Exploring neural heterogeneity in inattention and hyperactivity

Zdorovtsova, Natalia; Jones, Jonathan Spencer; Akarca, Danyal; Benhamou, Elia; Team, The CALM.; Astle, Duncan E.

doi:10.1016/j.cortex.2023.04.001

Cited by 3 publications

(2 citation statements)

References 137 publications

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“…In addition, given the choice of the neuropsychological tests and measures adopted in the ABCD study, our study utilized picture vocabulary and matrix reasoning scores as metrics for IQ, instead of full-scale IQ. Furthermore, working memory processes are subserved by widespread brain regions, including distinct activation patterns in the fronto-parietal regions, along with a considerable heterogeneity in connectome organization [26,89,105]. Therefore, future research adopting a network analysis approach and assessing topological features that may differ between ADHD-F and ADHD-NF would benefit the field.…”

Section: Discussionmentioning

confidence: 99%

“…Although EF deficits are implicated in ADHD, evidence for these deficits in ADHD is inconsistent [22]. ADHD participant groups show a notable heterogeneity in EF and working memory deficits [9,[23][24][25][26]. Furthermore, inconsistencies have been noted regarding the specific EF and working memory domains in which participants with ADHD show impairments [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Working Memory-Related Neurofunctional Correlates Associated with the Frontal Lobe in Children with Familial vs. Non-Familial Attention Deficit/Hyperactivity Disorder

Li,

Motwani,

Cao

et al. 2023

Brain Sciences

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Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with high prevalence, heritability, and heterogeneity. Children with a positive family history of ADHD have a heightened risk of ADHD emergence, persistence, and executive function deficits, with the neural mechanisms having been under investigated. The objective of this study was to investigate working memory-related functional brain activation patterns in children with ADHD (with vs. without positive family histories (ADHD-F vs. ADHD-NF)) and matched typically developing children (TDC). Voxel-based and region of interest analyses were conducted on two-back task-based fMRI data of 362 subjects, including 186, 96, and 80 children in groups of TDC, ADHD-NF, and ADHD-F, respectively. Relative to TDC, both ADHD groups had significantly reduced activation in the left inferior frontal gyrus (IFG). And the ADHD-F group demonstrated a significant positive association of left IFG activation with task reaction time, a negative association of the right IFG with ADHD symptomatology, and a negative association of the IFG activation laterality index with the inattention symptom score. These results suggest that working memory-related functional alterations in bilateral IFGs may play distinct roles in ADHD-F, with the functional underdevelopment of the left IFG significantly informing the onset of ADHD symptoms. Our findings have the potential to assist in tailored diagnoses and targeted interventions in children with ADHD-F.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Working Memory-Related Neurofunctional Correlates Associated with the Frontal Lobe in Children with Familial vs. Non-Familial Attention Deficit/Hyperactivity Disorder

Li,

Motwani,

Cao

et al. 2023

Brain Sciences

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The Complex Relationship Between IQ and ADHD: Insights from Behaviors, Cognition, Brain and impairment in 5,138 Children with ADHD

Liu,

Jia,

Wang

et al. 2024

Preprint

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Background Intelligence quotient (IQ) has traditionally been considered a protective factor against attention-deficit/hyperactivity disorder (ADHD). However, recent findings suggest a more complex relationship. This study aimed to elucidate the nuanced associations between IQ and ADHD. We leveraged a high-quality, multi-dimensional dataset encompassing behavioural, cognitive, and neuroimaging measures from a large ADHD cohort to investigate these relationships comprehensively. Methods We conducted a study with 5,138 children, aged 6–16 years, who have been professionally diagnosed with ADHD and have a wide range of IQ levels. General Linear Models were used to fit the relationship between IQ [Full-Scale IQ (FSIQ), Verbal IQ (VIQ), and Performance IQ (PIQ)] and ADHD core symptoms [total scores (TO), inattention (IA), hyperactivity/impulsivity (HI)] while considering sex and age as covariates. Furthermore, we examined if the complex associations between IQ and ADHD symptoms could be validated in executive functions and brain structure associated with ADHD. Finally, Latent class analysis was used to identify IQ and symptomatic characteristics in profiles of functional impairment of ADHD. Results Consistent with previous research, our study demonstrated a linear negative correlation between FSIQ and ADHD core symptoms. However, PIQ exhibited an inverted U-shaped relationship with HI scores, peaking at a PIQ of 103. These findings were partially corroborated in the relationships between IQ and executive functions, as well as IQ and brain surface area. Latent profile analysis identified three distinct ADHD profiles: Mild Impairment, Severe Academic Deficit, and Social-Academic Deficit. Notably, while higher IQ was associated with less impairment in academic performance, it did not consistently predict social functional impairment. Paradoxically, the group with the least overall impairment exhibited the highest levels of hyperactivity/impulsivity symptoms. Conclusion This study elucidates the intricate relationship between IQ and ADHD, revealing that elevated Performance IQ is associated with increased hyperactivity/impulsivity symptoms, while higher symptom levels may not always indicate worse outcomes.

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Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach

Zheng,

Sugino,

Jimbo

et al. 2024

Front. Comput. Neurosci.

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Top-down visual attention is a fundamental cognitive process that allows individuals to selectively attend to salient visual stimuli in the environment. Recent empirical findings have revealed that gamma oscillations participate in the modulation of visual attention. However, computational studies face challenges when analyzing the attentional process in the context of gamma oscillation due to the unstable nature of gamma oscillations and the complexity induced by the layered fashion in the visual cortex. In this study, we propose a layer-dependent network-of-networks approach to analyze such attention with gamma oscillations. The model is validated by reproducing empirical findings on orientation preference and the enhancement of neuronal response due to top-down attention. We perform parameter plane analysis to classify neuronal responses into several patterns and find that the neuronal response to sensory and attention signals was modulated by the heterogeneity of the neuronal population. Furthermore, we revealed a counter-intuitive scenario that the excitatory populations in layer 2/3 and layer 5 exhibit opposite responses to the attentional input. By modification of the original model, we confirmed layer 6 plays an indispensable role in such cases. Our findings uncover the layer-dependent dynamics in the cortical processing of visual attention and open up new possibilities for further research on layer-dependent properties in the cerebral cortex.

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Exploring neural heterogeneity in inattention and hyperactivity

Cited by 3 publications

References 137 publications

Working Memory-Related Neurofunctional Correlates Associated with the Frontal Lobe in Children with Familial vs. Non-Familial Attention Deficit/Hyperactivity Disorder

Working Memory-Related Neurofunctional Correlates Associated with the Frontal Lobe in Children with Familial vs. Non-Familial Attention Deficit/Hyperactivity Disorder

The Complex Relationship Between IQ and ADHD: Insights from Behaviors, Cognition, Brain and impairment in 5,138 Children with ADHD

Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach

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