like dynamic presentations, but demonstrated deficits in the processing of normal-paced dynamic expressions. The question remains whether preserving the natural dynamic transitions of emotional facial expressions allows individuals with ASD to successfully interpret the depicted emotion, or not. The ability to process the dynamic transitions of facial expressions is an important measure of the processing skills required during successful social interaction.

The studies mentioned so far rely on explicit processing tasks involving the recognition, labeling, or matching of emotional facial expressions. Studies of typical individuals show that implicit face processing may develop before explicit recognition ability reaches maturity ( Lobaugh, Gibson, & Taylor, 2006) and involve different neuronal activation patterns ( Habel et al. 2007, Hariri, Bookheimer, & Mazziotta, 2000), especially for fearful faces ( Lange et al. 2003). Very little work has been done to date on implicit emotional facial expression processing among individuals with ASD. Critchley et al. (2000) found that adults with ASD and a typical control group had less neurological activation for implicit than explicit processing of facial expression stimuli, but that the ASD group showed significantly less neuronal activation for both explicit and implicit FEE processing compared to their TD peers. These neuro-imaging data raise the as yet unanswered question of whether individuals with ASD differ in their behavioral approach to implicit emotion processing as well. Implicit processing of emotional faces is an important component of social interaction. Understanding the ability of individuals with ASD to process facial expressions implicitly is highly relevant to understanding their ability to function in a social environment.

Although it is likely that individuals with ASD have deficits in the processing of dynamic emotional facial expressions, at least for rapid dynamic stimuli in explicit task designs, there is evidence to suggest that they are as competent as their typically developing peers to recognize visual speech, i.e. lipreading. Lipreading is used by TD individuals during daily conversation and can boost comprehension of spoken language in noisy or distracting environments (e.g. Sumby & Pollack 1954, Lidestam, Lyxell, & Lundeberg, 2001, Ross, Saint-Amour, Leavitt, Javitt, & Foxe, 2007). Typical infants are sensitive to the facial movements that accompany speech as early as two months of age ( Dodd 1979, Kuhl & Metzoff, 1982, 1984) and can detect audio-visual mismatches through speech reading by five months (e.g. Rosenblum, Schmuckler, & Johnson, 1997). Their silent lipreading skills, however, are relatively poor through late adolescence ( Kishon-Rabin & Henkin, 2000) and are inferior to the lipreading skills of hearing-impaired adolescents who presumably rely more heavily on lipreading in daily life ( Elphick, 1996, Lyxell & Holmberg, 2000).

Individuals with ASD pay greater attention to the mouth regions of the face (e.g. Klin et al., 2002) and may therefore have acquired an enhanced lipreading expertise. Again, the evidence is contradictory. Some studies showed that ASD participants performed more poorly than TD controls on a static face matching task involving lipreading ( Deruelle, Rondan, Gepner, & Tardif, 2004), and on a silent lipreading task using a dynamic computer generated face ( Williams et al. 2004), while others found preserved matching skills for lipreading in the ASD population based on static images of real faces portraying the intonation of singles vowels ( Gepner, et al. 2001) or dynamic videos of vowel-consonant-vowel syllables ( De Gelder, Vroomen, & van der Heide,1991). To some extent these different findings may be due to differences in stimuli, which range from static to dynamic and real to computer generated. There is evidence to suggest that typical individuals are less successful at processing full language visual speech information from computer generated "speakers" than real people ( Lidestam & Beskow, 2006, Lidestam, et al., 2001), which might explain the differences in results obtained for these two types of stimuli. It is noteworthy that the task using dynamic videos of real faces and speech sounds ( de Gelder et al. 1991) determined that individuals with ASD had good lipreading skills, even compared to their TD peers. The question remains whether the use of real faces and preserved dynamic transitions will allow individuals with ASD to speech-read full language stimuli accurately. Being able to use lip-reading could help individuals with ASD to comprehend spoken language in daily conversation.

The studies described in this paper were designed to answer several of the questions that emerge from the literature, specifically regarding the ability of adolescents with autism to implicitly process dynamic facial expressions of emotion, a skill required for everyday social interaction. We also aim to investigate whether the documented attention to the mouth region in this population has led to enhanced lipreading skills which could be used to enhance spoken language comprehension. The direct comparison of these face-based stimuli enables us to assess the autism and TD groups' competence at extracting information from emotional and speech-based facial expressions encountered during everyday communication.

Experiment 1

In Experiment 1, we aim to determine whether high functioning adolescents with autism are as familiar as typical controls with the dynamic properties of four basic emotions (happy, sad, fear, disgust) by asking them to recreate the dynamic sequence of emotional facial expressions using six still images extracted from a video clip, a task assumed to require attention to the entire face and particularly the eyes. We based our experimental design on the paradigm developed by Edwards (1998), who found that typical adults were able to recreate the dynamic sequences of several basic emotions using 14-18 images taken from video clips depicting their onset, apex, and offset. In order to ascertain whether individuals with autism derive benefit from their reported preferential attention to the mouth region, we also investigated their ability to recreate the dynamic sequences of four words (with, watch, bath, thumb), a task that relies exclusively on attention to the mouth. Our task is designed not to require explicit labeling or matching so that we can investigate knowledge of facial expressions in the absence of secondary verbal task demands. Based on the target group's reduced neurological involvement for implicit processing, as well as their deficits in explicit dynamic FEE processing, we hypothesized that participants with autism would perform worse than controls on the emotional facial expression task. However, due to their preferential attention to the mouth, and thereby possibly acquired relative lipreading expertise, we expected them to perform better than controls on the lipreading task.

Method

Results

The data for this study are presented in . A 2 (group) by 2(category: emotion vs. word) repeated measures ANOVA of accuracy revealed a significant group by task interaction ( F(1,48)=11.21, p=.002), but no main effect for group ( F(1,48)=.38, p=.54) or task ( F(1,48)=1.45, p=.23). Pairwise comparisons within category showed that the TD participants were significantly better at recreating sequences of emotion than the participants with autism ( t(48)=2.6, p=.012), although the autistic group was significantly better at recreating the word stimuli than their TD peers ( t(48)=2.26, p=.029). Pairwise comparisons within group revealed a significant advantage for emotions over words in the TD participants ( t(24)=2.8, p=.01), and a trend towards significance in the autism group for words over emotions ( t(23)=1.9, p=.076).

