Hostname: page-component-77c89778f8-m42fx Total loading time: 0 Render date: 2024-07-17T03:46:57.009Z Has data issue: false hasContentIssue false

Mapping the neuroanatomic substrates of cognition in familial attention deficit hyperactivity disorder

Published online by Cambridge University Press:  24 May 2018

Rachel Muster
Affiliation:
Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and the National Institute of Mental Health, NIH, Building 31, Bethesda B1B37, USA
Saadia Choudhury
Affiliation:
Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and the National Institute of Mental Health, NIH, Building 31, Bethesda B1B37, USA
Wendy Sharp
Affiliation:
Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and the National Institute of Mental Health, NIH, Building 31, Bethesda B1B37, USA
Steven Kasparek
Affiliation:
Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and the National Institute of Mental Health, NIH, Building 31, Bethesda B1B37, USA
Gustavo Sudre
Affiliation:
Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and the National Institute of Mental Health, NIH, Building 31, Bethesda B1B37, USA
Philip Shaw*
Affiliation:
Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and the National Institute of Mental Health, NIH, Building 31, Bethesda B1B37, USA
*
Author for correspondence: Philip Shaw, E-mail: shawp@mail.nih.gov

Abstract

Background

While the neuroanatomic substrates of symptoms of attention deficit hyperactivity disorder (ADHD) have been investigated, less is known about the neuroanatomic correlates of cognitive abilities pertinent to the disorder, particularly in adults. Here we define the neuroanatomic correlates of key cognitive abilities and determine if there are associations with histories of psychostimulant medication.

Methods

We acquired neuroanatomic magnetic resonance imaging data from 264 members of 60 families (mean age 29.5; s.d. 18.4, 116 with ADHD). Using linear mixed model regression, we tested for associations between cognitive abilities (working memory, information processing, intelligence, and attention), symptoms and both cortical and subcortical volumes.

Results

Symptom severity was associated with spatial working memory (t = −3.77, p = 0.0002), processing speed (t = −2.95, p = 0.004) and a measure of impulsive responding (t = 2.19, p = 0.03); these associations did not vary with age (all p > 0.1). Neuroanatomic associations of cognition varied by task but centered on prefrontal, lateral parietal and temporal cortical regions, the thalamus and putamen. The neuroanatomic correlates of ADHD symptoms overlapped significantly with those of working memory (Dice's overlap coefficient: spatial, p = 0.003; verbal, p = 0.001) and information processing (p = 0.02). Psychostimulant medication history was associated with neither cognitive skills nor with a brain–cognition relationships.

Conclusions

Diagnostic differences in the cognitive profile of ADHD does not vary significantly with age; nor were cognitive differences associated with psychostimulant medication history. The neuroanatomic substrates of working memory and information overlapped with those for symptoms within these extended families, consistent with a pathophysiological role for these cognitive skills in familial ADHD.

Type
Original Articles
Copyright
© Crown Copyright. Published by 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

