An Integrated Analysis of Neural Network Correlates of Categorical and Dimensional Models of Attention-Deficit/Hyperactivity Disorder

Raimon H.R. Pruim, Christian F. Beckmann, Marianne Oldehinkel, Jaap Oosterlaan, Dirk Heslenfeld, Catharina A. Hartman, Pieter J. Hoekstra, Stephen V. Faraone, Barbara Franke, Jan K. Buitelaar, Maarten Mennes

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder, putatively induced by dissociable dysfunctional biobehavioral pathways. Here, we present a proof-of-concept study to parse ADHD-related heterogeneity in its underlying neurobiology by investigating functional connectivity across multiple brain networks to 1) disentangle categorical diagnosis-related effects from dimensional behavior-related effects and 2) functionally map these neural correlates to neurocognitive measures. Methods: We identified functional connectivity abnormalities related to ADHD across 14 networks within a large resting-state functional magnetic resonance imaging dataset (n = 409; age = 17.5 ± 3.3 years). We tested these abnormalities for their association with the categorical ADHD diagnosis and with dimensional inattention and hyperactivity/impulsivity scores using a novel modeling framework, creating orthogonalized models. Next, we evaluated the relationship of these findings with neurocognitive measures (working memory, response inhibition, reaction time variability, reward sensitivity). Results: Within the default mode network, we mainly observed categorical ADHD-related functional connectivity abnormalities, unrelated to neurocognitive measures. Clusters within the visual networks primarily related to dimensional scores of inattention and reaction time variability, while findings within the sensorimotor networks were mainly linked to hyperactivity/impulsivity and both reward sensitivity and working memory. Findings within the cerebellum network and salience network related to both categorical and dimensional ADHD measures and were linked to response inhibition and reaction time variability. Conclusions: This proof-of-concept study identified ADHD-related neural correlates across multiple functional networks, showing distinct categorical and dimensional mechanisms and their links to neurocognitive functioning.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
DOIs
Publication statusAccepted/In press - 20 Dec 2018

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Attention Deficit Disorder with Hyperactivity
Reaction Time
Impulsive Behavior
Reward
Short-Term Memory
Neurobiology
Cerebellum
Magnetic Resonance Imaging
Brain

Keywords

  • Attention-deficit/hyperactivity disorder
  • Categorical-dimensional analysis
  • Connectivity
  • Neural networks
  • Neuropsychology
  • Resting-state functional magnetic resonance imaging
  • rfMRI

Cite this

Pruim, Raimon H.R. ; Beckmann, Christian F. ; Oldehinkel, Marianne ; Oosterlaan, Jaap ; Heslenfeld, Dirk ; Hartman, Catharina A. ; Hoekstra, Pieter J. ; Faraone, Stephen V. ; Franke, Barbara ; Buitelaar, Jan K. ; Mennes, Maarten. / An Integrated Analysis of Neural Network Correlates of Categorical and Dimensional Models of Attention-Deficit/Hyperactivity Disorder. In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2018 ; pp. 1-12.
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An Integrated Analysis of Neural Network Correlates of Categorical and Dimensional Models of Attention-Deficit/Hyperactivity Disorder. / Pruim, Raimon H.R.; Beckmann, Christian F.; Oldehinkel, Marianne; Oosterlaan, Jaap; Heslenfeld, Dirk; Hartman, Catharina A.; Hoekstra, Pieter J.; Faraone, Stephen V.; Franke, Barbara; Buitelaar, Jan K.; Mennes, Maarten.

In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 20.12.2018, p. 1-12.

Research output: Contribution to JournalArticleAcademicpeer-review

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T1 - An Integrated Analysis of Neural Network Correlates of Categorical and Dimensional Models of Attention-Deficit/Hyperactivity Disorder

AU - Pruim, Raimon H.R.

AU - Beckmann, Christian F.

AU - Oldehinkel, Marianne

AU - Oosterlaan, Jaap

AU - Heslenfeld, Dirk

AU - Hartman, Catharina A.

AU - Hoekstra, Pieter J.

AU - Faraone, Stephen V.

AU - Franke, Barbara

AU - Buitelaar, Jan K.

AU - Mennes, Maarten

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N2 - Background: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder, putatively induced by dissociable dysfunctional biobehavioral pathways. Here, we present a proof-of-concept study to parse ADHD-related heterogeneity in its underlying neurobiology by investigating functional connectivity across multiple brain networks to 1) disentangle categorical diagnosis-related effects from dimensional behavior-related effects and 2) functionally map these neural correlates to neurocognitive measures. Methods: We identified functional connectivity abnormalities related to ADHD across 14 networks within a large resting-state functional magnetic resonance imaging dataset (n = 409; age = 17.5 ± 3.3 years). We tested these abnormalities for their association with the categorical ADHD diagnosis and with dimensional inattention and hyperactivity/impulsivity scores using a novel modeling framework, creating orthogonalized models. Next, we evaluated the relationship of these findings with neurocognitive measures (working memory, response inhibition, reaction time variability, reward sensitivity). Results: Within the default mode network, we mainly observed categorical ADHD-related functional connectivity abnormalities, unrelated to neurocognitive measures. Clusters within the visual networks primarily related to dimensional scores of inattention and reaction time variability, while findings within the sensorimotor networks were mainly linked to hyperactivity/impulsivity and both reward sensitivity and working memory. Findings within the cerebellum network and salience network related to both categorical and dimensional ADHD measures and were linked to response inhibition and reaction time variability. Conclusions: This proof-of-concept study identified ADHD-related neural correlates across multiple functional networks, showing distinct categorical and dimensional mechanisms and their links to neurocognitive functioning.

AB - Background: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder, putatively induced by dissociable dysfunctional biobehavioral pathways. Here, we present a proof-of-concept study to parse ADHD-related heterogeneity in its underlying neurobiology by investigating functional connectivity across multiple brain networks to 1) disentangle categorical diagnosis-related effects from dimensional behavior-related effects and 2) functionally map these neural correlates to neurocognitive measures. Methods: We identified functional connectivity abnormalities related to ADHD across 14 networks within a large resting-state functional magnetic resonance imaging dataset (n = 409; age = 17.5 ± 3.3 years). We tested these abnormalities for their association with the categorical ADHD diagnosis and with dimensional inattention and hyperactivity/impulsivity scores using a novel modeling framework, creating orthogonalized models. Next, we evaluated the relationship of these findings with neurocognitive measures (working memory, response inhibition, reaction time variability, reward sensitivity). Results: Within the default mode network, we mainly observed categorical ADHD-related functional connectivity abnormalities, unrelated to neurocognitive measures. Clusters within the visual networks primarily related to dimensional scores of inattention and reaction time variability, while findings within the sensorimotor networks were mainly linked to hyperactivity/impulsivity and both reward sensitivity and working memory. Findings within the cerebellum network and salience network related to both categorical and dimensional ADHD measures and were linked to response inhibition and reaction time variability. Conclusions: This proof-of-concept study identified ADHD-related neural correlates across multiple functional networks, showing distinct categorical and dimensional mechanisms and their links to neurocognitive functioning.

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