Functional Neuroimaging of Visuospatial Working Memory Tasks Enables Accurate Detection of Attention Deficit and Hyperactivity Disorder

Overview

Journal Neuroimage Clin

Publisher Elsevier

Specialties Neurology
Radiology

Date 2015 Oct 29

PMID 26509111

Citations 8

Authors

Rubi Hammer

Gillian E Cooke

Mark A Stein

James R Booth

Affiliations

Soon will be listed here.

Abstract

Finding neurobiological markers for neurodevelopmental disorders, such as attention deficit and hyperactivity disorder (ADHD), is a major objective of clinicians and neuroscientists. We examined if functional Magnetic Resonance Imaging (fMRI) data from a few distinct visuospatial working memory (VSWM) tasks enables accurately detecting cases with ADHD. We tested 20 boys with ADHD combined type and 20 typically developed (TD) boys in four VSWM tasks that differed in feedback availability (feedback, no-feedback) and reward size (large, small). We used a multimodal analysis based on brain activity in 16 regions of interest, significantly activated or deactivated in the four VSWM tasks (based on the entire participants' sample). Dimensionality of the data was reduced into 10 principal components that were used as the input variables to a logistic regression classifier. fMRI data from the four VSWM tasks enabled a classification accuracy of 92.5%, with high predicted ADHD probability values for most clinical cases, and low predicted ADHD probabilities for most TDs. This accuracy level was higher than those achieved by using the fMRI data of any single task, or the respective behavioral data. This indicates that task-based fMRI data acquired while participants perform a few distinct VSWM tasks enables improved detection of clinical cases.

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References

Johnston B, Mwangi B, Matthews K, Coghill D, Konrad K, Douglas Steele J . Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification. Hum Brain Mapp. 2014; 35(10):5179-89. PMC: 6869620. DOI: 10.1002/hbm.22542. View

Hoekzema E, Carmona S, Ramos-Quiroga J, Richarte Fernandez V, Bosch R, Soliva J . An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD. Hum Brain Mapp. 2013; 35(4):1261-72. PMC: 6869838. DOI: 10.1002/hbm.22250. View

Volkow N, Wang G, Kollins S, Wigal T, Newcorn J, Telang F . Evaluating dopamine reward pathway in ADHD: clinical implications. JAMA. 2009; 302(10):1084-91. PMC: 2958516. DOI: 10.1001/jama.2009.1308. View

Peters S, Koolschijn P, Crone E, van Duijvenvoorde A, Raijmakers M . Strategies influence neural activity for feedback learning across child and adolescent development. Neuropsychologia. 2014; 62:365-74. DOI: 10.1016/j.neuropsychologia.2014.07.006. View

Sonuga-Barke E, Fairchild G . Neuroeconomics of attention-deficit/hyperactivity disorder: differential influences of medial, dorsal, and ventral prefrontal brain networks on suboptimal decision making?. Biol Psychiatry. 2012; 72(2):126-33. DOI: 10.1016/j.biopsych.2012.04.004. View

Ehlis A, Bahne C, Jacob C, Herrmann M, Fallgatter A . Reduced lateral prefrontal activation in adult patients with attention-deficit/hyperactivity disorder (ADHD) during a working memory task: a functional near-infrared spectroscopy (fNIRS) study. J Psychiatr Res. 2008; 42(13):1060-7. DOI: 10.1016/j.jpsychires.2007.11.011. View

Menon V, Uddin L . Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010; 214(5-6):655-67. PMC: 2899886. DOI: 10.1007/s00429-010-0262-0. View

Dovis S, Van der Oord S, Huizenga H, Wiers R, Prins P . Prevalence and diagnostic validity of motivational impairments and deficits in visuospatial short-term memory and working memory in ADHD subtypes. Eur Child Adolesc Psychiatry. 2014; 24(5):575-90. DOI: 10.1007/s00787-014-0612-1. View

Zion Golumbic E, Ding N, Bickel S, Lakatos P, Schevon C, McKhann G . Mechanisms underlying selective neuronal tracking of attended speech at a "cocktail party". Neuron. 2013; 77(5):980-91. PMC: 3891478. DOI: 10.1016/j.neuron.2012.12.037. View

10.

. The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience. Front Syst Neurosci. 2012; 6:62. PMC: 3433679. DOI: 10.3389/fnsys.2012.00062. View

11.

Demir O, Prado J, Booth J . The differential role of verbal and spatial working memory in the neural basis of arithmetic. Dev Neuropsychol. 2014; 39(6):440-58. PMC: 4142562. DOI: 10.1080/87565641.2014.939182. View

12.

Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P . Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997; 36(7):980-8. DOI: 10.1097/00004583-199707000-00021. View

13.

Cubillo A, Halari R, Ecker C, Giampietro V, Taylor E, Rubia K . Reduced activation and inter-regional functional connectivity of fronto-striatal networks in adults with childhood Attention-Deficit Hyperactivity Disorder (ADHD) and persisting symptoms during tasks of motor inhibition and cognitive switching. J Psychiatr Res. 2010; 44(10):629-39. DOI: 10.1016/j.jpsychires.2009.11.016. View

14.

Kloppel S, Abdulkadir A, Jack Jr C, Koutsouleris N, Mourao-Miranda J, Vemuri P . Diagnostic neuroimaging across diseases. Neuroimage. 2011; 61(2):457-63. PMC: 3420067. DOI: 10.1016/j.neuroimage.2011.11.002. View

15.

Kahnt T, Heinzle J, Park S, Haynes J . The neural code of reward anticipation in human orbitofrontal cortex. Proc Natl Acad Sci U S A. 2010; 107(13):6010-5. PMC: 2851854. DOI: 10.1073/pnas.0912838107. View

16.

Calhoun V, Adali T, Stevens M, Kiehl K, Pekar J . Semi-blind ICA of fMRI: A method for utilizing hypothesis-derived time courses in a spatial ICA analysis. Neuroimage. 2005; 25(2):527-38. DOI: 10.1016/j.neuroimage.2004.12.012. View

17.

Peng X, Lin P, Zhang T, Wang J . Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS One. 2013; 8(11):e79476. PMC: 3834213. DOI: 10.1371/journal.pone.0079476. View

18.

Castellanos F, Proal E . Large-scale brain systems in ADHD: beyond the prefrontal-striatal model. Trends Cogn Sci. 2011; 16(1):17-26. PMC: 3272832. DOI: 10.1016/j.tics.2011.11.007. View

19.

Friston K . Ten ironic rules for non-statistical reviewers. Neuroimage. 2012; 61(4):1300-10. DOI: 10.1016/j.neuroimage.2012.04.018. View

20.

Clark L, Blackwell A, Aron A, Turner D, Dowson J, Robbins T . Association between response inhibition and working memory in adult ADHD: a link to right frontal cortex pathology?. Biol Psychiatry. 2006; 61(12):1395-401. DOI: 10.1016/j.biopsych.2006.07.020. View