Event Abstract

Back to Event

Are ‘neurotypical controls’ really neurotypical? Individual differences in mental health traits in ‘control’ populations

Eri Ichijo1*, Mirta Stantic1, Sammi Chekroud1, Jennifer Murphy2, Midel-Pierre Coll1 and Geoffrey Bird3
  • 1 Department of Experimental Psychology, Medical Sciences Division, University of Oxford, United Kingdom
  • 2 Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
  • 3 University of Oxford, United Kingdom

A continuous challenge in psychiatric research is selection of comparison samples that adequately reflect the general population. In 2010, a group of Canadian researchers argued that studies of human psychology and behaviour published in the world’s top journals generalize too broadly from data collected in niche groups of people they called WEIRD - Western, Educated, Industrialised, Rich, and Democratic (Henrich et al., 2010). Reporting and matching basic demographic information, such as gender and age, is a common practice in all behavioural research. Few studies, however, match or control for mental health traits despite a general rise in the prevalence of various clinical and subclinical conditions (World Health Organization, 2017) that are known to affect cognitive abilities ranging from pain perception to facial expression recognition (Benedetti et al., 2006; Douglas and Porter, 2010; Cook et al., 2013). In experimental between groups designs, such as randomised control trials (RCTs), participants are allocated to either treatment or control groups randomly (Kim and Shin, 2014). In case-control, between-group designs, which is the focus of this paper, individuals are assigned to a group depending on the presence or an absence of a predefined feature (Weinberg and Sandler, 1991). The lack of matching for mental health traits in case-control, between-group designs is generally justified by randomly assigning participants to groups. In between-group mental health-related studies, neurotypical control participants (henceforth ‘controls’) are usually selected on the basis that they report no psychiatric diagnoses and no use of psychotropic medications. However, absence of clinical diagnoses does not necessarily indicate the absence of psychiatric traits, which exist on a continuum and are observed in neurotypical populations who do not have a clinical diagnosis (McCrae and Costa, 2004; Crawford et al., 2011; Löwe et al., 2008). A primary issue is that mental health traits tend to be viewed as discrete variables; researchers often use arbitrary cut-offs that promote a binary system. This is not reflective of the reality of various mental health conditions and may differ across age groups and clinical populations (Julian, 2011; White et al., 2009; Castro-Costa et al., 2007). Here, we focus specifically on individual differences in mental health traits in populations that ostensibly meet criteria to be considered controls. We argue that failing to match for mental health trait measures (such as anxiety and depression) in between-subjects designs can substantially alter the implications of one’s findings. 805 individuals completed an online questionnaire study including the 21-item Depression Anxiety Stress Scale (DASS-21; Lovibond and Lovibond, 1995). The DASS-21 was selected as the scale distinctly captures depression and anxiety, which are highly correlated. Participants were recruited via social media, pre-existing participant databases and local advertisements. In line with standard practice, participants who reported psychiatric diagnoses or use of psychotropic drugs were excluded from further analysis. Additionally, we conservatively excluded participants who reported self or informal diagnosis without a formal evaluation. After exclusions, 402 participants met the criteria to be considered controls in a typical study (Table 1). Table 1. Demographics data Group N Mean (age) SD (age) All 402 27.50 11.45 Female 325 27.50 9.11 Male 77 27.51 10.42 Figure 1 depicts the distribution of depression, anxiety, and stress traits. While the majority of participants fall in the normal range on depressive (58.0%), anxious (57.5%) and stress scores (72.1%), a concerning proportion of this conservatively-selected population scores in the atypical range. Even in this population, 26.9% of the controls score in the range of moderate to extremely severe depression scores, 32.8%% in the moderate to extremely severe anxiety scores, and 15.9% in the moderate to extremely severe stress scores. Given the increasing trend of mental health disorder prevalence in the general population (WHO, 2017) and an increase of 40% in reported compulsory detentions in psychiatric hospitals in the UK (Wessely et al., 2018), these findings are perhaps not surprising. Nonetheless, it is alarming that such a significant proportion of those who would be treated as controls in studies demonstrate considerable anxious and depressive traits. While it should be noted that the stability of depressive and anxiety traits can be affected by various environmental factors (Rosenthal, 1984; Hadley and Patil, 2008), and a comprehensive assessment is required to establish the presence of a clinical condition, it is unsettling that between a quarter and a third of the control population scored in the moderate to extremely severe range for mental health traits. It also suggests the futility of using cut-off scores in questionnaires such as DASS-21 for the purpose of inclusion and exclusion in neurotypical populations. Of course, there are limitations to recruitment and selection of participants in an online study like this one, such as limited control over methods of study distribution and relative anonymity of online participants. Nevertheless, the proportion of participants with significant anxious and depressive traits without a formal diagnosis warrants further investigation and consideration. We argue that this finding is relevant not just because the number of online studies available has been hugely increasing in recent years, but also because laboratory-based studies do not tend to control for these traits. DASS-21 was calibrated twenty-five years ago using a WEIRD sample of first-year psychology students. Changes in global environment, including economic and political uncertainty, may have shifted the distribution of population traits over the years. This suggests that perhaps the DASS scale needs to be recalibrated with a more representative contemporary sample. It may also be preferable to employ measures built based on the symptoms defined in Diagnostic and Statistical Manuals, such as the Beck Depression Inventory Second Edition (BDI-II; Beck et al., 1996). Given this distribution of traits, we argue that as researchers, we ought to be more aware about traits we match for in between-group experimental designs. We appreciate that matching for every possible confounding variable is impossible. Despite that, previous research on various cognitive processes influenced by mental health traits indicates that matching groups on mental health traits is critical. Of course, this does not discount the importance of standard matching on basic demographic information like age and gender. The solution is not to exclude people who demonstrate high levels of clinically-relevant traits, which would lead to non-representative samples, nor to control for these traits statistically, which would lead to artificial comparisons since many of the mental health issues are correlated and/or comorbid. Instead, we propose reconsideration of inclusion and exclusion criteria, use of larger and varied population samples, and better matching between case- and control-populations on multiple dimensions.

