Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T23:09:11.076Z Has data issue: false hasContentIssue false
  • English
  • Français

Distinct neuropsychological profiles within ADHD: a latent class analysis of cognitive control, reward sensitivity and timing

Published online by Cambridge University Press:  07 August 2014

B. M. van Hulst ,
P. de Zeeuw  and
S. Durston
B. M. van Hulst*
Affiliation:
NICHE Laboratory, Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
P. de Zeeuw
Affiliation:
NICHE Laboratory, Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
S. Durston
Affiliation:
NICHE Laboratory, Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
*
*Address for correspondence: B. M. van Hulst, MD, Department of Psychiatry – HP A 01.126, University Medical Center Utrecht, 3500 VW Utrecht, The Netherlands. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Multiple pathway models of attention deficit hyperactivity disorder (ADHD) suggest that this disorder is the behavioural expression of dysfunction in one of several separable brain systems. One such model focuses on the brain systems underlying cognitive control, timing and reward sensitivity. It predicts separable subgroups among individuals with ADHD, with performance deficits in only one of these domains. We used latent class analysis (LCA) to identify subgroups of individuals with ADHD based on their overall pattern of neuropsychological performance, rather than grouping them based on cut-off criteria. We hypothesized that we would find separable subgroups with deficits in cognitive control, timing and reward sensitivity respectively.

Method

Ninety-six subjects with ADHD (of any subtype) and 121 typically developing controls performed a battery assessing cognitive control, timing and reward sensitivity. LCA was used to identify subgroups of individuals with ADHD with a distinct neuropsychological profile. A similar analysis was performed for controls.

Results

Three subgroups represented 87% of subjects with ADHD. Two of our three hypothesized subgroups were identified, with poor cognitive control and timing. Two of the ADHD subgroups had similar profiles to control subgroups, whereas one subgroup had no equivalent in controls.

Conclusions

Our findings support multiple pathway models of ADHD, as we were able to define separable subgroups with differing cognitive profiles. Furthermore, we found both quantitative and qualitative differences from controls, suggesting that ADHD may represent both categorical and dimensional differences. These results show that by addressing heterogeneity in ADHD, we can identify more homogeneous subsets of individuals to further investigate.

Keywords

ADHDcognitive controlheterogeneitylatent class analysis (LCA)neuropsychologyreward sensitivitytiming
Type
Original Articles
Information
Psychological Medicine , Volume 45 , Issue 4 , March 2015 , pp. 735 - 745
Copyright
Copyright © Cambridge University Press 2014 

