Literature DB >> 27629707

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention.

Monica D Rosenberg1, Sheng Zhang2, Wei-Ting Hsu3, Dustin Scheinost4, Emily S Finn5, Xilin Shen4, R Todd Constable6, Chiang-Shan R Li7, Marvin M Chun8.   

Abstract

UNLABELLED: Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT: Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention.
Copyright © 2016 the authors 0270-6474/16/369547-11$15.00/0.

Entities:  

Keywords:  fMRI; functional connectivity; methylphenidate; neuromarker; predictive marker; sustained attention

Mesh:

Substances:

Year:  2016        PMID: 27629707      PMCID: PMC5039242          DOI: 10.1523/JNEUROSCI.1746-16.2016

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  44 in total

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10.  The minimal preprocessing pipelines for the Human Connectome Project.

Authors:  Matthew F Glasser; Stamatios N Sotiropoulos; J Anthony Wilson; Timothy S Coalson; Bruce Fischl; Jesper L Andersson; Junqian Xu; Saad Jbabdi; Matthew Webster; Jonathan R Polimeni; David C Van Essen; Mark Jenkinson
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  35 in total

1.  Functional connectivity predicts changes in attention observed across minutes, days, and months.

Authors:  Monica D Rosenberg; Dustin Scheinost; Abigail S Greene; Emily W Avery; Young Hye Kwon; Emily S Finn; Ramachandran Ramani; Maolin Qiu; R Todd Constable; Marvin M Chun
Journal:  Proc Natl Acad Sci U S A       Date:  2020-02-04       Impact factor: 11.205

2.  Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets.

Authors:  Kwangsun Yoo; Monica D Rosenberg; Wei-Ting Hsu; Sheng Zhang; Chiang-Shan R Li; Dustin Scheinost; R Todd Constable; Marvin M Chun
Journal:  Neuroimage       Date:  2017-11-06       Impact factor: 6.556

Review 3.  [Doping for the brain].

Authors:  Bernhard Iglseder
Journal:  Z Gerontol Geriatr       Date:  2017-12-05       Impact factor: 1.281

4.  Functional Connectivity Parcellation of the Human Thalamus by Independent Component Analysis.

Authors:  Sheng Zhang; Chiang-Shan R Li
Journal:  Brain Connect       Date:  2017-11

Review 5.  How the motor system integrates with working memory.

Authors:  Cherie L Marvel; Owen P Morgan; Sharif I Kronemer
Journal:  Neurosci Biobehav Rev       Date:  2019-04-27       Impact factor: 8.989

6.  A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task.

Authors:  David C Jangraw; Javier Gonzalez-Castillo; Daniel A Handwerker; Merage Ghane; Monica D Rosenberg; Puja Panwar; Peter A Bandettini
Journal:  Neuroimage       Date:  2017-10-12       Impact factor: 6.556

7.  Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies.

Authors:  Angus Ho Ching Fong; Kwangsun Yoo; Monica D Rosenberg; Sheng Zhang; Chiang-Shan R Li; Dustin Scheinost; R Todd Constable; Marvin M Chun
Journal:  Neuroimage       Date:  2018-12-03       Impact factor: 6.556

8.  Effects of methamphetamine on neural responses to visual stimuli.

Authors:  Kathryne Van Hedger; Sarah K Keedy; Kathryn E Schertz; Marc G Berman; Harriet de Wit
Journal:  Psychopharmacology (Berl)       Date:  2019-01-02       Impact factor: 4.530

9.  Connectome-Based Prediction of Cocaine Abstinence.

Authors:  Sarah W Yip; Dustin Scheinost; Marc N Potenza; Kathleen M Carroll
Journal:  Am J Psychiatry       Date:  2019-01-04       Impact factor: 18.112

10.  Perceptual and response factors in the gradual onset continuous performance tasks.

Authors:  Jihyang Jun; Vanessa G Lee
Journal:  Atten Percept Psychophys       Date:  2021-08-05       Impact factor: 2.199

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