Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-05-07T23:12:30.944Z Has data issue: false hasContentIssue false

A haplotype of the dopamine transporter gene modulates regional homogeneity, gray matter volume, and visual memory in children with attention-deficit/hyperactivity disorder

Published online by Cambridge University Press:  13 February 2018

C. Y. Shang
Affiliation:
Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
H. Y. Lin
Affiliation:
Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
W. Y. Tseng
Affiliation:
Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan Molecular Imaging Center, National Taiwan University, Taipei, Taiwan
S. S. Gau*
Affiliation:
Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan
*
Author for correspondence: S. S. Gau, E-mail: gaushufe@ntu.edu.tw

Abstract

Background

The dopamine transporter gene (DAT1) and visual memory deficits have been consistently reported to be associated with attention-deficit/hyperactivity disorder (ADHD). This study aimed to examine whether a DAT1 haplotype affected functional and structural brain alterations in children with ADHD and whether those alterations were associated with visual memory.

Method

We recruited a total of 37 drug-naïve children with ADHD (17 with the DAT1 rs27048 (C)/rs429699 (T) haplotype and 20 without the CT haplotype) and 37 typically developing children (17 with the CT haplotype and 20 without the CT haplotype). Visual memory was assessed by the pattern recognition memory (PRM) and spatial recognition memory (SRM) tasks. We analyzed functional and structural brain architecture with regional homogeneity (ReHo) and gray matter volume (GMV).

Results

The CT haplotype was associated with decreased ReHo in the left superior occipital gyrus, cuneus, and precuneus; and decreased GMV in the left superior occipital gyrus, cuneus, and precuneus, and in the right angular gyrus. Significant interactions of ADHD and the CT haplotype were found in the right postcentral gyrus for ReHo and in the right supplementary motor area for GMV. For the ADHD-CT group, we found negative correlations of total correct responses in PRM and SRM and positive correlations of mean latency of correct responses in PRM with the GMV in the left superior occipital gyrus, cuneus, and precuneus.