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

Alderson, RM, Rapport, MD, Kofler, MJ (2007). Attention-deficit/hyperactivity disorder and behavioral inhibition: a meta-analytic review of the stop-signal paradigm. Journal of Abnormal Child Psychology 35, 745–758.Google Scholar
APA (2000). Attention-deficit and disruptive behavior disorders. In: Diagnostic and Statistical Manual of Mental Disorders, 4th edn, text rev. American Psychiatric Association: Washington, DC.Google Scholar
APA (2013). Neurodevelopmental disorders. In Diagnostic and Statistical Manual of Mental Disorders, 5th edn. American Psychiatric Association: Washington, DC.Google Scholar
Barkley, RA (1997). Behavioral inhibition, sustained attention, and executive functions: constructing a unifying theory of ADHD. Psychological Bulletin 121, 65–94.Google Scholar
Casey, BJ, Tottenham, N, Liston, C, Durston, S (2005). Imaging the developing brain: what have we learned about cognitive development? Trends in Cognitive Sciences 9, 104–110.Google Scholar
Castellanos, FX, Tannock, R (2002). Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes. Nature Reviews. Neuroscience 3, 617–628.Google Scholar
Coghill, D, Sonuga-Barke, EJS (2012). Annual research review: categories versus dimensions in the classification and conceptualisation of child and adolescent mental disorders–implications of recent empirical study. Journal of Child Psychology and Psychiatry, and Allied Disciplines 53, 469–489.Google Scholar
Cortese, S, Kelly, C, Chabernaud, C, Proal, E, Di Martino, A, 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
Daviss, WB (2008). A review of co-morbid depression in pediatric ADHD: etiology, phenomenology, and treatment. Journal of Child and Adolescent Psychopharmacology 18, 565–571.Google Scholar
de Zeeuw, P, Weusten, J, van Dijk, S, van Belle, J, Durston, S (2012). Deficits in cognitive control, timing and reward sensitivity appear to be dissociable in ADHD. PLoS ONE 7, e51416.Google Scholar
Durston, S (2010). Imaging genetics in ADHD. NeuroImage 53, 832–838.Google Scholar
Durston, S, Davidson, MC, Mulder, MJ, Spicer, JA, Galvan, A, Tottenham, N, Scheres, A, Xavier Castellanos, F, van Engeland, H, Casey, BJ (2007). Neural and behavioral correlates of expectancy violations in attention-deficit hyperactivity disorder. Journal of Child Psychology and Psychiatry 48, 881–889.Google Scholar
Durston, S, van Belle, J, de Zeeuw, P (2011). Differentiating frontostriatal and fronto-cerebellar circuits in attention-deficit/hyperactivity disorder. Biological Psychiatry 69, 1178–1184.Google Scholar
Fair, DA, Bathula, D, Nikolas, MA, Nigg, JT (2012). Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proceedings of the National Academy of Sciences USA 109, 6769–6774.Google Scholar
Faraone, SV, Biederman, J, Weber, W, Russell, RL (1998). Psychiatric, neuropsychological, and psychosocial features of DSM-IV subtypes of attention-deficit/hyperactivity disorder: results from a clinically referred sample. Journal of the American Academy of Child and Adolescent Psychiatry 37, 185–193.Google Scholar
Franke, B, Faraone, SV, Asherson, P, Buitelaar, J, Bau, CHD, Ramos-Quiroga, JA, Mick, E, Grevet, EH, Johansson, S, Haavik, J, Lesch, K-P, Cormand, B, Reif, A (2011). The genetics of attention deficit/hyperactivity disorder in adults, a review. Molecular Psychiatry, 1–28.Google Scholar
Gottesman, II, Gould, TD (2003). The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry 160, 636–645.Google Scholar
Knutson, B, Adams, CM, Fong, GW, Hommer, D (2001). Anticipation of increasing monetary reward selectively recruits nucleus accumbens. Journal of Neuroscience 21, RC159.Google Scholar
Li, JJ, Lee, SS (2012). Interaction of dopamine transporter (DAT1) genotype and maltreatment for ADHD: a latent class analysis. Journal of Child Psychology and Psychiatry, and Allied Disciplines 53, 997–1005.Google Scholar
Luman, M, Tripp, G, Scheres, A (2010). Identifying the neurobiology of altered reinforcement sensitivity in ADHD: a review and research agenda. Neuroscience and Biobehavioral Reviews 34, 744–754.Google Scholar
Magidson, J, Vermunt, JK (2004). Latent class models. In The SAGE Handbook of Quantitative Methodology for the Social Sciences (ed. Kaplan, D.), pp. 1–24. Sage Publications: Thousand Oaks.Google Scholar
Mulder, MJ, Baeyens, D, Davidson, MC, Casey, BJ, van den Ban, E, van Engeland, H, Durston, S (2008). Familial vulnerability to ADHD affects activity in the cerebellum in addition to the prefrontal systems. Journal of the American Academy of Child and Adolescent Psychiatry 47, 68–75.Google Scholar