Figure 1
Image

Acknowledgements

M Stantic is funded by ESRC studentship and a Wilfrid Knapp Science Scholarship. S Chekroud is funded by Glyn Humphreys Scholarship. J Murphy is supported by a doctoral studentship from the Economic and Social Research Council [1599941; ES/J500057/1]. MP Coll is funded by a postdoctoral fellowship from the Fonds de recherche du Québec—Santé. G Bird is supported by the Baily Thomas Charitable Trust.

References

Beck, A. T., Steer, R. A., Ball, R., and Ranieri, W. (1996). Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J. Pers. Assess. 67, 588–597. doi:10.1207/s15327752jpa6703_13. Benedetti, F., Amanzio, M., Vighetti, S., and Asteggiano, G. (2006). The biochemical and neuroendocrine bases of the hyperalgesic nocebo effect. J. Neurosci. 26, 12014–12022. doi:10.1523/JNEUROSCI.2947-06.2006. Castro-Costa, E., Dewey, M., Stewart, R., Banerjee, S., Huppert, F., Mendonca-Lima, C., Bula, C., Reisches, F., Wancata, J., Ritchie, K., et al. (2007). Prevalence of depressive symptoms and syndromes in later life in ten European countries: the SHARE study. Br. J. Psychiatry 191, 393–401. doi:10.1192/bjp.bp.107.036772. Cook, R., Brewer, R., Shah, P., and Bird, G. (2013). Alexithymia, not autism, predicts poor recognition of emotional facial expressions. Psychol. Sci. 24, 723–732. doi:10.1177/0956797612463582. Crawford, J., Cayley, C., Lovibond, P. F., Wilson, P. H., and Hartley, C. (2011). Percentile Norms and Accompanying Interval Estimates from an Australian General Adult Population Sample for Self-Report Mood Scales (BAI, BDI, CRSD, CES-D, DASS, DASS-21, STAI-X, STAI-Y, SRDS, and SRAS). Aust Psychol 46, 3–14. doi:10.1111/j.1742-9544.2010.00003.x. Douglas, K. M., and Porter, R. J. (2010). Recognition of disgusted facial expressions in severe depression. Br. J. Psychiatry 197, 156–157. doi:10.1192/bjp.bp.110.078113. Hadley, C., and Patil, C. L. (2008). Seasonal changes in household food insecurity and symptoms of anxiety and depression. Am. J. Phys. Anthropol. 135, 225–232. doi:10.1002/ajpa.20724. Henrich, J., Heine, S. J., and Norenzayan, A. (2010). The weirdest people in the world? Behav. Brain Sci. 33, 61–83; discussion 83. doi:10.1017/S0140525X0999152X. Julian, L. J. (2011). Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Arthritis Care Res. (Hoboken) 63 Suppl 11, S467-72. doi:10.1002/acr.20561. Kim, J., and Shin, W. (2014). How to do random allocation (randomization). Clin Orthop Surg 6, 103–109. doi:10.4055/cios.2014.6.1.103. Lovibond, P. F., and Lovibond, S. H. (1995). The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav. Res. Ther. 33, 335–343. doi:10.1016/0005-7967(94)00075-U. Löwe, B., Decker, O., Müller, S., Brähler, E., Schellberg, D., Herzog, W., and Herzberg, P. Y. (2008). Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Med. Care 46, 266–274. doi:10.1097/MLR.0b013e318160d093. McCrae, R. R., and Costa, P. T. (2004). A contemplated revision of the NEO Five-Factor Inventory. Pers. Individ. Dif. 36, 587–596. doi:10.1016/S0191-8869(03)00118-1. Rosenthal, N. E. (1984). Seasonal Affective Disorder. Arch. Gen. Psychiatry 41, 72. doi:10.1001/archpsyc.1984.01790120076010. Weinberg, C. R., and Sandler, D. P. (1991). Randomized recruitment in case-control studies. Am. J. Epidemiol. 134, 421–432. Wessely, S., Gilbert, S., Hedley, M., and Neuberger, J. (2018). Modernising the Mental Health Act: Increasing choice, reducing compulsion - Final report of the Independent Review of the Mental Health Act 1983. GOV.UK. White, S. W., Oswald, D., Ollendick, T., and Scahill, L. (2009). Anxiety in children and adolescents with autism spectrum disorders. Clin. Psychol. Rev. 29, 216–229. doi:10.1016/j.cpr.2009.01.003. World Health Organization (2017). Depression and other common mental disorders: global health estimates. apps.who.int.

Keywords: Mental Health, Study Design, Depression - psychology, Anxiety, General population (GP)

Conference: 4th International Conference on Educational Neuroscience, Abu Dhabi, United Arab Emirates, 10 Mar - 11 Mar, 2019.

Presentation Type: Oral Presentation (invited speakers only)

Topic: Educational Neuroscience

Citation: Ichijo E, Stantic M, Chekroud S, Murphy J, Coll M and Bird G (2019). Are ‘neurotypical controls’ really neurotypical? Individual differences in mental health traits in ‘control’ populations. Conference Abstract: 4th International Conference on Educational Neuroscience. doi: 10.3389/conf.fnhum.2019.229.00024

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 10 Feb 2019; Published Online: 27 Sep 2019.

* Correspondence: Miss. Eri Ichijo, Department of Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, England, OX1 3PH, United Kingdom, 685729@frontiersin.org