Information‐based multivariate decoding reveals imprecise neural encoding in children with attention deficit hyperactivity disorder during visual selective attention

Abstract

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in school‐age children. Attentional orientation is a potential clinical diagnostic marker to aid in the early diagnosis of ADHD. However, the underlying pathophysiological substrates of impaired attentional orienting in childhood ADHD remain unclear. Electroencephalography (EEG) was measured in 135 school‐age children (70 with childhood ADHD and 65 matched typically developing children) to directly investigate target localization during spatial selective attention through univariate ERP analysis and information‐based multivariate pattern machine learning analysis. Compared with children with typical development, a smaller N2pc was found in the ADHD group through univariate ERP analysis. Children with ADHD showed a lower parieto‐occipital multivariate decoding accuracy approximately 240–340 ms after visual search onset, which predicts a slower reaction time and larger standard deviation of reaction time. Furthermore, a significant correlation was found between N2pc and decoding accuracy in typically developing children but not in children with ADHD. These observations reveal that impaired attentional orienting in ADHD may be due to inefficient neural encoding responses. By using a personalized information‐based multivariate machine learning approach, we have advanced the understanding of cognitive deficits in neurodevelopmental disorders. Our study provides potential research directions for the early diagnosis and optimization of personalized intervention in children with ADHD.

Prolonged neural encoding of visual information in autism

“…Second, and most relevant to the current study, recent evidence has demonstrated a greater efficacy of the MVPA of EEG data in identifying hidden neural dynamics exhibited by individuals with neurodevelopmental (Williams Syndrome: Farran et al, 2020; ADHD: Li et al, 2023) and psychiatric (Schizophrenia: Bae et al, 2020) disorders. Indeed, for populations in which the observed behavior is the result of an alternative developmental pathway, potentially supported by distinct neural mechanisms, a multivariate approach may provide a more explanatory understanding of their sensory and cognitive profile.…”

“…In addition, similarly to some recent studies (Bae et al, 2020;Farran et al, 2020;Li et al, 2023;Mares et al, 2020), we analyzed different key decoding parameters that allow a deeper investigation of the neural activation profiles differences associated with the discrimination of the experimental classes between ASD and TD groups. These parameters, which were tested using independent sample ttests, are: (i) decoding onset, that is, the first timepoint at which decoding becomes significant; (ii) decoding sustainability, that is, the percentage of significant timepoints decoded over time; (iii) decoding peak, that is, the highest decoding accuracy value (AUC); (iv) decoding peak latency, that is, the timepoint at which accuracy (AUC) is highest.…”

“…Multivariate pattern analysis (MVPA) of EEG data is a particularly promising method to investigate the fast and transient neural dynamics orchestrating attentional processes (Battistoni et al, 2020; Marti & Dehaene, 2017; Moerel et al, 2022; Munneke et al, 2021). Recently, MVPA has proven to be a powerful methodological tool to unveil the hidden dysfunctional neural dynamics underlying anomalies in different cognitive domains (Bae et al, 2020; Farran et al, 2020; Li et al, 2023). Accordingly, MVPA could be used to deepen the understanding of visuo‐spatial attentional anomalies in children with ASD for several reasons.…”

Improvement of semantic processing ability of Chinese characters in school children: A comparative study based on 2009 and 2019 data

“…We observed language development in typically developing children. So all participants exhibited IQ ≥80 according to the Wechsler Intelligence Scale for Chinese Children-Revised (WISCC-R) ( Wang et al, 2021 ; Li et al, 2023 ). Children diagnosed by a specialist with attention deficit hyperactivity disorder, autism, psychological disorders, intellectual disability, and a history of psychiatric disorders or brain damage were excluded.…”