my autism has dominated my social skills a lot, so i cant really ever “relax and be myself” in a social setting, social interactions have always been very unnatural to me and i’m weird and i talk too much and i talk abt wrong stuff in a wrong way and embarrass myself and ultimately make ppl feel uncomfortable bc my “being myself” is not up to society’s perfect allistic standards
i got ostracized by my neurotypical peers for most of my life until i was like 17 and finally started to learn how to hide my autism in social settings. i’m not as embarrassing and weird as before, but i still slip up when i get too comfortable
i just wish meeting ppl & making friends didnt feel like some kind of an obstacle course that i have to memorize & manually perform every time, until i feel like i’m allowed to relax and be my weird self. its so odd that to other people it all comes naturally and isnt stressful or anxiety-provoking.
- your special interests don’t have to, be useful
- other people’s opinions do NOT define you
- your ability to socialize does not define your worth
- you do NOT need to change too meet someone’s else’s expectations
Things You Might Relate To If You're Socially awkward
- Feeling nervous in social situations
- Your jealous of people with good social skill
- Making eye contact is difficult
- You don’t like making phone calls, you would rather text
- You’re not great at starting or ending conversations
- You dread going to hairdressers
- You avoid large gatherings
- You feel uncomfortable when people give you compliments “umm thanks…”
- Using self scan to avoid an awkward conversation with the cashier
- Pretending to be busy
- getting labelled as arrogant
Many doctors and scientists think they could improve the diagnosis
and understanding of autism spectrum disorders if they had reliable
means to identify specific abnormalities in the brain. Such “biomarkers”
have proven elusive, often because methods that show promise with one
group of patients fail when applied to another. In a new study in Nature Communications,
however, scientists report a new degree of success. Their proposed
biomarker worked with a comparably high degree of accuracy in assessing
two diverse sets of adults.
A map of the brain
connections that proved useful in distinguishing patients diagnosed with
autism from people without an autism diagnosis)
The technology, principally developed at the Advanced
Telecommunications Research Institute International in Kyoto, Japan,
with the major contributions from three co-authors at Brown University,
is a computer algorithm called a “classifier” because it can classify
sets of subjects – those with an autism spectrum disorder and those
without – based on functional magnetic resonance imaging (fMRI) brain
scans. By analyzing thousands of connections of brain network
connectivity in scores of people with and without autism, the software
found 16 key interregional functional connections that allowed it to
tell, with high accuracy, who had been traditionally diagnosed with
autism and who had not. The team developed the classifier with 181 adult
volunteers at three sites in Japan and then applied it in a group of 88
American adults at seven sites. All the study volunteers with autism
diagnoses had no intellectual disability.
“It is the first study to [successfully] apply a classifier to a
totally different cohort,” said co-corresponding author Yuka Sasaki, a
research associate professor of cognitive, linguistic and psychological
sciences at Brown. “There have been numerous attempts before. We finally
overcame the problem.”
The classifier, which blends two machine-learning algorithms, worked
well in each population, averaging 85 percent accuracy among the
Japanese volunteers and 75 percent accuracy among the Americans. The
researchers calculated that the probability of seeing this degree of
cross-population performance purely by chance was 1.4 in a million.
“These results indicate that although we developed a highly reliable
classifier using the training data only in Japan, it is sufficiently
universal to classify [autism] in the U.S.A. validation cohort,” wrote
the team of clinicians and basic researchers led by Mitsuo Kawato of
In another way of validating the classifier, the researchers asked
whether the differences it notes in the 16 connections were predictive
not only of whether a person had an autism diagnosis at all, but whether
they relate to performance on the main diagnostic method currently
available to clinicians, the Autism Diagnostic Observation Schedule.
ADOS is based not on markers of biology or physiology, but instead on a
doctor’s interviews and observations of behavior. The classifier was
able to predict scores on the ADOS communications component with a
statistically significant correlation of 0.44.
The correlation suggests that the 16 connections identified by the
classifier relate to attributes of importance in ADOS. When the
researchers examined where these 16 connections are and what brain
networks they affect, they found that 41 percent of the specific brain
regions in which the 16 connections reside belonged within the
cingulo-opercular network, which matters to brain functions such as
conceiving of other people, face processing and emotional processing.
Difficulties with such social and emotional perception tasks are
important symptoms in autism spectrum disorders.
Finally, the team looked to see whether the classifier appropriately
reflects the similarities and differences between autism spectrum
disorders and other psychiatric conditions. Autism, for example, is
known to share some similarities with schizophrenia but not with
depression or attention deficit hyperactivity disorder, as indicated by a
previous genome study. Applied to patients with each of these other
disorders compared to similar people without the conditions, the
classifier showed moderate but statistically significant accuracy in
distinguishing schizophrenia patients, but not depression or ADHD
Eventual clinical usefulness?
The MRI scans required to gather the data were simple, Sasaki said.
Subjects only needed to spend about 10 minutes in the machine and didn’t
have to perform any special tasks. They just had to stay still and
Despite that simplicity and even though the classifier performed
unprecedentedly well as a matter of research, Sasaki said, it is not yet
ready to be a clinical tool. While the future may bring that
development, refinements will be necessary first.
“The accuracy level needs to be much higher,” Sasaki said. “Eighty percent accuracy may not be useful in the real world.”
It’s also not clear how it would work among children, as the volunteers in this study were all adults.
But if the classifier’s accuracy can be improved further, the
researchers hope that it can be used not only as a physiology-based
diagnostic tool but also for monitoring treatment. Doctors perhaps will
be able to use the tool someday to monitor whether therapies produce
changes in brain connectivity, Sasaki said.