A separate ANOVA for emotion within each group (Bonferroni corrected) revealed a main effect for emotion in the autism group ( F(3,72)=3.1, p=.033), but not the TD group ( F(3,72)=1.87, p=.14). Participants with autism were most accurate on fear (M=1.92) and least accurate on happy (M=1.08); the difference between these two emotions was statistically significant ( p=.038). No other emotion comparisons reached statistical significance in the autism group. There was also a main effect for words in the autism group ( F(3,72)=4.78, p=.004), but not the TD group ( F(3,72)=1.92, p=.13). Pairwise comparisons of word accuracy scores in the autism group revealed significant differences between the lowest accuracy score (" with," M=1.08) and the highest scores (" watch" and " thumb", both M=1.88) at p=.024 for "watch" and p=.002 for "thumb".

Discussion

The findings of this experiment support our initial hypothesis that the autism group will be better at sequencing words than emotions while the typical controls will show the reverse pattern, with a significant advantage of emotions over words. The data suggest that the documented attention to the mouth region in individuals with ASD may indeed have led to greater expertise with the dynamic properties of visual speech and therefore higher accuracy scores for the sequencing of word stimuli. Although most emotional expressions are formed by the movements of the mouth as well as the eye region, visual speech relies exclusively on mouth movement and should therefore be more affected by increased attention to the mouth. The autism group's relative difficulties at processing pseudo-dynamic facial expressions of emotion may at least partially be due to a lack of familiarity with the dynamic properties of the eye region of faces which is crucial to the successful processing of facial emotion.

An unexpected result was the high accuracy achieved for the sequencing of fearful faces in both participant groups. Although this difference only reached statistical significance for the adolescents with autism, it was still present in the TD group ().This finding stands in contrast to other studies that have found relatively poor performance on the processing of fear among typical populations and individuals with ASD (e.g. Ashwin, Baron-Cohen, Wheelwright, O'Riordan, & Bullmore 2007, Humphreys, Minshew, Leonard, & Behrmann, 2007). Many studies have also documented an advantage for happy face processing, even in individuals with ASD (e.g. Grossman, Klin, Carter, & Volkmar 2000). Our results, however, indicate that the TD group was not more accurate for happy faces than any of the other expressions and that the autism group even demonstrated their poorest accuracy levels for these expressions. It is possible that the implicit nature of our task design allowed participants to demonstrate their familiarity with the dynamic contours of the evolutionarily important fear faces in the absence of secondary verbal task demands.

Overall, the results support our hypothesis that adolescents with autism prefer local processing of the mouth region for both types of facial stimuli. This preferential processing of the mouth may explain their advantage over the TD group for the word stimuli, which are exclusively conveyed by the mouth, as well as their relatively poor performance on the emotions, for which information from the eyes is critical. The question raised by this task is whether individuals with autism derive any benefit from the information conveyed by the eye region at all and whether emotions can be successfully processed by attending only to the mouth, even by TD individuals. We pursued this question in the second experiment.

Experiment 2

Experiment 2 replicates the design of Experiment 1, except that the eye region of the stimuli is obscured. This study was designed to determine whether individuals with autism derive any benefit from the eye region of faces during face processing, and also to demonstrate that the eyes are crucial to the determination of facial emotion in typical adolescents. Our hypothesis underlying this task was that the autism group would show no difference in their performance on emotion and speech stimuli compared to their results in Experiment 1. In contrast, we expected the lack of information from the eyes to cause TD participants to perform more poorly on the emotion task than in Experiment 1, but they would show the same level of accuracy for speech stimuli as before.

Method

Results

The results for this study are presented in . We conducted a 2(group) by 2(category) ANOVA for total accuracy, which revealed no main effect for task ( F(1,42)=2.07, p=.16), group ( F(1,42)=.075, p=.79), or group by task interaction ( F(1,42)=0.38, p=.54). Separate ANOVAs within each participant group revealed a main effect for emotion in the autism group ( F(3,63)=3.24, p=.028) and the TD group ( F(3,63)=8.05, p<.001). Post-hoc pairwise comparisons (Bonferroni corrected) showed that the autistic participants were significantly better at recreating the sequence for the highest scoring fear expressions (M=1.9) than the lowest scoring sad faces (M=1.05), p=.01. The TD group's highest accuracy levels were also for fearful faces (M=2.23) and their lowest scores were for disgust (M=.91). The accuracy scores for fear in the TD group were significantly higher than those for sad (M=1.55, p=.046), happy (M=1.32, p=.006) and disgust (M=.91, p<.001). A comparison of word scores revealed no main effect for words in the TD group ( F(3,63)=2.33 , p=.83) or the autism group ( F(3,63)=1.09 , p=.36) ().

Discussion

The aim of these experiments was to determine how adolescents with autism approach implicit processing of communicative facial stimuli and how familiar they are with the dynamic properties of emotional facial expressions and visual speech. We presented emotions and words in a pseudo-dynamic form, preserving dynamic contours, but reducing presentation speed and complexity of dynamic transitions by extracting individual frames from a video sequence. The group by task interaction in Experiment 1 clearly supports our hypothesis, with the autism group outperforming the TD group on words, but scoring lower than their typical peers on emotion stimuli. These results support findings in the literature that describe deficits in explicit processing of dynamic emotional expression in individuals with ASD ( Lindner & Rosén, 2006, Back et al., 2007), but preserved lipreading skills ( Gepner et al. 2001, De Gelder et al. 1991). Our results go further and demonstrate not just preserved, but enhanced lipreading skills in adolescents with autism for these pseudo-dynamic stimuli.

Comparing the performance of both groups across the two study tasks, we find that the autism group showed no difference in accuracy levels for masked vs. unmasked stimuli. One possible interpretation is that individuals with autism do not use the information contained in the eye region of emotional facial expressions. A second interpretation is that the eyes are simply not necessary to process and recreate the dynamic properties of FEEs. The second interpretation can be discarded, because of the performance difference in the TD group across the masked and unmasked modalities. The TD individuals demonstrated a significant advantage for emotions over words in Experiment 1. However, in Experiment 2, where the eye region was not visble, the typical group's accuracy results for emotional expressions dropped significantly and were no longer distinguishable either from their own low accuracy scores for words, or the emotion processing scores of the autism group. Based on this significant performance gap in the TD group across the two studies and the lack of change in the corresponding accuracy levels of the autism group, we conclude that the eyes do provide crucial information towards the processing of dynamic facial expressions and that adolescents with autism do not have access to, or choose to ignore, this information when attempting to recreate the dynamic properties of basic emotions.

