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Information gain in the brain's resting state: A new perspective on autism
Information gain in the brain's resting state: A new perspective on autism
Along with the study of brain activity evoked by external stimuli, an increased interest in the research of background, “noisy” brain activity is fast developing in current neuroscience. It is becoming apparent that this “resting-state” activity is a major factor determining other, more particular, responses to stimuli and hence it can be argued that background activity carries important information used by the nervous systems for adaptive behaviors. In this context, we investigated the generation of information in ongoing brain activity recorded with magnetoencephalography (MEG) in children with autism spectrum disorder (ASD) and non-autistic children. Using a stochastic dynamical model of brain dynamics, we were able to resolve not only the deterministic interactions between brain regions, i.e., the brain's functional connectivity, but also the stochastic inputs to the brain in the resting state; an important component of large-scale neural dynamics that no other method can resolve to date. We then computed the Kullback-Leibler (KLD) divergence, also known as information gain or relative entropy, between the stochastic inputs and the brain activity at different locations (outputs) in children with ASD compared to controls. The divergence between the input noise and the brain's ongoing activity extracted from our stochastic model was significantly higher in autistic relative to non-autistic children. This suggests that brains of subjects with autism create more informatio...
·frontiersin.org·
Information gain in the brain's resting state: A new perspective on autism
Psychometric exploration of the RAADS-R with autistic adults: Implications for research and clinical practice - Alexandra Sturm, Sijia Huang, Vanessa Bal, Ben Schwartzman, 2024
Psychometric exploration of the RAADS-R with autistic adults: Implications for research and clinical practice - Alexandra Sturm, Sijia Huang, Vanessa Bal, Ben Schwartzman, 2024
Several validated adult autism symptom screening tools exist; however, there are concerns about the validity of instruments in adults who self-identify and thos...
·journals.sagepub.com·
Psychometric exploration of the RAADS-R with autistic adults: Implications for research and clinical practice - Alexandra Sturm, Sijia Huang, Vanessa Bal, Ben Schwartzman, 2024
A reverse Turing-test for predicting social deficits in people with Autism
A reverse Turing-test for predicting social deficits in people with Autism
Social symptoms of autism spectrum disorder (ASD) are typically viewed as consequences of an impaired Theory of Mind, i.e. the ability to understand others’ covert mental states. Here, we test the assumption that such “mind blindness” may be due to the inability to exploit contextual knowledge about, e.g., the stakes of social interactions, to make sense of otherwise ambiguous cues (e.g., idiosyncratic responses to social competition). In this view, social cognition in ASD may simply reduce to non-social cognition, i.e. cognition that is not informed by the social context. We compared 24 adult participants with ASD to 24 neurotypic participants in a repeated dyadic competitive game against artificial agents with calibrated mentalizing sophistication. Critically, participants were framed to believe that they were competing against humans (social framing) or not (non-social framing), hence the “reverse Turing test”. In contrast to control participants, the strategy of people with ASD is insensitive to the game’s framing, i.e. they do not constrain their understanding of others’ behaviour with the contextual knowledge about the game (cf. competitive social framing). They also outperform controls when playing against simple agents, but are outperformed by them against recursive algorithms framed as human opponents. Moreover, computational analyses of trial-by-trial choice sequences in the game show that individuals with ASD rely on a distinctive cognitive strategy with subnormal flexibility and mentalizing sophistication. These computational phenotypes yield 79% diagnosis classification accuracy and explain 62% of the severity of social symptoms in people with ASD.
A reverse Turing-test for predicting social deficits in people with Autism
·biorxiv.org·
A reverse Turing-test for predicting social deficits in people with Autism
Autistic peer-to-peer information transfer is highly effective - Catherine J Crompton, Danielle Ropar, Claire VM Evans-Williams, Emma G Flynn, Sue Fletcher-Watson, 2020
Autistic peer-to-peer information transfer is highly effective - Catherine J Crompton, Danielle Ropar, Claire VM Evans-Williams, Emma G Flynn, Sue Fletcher-Watson, 2020
Effective information transfer requires social communication skills. As autism is clinically defined by social communication deficits, it may be expected that i...
·journals.sagepub.com·
Autistic peer-to-peer information transfer is highly effective - Catherine J Crompton, Danielle Ropar, Claire VM Evans-Williams, Emma G Flynn, Sue Fletcher-Watson, 2020