Brain Structure and Function in School-Aged Children With Sluggish Cognitive Tempo Symptoms

Camprodon‐Rosanas, Ester; Pujol, Jesús; Martínez‐Vilavella, Gerard; Blanco‐Hinojo, Laura; Medrano-Martorell, Santiago; Batlle, Santiago; Forns, Joan; Ribas-Fitó, Núria; Dolz, Montserrat; Sunyer, Jordi

doi:10.1016/j.jaac.2018.09.441

Cited by 20 publications

(17 citation statements)

References 41 publications

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“…Finally, existing studies looking at ASD, ADHD, and ASD+ADHD groups investigated conditions such as response time variability in the three groups (Tye et al, 2016 ), and recent studies on sluggish cognitive tempo (SCT); however, with little mention or emphasis on the DMN FC of the three groups (Camprodon-Rosanas et al, 2019 ; Duncan et al, 2019 ). Duncan et al ( 2019 ) noted that SCT is highly comorbid with ASD, with social cognitive deficits in comorbid SCT-ASD individuals related to clinical symptoms such as increased ASD symptomatology and internalizing symptoms.…”

Section: Asd+adhd Comorbid Group Studies: a Brief Discussionmentioning

confidence: 99%

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder

et al. 2021

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Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD. Impact statement In this work, we review the default mode network in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), as well as comorbid ASD+ADHD literature. Such a review has not been constructed in the field of cognitive neuroscience at this time, and it would greatly aid other behavioral and cognitive neuroscientists in identifying gaps in the field. In addition, the need to consider disorders to be on a continuum, as suggested by the Research Domain Criteria (RDoC), is important while identifying abnormal patterns in resting-state functional connectivity. This timely review will impact the field in a meaningful way, such that more research on the overlaps between ASD and ADHD is conducted along a spectrum.

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Section: Asd+adhd Comorbid Group Studies: a Brief Discussionmentioning

confidence: 99%

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder

et al. 2021

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“…Although studies are needed to directly address this possibility, "daydreams" is the most frequently used behavioral item for assessing SCT [1] and is also a key feature of mind wandering [33,34]. Further, both mind wandering [35] and SCT symptoms [36] are associated with altered default mode network connectivity. A small but growing body of research has linked mind wandering to poorer motor task performance [37].…”

Section: Sct and Processing Speed: Do Motor Demands Matter?mentioning

confidence: 99%

Sluggish cognitive tempo and processing speed in adolescents with ADHD: do findings vary based on informant and task?

Becker

Marsh

Holdaway

et al. 2019

Eur Child Adolesc Psychiatry

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Few studies have examined whether behavioral sluggish cognitive tempo (SCT) symptoms are related to speeded task performance. Mixed findings in existing research could be due to previous studies using a broad conceptualization of processing speed, not including self-report of SCT symptoms, and relying on non-optimal measures of SCT. Using a multi-informant design with both parent-and adolescent-reported SCT symptoms, the present study provides a preliminary test of the hypothesis that SCT symptoms would be associated with slower performance on tasks having greater graphomotor and fine motor demands. Participants were 80 adolescents (ages 13-17 years; 71% male) with attention-deficit/hyperactivity disorder (ADHD). Adolescents and parents completed ratings of SCT. Adolescents were administered the Wechsler Symbol Search and Coding subtests and the Grooved Pegboard Test. When adjusting for age, sex, and ADHD symptom severity, parent-reported SCT symptoms were not significantly associated with Symbol Search or Coding scores but were significantly associated with slower Grooved Pegboard time. Adolescent-reported SCT symptoms were not significantly associated with Symbol Search but were significantly associated with lower Coding scores and slower Grooved Pegboard time. Findings provide preliminary support for the hypothesis that SCT may be more clearly associated with processing speed task performance as motor demands increase and provide a potential explanation for the mixed literature on SCT in relation to processing speed by demonstrating that the presence and magnitude of associations vary by informant and task.

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“…Literature also reflects in the same light where ADHD activities are more visible in frontal areas of the brain. [53][54][55]…”

Section: Resultsmentioning

confidence: 99%

Detection of ADHD From EEG Signals Using Different Entropy Measures and ANN

et al. 2021

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Attention deficit hyperactivity disorder (ADHD) is a prevalent behavioral, cognitive, neurodevelopmental pediatric disorder. Clinical evaluations, symptom surveys, and neuropsychological assessments are some of the ADHD assessment methods, which are time-consuming processes and have a certain degree of uncertainty. This research investigates an efficient computer-aided technological solution for detecting ADHD from the acquired electroencephalography (EEG) signals based on different nonlinear entropy estimators and an artificial neural network classifier. Features extracted through fuzzy entropy, log energy entropy, permutation entropy, SURE entropy, and Shannon entropy are analyzed for effective discrimination of ADHD subjects from the control group. The experimented results confirm that the proposed techniques can effectively detect and classify ADHD subjects. The permutation entropy gives the highest classification accuracy of 99.82%, sensitivity of 98.21%, and specificity of 98.82%. Also, the potency of different entropy estimators derived from the t-test reflects that the Shannon entropy has a higher P-value (>.001); therefore, it has a limited scope than other entropy estimators for ADHD diagnosis. Furthermore, the considerable variance found from potential features obtained in the frontal polar (FP) and frontal (F) lobes using different entropy estimators under the eyes-closed condition shows that the signals received in these lobes will have more significance in distinguishing ADHD from normal subjects.

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Brain Structure and Function in School-Aged Children With Sluggish Cognitive Tempo Symptoms

Cited by 20 publications

References 41 publications

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder

Sluggish cognitive tempo and processing speed in adolescents with ADHD: do findings vary based on informant and task?

Detection of ADHD From EEG Signals Using Different Entropy Measures and ANN

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