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Neural correlates of inhibitory control and visual processing in youths with attention deficit hyperactivity disorder: a counting Stroop functional MRI study

Published online by Cambridge University Press:  22 January 2014

L.-Y. Fan ,
S. S.-F. Gau  and
T.-L. Chou
L.-Y. Fan
Affiliation:
Department of Psychology, National Taiwan University, Taipei, Taiwan
S. S.-F. Gau*
Affiliation:
Department of Psychology, National Taiwan University, Taipei, Taiwan Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan
T.-L. Chou*
Affiliation:
Department of Psychology, National Taiwan University, Taipei, Taiwan Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan
*
*Address for correspondence: T.-L. Chou, Ph.D., Department of Psychology, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 106, Taiwan. (Email: [email protected]) [T.-L. Chou] (Email: [email protected]) [S. S.-F. Gau]
*Address for correspondence: T.-L. Chou, Ph.D., Department of Psychology, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 106, Taiwan. (Email: [email protected]) [T.-L. Chou] (Email: [email protected]) [S. S.-F. Gau]
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Abstract

Background

Despite evidence of inhibitory control and visual processing impairment in attention deficit hyperactivity disorder (ADHD), knowledge about its corresponding alterations in the brain is still evolving. The current study used counting Stroop functional MRI and the Cambridge Neuropsychological Test Automated Battery (CANTAB) to investigate if brain activation of inhibitory control and visual processing would differ in youths with ADHD relative to neurotypical youths.

Method

We assessed 25 youths with ADHD [mean age 10.9 (s.d. = 2.2) years] and 23 age-, gender- and IQ-matched neurotypical youths [mean age 11.2 (s.d. = 2.9) years]. The participants were assessed by using the Wechsler Intelligence Scale for Children, third edition, and two tests from the CANTAB: rapid visual information processing (RVP) and pattern recognition memory (PRM) outside the scanner.

Results

Youths with ADHD showed more activation than neurotypical youths in the right inferior frontal gyrus [Brodmann area (BA) 45] and anterior cingulate cortex, which were correlated with poorer performance on the RVP test in the CANTAB. In contrast, youths with ADHD showed less activation than neurotypical youths in the left superior parietal lobule (BA 5/7), which was correlated with the percentage of correct responses on the PRM test in the CANTAB.

Conclusions

Our findings suggest that youths with ADHD might need more inhibitory control to suppress interference between number and meaning and may involve less visual processing to process the numbers in the counting Stroop task than neurotypical youths.

Keywords

Cambridge Neuropsychological Test Automated Battery (CANTAB)counting Stroop functional MRIinhibitory controlvisual processing
Type
Original Articles
Information
Psychological Medicine , Volume 44 , Issue 12 , September 2014 , pp. 2661 - 2671
Copyright
Copyright © Cambridge University Press 2014 