Our results support prior findings that adolescents with ASD have deficits in their ability to explicitly process basic dynamic facial emotions (e.g. Lindner & Rosén, 2006), but Gepner et al. (2001), who found preserved facial emotion processing in ASD for slowed, strobe-like presentations. One explanation for this discrepancy can be found in the differences in stimuli and task designs across studies. Gepner et al. (2001) used a more explicit task involving matching of a still photograph to a dynamic sequence. It is also not clear whether our cohort of participants with ASD is comparable to the group studied by Gepner et al., since different assessment tools were used to determine autism symptomology in the two studies. But more importantly, the emotion stimuli used in Gepner et al.'s study portrayed only the onset of the emotion up to the maximum expression; the emotional sequences in the experiments presented here contained the entire dynamic range from onset through peak and offset. It is possible that including the entire dynamic contour of emotional facial expressions increased the processing demands sufficiently to lower the accuracy scores of our autistic participants. It is also likely that the implicit vs. explicit processing demands of the two tasks account for at least some of the differences between our results. Overall, our data confirm the documented difficulties of individuals with ASD to process FEEs and suggest that their deficits in interpreting emotional expressions are tied to an avoidance of the eye region of the face. We conclude that the autism group has less familiarity with the dynamic aspects of emotional facial expressions than the TD group, as demonstrated by their relatively poor performance on all emotion stimuli when compared to the TD group in Experiment 1, and compared to their own performance on words.

In contrast to their relatively poor performance on emotional expressions, the autism group outperformed the TD group on the sequencing of word stimuli, due in part to the fact that the TD group scored very low on the word task. A possible explanation for this poor performance on lipreading in the typical group can be found in the documented developmental aspect of lipreading ability in typical individuals ( Bar-Haim et al., 2006). Full adult lipreading ability did not appear to develop until late adolescence, effectively placing the majority of our TD participants below the threshold of lipreading competence. The autism group, on the other hand, may have gained earlier expertise in this domain due to their preferential visual attention to the lower part of the face and the mouth during social interaction ( Klin et al. 2002, Joseph & Tanaka, 2003, Langdell, 1978). The fact that lipreading is a skill that can be trained and improved upon through daily exposure has been demonstrated by studies of adolescents with hearing impairments. These individuals, due to their greater need for daily lipreading, acquire earlier expertise for the processing and interpretation of dynamic properties of the mouth during visual speech ( Elphick, 1996, Lyxell & Holmberg, 2000). In the same vein, our participants with autism may have developed greater familiarity with speech-related mouth movements due to their presumed greater daily visual attention to the mouth regions of their conversation partners.

An alternative interpretation of these data is that adolescents with autism do not actually have greater skill at lipreading, but rather a developmental advantage at the age of sampling that may disappear with time. Their preferential attention to the mouth region of a speaker may have led them to develop full adult competence for lipreading earlier than their typical peers, rather than reach a higher skill level in this area overall. Further studies of lipreading using groups of adult participants with autism and typical controls are necessary to decide whether the lipreading advantage we found in individuals with autism remains constant as they age.

A surprising result is that both groups consistently achieved the highest accuracy levels of any emotion for the fearful faces in both the masked and unmasked tasks. This good performance for fear faces is the only consistent ranking finding among the emotions or words in either participant group. There are no clear patterns of accuracy rankings among the words and no single emotion that consistently achieves the lowest accuracy levels. Although the literature shows that the highest accuracy levels and fastest reaction times are often reached for the identification of happy faces ( Bullock & Russell, 1986; Custrini & Feldman, 1989; Grossman et al. 2000, Phillippot & Feldman, 1990), and that participants, including those with ASD, are specifically poor at processing fearful faces ( Ashwin et al. 2007, Humphreys, et al. 2007, Dawson et al., 2004, Ogai et al., 2003, Pelphrey et al., 2002, Howard et al. 2000), we found no such pattern here. There are at least some data to support that different emotions are processed equally well ( Egan, 1998), or even that happy and fear are processed better than other basic emotions during an implicit task in typical adults ( Edwards, 1998). But it is still surprising that both groups maintained this processing advantage for fear faces even in Experiment 2, which provided no information from the eye region of the face.

Although fearful faces carry many of their defining facial characteristics in the eyes (e.g. Morris, deBonis & Dolan, 2002), they do also involve defining movements of the lips and jaw ( Ekman & Friesen, 1978), thereby possibly allowing our participants to accurately process this facial emotion from the mouth movements alone, as they apparently did in Experiment 2. Also, Dadds et al. (2006) found that children with mild callus-unemotional traits showed no difference in fear processing accuracy whether their gaze was exclusively directed to the eyes or the mouth region of a face, indicating that it is possible to accurately identify fearful faces through attention only to the mouth. These data combined with the Facial Action Coding System (FACS) defined involvement of the lips and jaw in producing fearful faces support our claim that the mouth does carry crucial information for the processing of fearful faces, which may help explain the high accuracy results achieved by our participants for this emotion.