Conclusions

Our findings suggest that the DAT1-related GMV alterations in the posterior cortical regions may contribute to visual memory performance in children with ADHD.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aarts, E, Roelofs, A, Franke, B, Rijpkema, M, Fernandez, G, Helmich, RC et al. (2010) Striatal dopamine mediates the interface between motivational and cognitive control in humans: evidence from genetic imaging. Neuropsychopharmacology 35, 19431951.Google Scholar
Albrecht, MA, Roberts, G, Price, G, Lee, J, Iyyalol, R and Martin-Iverson, MT (2016) The effects of dexamphetamine on the resting-state electroencephalogram and functional connectivity. Human Brain Mapping 37, 570588.Google Scholar
Alonso Bde, C, Hidalgo Tobon, S, Dies Suarez, P, Garcia Flores, J, de Celis Carrillo, B and Barragan Perez, E (2014) A multi-methodological MR resting state network analysis to assess the changes in brain physiology of children with ADHD. PLoS ONE 9, e99119.Google Scholar
Ashburner, J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38, 95113.Google Scholar
Banoei, MM, Chaleshtori, MH, Sanati, MH, Panahi, MS, Majidizadeh, T, Rostami, M et al. (2007) Diversity and relationship between Iranian ethnic groups: human dopamine transporter gene (DAT1) VNTR genotyping. American Journal of Human Biology 19, 821826.Google Scholar
Bedard, AC, Schulz, KP, Cook, EH Jr., Fan, J, Clerkin, SM, Ivanov, I et al. (2010) Dopamine transporter gene variation modulates activation of striatum in youth with ADHD. NeuroImage 53, 935942.Google Scholar
Behzadi, Y, Restom, K, Liau, J and Liu, TT (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90101.Google Scholar
Bhaskar, LV, Thangaraj, K, Mulligan, CJ, Wasnik, S, Nandan, A, Sharma, VK et al. (2009) Dopamine transporter (DAT1) VNTR polymorphism in 12 Indian populations. Neurological Sciences 30, 487493.Google Scholar
Capotosto, P, Babiloni, C, Romani, GL and Corbetta, M (2009) Frontoparietal cortex controls spatial attention through modulation of anticipatory alpha rhythms. The Journal of Neuroscience 29, 58635872.Google Scholar
Castellanos, FX, Margulies, DS, Kelly, C, Uddin, LQ, Ghaffari, M, Kirsch, A et al. (2008) Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biological Psychiatry 63, 332337.Google Scholar
Castellanos, FX and Proal, E (2012) Large-scale brain systems in ADHD: beyond the prefrontal-striatal model. Trends in Cognitive Sciences 16, 1726.Google Scholar
Chien, YL, Wu, YY, Chiu, YN, Liu, SK, Tsai, WC, Lin, PI et al. (2011) Association study of the CNS patterning genes and autism in Han Chinese in Taiwan. Progress in Neuropsychopharmacology & Biological Psychiatry 35, 15121517.Google Scholar
Ciliax, BJ, Drash, GW, Staley, JK, Haber, S, Mobley, CJ, Miller, GW et al. (1999) Immunocytochemical localization of the dopamine transporter in human brain. The Journal of Comparative Neurology 409, 3856.Google Scholar
Cummins, TD, Hawi, Z, Hocking, J, Strudwick, M, Hester, R, Garavan, H et al. (2012) Dopamine transporter genotype predicts behavioural and neural measures of response inhibition. Molecular Psychiatry 17, 10861092.Google Scholar
D'Agati, E, Casarelli, L, Pitzianti, MB and Pasini, A (2010) Overflow movements and white matter abnormalities in ADHD. Progress in Neuropsychopharmacology & Biological Psychiatry 34, 441445.Google Scholar
Duerden, EG, Tannock, R and Dockstader, C (2012) Altered cortical morphology in sensorimotor processing regions in adolescents and adults with attention-deficit/hyperactivity disorder. Brain Research 1445, 8291.Google Scholar
DuPaul, GJ, Power, TJ, Anastopoulos, AD and Reid, R (1998) ADHD Rating Scale-IV: Checklists, Norms, and Clinical Interpretations. New York: Guilford.Google Scholar
Durston, S (2010) Imaging genetics in ADHD. NeuroImage 53, 832838.Google Scholar
Durston, S, Fossella, JA, Mulder, MJ, Casey, BJ, Ziermans, TB, Vessaz, MN et al. (2008) Dopamine transporter genotype conveys familial risk of attention-deficit/hyperactivity disorder through striatal activation. Journal of the American Academy of Child and Adolescent Psychiatry 47, 6167.Google Scholar
Faraone, SV, Perlis, RH, Doyle, AE, Smoller, JW, Goralnick, JJ, Holmgren, MA et al. (2005) Molecular genetics of attention-deficit/hyperactivity disorder. Biological Psychiatry 57, 13131323.Google Scholar
Gaddis, A, Rosch, KS, Dirlikov, B, Crocetti, D, MacNeil, L, Barber, AD et al. (2015) Motor overflow in children with attention-deficit/hyperactivity disorder is associated with decreased extent of neural activation in the motor cortex. Psychiatry Research 233, 488495.Google Scholar