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References

Barkley, RA, Routh, DK (1974). Reduction of children's locomotor activity by modeling and the promise of contingent reward. Journal of Abnormal Child Psychology 2, 117–131.CrossRefGoogle ScholarPubMed
Burock, MA, Buckner, RL, Woldorff, MG, Rosen, BR, Dale, AM (1998). Randomized event-related experimental designs allow for extremely rapid presentation rates using functional MRI. Neuroreport 9, 3735–3739.Google Scholar
Bush, G (2011). Cingulate, frontal, and parietal cortical dysfunction in attention-deficit/hyperactivity disorder. Biological Psychiatry 69, 1160–1167.CrossRefGoogle ScholarPubMed
Bush, G, Frazier, JA, Rauch, SL, Seidman, LJ, Whalen, PJ, Jenike, MA, Rosen, BR, Biederman, J (1999). Anterior cingulate cortex dysfunction in attention deficit/hyperactivity disorder revealed by fMRI and the counting Stroop. Biological Psychiatry 45, 1542–1552.CrossRefGoogle ScholarPubMed
Bush, G, Whalen, PJ, Rosen, BR, Jenike, MA, Mcinerney, SC, Rauch, SL (1998). The counting Stroop: an interference task specialized for functional neuroimaging–validation study with functional MRI. Human Brain Mapping 6, 270–282.Google Scholar
Cavanna, AE, Trimble, MR (2006). The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129, 564–583.Google Scholar
Coghill, DR, Rhodes, SM, Matthews, K (2007). The neuropsychological effects of chronic methylphenidate on drug-naive boys with attention-deficit/hyperactivity disorder. Biological Psychiatry 62, 954–962.CrossRefGoogle ScholarPubMed
Cortese, S, Kelly, C, Chabernaud, C, Proal, E, Martino, AD, Milham, MP, Castellanos, FX (2012). Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. American Journal of Psychiatry 169, 1038–1055.Google Scholar
Dehaene, S, Piazza, M, Pinel, P, Cohen, L (2003). Three parietal circuits for number processing. Cognitive Neuropsychology 20, 487–506.CrossRefGoogle ScholarPubMed
Doyle, AE, Willcuttc, EG, Seidman, LJ, Biederman, J, Chouinard, VA, Silva, J, Faraone, SV (2005). Attention-deficit/hyperactivity disorder endophenotypes. Biological Psychiatry 57, 1324–1335.Google Scholar
Durston, S (2003). A review of the biological bases of ADHD: what have we learned from imaging studies? Mental Retardation and Developmental Disabilities Research Reviews 9, 184–195.CrossRefGoogle ScholarPubMed
Fassbender, C, Schweitzer, JB (2006). Is there evidence for neural compensation in attention deficit hyperactivity disorder? A review of the functional neuroimaging literature. Clinical Psychology Review 26, 445–465.CrossRefGoogle ScholarPubMed
Gau, SS, Chiu, C-D, Shang, C-Y, Cheng, AT-A, Soong, W-T (2009). Executive function in adolescence among children with attention-deficit/hyperactivity disorder in Taiwan. Journal of Developmental and Behavioral Pediatrics 30, 525–534.CrossRefGoogle ScholarPubMed
Gau, SS, Chong, MY, Chen, THH, Cheng, ATA (2005). A 3-year panel study of mental disorders among adolescents in Taiwan. American Journal of Psychiatry 162, 1344–1350.CrossRefGoogle ScholarPubMed
Gau, SS, Huang, W-L (2014). Rapid visual information processing as a cognitive endophenotype for attention deficit hyperactivity disorder. Psychological Medicine 44, 435–446.Google Scholar
Gau, SS, Shang, C-Y (2010 a). Executive functions as endophenotypes in ADHD: evidence from the Cambridge Neuropsychological Test Battery (CANTAB). Journal of Child Psychology and Psychiatry 51, 838–849.CrossRefGoogle ScholarPubMed
Gau, SS, Shang, C-Y (2010 b). Improvement of executive functions in boys with attention deficit hyperactivity disorder: an open-label follow-up study with once-daily atomoxetine. International Journal of Neuropsychopharmacology 13, 243–256.Google Scholar
Hampshire, A, Chamberlain, SR, Monti, MM, Duncan, J, Owen, AM (2010). The role of the right inferior frontal gyrus: inhibition and attentional control. Neuroimage 50, 1313–1319.CrossRefGoogle ScholarPubMed
Hart, H, Radua, J, Nakao, T, Mataix-Cols, D, Rubia, K (2013). Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder. JAMA Psychiatry 70, 185–198.Google Scholar
Josephs, O, Henson, RNA (1999). Event-related functional magnetic resonance imaging: modelling, inference and optimization. Philosophical Transactions of the Royal Society B: Biological Sciences 354, 1215–1228.CrossRefGoogle ScholarPubMed
Konrad, A, Dielentheis, TF, El Masri, D, Dellani, PR, Stoeter, P, Vucurevic, G, Winterer, G (2012). White matter abnormalities and their impact on attentional performance in adult attention-deficit/hyperactivity disorder. European Archives of Psychiatry and Clinical Neuroscience 262, 351–360.CrossRefGoogle ScholarPubMed
Lévesque, J, Beauregard, M, Mensour, B (2006). Effect of neurofeedback training on the neural substrates of selective attention in children with attention-deficit/hyperactivity disorder: a functional magnetic resonance imaging study. Neuroscience Letters 394, 216–221.CrossRefGoogle ScholarPubMed
