Literature DB >> 28176434

Network-level assessment of reward-related activation in patients with ADHD and healthy individuals.

Daniel von Rhein1,2, Christian F Beckmann1,2,3, Barbara Franke4,5, Jaap Oosterlaan6, Dirk J Heslenfeld6, Pieter J Hoekstra7, Catharina A Hartman7, Marjolein Luman6, Stephen V Faraone8,9, Roshan Cools1,2, Jan K Buitelaar1,10, Maarten Mennes2.   

Abstract

INTRODUCTION: Reward processing is a key aspect of cognitive control processes, putatively instantiated by mesolimbic and mesocortical brain circuits. Deficient signaling within these circuits has been associated with psychopathology. We applied a network discovery approach to assess specific functional networks associated with reward processing in participants with attention-deficit/hyperactivity disorder (ADHD).
METHODS: To describe task-related processes in terms of integrated functional networks, we applied independent component analysis (ICA) to task response maps of 60 healthy participants who performed a monetary incentive delay (MID) task. The resulting components were interpreted on the basis of their similarity with group-level task responses as well as their similarity with brain networks derived from resting state fMRI analyses. ADHD-related effects on network characteristics including functional connectivity and communication between networks were examined in an independent sample comprising 150 participants with ADHD and 48 healthy controls.
RESULTS: We identified 23 components to be associated with 4 large-scale functional networks: the default-mode, visual, executive control, and salience networks. The salience network showed a specific association with reward processing as well as the highest degree of within-network integration. ADHD was associated with decreased functional connectivity between the salience and executive control networks as well as with peripheral brain regions.
CONCLUSIONS: Reward processing as measured with the MID task involves one reward-specific and three general functional networks. Participants with ADHD exhibited alterations in connectivity of both the salience and executive control networks and associated brain regions during task performance. Hum Brain Mapp 38:2359-2369, 2017.
© 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  ADHD; brain networks; imaging; reward processing

Mesh:

Substances:

Year:  2017        PMID: 28176434      PMCID: PMC6584954          DOI: 10.1002/hbm.23522

Source DB:  PubMed          Journal:  Hum Brain Mapp        ISSN: 1065-9471            Impact factor:   5.038


  38 in total

1.  Dissociation of reward anticipation and outcome with event-related fMRI.

Authors:  B Knutson; G W Fong; C M Adams; J L Varner; D Hommer
Journal:  Neuroreport       Date:  2001-12-04       Impact factor: 1.837

Review 2.  The cognitive-energetic model: an empirical approach to attention-deficit hyperactivity disorder.

Authors:  J Sergeant
Journal:  Neurosci Biobehav Rev       Date:  2000-01       Impact factor: 8.989

Review 3.  Organization, development and function of complex brain networks.

Authors:  Olaf Sporns; Dante R Chialvo; Marcus Kaiser; Claus C Hilgetag
Journal:  Trends Cogn Sci       Date:  2004-09       Impact factor: 20.229

4.  Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.

Authors:  Mark W Woolrich; Timothy E J Behrens; Christian F Beckmann; Stephen M Smith
Journal:  IEEE Trans Med Imaging       Date:  2005-01       Impact factor: 10.048

5.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks.

Authors:  Michael D Fox; Abraham Z Snyder; Justin L Vincent; Maurizio Corbetta; David C Van Essen; Marcus E Raichle
Journal:  Proc Natl Acad Sci U S A       Date:  2005-06-23       Impact factor: 11.205

Review 6.  Advances in functional and structural MR image analysis and implementation as FSL.

Authors:  Stephen M Smith; Mark Jenkinson; Mark W Woolrich; Christian F Beckmann; Timothy E J Behrens; Heidi Johansen-Berg; Peter R Bannister; Marilena De Luca; Ivana Drobnjak; David E Flitney; Rami K Niazy; James Saunders; John Vickers; Yongyue Zhang; Nicola De Stefano; J Michael Brady; Paul M Matthews
Journal:  Neuroimage       Date:  2004       Impact factor: 6.556

7.  Dynamic causal modelling.

Authors:  K J Friston; L Harrison; W Penny
Journal:  Neuroimage       Date:  2003-08       Impact factor: 6.556

Review 8.  Parsing reward.

Authors:  Kent C Berridge; Terry E Robinson
Journal:  Trends Neurosci       Date:  2003-09       Impact factor: 13.837

Review 9.  Psychological heterogeneity in AD/HD--a dual pathway model of behaviour and cognition.

Authors:  Edmund J S Sonuga-Barke
Journal:  Behav Brain Res       Date:  2002-03-10       Impact factor: 3.332

10.  Dynamic dopamine modulation in the basal ganglia: a neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism.

Authors:  Michael J Frank
Journal:  J Cogn Neurosci       Date:  2005-01       Impact factor: 3.225

View more
  7 in total

1.  Salience network coupling is linked to both tobacco smoking and symptoms of attention deficit hyperactivity disorder (ADHD).

Authors:  A C Janes; J M Gilman; B B Frederick; M Radoman; G Pachas; M Fava; A E Evins
Journal:  Drug Alcohol Depend       Date:  2017-11-21       Impact factor: 4.492

2.  DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders.

Authors:  Mohammed A Syed; Zhi Yang; D Rangaprakash; Xiaoping Hu; Michael N Dretsch; Jeffrey S Katz; Thomas S Denney; Gopikrishna Deshpande
Journal:  Neuroinformatics       Date:  2020-01

3.  Trauma-associated anterior cingulate connectivity during reward learning predicts affective and anxiety states in young adults.

Authors:  Kristen L Eckstrand; Lindsay C Hanford; Michele A Bertocci; Henry W Chase; Tsafrir Greenberg; Jeanette Lockovich; Ricki Stiffler; Haris A Aslam; Simona Graur; Genna Bebko; Erika E Forbes; Mary L Phillips
Journal:  Psychol Med       Date:  2018-09-19       Impact factor: 7.723

4.  Dopamine Adaptations as a Common Pathway for Neurocognitive Impairment in Diabetes and Obesity: A Neuropsychological Perspective.

Authors:  Dana M Small
Journal:  Front Neurosci       Date:  2017-03-28       Impact factor: 4.677

5.  Aberrant functional connectivity in resting state networks of ADHD patients revealed by independent component analysis.

Authors:  Huayu Zhang; Yue Zhao; Weifang Cao; Dong Cui; Qing Jiao; Weizhao Lu; Hongyu Li; Jianfeng Qiu
Journal:  BMC Neurosci       Date:  2020-09-18       Impact factor: 3.288

6.  Identification of Brain Regions with Enhanced Functional Connectivity with the Cerebellum Region in Children with Attention Deficit Hyperactivity Disorder: A Resting-State fMRI Study.

Authors:  Li Ding; Gaofeng Pang
Journal:  Int J Gen Med       Date:  2021-05-27

7.  Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach.

Authors:  Yunkai Sun; Lei Zhao; Zhihui Lan; Xi-Ze Jia; Shao-Wei Xue
Journal:  Neuropsychiatr Dis Treat       Date:  2020-03-10       Impact factor: 2.570

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.