Gau, SS, Huang, YS, Soong, WT, Chou, MC, Chou, WJ, Shang, CY et al. (2007) A randomized, double-blind, placebo-controlled clinical trial on once-daily atomoxetine in Taiwanese children and adolescents with attention-deficit/hyperactivity disorder. Journal of Child and Adolescent Psychopharmacology 17, 447460.Google Scholar
Ghanizadeh, A (2011) Sensory processing problems in children with ADHD, a systematic review. Psychiatry Investigation 8, 8994.Google Scholar
Gizer, IR, Ficks, C and Waldman, ID (2009) Candidate gene studies of ADHD: a meta-analytic review. Human Genetics 126, 5190.Google Scholar
Gordon, EM, Devaney, JM, Bean, S and Vaidya, CJ (2015) Resting-state striato-frontal functional connectivity is sensitive to DAT1 genotype and predicts executive function. Cererbral Cortex 25, 336345.Google Scholar
Guggenmos, M, Thoma, V, Cichy, RM, Haynes, JD, Sterzer, P and Richardson-Klavehn, A (2015) Non-holistic coding of objects in lateral occipital complex with and without attention. NeuroImage 107, 356363.Google Scholar
Hallquist, MN, Hwang, K and Luna, B (2013) The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. NeuroImage 82, 208225.Google Scholar
Harmer, CJ, McTavish, SF, Clark, L, Goodwin, GM and Cowen, PJ (2001) Tyrosine depletion attenuates dopamine function in healthy volunteers. Psychopharmacology 154, 105111.Google Scholar
Jenkinson, M, Bannister, P, Brady, M and Smith, S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825841.Google Scholar
Kempton, S, Vance, A, Maruff, P, Luk, E, Costin, J and Pantelis, C (1999) Executive function and attention deficit hyperactivity disorder: stimulant medication and better executive function performance in children. Psychological Medicine 29, 527538.Google Scholar
Klein, M, Onnink, M, van Donkelaar, M, Wolfers, T, Harich, B, Shi, Y et al. (2017) Brain imaging genetics in ADHD and beyond – mapping pathways from gene to disorder at different levels of complexity. Neuroscience and Biobehavioral Review 80, 115155.Google Scholar
Ko, CH, Yen, JY, Yen, CF, Chen, CS, Lin, WC, Wang, PW et al. (2013) Brain activation deficit in increased-load working memory tasks among adults with ADHD using fMRI. European Archives of Psychiatry and Clinical Neuroscience 263, 561573.Google Scholar
Lei, D, Ma, J, Du, X, Shen, G, Jin, X and Gong, Q (2014) Microstructural abnormalities in the combined and inattentive subtypes of attention deficit hyperactivity disorder: a diffusion tensor imaging study. Scientific Reports 4, 6875.Google Scholar
Metin, B, Roeyers, H, Wiersema, JR, van der Meere, JJ, Thompson, M and Sonuga-Barke, E (2013) ADHD performance reflects inefficient but not impulsive information processing: a diffusion model analysis. Neuropsychology 27, 193200.Google Scholar
Nakama, H, Chang, L, Fein, G, Shimotsu, R, Jiang, CS and Ernst, T (2011) Methamphetamine users show greater than normal age-related cortical gray matter loss. Addiction 106, 14741483.Google Scholar
Pruim, RH, Mennes, M, Buitelaar, JK and Beckmann, CF (2015a). Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI. NeuroImage 112, 278287.Google Scholar
Pruim, RH, Mennes, M, van Rooij, D, Llera, A, Buitelaar, JK and Beckmann, CF (2015b). ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage 112, 267277.Google Scholar
Rhodes, SM, Coghill, DR and Matthews, K (2004) Methylphenidate restores visual memory, but not working memory function in attention deficit-hyperkinetic disorder. Psychopharmacology 175, 319330.Google Scholar
Rhodes, SM, Coghill, DR and Matthews, K (2005) Neuropsychological functioning in stimulant-naive boys with hyperkinetic disorder. Psychological Medicine 35, 11091120.Google Scholar
Rhodes, SM, Coghill, DR and Matthews, K (2006) Acute neuropsychological effects of methylphenidate in stimulant drug-naive boys with ADHD II – broader executive and non-executive domains. Journal of Child Psychology and Psychiatry 47, 11841194.Google Scholar
Rodi, CP, Darnhofer-Patel, B, Stanssens, P, Zabeau, M and van den Boom, D (2002) A strategy for the rapid discovery of disease markers using the MassARRAY system. BioTechniques Suppl, 62–66, 6869.Google Scholar
Rommelse, NN, Altink, ME, Arias-Vasquez, A, Buschgens, CJ, Fliers, E, Faraone, SV et al. (2008) A review and analysis of the relationship between neuropsychological measures and DAT1 in ADHD. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 147B, 15361546.Google Scholar
Shang, CY, Chiang, HL and Gau, SS (2015) A haplotype of the norepinephrine transporter gene (SLC6A2) is associated with visual memory in attention-deficit/hyperactivity disorder. Progress in Neuropsychopharmacology & Biological Psychiatry 58, 8996.Google Scholar