Another difference between our study and prior research on the processing of fearful faces in individuals with ASD is that our stimuli preserved the dynamic contours, although not the dynamic speed, of the emotional expressions. Natural conversational facial expressions are defined not only by their constellations of facial features at any given moment, but rather by the dynamic changes and interactions of these features over time. We hypothesize that the dynamic properties of fearful faces, which evoke an immediate neurological reaction in humans ( Whalen, et al., 1998, 2004), are quite familiar to adolescents with autism and successfully recreated, and that a different level of cognitive processing is needed to successfully connect the expression with a verbal label or visual match. In a recent study by Humphreys et al. (2007), individuals with ASD were significantly worse at labeling fearful faces than at recognizing them in a same-different task. Humphreys et al. (2007) argued that this performance difference in the ASD group was based on the added language component of the labeling task, which increased difficulty and therefore lowered accuracy. Our results support that hypothesis. In our implicit emotion processing task, adolescents with ASD and their TD peers were able to recreate the dynamic properties of fearful faces significantly better than any of the other emotions presented, contrary to findings of explicit emotion processing tasks with the same populations. It is possible that the lack of explicit second-layer task demands and preservation of dynamic contours in our design enabled participants in both groups to deal with these expressions on a more immediate level, demonstrating easy familiarity with fearful faces over other basic emotions, which do not evoke the same hard-wired neuro-physiological responses as fear. However, considering that our results are based on a single sample of fear, we cannot generalize these findings, even though our stimuli were judged to be clear expressions of their target emotion during the stimulus creation process. Further studies of implicit processing of dynamic fearful faces by individuals with autism are required to determine the general validity of this finding.

Conclusion

In an implicit processing task assessing familiarity with the dynamic properties of emotional facial expressions and visual speech, adolescents with autism show a reverse pattern of competence for emotion and word sequencing than their TD peers. The autism group is more accurate for words than emotions and the TD group is more accurate for emotions than words. The autism group's performance does not change whether information from the eye region is accessible, indicating that they do not use this information for processing of basic emotions. The TD group, on the other hand, demonstrates a significant drop in accuracy for recreating dynamic properties of facial emotional expressions in trials with obscured eye regions, suggesting that the dynamic information contained in the eyes, brows, lower forehead and upper cheeks is crucial to the successful processing and recreation of emotional facial expressions in typical adolescents. The attention preference to the lower face among individuals with autism may also lead to an acquired lipreading expertise and explain their advantage for the processing of visual speech compared to their typical peers. Surprisingly, both groups reach their highest accuracy rates for fearful faces, contradicting literature showing poor performance for the recognition, labeling, or matching of fear faces in both typical and autistic populations. We propose that an implicit task design and preservation of dynamic contours in the emotional stimuli play a significant role in the ability of both groups of adolescents to interpret fearful faces.

During social interactions, processing of emotional or speech-based facial actions happens on an implicit level. Unfortunately, implicit processing of facial stimuli in individuals with ASD has not been studied extensively to date. This paper's results highlight the reduced ability of adolescents with autism to implicitly process facial emotion as well as their enhanced capacity for processing facial speech. Further studies are required to ascertain how individuals with ASD implicitly process a speaker's emotional state as well as possibly enhance their comprehension of spoken language through lipreading.

Acknowledgments

The authors wish to thank Rhyannon Bemis, Chris Connolly, Danielle Delosh, Alex B. Fine, Meaghan Kennedy, and Janice Lomibao for their assistance in conducting this study. We also thank Geraldine Owen, whose face is portrayed in our stimuli and the children and families who gave their time to participate in this study.

Funding was provided by NAAR, NIDCD (U19 DC03610; H. Tager-Flusberg, PI) which is part of the NICHD/NIDCD Collaborative Programs of Excellence in Autism, and by grant M01-RR00533 from the General Clinical Research Ctr. program of the National Center for Research Resources, National Institutes of Health.

Footnotes

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References

Whalen PJ, Rauch SL, Etcoff NL, McInerney SC, Lee M, Jenike MA. Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. Journal of Neuroscience. 1998;18:411-418. [[PMC free article] [PubMed] Google Scholar]

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Reading Faces for Information about Words and Emotions in Adolescents with Autism

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Studies of explicit processing of facial expressions by individuals with autism spectrum disorder (ASD) have found a variety of deficits and preserved abilities compared to their typically developing (TD) peers. However, little attention has been paid to their implicit processing abilities for emotional facial expressions.

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https://youtu.be/PPOBAhU_yMQ

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https://www.tandfonline.com/doi/abs/10.1080/14702029.2013.10820083

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(2013). Dogme 95 and disabled identity on film. Journal of Visual Art Practice: Vol. 12, No. 3, pp. 291-304.

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https://youtu.be/iyqKy5P1Y0Q

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https://youtu.be/rMtkdFJTmFk

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https://pin.it/1tIQbyc

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0 Autism documentary shot inspo ideas in 2022

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Jan 2, 2022 - Explore anna rooney's board "Autism documentary shot inspo" on Pinterest.

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https://youtu.be/sMkWi86JiPs

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What Is It Like to Live With Autism? | Make Me Normal | Real Families

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https://youtu.be/2wCsr5aK0_w

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How Scientists Are Changing Life With Autism

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https://youtu.be/-Xk59VKoCGI

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Julien Donkey-Boy (Chloe Sevigny singing)

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https://youtu.be/sMPojIsgouI

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https://www.shortoftheweek.com/2014/10/03/ill-be-fine/

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Considering how often autism is depicted in movies and discussed on the nightly news (for the record, vaccines are not a cause), it's sort of appalling how little we actually understand the disorder. Now, when I write that, I don't mean "be aware of" but rather I'm referring to an actual understanding of how the condition affects people in their day to day lives. There's a link missing between our visions of "Rain Man" savants and the less glamorous truth.

Fortunately, director Micha Hilliard provides a much needed glimpse into that reality with his short documentary, I'll Be Fine -a simple, delicate portrait of Tim, a 19-year-old who has autism. This isn't a showy short (the slow pacing might turn some internet viewers off), but it is an incredibly honest look at a family dealing with autism. Tim's entire life has been lived under the shadow of the disorder, and in consequence, so too have the lives of his parents. In turn, this film is an inspiring depiction of how humans adapt in order to survive. For Tim's parents, it's been a journey pegged with difficulty and compromises. Yet, life continues to go on for all of them. It's hard not to admire that sense of familial fortitude.

Fortunately for us, Hilliard allows Tim's voice to take center stage. Too often in documentaries surrounding a particular condition, we never actually hear from the one living with it. No so here-Tim's characteristic way of speech dominates the film, and as such, we're giving an authentic look into his everyday trials and interests. His attraction to video games is especially prevalent and provides a fascinating glimpse to how certain sufferers of autism can form deeply rooted bonds with specific things. The look on his face when he sees a Nintendo press conference is priceless-a sense of awe that is rarely seen in someone his age. In that sense, Hilliard's representation is that of a fully realized person. He's not a nameless sufferer, but rather a young man with fears, hobbies, and dreams.