Makris, N, Buka, SL, Biederman, J, Papadimitriou, GM, Hodge, SM, Valera, EM, Brown, AB, Bush, G, Monuteaux, MC, Caviness, VS, Kennedy, DN, Seidman, LJ (2008). Attention and executive systems abnormalities in adults with childhood ADHD: a DT-MRI study of connections. Cerebral Cortex 18, 1210–1220.CrossRefGoogle ScholarPubMed
Nakao, T, Radua, J, Rubia, K, Mataix-Cols, D (2011). Gray matter volume abnormalities in ADHD: voxel-based meta-analysis exploring the effects of age and stimulant medication. American Journal of Psychiatry 168, 1154–1163.Google Scholar
Pardo, JV, Pardo, PJ, Janer, KW, Raichle, ME (1990). The anterior cingulate cortex mediates processing selection in the Stroop attentional conflict paradigm. Proceedings of the National Academy of Sciences USA 87, 256–259.Google Scholar
Rasmussen, C, Bisanz, J (2005). Representation and working memory in early arithmetic. Journal Experimental Child Psychology 91, 137–157.Google Scholar
Rhodes, SM, Coghill, DR, Matthews, K (2005). Neuropsychological functioning in stimulant-naïve boys with hyperkinetic disorder. Psychological Medicine 35, 1109–1120.Google Scholar
Rubia, K, Overmeyer, S, Taylor, E, Brammer, M, Williams, SCR, Simmons, A, Bullmore, ET (1999). Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: a study with functional MRI. American Journal of Psychiatry 156, 891–896.CrossRefGoogle ScholarPubMed
Sahakian, BJ, Jones, G, Levy, R, Gray, J, Warburton, D (1989). The effects of nicotine on attention, information processing, and short-term memory in patients with dementia of the Alzheimer type. British Journal of Psychiatry 154, 797–800.CrossRefGoogle ScholarPubMed
Sahakian, BJ, Morris, RG, Evenden, JL, Heald, A, Levy, R, Philpot, M, Robbins, TW (1988). A comparative study of visuospatial memory and learning in Alzheimer-type dementia and Parkinson's disease. Brain 111, 695–718.Google Scholar
Sahgal, A (1987). Some limitations of indices derived from signal detection theory: evaluation of an alternative index for measuring bias in memory tasks. Psychopharmacology 91, 517–520.Google Scholar
Schneider, MF, Krick, CM, Retz, W, Hengesch, G, Retz-Junginger, P, Reith, W, Rösler, M (2010). Impairment of fronto-striatal and parietal cerebral networks correlates with attention deficit hyperactivity disorder (ADHD) psychopathology in adults – a functional magnetic resonance imaging (fMRI) study. Psychiatry Research: Neuroimaging 183, 75–84.Google Scholar
Schulz, KP, Fan, J, Tang, CY, Newcorn, JH, Buchsbaum, MS, Cheung, AM, Halperin, JM (2004). Response inhibition in adolescents diagnosed with attention deficit hyperactivity disorder during childhood: an event-related fMRI study. American Journal of Psychiatry 161, 1650–1657.Google Scholar
Shang, C-Y, Gau, SS (2011). Visual memory as a potential cognitive endophenotype of attention deficit hyperactivity disorder. Psychological Medicine 41, 2603–2614.Google Scholar
Shang, C-Y, Gau, SS (2012). Improving visual memory, attention, and school function with atomoxetine in boys with attention-deficit/hyperactivity disorder. Journal of Child and Adolescent Psychopharmacology 22, 353–363.CrossRefGoogle ScholarPubMed
Shang, CY, Wu, YH, Gau, SS, Tseng, WY (2012). Disturbed microstructural integrity of the frontostriatal fiber pathways and executive dysfunction in children with attention deficit hyperactivity disorder. Psychological Medicine 43, 1093–1107.Google Scholar
Silk, TJ, Vance, A, Rinehart, N, Bradshaw, JL, Cunnington, R (2008). Dysfunction in the fronto-parietal network in attention deficit hyperactivity disorder (ADHD): an fMRI study. Brain Imaging and Behavior 2, 123–131.Google Scholar
Slaats-Willemse, DI, Swaab-Barneveld, HJ, de Sonneville, LM, Buitelaar, JK (2007). Family-genetic study of executive functioning in attention-deficit/hyperactivity disorder: evidence for an endophenotype? Neuropsychology 21, 751–760.Google Scholar
Trommer, BL, Hoeppner, JA, Lorber, R, Armstrong, KJ (1988). The Go-No-Go paradigm in attention deficit disorder. Annals of Neurology 24, 610–614.Google Scholar
Wechsler, D (1991). Wechsler Intelligence Scale for Children, 3rd edn. The Psychological Corporation: San Antonio, TX.Google Scholar
Whelan, R, Conrod, PJ, Poline, J-B, Lourdusamy, A, Banaschewski, T, Barker, GJ, Bellgrove, MA, Büchel, C, Byrne, M, Cummins, TDR, Fauth-Bühler, M, Flor, H, Gallinat, J, Heinz, A, Ittermann, B, Mann, K, Martinot, J-L, Lalor, EC, Lathrop, M, Loth, E, Nees, F, Paus, T, Rietschel, M, Smolka, MN, Spanagel, R, Stephens, DN, Struve, M, Thyreau, B, Vollstaedt-Klein, S, Robbins, TW, Schumann, G, Garavan, H, Imagen Consortium (2012). Adolescent impulsivity phenotypes characterized by distinct brain networks. Nature Neuroscience 15, 920–925.Google Scholar
Wu, YH, Gau, SS, Lo, YC, Tseng, WY (2014). White matter tract integrity of frontostriatal circuit in attention deficit hyperactivity disorder: association with attention performance and symptoms. Human Brain Mapping 35, 199–212.Google Scholar
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