Advokat, CD and Scheithauer, M (2013) Attention-deficit hyperactivity disorder (ADHD) stimulant medications as cognitive enhancers. Frontiers in Neuroscience 7, 82.Google Scholar
Alderson, RM, Hudec, KL, Patros, CH and Kasper, LJ (2013) Working memory deficits in adults with attention-deficit/hyperactivity disorder (ADHD): an examination of central executive and storage/rehearsal processes. Journal of Abnormal Psychology 122, 532.Google Scholar
Baroni, A and Castellanos, FX (2015) Stimulants, cognition and ADHD. Current Opinion in Behavioral Sciences 4, 109114.Google Scholar
Benjamini, Y and Hochberg, Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57, 289300.Google Scholar
Bidwell, L, Willcutt, EG, DeFries, JC and Pennington, BF (2007) Testing for neuropsychological endophenotypes in siblings discordant for attention-deficit/hyperactivity disorder. Biological Psychiatry 62, 991998.Google Scholar
Burgaleta, M, MacDonald, PA, Martínez, K, Roman, FJ, Álvarez-Linera, J, Gonzalez, AR, Karama, S and Colom, R (2014) Subcortical regional morphology correlates with fluid and spatial intelligence. Human Brain Mapping 35, 19571968.Google Scholar
Casey, BJ, Epstein, JN, Buhle, J, Liston, C, Davidson, MC, Tonev, ST, Spicer, J, Niogi, S, Millner, AJ, Reiss, A, Garrett, A, Hinshaw, SP, Greenhill, LL, Shafritz, KM, Vitolo, A, Kotler, LA, Jarrett, MA and Glover, G (2007) Frontostriatal connectivity and its role in cognitive control in parent-child dyads with ADHD. American Journal of Psychiatry 164, 17291736.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
Colom, R, Jung, RE and Haier, RJ (2006) Distributed brain sites for the g-factor of intelligence. Neuroimage 31, 13591365.10.1016/j.neuroimage.2006.01.006Google Scholar
Conners, KC (2004) Conners' Continuous Performance Test (CPT II): Computer Program for Windows Technical Guide and Software Manual. North Tonawanda, NY: Multi-Health Systems.Google Scholar
Cortese, S, Kelly, C, Chabernaud, C, Proal, E, Di Martino, A, Milham, MP and Castellanos, FX (2012) Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. American Journal of Psychiatry 169, 10381055.Google Scholar
Deary, IJ, Penke, L and Johnson, W (2010) The neuroscience of human intelligence differences. Nature Reviews Neuroscience 11, 201211.Google Scholar
Doshi, JA, Hodgkins, P, Kahle, J, Sikirica, V, Cangelosi, MJ, Setyawan, J, Erder, MH and Neumann, PJ (2012) Economic impact of childhood and adult attention-deficit/hyperactivity disorder in the United States. Journal of the American Academy of Child and Adolescent Psychiatry 51, 9901002.10.1016/j.jaac.2012.07.008Google Scholar
Durston, S, van Belle, J and de Zeeuw, P (2011) Differentiating frontostriatal and fronto-cerebellar circuits in attention-deficit/hyperactivity disorder. Biological Psychiatry 69, 11781184.Google Scholar
Eckert, MA, Keren, NI, Roberts, DR, Calhoun, VD and Harris, KC (2010) Age-related changes in processing speed: unique contributions of cerebellar and prefrontal cortex. Frontiers in Human Neuroscience 4, 10.Google Scholar
Egeland, J and Kovalik-Gran, I (2008) Measuring several aspects of attention in one test: the factor structure of Conners's Continuous Performance Test. Journal of Attention Disorders 13, 339357.10.1177/1087054708323019Google Scholar
Epstein, J and Johnson, D (2001) Conners’ Adult ADHD Diagnostic Interview for DSM-IV. North Tonawanda: Multi-Health Systems.Google Scholar
First, M, Spetzer, R, Gibbon, M and Williams, J (2002) Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Non-Patient Edition (with Psychotic Screen) (SCID-I/NP/W/PSYCHOTIC SCREEN). New York: Biometrics Research, New York State Psychiatric Institute.Google Scholar
Friedman, LA and Rapoport, JL (2015) Brain development in ADHD. Current Opinion in Neurobiology 30, 106111.Google Scholar
Frodl, T and Skokauskas, N (2012) Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects. Acta Psychiatrica Scandinavica 125, 114126.Google Scholar
Gau, SSF and Shang, CY (2010) Executive functions as endophenotypes in ADHD: evidence from the Cambridge Neuropsychological Test Battery (CANTAB). Journal of Child Psychology and Psychiatry 51, 838849.Google Scholar
Genova, HM, Hillary, FG, Wylie, G, Rypma, B and Deluca, J (2009) Examination of processing speed deficits in multiple sclerosis using functional magnetic resonance imaging. Journal of the International Neuropsychological Society 15, 383393.Google Scholar