Shang, CY and Gau, SS (2011) Visual memory as a potential cognitive endophenotype of attention deficit hyperactivity disorder. Psychological Medicine 41, 26032614.Google Scholar
Shang, CY and Gau, SS (2014) Association between the DAT1 gene and spatial working memory in attention deficit hyperactivity disorder. The International Journal of Neuropsychopharmacology 17, 921.Google Scholar
Shang, CY, Gau, SS, Liu, CM and Hwu, HG (2011) Association between the dopamine transporter gene and the inattentive subtype of attention deficit hyperactivity disorder in Taiwan. Progress in Neuropsychopharmacology & Biological Psychiatry 35, 421428.Google Scholar
Shulman, GL, Astafiev, SV, Franke, D, Pope, DL, Snyder, AZ, McAvoy, MP et al. (2009) Interaction of stimulus-driven reorienting and expectation in ventral and dorsal frontoparietal and basal ganglia-cortical networks. The Journal of Neuroscience 29, 43924407.Google Scholar
Simons, JS, Graham, KS, Owen, AM, Patterson, K and Hodges, JR (2001) Perceptual and semantic components of memory for objects and faces: a pet study. Journal of Cognitive Neuroscience 13, 430443.Google Scholar
Soros, P, Bachmann, K, Lam, AP, Kanat, M, Hoxhaj, E, Matthies, S et al. (2017) Inattention predicts increased thickness of left occipital cortex in Men with attention-deficit/hyperactivity disorder. Frontiers in Psychiatry 8, 170.Google Scholar
Stollstorff, M, Foss-Feig, J, Cook, EH Jr., Stein, MA, Gaillard, WD and Vaidya, CJ (2010) Neural response to working memory load varies by dopamine transporter genotype in children. NeuroImage 53, 970977.Google Scholar
Sutcubasi Kaya, B, Metin, B, Tas, ZC, Buyukaslan, A, Soysal, A, Hatiloglu, D et al. (2016) Gray matter increase in motor cortex in pediatric ADHD: a voxel-based morphometry study. Journal of Attention Disorders. doi: 10.1177/1087054716659139 [Epub ahead of print].Google Scholar
Tzourio-Mazoyer, N, Landeau, B, Papathanassiou, D, Crivello, F, Etard, O, Delcroix, N et al. (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273289.Google Scholar
Vilor-Tejedor, N, Caceres, A, Pujol, J, Sunyer, J and Gonzalez, JR (2016) Imaging genetics in attention-deficit/hyperactivity disorder and related neurodevelopmental domains: state of the art. Brain Imaging and Behavior 11, 19221931.Google Scholar
Volz, TJ (2008) Neuropharmacological mechanisms underlying the neuroprotective effects of methylphenidate. Current Neuropharmacology 6, 379385.Google Scholar
Wang, X, Jiao, Y, Tang, T, Wang, H and Lu, Z (2013) Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder. European Journal of Radiology 82, 15521557.Google Scholar
Wehmeier, PM, Schacht, A and Barkley, RA (2010) Social and emotional impairment in children and adolescents with ADHD and the impact on quality of life. The Journal of Adolescent Health 46, 209217.Google Scholar
Whitfield-Gabrieli, S and Nieto-Castanon, A (2012) Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity 2, 125141.Google Scholar
Wiggins, JL, Bedoyan, JK, Peltier, SJ, Ashinoff, S, Carrasco, M, Weng, SJ et al. (2012) The impact of serotonin transporter (5-HTTLPR) genotype on the development of resting-state functional connectivity in children and adolescents: a preliminary report. NeuroImage 59, 27602770.Google Scholar
Wilke, M, Holland, SK, Altaye, M and Gaser, C (2008) Template-O-Matic: a toolbox for creating customized pediatric templates. NeuroImage 41, 903913.Google Scholar
Wolf, RC, Plichta, MM, Sambataro, F, Fallgatter, AJ, Jacob, C, Lesch, KP et al. (2009) Regional brain activation changes and abnormal functional connectivity of the ventrolateral prefrontal cortex during working memory processing in adults with attention-deficit/hyperactivity disorder. Human Brain Mapping 30, 22522266.Google Scholar
Wu, Z, Yang, L and Wang, Y (2014) Applying imaging genetics to ADHD: the promises and the challenges. Molecular Neurobiology 50, 449462.Google Scholar
Yan, CG, Cheung, B, Kelly, C, Colcombe, S, Craddock, RC, Di Martino, A et al. (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76, 183201.Google Scholar
Yan, CG and Zang, YF (2010) DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience 4, 13.Google Scholar
Zang, Y, Jiang, T, Lu, Y, He, Y and Tian, L (2004) Regional homogeneity approach to fMRI data analysis. NeuroImage 22, 394400.Google Scholar
Supplementary material: File

Shang et al. supplementary material

Table S1

Download Shang et al. supplementary material(File)
File 35.8 KB
Supplementary material: File

Shang et al. supplementary material

Table S2

Download Shang et al. supplementary material(File)
File 39.9 KB