In order to make such an honest portrait, Hilliard, who is a relative to Tim and his family, actually moved into their basement, living with them for two months in order to get the footage and scenes he needed. This time and unfettered access was critical to the film's success-it not only allowed him to establish trust with his subject but also caused the story itself to evolve organically.

As Hilliard relates, "The film I ended up making is different from the film I set out to make. In an effort to take charge and predict the course of the story, I started to rely more heavily on my subject's parents...Rather than looking at the world through my subject's eyes, I eventually observed him from his parents' standpoint. I think the resulting film is truer to reality than what I had in mind."

In effect, Hilliard's film is not solely a portrait of a young man living with autism-it's also a snapshot of a family working through the resulting trials. It's this connection-this sense of selfless kinship-that makes I'll Be Fine such a special short. There is no "beating" autism. But, there is a quiet wonder and sense of admiration that comes from watching a family face the challenges together.

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Considering how often autism is depicted in movies and discussed on the nightly news (for the record, vaccines are not a cause), it's sort of appalling how little we actually understand the disorder. Now, when I write that, I don't mean "be aware of" but rather I'm referring to an actual understanding of how the condition affects people in their day to day lives.

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autism and depression and identifying feelings

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autism and depression and identifying feelings

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https://www.nature.com/articles/s41598-021-81696-5

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Introduction

Autism traits and depression

Autism spectrum disorder (ASD) is characterized as a developmental disorder that entails pervasive peculiarities in communication and social interaction, as well as restricted and repetitive behavior, which exist as main diagnostic categories according to ICD-10. Depression remains a prevailing comorbidity in ASD populations. A recent meta-analysis of 29 studies showed a heightened prevalence of current (23%) and lifetime (37%) depression in autistic adults. A subsequent question is whether traits that are inherent to autism suggest a cause for heightened depressive symptoms. It could be assumed that increased autism traits, such as social difficulties and behavioral maladaptation, constitute a possible loading factor. However, mixed empirical findings riddle the literature. For example, a longitudinal study found higher depression scores in children with autism ASD, and higher autism traits predicted higher depression rates in adolescence. In line with that, studies found positive correlations of autism traits with depression in non-clinical samples of adults and a sample of adolescents and adults with ASD. Other studies reported no direct modulation of depressive symptoms by autism traits in adults with ASD. Thus, it remains unclear whether autism traits have a direct effect on depression that is not explained by other comorbidities in ASD.

Alexithymia traits and depression

Besides autism traits as a potential factor for depression in ASD, comorbid factors may account for heightened depressive symptoms. An increasingly discussed psychopathological construct, alexithymia, often co-occurs with ASD and is especially interesting in the context of depression in ASD. This condition was first described by Sifneos and is also referred to as emotional blindness. In the general population, the estimated prevalence of alexithymia is 10-20%. In their recent meta-analysis, Kinnaird et al. reported a markedly increased prevalence of alexithymia in ASD of 50%. Alexithymia measures usually comprise three different traits: Difficulties in Identifying Feelings (DIF), Difficulties Describing Feelings (DDF), and Externally-Oriented Thinking (EOT). Alexithymia has frequently been linked to depression. Regarding the distinct alexithymia subdomains, a meta-analysis of studies found medium-sized effects of difficulties identifying and describing feelings but only a weak relationship of externally-oriented thinking with self-reported depression.

Alexithymia traits, autism traits, and depression

Considering the high rates of alexithymia in ASD and an association of alexithymia with depression irrespective of ASD, it could be assumed that alexithymia traits modulate depression in ASD. By observing ASD and alexithymia as parallel factors for depression, Fietz and colleagues applied structural equation models on data from an online study addressed to participants from the general population. They showed that autism and alexithymia traits had medium-sized effects on depression. Morie and colleagues conducted an online questionnaire and investigated the effect of autism symptomatology and alexithymia with depressive symptoms in adults with ASD. Their serial mediation analysis provides evidence that autism traits are not directly associated with depression. However, an indirect effect was found, which indicated that autism traits could lead to enhanced depressive symptoms through a positive association with alexithymia and weaker emotion regulation abilities. The validity and generalizability of this online study is limited as the small sample consisted primarily of females (47*f/17*m), and diagnoses were not confirmed by any clinical assessment. Furthermore, the association with depression of both autism and alexithymia raises questions concerning the interpretability of many previous studies testing only one of these predictors or both without controlling their covariation. To date, no study has tested the potentially different effects of alexithymia traits (DIF, DDF, EOT) on depression in a clinical population of individuals with and without ASD, allowing for judgments of specificity.

Consequently, the aims of the current study are to (a) clarify whether autism traits or alexithymia traits better explain depressive symptoms in adults with ASD, (b) establish whether there is a differential predictive power of the three alexithymia traits, and (c) determine whether such predictive associations with depression are specific to ASD. For this purpose, we analyzed data of a representative clinical population of adults referred to outpatient clinics for autism diagnostics. Groups were split into cases with a confirmed ASD diagnosis ( N = 281) and cases with a ruled out ASD diagnosis ( N  = 119), allowing for direct clinical comparison and statistically accounting for the strong association between autism and alexithymia traits by employing dominance analysis.

Method

Participants

Participant characteristics are reported in Table 1. The data analyzed in this study are based on post-hoc naturalistic and comprehensive sampling from the referral population in the outpatient clinic for autism in adulthood at the University Hospital Cologne. Data was collected from 2006 until 2019. The project has been approved by the ethical commission of the medical faculty of the University of Cologne (case number: 20-1432). Data analyzed in this study was fully anonymized. The need to obtain informed consent from the subjects included in this retrospective study has been declared dispensable by the ethics committee of the medical faculty of the University of Cologne. According to §6 para. 1 sentence 1 GDSG NRW ('Gesundheitsdatenschutzgesetz Nordrhein-Westfalen') academic staff is allowed to make scientific use of data to which they have access because of their activities, without the consent of the persons concerned being necessary. Individuals were referred to the outpatient clinic for ASD by medical consultants based on suspicion of a possible ASD diagnosis due to reported social-emotional symptoms. Diagnostic procedures throughout the period of data collection were conducted in accordance with German guidelines for the diagnosis of ASD in adulthood, comprising neuropsychological testing and clinical assessment of at least two independent clinicians and a clinical consensus decision. An ASD diagnosis was given if patients met all diagnostic criteria of ICD-10.