Goos, LM, Crosbie, J, Payne, S and Schachar, R (2009) Validation and extension of the endophenotype model in ADHD patterns of inheritance in a family study of inhibitory control. American Journal of Psychiatry 166, 711717.Google Scholar
Hart, H, Radua, J, Nakao, T, Mataix-Cols, D and Rubia, K (2013) Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder: exploring task-specific, stimulant medication, and age effects. ADHD functional MR imaging studies meta-analysis. JAMA Psychiatry 70, 185198.10.1001/jamapsychiatry.2013.277Google Scholar
Hoogman, M, Bralten, J, Hibar, DP, Mennes, M, Zwiers, MP, Schweren, LS, van Hulzen, KJ, Medland, SE, Shumskaya, E and Jahanshad, N (2017) Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. The Lancet Psychiatry 4, 310319.Google Scholar
Huang-Pollock, CL, Karalunas, SL, Tam, H and Moore, AN (2012) Evaluating vigilance deficits in ADHD: a meta-analysis of CPT performance. Journal of Abnormal Psychology 121, 360371.Google Scholar
Hudec, KL, Alderson, RM, Kasper, LJ and Patros, CH (2014) Working memory contributes to elevated motor activity in adults with ADHD: an examination of the role of central executive and storage/rehearsal processes. Journal of Attention Disorders 18, 357368.Google Scholar
Ivry, R (1997) Cerebellar timing systems. International Review of Neurobiology 41, 555573.10.1016/S0074-7742(08)60370-0Google Scholar
Kasper, LJ, Alderson, RM and Hudec, KL (2012) Moderators of working memory deficits in children with attention-deficit/hyperactivity disorder (ADHD): a meta-analytic review. Clinical Psychology Review 32, 605617.Google Scholar
Lijffijt, M, Kenemans, JL, Verbaten, MN and van Engeland, H (2005). A meta-analytic review of stopping performance in attention-deficit/hyperactivity disorder: deficient inhibitory motor control? Journal of Abnormal Psychology 114, 216222.Google Scholar
Makris, N, Seidman, LJ, Valera, EM, Biederman, J, Monuteaux, MC, Kennedy, DN, Caviness, VS Jr, Bush, G, Crum, K and Brown, AB (2010) Anterior cingulate volumetric alterations in treatment-naive adults with ADHD: a pilot study. Journal of Attention Disorders 13, 407413.Google Scholar
Martinussen, R, Hayden, J, Hogg-Johnson, S and Tannock, R (2005) A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry 44, 377384.10.1097/01.chi.0000153228.72591.73Google Scholar
Moore, DM, D'Mello, AM, McGrath, LM and Stoodley, CJ (2017) The developmental relationship between specific cognitive domains and grey matter in the cerebellum. Developmental Cognitive Neuroscience 24, 111.Google Scholar
Nakao, T, Radua, J, Rubia, K and 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 2011, 24.Google Scholar
Owens, MM, Duda, B, Sweet, LH and MacKillop, J (2018) Distinct functional and structural neural underpinnings of working memory. NeuroImage 174, 463471.Google Scholar
Petersen, SE and Posner, MI (2012) The attention system of the human brain: 20 years after. Annual Review of Neuroscience 35, 7389.Google Scholar
Pinheiro, JC and Bates, DM (2000) Mixed-effects Models in S and S-PLUS. New York: Springer.10.1007/978-1-4419-0318-1Google Scholar
Pinheiro, J, Bates, D, DebRoy, S and Sarkar, D (2014) R Core Team (2014) nlme: linear and nonlinear mixed effects models. R package version 3.1-117. Available at http://CRAN.R-project.org/package=nlme.Google Scholar
Plichta, MM and Scheres, A (2014) Ventral–striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: a meta-analytic review of the fMRI literature. Neuroscience & Biobehavioral Reviews 38, 125134.Google Scholar
Polanczyk, G, de Lima, M, Bernardo, H, Biederman, J and Rohde, L (2007) The worldwide prevalence of ADHD: a systematic review and metaregression analysis. American Journal of Psychiatry 164, 942948.Google Scholar
Rapport, MD, Bolden, J, Kofler, MJ, Sarver, DE, Raiker, JS and Alderson, RM (2009) Hyperactivity in boys with attention-deficit/hyperactivity disorder (ADHD): a ubiquitous core symptom or manifestation of working memory deficits? Journal of Abnormal Child Psychology 37, 521534.Google Scholar
Rapport, MD, Orban, SA, Kofler, MJ and Friedman, LM (2013) Do programs designed to train working memory, other executive functions, and attention benefit children with ADHD? A meta-analytic review of cognitive, academic, and behavioral outcomes. Clinical Psychology Review 33, 12371252.Google Scholar
Reich, W (2000) Diagnostic interview for children and adolescents (DICA). Journal of the American Academy of Child & Adolescent Psychiatry 39, 5966.Google Scholar
Rommelse, NN, Altink, ME, De Sonneville, LM, Buschgens, CJ, Buitelaar, J, Oosterlaan, J and Sergeant, JA (2007) Are motor inhibition and cognitive flexibility dead ends in ADHD? Journal of Abnormal Child Psychology 35, 957967.Google Scholar
