Representational similarity analysis reveals atypical age-related changes in brain regions supporting face and car recognition in autism

O’Hearn, Kirsten; Larsen, Bart; Fedor, Jennifer; Luna, Beatríz; Lynn, Andrew

doi:10.1016/j.neuroimage.2019.116322

Cited by 19 publications

(22 citation statements)

References 64 publications

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“…As we hypothesized, the current study replicated the well-documented findings that children with ASD have general face memory deficits [22,61,62], and further affirmed that deficits are present both in the encoding and retrieval phases. In the behavioral aspect, Gaigg et al [25] found that children with ASD present a lower accuracy than NTC in a three-order load paradigm of memory encoding, while Cooper et al [27] found that they made more errors in a continuous memory recalling task.…”

Section: Transdiagnostic Features Of Face Memory In Children With Asd and Adhdsupporting

confidence: 87%

The Effect of Comorbid Attention-Deficit/Hyperactivity Disorder Symptoms on Face Memory in Children with Autism Spectrum Disorder: Insights from Transdiagnostic Profiles

et al. 2021

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Face memory impairments are common but heterogeneous in autism spectrum disorder (ASD), which may be influenced by co-occurrence with attention-deficit/hyperactivity disorder (ADHD). Here, we aimed to investigate the phenotype change of face memory in children with ASD comorbid ADHD symptoms, and discuss the potential role of executive function (EF). Ninety-eight children were analyzed in the present study, including ASD− (ASD-only, n = 24), ADHD (n = 23), ASD+ (with ADHD symptoms, n = 23) and neurotypical controls (NTC, n = 28). All participants completed two tests: face encoding and retrieving task and Wisconsin Card Sorting Test (WCST) for measuring face memory and EF, respectively. Results revealed that: compared with the NTC group, children with ASD− exhibited lower accuracy in both face encoding and retrieving, and participants with ASD+ showed lower accuracy only in the retrieving, whereas no differences were found among participants with ADHD. Moreover, in the ASD+ group, face encoding performance was correlated with response perseverative errors (RPE) and failure to maintain sets (FMS) of WCST; significantly, there were no group differences between ASD+ and NTC in these two indices. The transdiagnostic profiles indicated that comorbid ADHD symptoms could modulate the face encoding deficiency of ASD, which may be partially compensated by EF. Shared and distinct intervention strategies to improve social cognition are recommended for children undergoing treatment for each condition.

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Section: Transdiagnostic Features Of Face Memory In Children With Asd and Adhdsupporting

confidence: 87%

The Effect of Comorbid Attention-Deficit/Hyperactivity Disorder Symptoms on Face Memory in Children with Autism Spectrum Disorder: Insights from Transdiagnostic Profiles

et al. 2021

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“…However, it does not explain the technological impact on obesity. [29] Focusing on autism associated with unusual activation during face processing representational similarity analysis…”

Section: Quasi-experimental Pragmatic Trialmentioning

confidence: 99%

Child tracking and hidden activities observation system through mobile app

Alam

Chowdhury

Hossain

et al. 2021

IJEECS

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The world is changing rapidly due to information technology, and all the people around the world are busy with their jobs, and business, adjusting to this race. Now, parents are giving more time at their business, office, and jobs, instead of passing the time at home, but they always get worried and scared about their children due to abuse of Information Technology and the country's law and order situation. So, parents are wanted to track and monitor their child's activities and location from anywhere to resolve their pressure. But is not possible for every parent to monitor their child physically due to many reasons. This paper presents a system that will help parents monitor their child activities from anywhere using a mobile phone to solve the problem. This android app uses global positioning system (GPS) and mobile services to find the child location and secretly stored all the call logs, hort message service (SMS) logs, contact lists, and accurate locations without knowing the children. Children assume that they are using Facebook, browsing the net or watching videos from youtube. It will not hamper any activities of the child. Parents can check all the activities of children using this app.

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“…While age-related differences in representational properties are a relatively longstanding topic in neurocognitive aging research – in particular the hypothesis and evidence regarding neural dedifferentiation ( Carp et al, 2011 , Kobelt et al, 2021 , Koen and Rugg, 2019 , Li et al, 2001 , Li et al, 2000 , Park et al, 2004 , Park et al, 2010 ) – childhood development has only recently been investigated from a representational perspective, and mainly with fMRI so far (cf. Cohen et al, 2019 ; Fandakova et al, 2019 ; O’Hearn et al, 2020 ). Accordingly, RSA is not yet a commonly utilized approach in developmental cognitive neuroscience, especially not for M/EEG studies.…”

Section: Introductionmentioning

confidence: 99%

Spectral pattern similarity analysis: Tutorial and application in developmental cognitive neuroscience

Sommer

Mount

Weigelt

et al. 2022

Developmental Cognitive Neuroscience

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The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists.

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Representational similarity analysis reveals atypical age-related changes in brain regions supporting face and car recognition in autism

Cited by 19 publications

References 64 publications

The Effect of Comorbid Attention-Deficit/Hyperactivity Disorder Symptoms on Face Memory in Children with Autism Spectrum Disorder: Insights from Transdiagnostic Profiles

The Effect of Comorbid Attention-Deficit/Hyperactivity Disorder Symptoms on Face Memory in Children with Autism Spectrum Disorder: Insights from Transdiagnostic Profiles

Child tracking and hidden activities observation system through mobile app

Spectral pattern similarity analysis: Tutorial and application in developmental cognitive neuroscience

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