Table 1 Characteristics of both diagnostic samples: confirmed (ASD+) and ruled out (ASD−) diagnosis of ASD.

The comprehensive clinical population was split into ASD+ cases ( N = 281), consisting of individuals who received a diagnosis of F84.5 ( n = 242; Asperger Syndrome), F84.1 ( n = 18; Atypical Autism), or F84.0 ( n = 21; Childhood Autism) according to ICD-10, and cases consisting of individuals for whom any F84 diagnosis was ruled out, hence referred to as ASD− ( N = 119). Thus, the ASD− group comprises individuals who exhibited social difficulties but specifically did not fulfill the criteria for an ASD diagnosis. Patients who received a diagnosis of F84.8 ( n = 21, other pervasive developmental disorders) or F84.9 ( n = 50, pervasive developmental disorder-not otherwise specified, PDD-NOS) were not included in the analyzes because of the ambiguity of group assignment.

Further inclusion criteria were total IQ scores > 70, measured with the German version of the Wechsler Adult Intelligence Scale (WIE-III) which includes measures of performance-based IQ (PIQ) and verbal IQ (VIQ), and less than four missing items on the Autism-Spectrum Quotient (AQ) and the 20-Item Toronto Alexithymia Scale (TAS-20). In total, n = 19 (ASD+) and n = 12 (ASD−) had one missing item, n = 5 (ASD+) and n = 5 (ASD−) had two missing items, and n = 2 (ASD+) had three missing AQ items. For TAS-20, n = 8 (ASD+) and n = 4 (ASD−) had one missing item, n = 2 (ASD+) had two missing items, and n = 1 (ASD+) had three missing items. Missing items were replaced by group means. One ASD+ case was not included in the analysis due to an extremely low outlier score on the AQ (< 3 SD from M).

Retrieved data

Depressive symptoms were assessed with the German version of the Beck Depression Inventory (BDI). BDI is a self-report questionnaire that assesses depressive symptoms over 21 items. Each item was answered on a four-point scale, scoring from 0-3, with higher scores indicating increased symptom severity. Scores in the range of 11-17 indicate mild to moderate depressive symptoms and scores ≥ 18 classify clinically relevant depression. In the ASD+ and ASD− groups, 25.27% and 20.17% of cases reported mild-moderate symptoms, respectively. In the ASD+ and ASD− groups, 30.60% and 41.18% reported clinically relevant depressive symptoms, respectively.

The AQ is a well-established 50-item self-report questionnaire assessing autism traits including social skills, difficulties in attention switching, the degree of attention to detail, and difficulties in communication and imagination. Each of the 50 items was answered on a Likert-scale, ranging from 1 =  agree to 4 =  disagree and re-coded to 1 or 0 for summing. Scores > 32 depict pronounced autism traits. In the ASD+ and ASD− groups, 83.63% and 78.15% of cases reported autism traits above this threshold, respectively. The internal consistency of the scale was good (α = 0.86).

The TAS-20 is a self-report questionnaire assessing alexithymia on three dimensions (DIF, DDF, and EOT). Each item was answered on a five-point Likert-scale, ranging from 5 =  strongly agree to 1 =  strongly disagree. The recommended cut-off for clinically relevant alexithymia is 61. In the ASD+ and the ASD− samples, 66.19% and 68.91% reported clinically relevant alexithymia traits, respectively. Cronbach's alphas for all subscales revealed good internal consistencies for DIF ( α = 0.85) and DDF ( α = 0.74) and acceptable internal consistency for EOT ( α  = 0.60).

Statistical procedures

Data preprocessing and analysis were conducted in RStudio using the statistical programming language R. Based on their extensive taxometric analysis, Parker et al. argued that the latent structure of alexithymia should be considered continuous rather than categorical. Accordingly, we analyzed alexithymia traits based on metric scales. Two sample t-tests were conducted to compare ASD+ and ASD− in terms of TAS-20, AQ, and BDI. Pearson zero-order correlations measures of interest were calculated with Bonferroni adjustment (Table 2). For multivariate analysis, regression models were calculated with autism traits (AQ) and alexithymia traits (DIF, DDF, EOT) as predictors of self-reported depressive symptoms (BDI) in ASD+ and ASD− samples. The samples differed significantly in IQ scores, with higher scores in the ASD+ sample ( t(398) =  − 2.055, p < 0.05, d =  − 0.230), driven by higher scores in VIQ ( t(398) =  − 2.942, p < 0.05, d =  − 0.326). We included age, sex, and measures of PIQ, and VIQ as control variables to account for potential confounds (see Table 1 for summary statistics and significance tests). The model for the ASD− sample met all assumptions for general linear models; namely, sample sizes were sufficient for the number of predictors, adequate model fit (< 5% of standardized residuals > 2), independence of errors (Durbin-Watson statistic ≈ 2), lack of multicollinearity (variance inflation factor < 2 for all predictors in all models), and homoscedasticity for the ASD− model. As for the model of the ASD+ sample, heteroscedasticity was implied by funneled residual plots and a significant result in the Breusch Pagan Test, robust regression with heteroscedasticity-consistent covariance matrix (HCCM) was conducted, using the sandwich and lmtest packages in R. HCCMs were retrieved by the vcovHC() function, applying the HC3 method based on recommendations by Long & Ervin. HCCM was proposed as an adequate method for adjustment of errors in case of heteroscedasticity by Rosopa et al..

Table 2 Descriptive statistics and correlations for variables of interest.

Correlations of autism and alexithymia traits could have problematic results when interpreting their relative effects through beta coefficients. Beta coefficients represent the total effects, whereas general dominance weights (GDW) constitute a global index of importance. This was suggested as a new comparison standard for multiple regression because it considers direct, total, and partial effects. This method was previously introduced regarding the inference of linear effects of alexithymia and ASD on empathy. GDWs were an adequate and easily interpretable effect size for answering the research questions concerning the relative effects of alexithymia and autism traits in our analysis. GDWs were calculated with their bootstrapped 95% confidence intervals (100 resamples) using the yhat package in R. GDWs were retrieved by dominance analysis, in which unique variance explained by each predictor was calculated by the squared semipartial correlation averaged across all models in all possible subsets that included that predictor. GDWs together sum up to the total determination factor R ² of the model. Therefore, they allowed for the calculation of the relative proportion of each predictor in variance explanation and, thus, the ranking of predictors within the models.