Rubia, K, Alegria, AA, Cubillo, AI, Smith, AB, Brammer, MJ and Radua, J (2014) Effects of stimulants on brain function in attention-deficit/hyperactivity disorder: a systematic review and meta-analysis. Biological Psychiatry 76, 616628.10.1016/j.biopsych.2013.10.016Google Scholar
Salum, G, Sergeant, J, Sonuga-Barke, E, Vandekerckhove, J, Gadelha, A, Pan, P, Moriyama, T, Graeff-Martins, A, de Alvarenga, PG and do Rosario, M (2014 a) Specificity of basic information processing and inhibitory control in attention deficit hyperactivity disorder. Psychological Medicine 44, 617631.Google Scholar
Salum, G, Sonuga-Barke, E, Sergeant, J, Vandekerckhove, J, Gadelha, A, Moriyama, T, Graeff-Martins, A, Manfro, G, Polanczyk, G and Rohde, L (2014 b) Mechanisms underpinning inattention and hyperactivity: neurocognitive support for ADHD dimensionality. Psychological Medicine 44, 31893201.Google Scholar
Schaefer, A and Gray, JR (2007) A role for the human amygdala in higher cognition. Reviews in the Neurosciences 18, 355364.Google Scholar
Seidman, LJ, Valera, EM, Makris, N, Monuteaux, MC, Boriel, DL, Kelkar, K, Kennedy, DN, Caviness, VS, Bush, G, Aleardi, M, Faraone, SV and Biederman, J (2006) Dorsolateral prefrontal and anterior cingulate cortex volumetric abnormalities in adults with attention-deficit/hyperactivity disorder identified by magnetic resonance imaging. Biological Psychiatry 60, 10711080.Google Scholar
Sergeant, JA, Oosterlaan, J and van der Meere, J (1999) Information processing and energetic factors in attention-deficit/hyperactivity disorder. In Handbook of Disruptive Behavior Disorders. Boston, MA: Springer.Google Scholar
Shaw, P (2007) Intelligence and the developing human brain. Bioessays 29, 962973.Google Scholar
Shaw, P, De Rossi, P, Watson, B, Wharton, A, Greenstein, D, Raznahan, A, Sharp, W, Lerch, JP and Chakravarty, MM (2014 a) Mapping the development of the basal ganglia in children With attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry 53, 780789.Google Scholar
Shaw, P, Stringaris, A, Nigg, J and Leibenluft, E (2014 b) Emotion dysregulation in attention deficit hyperactivity disorder. American Journal of Psychiatry 171, 276293.Google Scholar
Simon, V, Czobor, P, Balint, S, Meszaros, A and Bitter, I (2009) Prevalence and correlates of adult attention-deficit hyperactivity disorder: meta-analysis. The British Journal of Psychiatry 194, 204211.Google Scholar
Sonuga-Barke, EJ (2005) Causal models of attention-deficit/hyperactivity disorder: from common simple deficits to multiple developmental pathways. Biological Psychiatry 57, 12311238.Google Scholar
Sudre, G, Choudhuri, S, Szekely, E, Bonner, T, Goduni, E, Sharp, W and Shaw, P (2017) Estimating the heritability of structural and functional brain connectivity in families affected by attention-deficit/hyperactivity disorder. Jama Psychiatry 74, 7684.Google Scholar
Tillman, C, Eninger, L, Forssman, L and Bohlin, G (2011) The relation between working memory components and ADHD symptoms from a developmental perspective. Developmental Neuropsychology 36, 181198.Google Scholar
Wechsler, D (2001) Wechler Test of Adult Reading. San Antonio, TX: Psychological Corporation.Google Scholar
Wechsler, D (2003) Wechsler Intelligence Scale for Children- Fourth Edition (WISC-IV). San Antonio, TX: The Psychological Corporation.Google Scholar
Wechsler, D (2011) Wechsler Abbreivated Scale of Intelligence, 2nd Edn. San Antonio, TX: Psychological Corporation.Google Scholar
Willcutt, EG, Doyle, AE, Nigg, JT, Faraone, SV and Pennington, BF (2005 a) Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review. Biological Psychiatry 57, 13361346.Google Scholar
Willcutt, EG, Pennington, BF, Olson, RK, Chhabildas, N and Hulslander, J (2005 b) Neuropsychological analyses of comorbidity between reading disability and attention deficit hyperactivity disorder: in search of the common deficit. Developmental Neuropsychology 27, 3578.Google Scholar
Woodcock, RW, McGrew, KS and Mather, N (2001) Woodcock-Johnson III Tests of Cognitive Abilities. Itasca, IL: Riverside Publishing Company.Google Scholar
Yuan, P and Raz, N (2014) Prefrontal cortex and executive functions in healthy adults: a meta-analysis of structural neuroimaging studies. Neuroscience & Biobehavioral Reviews 42, 180192.Google Scholar
Supplementary material: File

Muster et al. supplementary material

Muster et al. supplementary material 1

Download Muster et al. supplementary material(File)
File 591.9 KB