The Validity of the SNAP-IV in Children Displaying ADHD Symptoms

Hall, Charlotte L; Guo, Boliang; Valentine, Althea; Groom, Madeleine J.; Daley, David; Sayal, Kapil; Hollis, Chris

doi:10.1177/1073191119842255

Cited by 83 publications

(75 citation statements)

References 35 publications

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“…Children with anxiety and those with ADHD may also have differences in their responses to unpredicted stimuli [Cornwell, Garrido, Overstreet, Pine, & Grillon, 2017; Gonzalez‐Gadea et al, 2015]. Given the high prevalence of these conditions in children with ASD [Avni, Ben‐Itzchak, & Zachor, 2018], the Screen for Child Anxiety Related Disorders (SCARED‐parent version) (individuals 8–18) [Birmaher et al, 1999] and Swanson, Nolan, and Pelham (SNAP‐IV) (individuals 6–18) [Hall et al, 2020] were used to account for these as possible confounders. Detailed medical history was collected for all participants.…”

Section: Methodsmentioning

confidence: 99%

Individuals With Autism Have No Detectable Deficit in Neural Markers of Prediction Error When Presented With Auditory Rhythms of Varied Temporal Complexity

et al. 2020

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The brain's ability to encode temporal patterns and predict upcoming events is critical for speech perception and other aspects of social communication. Deficits in predictive coding may contribute to difficulties with social communication and overreliance on repetitive predictable environments in individuals with autism spectrum disorder (ASD). Using a mismatch negativity (MMN) task involving rhythmic tone sequences of varying complexity, we tested the hypotheses that (1) individuals with ASD have reduced MMN response to auditory stimuli that deviate in presentation timing from expected patterns, particularly as pattern complexity increases and (2) amplitude of MMN signal is inversely correlated with level of impairment in social communication and repetitive behaviors. Electroencephalography was acquired as individuals (age 6-21 years) listened to repeated five-rhythm tones that varied in the Shannon entropy of the rhythm across three conditions (zero, medium-1 bit, and high-2 bit entropy). The majority of the tones conformed to the established rhythm (standard tones); occasionally the fourth tone was temporally shifted relative to its expected time of occurrence (deviant tones). Social communication and repetitive behaviors were measured using the Social Responsiveness Scale and Repetitive Behavior Scale-Revised. Both neurotypical controls (n = 19) and individuals with ASD (n = 21) show stepwise decreases in MMN as a function of increasing entropy. Contrary to the result forecasted by a predictive coding hypothesis, individuals with ASD do not differ from controls in these neural mechanisms of prediction error to auditory rhythms of varied temporal complexity, and there is no relationship between these signals and social communication or repetitive behavior measures.

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Section: Methodsmentioning

confidence: 99%

Individuals With Autism Have No Detectable Deficit in Neural Markers of Prediction Error When Presented With Auditory Rhythms of Varied Temporal Complexity

et al. 2020

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“…(Cornwell, Garrido, Overstreet, Pine, & Grillon, 2017;Gonzalez-Gadea et al, 2015). Given the high prevalence of these conditions in children with ASD (Avni, Ben-Itzchak, & Zachor, 2018), the Screen for Child Anxiety Related Disorders (SCARED-parent version) (individuals 8-18) (Birmaher et al, 1999) and Swanson, Nolan, and Pelham (SNAP-IV) (individuals 6-18) (Hall et al, 2019) were used to account for these as possible confounders. Detailed medical history was collected for all participants.…”

Section: Participants and Phenotypic Characterizationmentioning

confidence: 99%

Individuals with autism have no detectable deficit in neural markers of prediction error when presented with auditory rhythms of varied temporal complexity

