2023
DOI: 10.1101/2023.03.25.23287738
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Enhanced motor noise in an autism subtype with poor motor skills

Abstract: Early motor difficulties are a common in many, but not all, autistic individuals. These difficulties tend to be highly present in individuals carrying rare genetic mutations with high penetrance for autism. Many of these rare genetic mechanisms also cause neurophysiological dysregulation of excitation-inhibition balance (E:I). A predicted downstream consequence of E:I imbalance in motor circuitry would translate behaviorally into enhanced motor noise - that is, increased variability in execution of motor actio… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2
1

Relationship

3
0

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 83 publications
0
1
0
Order By: Relevance
“…We find evidence supporting the idea that within idiopathic autistic males, there are 2 robust and reproducible E:I neurosubtypes and that such subtypes can be identified with 92-98% accuracy in new independent data. Alongside our other past work taking a similar relative clustering validation approach 25,32,33 , this work showcases that we can begin with unsupervised data-driven discoveries and then immediately translate those discoveries into supervised prediction and classification models. Further utilization of such robust, reproducible, and highly generalizable stratification models may be useful for future work in the field.…”
Section: Discussionmentioning
confidence: 86%
“…We find evidence supporting the idea that within idiopathic autistic males, there are 2 robust and reproducible E:I neurosubtypes and that such subtypes can be identified with 92-98% accuracy in new independent data. Alongside our other past work taking a similar relative clustering validation approach 25,32,33 , this work showcases that we can begin with unsupervised data-driven discoveries and then immediately translate those discoveries into supervised prediction and classification models. Further utilization of such robust, reproducible, and highly generalizable stratification models may be useful for future work in the field.…”
Section: Discussionmentioning
confidence: 86%