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Neural circuitry underlying sustained attention in healthy adolescents and in ADHD symptomatology

  • Laura O'Halloran
  • , Zhipeng Cao
  • , Kathy Ruddy
  • , Lee Jollans
  • , Matthew D. Albaugh
  • , Andrea Aleni
  • , Alexandra S. Potter
  • , Nigel Vahey
  • , Tobias Banaschewski
  • , Sarah Hohmann
  • , Arun L.W. Bokde
  • , Uli Bromberg
  • , Christian Büchel
  • , Erin Burke Quinlan
  • , Sylvane Desrivières
  • , Herta Flor
  • , Vincent Frouin
  • , Penny Gowland
  • , Andreas Heinz
  • , Bernd Ittermann
  • Frauke Nees, Dimitri Papadopoulos Orfanos, Tomáš Paus, Michael N. Smolka, Henrik Walter, Gunter Schumann, Hugh Garavan, Clare Kelly, Robert Whelan*
*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Moment-to-moment reaction time variability on tasks of attention, often quantified by intra-individual response variability (IRV), provides a good indication of the degree to which an individual is vulnerable to lapses in sustained attention. Increased IRV is a hallmark of several disorders of attention, including Attention-Deficit/Hyperactivity Disorder (ADHD). Here, task-based fMRI was used to provide the first examination of how average brain activation and functional connectivity patterns in adolescents are related to individual differences in sustained attention as measured by IRV. We computed IRV in a large sample of adolescents (n = 758) across 'Go' trials of a Stop Signal Task (SST). A data-driven, multi-step analysis approach was used to identify networks associated with low IRV (i.e., good sustained attention) and high IRV (i.e., poorer sustained attention). Low IRV was associated with greater functional segregation (i.e., stronger negative connectivity) amongst an array of brain networks, particularly between cerebellum and motor, cerebellum and prefrontal, and occipital and motor networks. In contrast, high IRV was associated with stronger positive connectivity within the motor network bilaterally and between motor and parietal, prefrontal, and limbic networks. Consistent with these observations, a separate sample of adolescents exhibiting elevated ADHD symptoms had increased fMRI activation and stronger positive connectivity within the same motor network denoting poorer sustained attention, compared to a matched asymptomatic control sample. With respect to the functional connectivity signature of low IRV, there were no statistically significant differences in networks denoting good sustained attention between the ADHD symptom group and asymptomatic control group. We propose that sustained attentional processes are facilitated by an array of neural networks working together, and provide an empirical account of how the functional role of the cerebellum extends to cognition in adolescents. This work highlights the involvement of motor cortex in the integrity of sustained attention, and suggests that atypically strong connectivity within motor networks characterizes poor attentional capacity in both typically developing and ADHD symptomatic adolescents.
Original languageEnglish
Pages (from-to)395-406
Number of pages12
JournalNeuroImage
Volume169
Early online date09 Jan 2018
DOIs
Publication statusPublished - 01 Apr 2018
Externally publishedYes

Bibliographical note

Funding Information:
This work received support from the following sources: L. O'Halloran, L. Jollans and K. Ruddy are supported by The Irish Research Council ( GOIPG/2016/1635 ; GOIPG/2014/418 ; GOIPD/2017/798 ). Z. Cao is supported by China Scholarship Council . R. Whelan is supported by Science Foundation Ireland ( 16/ERCD/3797 ) and a Brain and Behavior Research Foundation Young Investigator award (# 23599 ). C. Kelly is supported by the National Institute of Mental Health ( R03MH104334 , PI: Kelly) and a B rain and Behavior Research Foundation Young Investigator award (# 24065 ). This work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) ( LSHM-CT- 2007-037286 ), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) ( 695313 ), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) ( PR-ST-0416-10004 ), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) ( MR/N027558/1 ), the FP7 projects IMAGEMEND ( 602450 ; IMAging GEnetics for MENtal Disorders) and MATRICS ( 603016 ), the Innovative Medicine Initiative Project EU-AIMS ( 115300-2 ), the Medical Research Council Grant 'c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) ( MR/N000390/1 ), the Swedish Research Council Formas , the Medical Research Council , the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London , the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152 ; 01EV0711 ; eMED SysAlc01ZX1311A ; Forschungsnetz AERIAL), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-1 , SM 80/7-2 , SFB 940/1 ). Further support was provided by grants from: ANR (project AF12-NEUR0008-01 - WM2NA , and ANR-12-SAMA-0004 ), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA) , the Fondation pour la Recherche Médicale ( DPA20140629802 ), the Fondation de l’Avenir, Paris-Sud University IDEX 2012; the National Institutes of Health, U.S.A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1 ), and by NIH Consortium grant U54 EB020403 , supported by a cross- NIH alliance that funds Big Data to Knowledge Centres of Excellence.

Publisher Copyright:
© 2017 Elsevier Inc.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • ADHD
  • Attention
  • fMRI
  • Functional connectivity
  • Reaction-time variability
  • SST

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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