Knight

Oakes

Hyman

et al. 2020

Preprint

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The brain's ability to encode temporal patterns and predict upcoming events is critical for speech perception and other aspects of social communication. Deficits in predictive coding may contribute to difficulties with social communication and overreliance on repetitive predictable environments in individuals with autism spectrum disorder (ASD). Using a mismatch negativity (MMN) task involving rhythmic tone sequences of varying complexity, we tested the hypotheses that 1) individuals with ASD have reduced MMN response to auditory stimuli that deviate in presentation timing from expected patterns, particularly as pattern complexity increases and 2) amplitude of MMN signal is inversely correlated with level of impairment in social communication and repetitive behaviors. Electroencephalography was acquired as individuals (age 6-21years) listened to repeated five-rhythm tones that varied in the Shannon entropy of the rhythm across three conditions (zero, medium-1 bit) and high-2 bit entropy). The majority of the tones conformed to the established rhythm (standard tones); occasionally the 4 th tone was temporally shifted relative to its expected time of occurrence (deviant tones). Social communication and repetitive behaviors were measured using the Social Responsiveness Scale and Repetitive Behavior Scale-Revised. Both neurotypical controls (n=19) and individuals with ASD (n=21) show stepwise decreases in MMN as a function of increasing entropy. Contrary to the result forecasted by a predictive coding hypothesis, individuals with ASD do not differ from controls in these neural mechanisms of prediction error to auditory rhythms of varied temporal complexity, and there is no relationship between these signals and social communication or repetitive behavior measures.

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“…Interestingly, we found that almost all oppositional questions from the teacher report were categorized into the high-order group, whereas oppositional questions from the parent report were in the low-order group (67). That is, according to these internationally well-known standardized scales used in our ADHD studies, teacher reports of oppositional symptoms had better discriminant validity in distinguishing ADHD from non-ADHD.…”

Section: Discussionmentioning

confidence: 67%

“…In addition, through the process of this deep learning approach, we can rank the questions of the rating scales in terms of the ability of the machine to learn from the data and to predict the missing values accurately. We hypothesized that questions assessing hyperactivity-impulsivity behaviors, particularly from teacher reports, would have high imputation accuracy and discriminating ability based on previous studies suggesting that these symptoms are observable (67) and that teachers may have more opportunities to observe ADHD-related behaviors such as oppositional defiant symptoms than parents do (35).…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Missing Data Imputation of Rating Scales Assessing Attention-Deficit Hyperactivity Disorder

Cheng

Tseng

Chang³

et al. 2020

Front. Psychiatry

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A variety of tools and methods have been used to measure behavioral symptoms of attention-deficit/hyperactivity disorder (ADHD). Missing data is a major concern in ADHD behavioral studies. This study used a deep learning method to impute missing data in ADHD rating scales and evaluated the ability of the imputed dataset (i.e., the imputed data replacing the original missing values) to distinguish youths with ADHD from youths without ADHD. The data were collected from 1220 youths, 799 of whom had an ADHD diagnosis, and 421 were typically developing (TD) youths without ADHD, recruited in Northern Taiwan. Participants were assessed using the Conners' Continuous Performance Test, the Chinese versions of the Conners' rating scale-revised: short form for parent and teacher reports, and the Swanson, Nolan, and Pelham, version IV scale for parent and teacher reports. We used deep learning, with information from the original complete dataset (referred to as the reference dataset), to perform missing data imputation and generate an imputation order according to the imputed accuracy of each question. We evaluated the effectiveness of imputation using support vector machine to classify the ADHD and TD groups in the imputed dataset. The imputed dataset can classify ADHD vs. TD up to 89% accuracy, which did not differ from the classification accuracy (89%) using the reference dataset. Most of the behaviors related to oppositional behaviors rated by teachers and hyperactivity/impulsivity rated by both parents and teachers showed high discriminatory accuracy to distinguish ADHD from non-ADHD. Our findings support a deep learning solution for missing data imputation without introducing bias to the data.

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The Validity of the SNAP-IV in Children Displaying ADHD Symptoms

Cited by 83 publications

References 35 publications

Individuals With Autism Have No Detectable Deficit in Neural Markers of Prediction Error When Presented With Auditory Rhythms of Varied Temporal Complexity

Individuals With Autism Have No Detectable Deficit in Neural Markers of Prediction Error When Presented With Auditory Rhythms of Varied Temporal Complexity

Individuals with autism have no detectable deficit in neural markers of prediction error when presented with auditory rhythms of varied temporal complexity

A Deep Learning Approach for Missing Data Imputation of Rating Scales Assessing Attention-Deficit Hyperactivity Disorder

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