Results

Table 2 depicts mean scores and correlations of the measures of interest in the groups. The ASD+ sample did not significantly differ from the ASD− sample in any TAS-20 subdomains, DIF ( t(398) = 1.698, p = 0.090, d = 0.183), DDF ( t(398) =  − 0.555, p = 0.579, d =  − 0.060), and EOT ( t(398) = 0.488, p = 0.626, d = 0.054). The groups did not differ significantly in their levels of AQ ( t(398) =  − 1.693, p = 0.091, d =  − 0.187) and BDI ( t(398) = 1.579, p = 0.115, d  = 0.172).

Regarding correlations with depressive symptoms in the ASD + sample, BDI scores significantly increased with AQ ( r = 0.25, p < 0.001, 95% CI [0.13, 0.35]), DIF ( r = 0.41, p < 0.001, 95% CI [0.31, 0.51]), and DDF ( r = 0.26, p < 0.001, 95% CI [0.15, 0.37]), but not with EOT ( r =  − 0.03, p = 0.643, 95% CI [ − 0.14, 0.09]). In the ASD− sample, BDI significantly increased with DIF ( r = 0.25, p < 0.05, 95% CI [0.08, 0.41]), but not with AQ ( r =  0.12, p = 0.189, 95% CI [ − 0.06, 0.29]), DDF ( r =  − 0.01, p < 0.877, 95% CI [ − 0.19, 0.16]) or EOT ( r = 0.06, p = 0.551, 95% CI [ − 0.13, 0.23]). Considering correlations of autism and alexithymia traits in the ASD + sample, DIF significantly increased with AQ ( r = 0.52, p < 0.001, 95% CI [0.43, 0.60]) and DDF ( r = 0.47, p < 0.001, 95% CI [0.38, 0.56]). Similarly, AQ significantly increased with DIF ( r = 0.44, p < 0.001, 95% CI [0.28, 0.57]) and DDF ( r = 0.47, p < 0.001, 95% CI [0.32, 0.60]) in the ASD− sample. EOT was not correlated with AQ in either sample (ASD + : r =   0.00, p = 0.985, 95% CI [ − 0.12, 0.12]/ASD−: r = 0.02, p = 0.830, 95% CI [ − 0.16, 0.20]).

Table 3 depicts regression estimates and results of dominance analysis with BDI as the dependent variable. The model in the ASD + sample explained 23.2% variance in BDI score ( F(8,272) = 10.27, p < 0.001). GDW estimates of AQ, DIF, DDF, and EOT allowed for ranking predictors in terms of importance by their proportion of variance explanation ( R 2). In the ASD + sample, DIF stood out as the strongest predictor ( GDW = 0.116, CI [0.062, 0.173], 50.0% of R 2), DDF was the second ( GDW = 0.029, CI [0.014, 0.059], 12.5% of R 2), and AQ the third strongest predictor ( GDW = 0.020, CI [0.009, 0.049], 8.6% of R 2) with negative coefficients. This indicates that autism traits did not affect depression beyond alexithymia traits. The model in the ASD− sample explained 18.3% variance of BDI scores ( F(8,110) = 3.08, p < 0.01). Estimates revealed a similar structure as the ASD + sample: DIF was the strongest predictor ( GDW = 0.068, CI [0.012, 0.144], 37.1% of R 2), DDF was ranked second ( GDW = 0.024, CI [0.005, 0.114], 13.1% of R 2), and AQ third ( GDW = 0.019, CI [0.002, 0.078], 10.4% of R 2). EOT was the weakest predictor of depressive symptoms with small effect sizes in the ASD+ sample ( GDW = 0.003, CI [0.001, 0.037], 1.3% of R 2) and in the ASD− sample ( GDW = 0.002, CI [0.001, 0.049], 1.1% of R 2).

Discussion

The key findings of the current study are:

1. (a)
Alexithymia traits (i.e. DIF & DDF) outweigh autism traits in the multivariate prediction of depressive symptoms in adults with ASD,

2. (b)
The alexithymia trait, difficulties identifying feelings, is the strongest predictor of depressive symptom severity, and

3. (c)
Similar patterns of prediction in both models, for adults with and without ASD, imply a general mechanism for depression in clinical samples.

Autism traits significantly correlated with depressive symptoms, which is in line with studies that also reported positive correlations. Nevertheless, considering the high correlation of alexithymia and autism traits, our multivariate analysis puts this association in a new perspective because autism traits were only ranked third after alexithymia traits difficulties identifying feelings and difficulties describing feelings. This aligns with past studies, particularly with the findings of Morie et al., as they resemble the direct effect of alexithymia on depressive symptoms in adults with ASD. Beyond replication, our results imply difficulties identifying feelings as the main factor in predicting depressive symptom severity. Furthermore, the effect of difficulties identifying feelings is in accordance with Li et al. who reported an overall medium effect size in their meta-analysis of studies from the general population and depression patients. Li et al. further reported medium effect sizes for difficulties describing feelings on depression, whereas we found only small effect sizes in the multivariate observation. As univariate correlations revealed a positive correlation with self-reported depressive symptoms, the small effect in the dominance analysis could hint at an intersection of variance explanation with autism traits. As AQ already includes communication difficulties, it may act as a suppressor for the scale assessing difficulties describing feelings.

Importantly, we need to understand whether prediction patterns of depression are unique to ASD for clinical decision making with respect to treatment planning. We found depressive symptoms to be similarly modulated by alexithymia traits and specifically explained by difficulties identifying feelings in a clinical population with a ruled out ASD diagnosis. Hence, alexithymia, in general, and difficulties identifying feelings, in particular, might be generally associated with depression requiring attention in treatment planning beyond ASD.