Mulder, MJ, van Belle, J, van Engeland, H, Durston, S (2011). Functional connectivity between cognitive control regions is sensitive to familial risk for ADHD. Human Brain Mapping 32, 1511–1518.Google Scholar
Nigg, JT, Casey, BJ (2005). An integrative theory of attention-deficit/hyperactivity disorder based on the cognitive and affective neurosciences. Development and Psychopathology 17, 785–806.Google Scholar
Nigg, JT, Willcutt, EG, Doyle, AE, Sonuga-Barke, EJS (2005). Causal heterogeneity in attention-deficit/hyperactivity disorder: do we need neuropsychologically impaired subtypes? Biological Psychiatry 57, 1224–1230.Google Scholar
Noreika, V, Falter, CM, Rubia, K (2013). Timing deficits in attention-deficit/hyperactivity disorder (ADHD): Evidence from neurocognitive and neuroimaging studies. Neuropsychologia 51, 1–32.Google Scholar
Scheres, A, Milham, MP, Knutson, B, Castellanos, FX (2007). Ventral striatal hyporesponsiveness during reward anticipation in attention-deficit/hyperactivity disorder. Biological Psychiatry 61, 720–724.Google Scholar
Sergeant, J (2005). Modeling attention-deficit/hyperactivity disorder: a critical appraisal of the cognitive–energetic model. Biological Psychiatry 57, 1248–1255.Google Scholar
Shaffer, D, Fisher, P, Lucas, CP, Dulcan, MK, Schwab-Stone, ME (2000). NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): description, differences from previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child and Adolescent Psychiatry 39, 28–38.Google Scholar
Sheehan, DV, Lecrubier, Y, Sheehan, KH, Amorim, P, Janavs, J, Weiller, E, Hergueta, T, Baker, R, Dunbar, GC (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry 59 (Suppl. 2), 22–33; quiz 34–57.Google Scholar
Sonuga-Barke, EJS (2002). Psychological heterogeneity in AD/HD – a dual pathway model of behaviour and cognition. Behavioural Brain Research 130, 29–36.Google Scholar
Sonuga-Barke, EJS (2005). Causal models of attention-deficit/hyperactivity disorder: from common simple deficits to multiple developmental pathways. Biological Psychiatry 57, 1231–1238.Google Scholar
Sonuga-Barke, EJS, Bitsakou, P, Thompson, M (2010). Beyond the dual pathway model: evidence for the dissociation of timing, inhibitory, and delay-related impairments in attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry 49, 345–355.Google Scholar
Sonuga-Barke, EJS, Castellanos, FX (2007). Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neuroscience and Biobehavioral Reviews 31, 977–986.Google Scholar
Ströhle, A, Stoy, M, Wrase, J, Schwarzer, S, Schlagenhauf, F, Huss, M, Hein, J, Nedderhut, A, Neumann, B, Gregor, A, Juckel, G, Knutson, B, Lehmkuhl, U, Bauer, M, Heinz, A (2008). Reward anticipation and outcomes in adult males with attention-deficit/hyperactivity disorder. NeuroImage 39, 966–972.Google Scholar
Swanson, J (1992). School-based Assessments and Interventions for ADD Students. K.C. Publications: Irvine, CA.Google Scholar
Toplak, ME, Dockstader, C, Tannock, R (2006). Temporal information processing in ADHD: findings to date and new methods. Journal of Neuroscience Methods 151, 15–29.Google Scholar
Tripp, G, Wickens, JR (2009). Neurobiology of ADHD. Neuropharmacology 57, 579–589.Google Scholar
van der Meer, JMJ, Oerlemans, AM, van Steijn, DJ, Lappenschaar, MGA, de Sonneville, LMJ, Buitelaar, JK, Rommelse, NNJ (2012). Are autism spectrum disorder and attention-deficit/hyperactivity disorder different manifestations of one overarching disorder? cognitive and symptom evidence from a clinical and population-based sample. Journal of the American Academy of Child & Adolescent Psychiatry 51, 1160–1172.Google Scholar
Verhulst, F, Van Der Ende, J, Koot, H (1996). Handleiding voor de CBCL/4–18 (Manual for the CBCL/4–18). Department of Child and Adolescent Psychiatry, Erasmus Academic Medical Center: Rotterdam.Google Scholar
Vermunt, JK, Magidson, J (2005). Latent GOLD 4.0 User's Guide. Statistical Innovations Inc: Belmont, Massachusetts.Google Scholar
Williams, LM, Hermens, DF, Palmer, D, Kohn, M, Clarke, S, Keage, H, Clark, CR, Gordon, E (2008). Misinterpreting emotional expressions in attention-deficit/hyperactivity disorder: evidence for a neural marker and stimulant effects. Biological Psychiatry 63, 917–926.Google Scholar
Supplementary material: Image

van Hulst Supplementary Material

Figure S1

Download van Hulst Supplementary Material(Image)
Image 175.8 KB
Supplementary material: File

van Hulst Supplementary Material

Tables S1-S2

Download van Hulst Supplementary Material(File)
File 17 KB