There are some important methodological and theoretical implications of this study. First, our results support the suggestion that the alexithymia subdomains should be considered individually to make inferences about distinct and subtle effects. As the subdomains showed different temporal stability, dissimilar internal consistencies, may distinctively relate to measures of psychological stress, and are semantically different. Thus, observations of individual subdomains should be standard. Subdomain splitting makes sense with regard to the different extent of social characteristics comprising the subdomains, especially in the context of ASD. While difficulties in identifying feelings and externally-oriented thinking are person-level domains, difficulties describing feelings represents a social domain of alexithymia. Thus, for individuals with pronounced autism traits comprising social communication difficulties, high scores in this domain might reflect a trend of social withdrawal rather than a cause of malfunctioning emotional access.

Another important aspect of our study is the replication of prior results that showed high correlations of autism and alexithymia traits. We encourage future research to consider this finding in their methodological procedures. For relative effects in the case of covariation of predictors, dominance weights demonstrate a reasonable alternative to beta coefficients as they represent a valid and easily interpretable estimate of relative importance of each predictor.

It has been suggested by different authors that alexithymia could be used to define subgroups within the autism spectrum. Based on our results, we would tentatively agree but additionally argue that alexithymia should be regarded in terms of subdomains and as continuous variables. Our results demonstrate that a subgroup of adults with ASD can be described by high levels of difficulties identifying feelings, and this subgroup may specifically profit from interventions that target emotional regulation abilities and foster introspection. Regarding this potential subgroup in ASD, it could be assumed that heightened rates of difficulties identifying feelings have their origin outside autism features. Szatmari et al. found that parents of children with ASD reported significantly higher degrees of difficulties identifying feelings compared to parents of children with Prader Willi syndrome. This result was recently replicated with the addition that difficulties identifying feelings were associated with depression in mothers and fathers of children with ASD. As studies showed that alexithymia is potentially passed on to children through heredity and family interactions, it could be suggested that heightened rates of difficulties in identifying feelings in a subgroup could be explained by external factors (family or affiliated persons). Likewise, it could be suggested that autism traits are potentially heightened within the family, which gives rise to alexithymia. Etiological pathways remain unclear, and future studies should be conducted that specifically target alexithymia in individuals with ASD and their caregivers in a longitudinal design. Such research could better inform the possible origins of pronounced alexithymia traits in ASD and allow for inference of alexithymia as a cause for, or consequence of, depression in ASD.

Aside from the methodological advantages and important implications, the current study has limitations. First of all, we applied a retrospective cross-sectional design that does not allow for causal inference. Questions about the directionality of effects and etiological pathways remain a target for future prospective longitudinal studies. Additionally, our results are based on post-hoc naturalistic and comprehensive sampling from the referral population in two outpatient clinics for adults with autism. While this approach has the asset of delivering directly translatable generalization into the clinical reality of adult outpatient clinics, the clinical comparison group of individuals with suspicion of, but ultimately ruled-out, ASD diagnosis results in a heterogeneous clinical sample. Future studies will need to add to the question of generalizability by testing homogeneous clinical samples characterized by, for instance, social phobia. Another limitation that should be considered is that all measures were retrieved via self-report. Deploying questionnaires for all measures potentially results in variance that is only due to this common method. Future studies should include various methods to compensate for this bias.

By deploying adequate methods for inference about the differential predictive power of alexithymia and autism traits in a large representative referral population for autism diagnostics in adulthood, the current results suggest that the alexithymia trait, difficulties identifying feelings, acts as a major predictor for depression in adults with ASD. Our results further show that this effect is not specific to ASD and potentially reflects a general mechanism in clinical samples.

Additional information

The use of language in this article was chosen based on suggestions in: Tepest, R. (2020). The meaning of diagnosis for different designations in talking about autism. Journal of Autism and Development. (DOI: https://doi.org/10.1007/s10803-020-04584-3).

Data availability

The aggregated data analyzed during the current study and the R Script used for analysis are available from the corresponding author on reasonable request.

Acknowledgements

We thank Afton Nelson, Ralf Tepest, Josephine Schmitt and Sebastian Lammers for proofreading, and Hanna Thaler and Claus Caspari for their methodological advices.

Funding

Open Access funding enabled and organized by Projekt DEAL. Funding was provided by Deutsche Forschungsgemeinschaft (Grant No. FA876/3-1).

Author information

Affiliations

1. Department of Psychiatry and Psychotherapy, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany

Carola Bloch, Lana Burghof, Fritz-Georg Lehnhardt & Kai Vogeley

2. Department of Psychiatry and Psychotherapy, Medical Faculty, LMU Munich, Nussbaumstr. 7, 80336, Munich, Germany

Carola Bloch & Christine Falter-Wagner

3. Institute of Neuroscience and Medicine (INM3), Research Center Jülich, Leo-Brandt-Str, 52428, Jülich, Germany

Kai Vogeley

4. Department of Psychology, University of Cologne, Gronewaldstr. 2, 50931, Cologne, Germany

Christine Falter-Wagner

Contributions

L.B. collected the initial sample for her bachelor and master theses. C.B. expanded the sample, conducted the analysis and wrote the first version of the manuscript. L.B., F.G.L., K.V. and C.F.W. read and edited the manuscript twice. Each author made substantial contribution to the design and conception of this work and the interpretation of data. C.B. and C.F.W. finalized the manuscript.

Corresponding author

Correspondence to Carola Bloch.

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The authors declare no competing interests.

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The original online version of this Article was revised: The original version of this Article contained errors that affected the descriptive statistics. Modifications have been made to Table 1, Table 2, the Method and the Results sections. Inferential statistics remained unaffected. Full information regarding the corrections made can be found in the correction for this Article.

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Bloch, C., Burghof, L., Lehnhardt, FG. et al. Alexithymia traits outweigh autism traits in the explanation of depression in adults with autism. Sci Rep 11, 2258 (2021). https://doi.org/10.1038/s41598-021-81696-5
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Alexithymia traits outweigh autism traits in the explanation of depression in adults with autism - Scientific Reports

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When contemplating the alarming depression rates in adults with autism spectrum disorder (ASD), there is a need to find factors explaining heightened symptoms of depression.

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The film was produced by Screen South for the Ignition Random Acts scheme, a series of ﬁlms celebrating England's most exciting 16 - 24 year-old artists and ﬁlmmakers in partnership with Arts Council England and Channel 4.

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