Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-10T16:38:11.008Z Has data issue: false hasContentIssue false
  • English
  • Français

Differentiating depression and ADHD without depression in adults with processing-speed measures

Published online by Cambridge University Press:  27 April 2020

Klaus Martiny Open the ORCID record for Klaus Martiny [Opens in a new window] ,
Niels Peter Nielsen  and
Elisabeth H. Wiig Open the ORCID record for Elisabeth H. Wiig [Opens in a new window]
Klaus Martiny
Affiliation:
Psychiatric Center Copenhagen, University of Copenhagen, Rigshospitalet, Copenhagen, Denmark
Niels Peter Nielsen
Affiliation:
Västervik Sjukhus, Department of Psychiatry, Västervik, Sweden
Elisabeth H. Wiig*
Affiliation:
Boston University and Knowledge Research Institute, Inc., Arlington, TX, USA
*
Author for correspondence: Elisabeth H. Wiig, Email: ehwiig@krii.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

We evaluated processing-speed and shift-cost measures in adults with depression or attention-deficit hyperactivity disorder (ADHD) and monitored the effects of treatment. We hypothesised that cognitive-speed and shift-cost measures might differentiate diagnostic groups.

Methods:

Colour, form, and colour–form stimuli were used to measure naming times. The shift costs were calculated as colour–form-naming time minus the sum of colour- and form-naming times. Measurements were done at baseline and end point for 42 adults with depression and 42 with ADHD without depression. Patients with depression were treated with transcranial pulsed electromagnetic fields and patients with ADHD with methylphenidate immediate release.

Results:

During depression treatment, reductions in naming times were recorded weekly. One-way analysis of variance indicated statistical between-group differences, with effect sizes in the medium range for form and colour–form. In both groups, naming times were longer before than after treatment. For the ADHD group, shift costs exceeded the average–normal range at baseline but were in the average–normal range after stabilisation with stimulant medication. For the depression group, shift costs were in the average–normal range at baseline and after treatment. Baseline colour–form-naming times predicted reductions in naming times for both groups, with the largest effect size and index of forecasting efficiency for the ADHD group.

Conclusions:

The cognitive-processing-speed (colour–form) and shift-cost measures before treatment proved most sensitive in differentiating patients with depression and ADHD. Reductions in naming times for the depression group were suggested to reflect improved psychomotor skills rather than improved cognitive control.

Keywords

depressionADHDmethylphenidateexecutive functiontreatment resistant depressiontranscranially applied pulsed electromagnetic field stimulation (T-PEMF)
Type
Original Article
Information
Acta Neuropsychiatrica , Volume 32 , Issue 5 , October 2020 , pp. 237 - 246
Copyright
© Scandinavian College of Neuropsychopharmacology 2020

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

Andersson, M, Wiig, EH, Minthon, L and Londos, E (2007) A Quick Test for Cognitive Speed: a measure of cognitive speed in dementia with Lewy bodies. American Journal of Alzheimer’s Disease & Other Dementias 22, 313–318.CrossRefGoogle ScholarPubMed
Baddeley, AD, Bressi, S, Della Sala, S, Logie, R and Spinnler, H (1991) The decline of working memory in Alzheimer’s disease. A longitudinal study. Brain 114 (Pt 6), 2521–2542.CrossRefGoogle ScholarPubMed
Barkley, RA and Brown, TE (2008) Unrecognized attention-deficit/hyperactivity disorder in adults presenting with other psychiatric disorders. CNS Spectrums 13, 977–984.CrossRefGoogle ScholarPubMed
Bech, P, Gefke, M, Lunde, M, Lauritzen, L and Martiny, K (2011) The Pharmacopsychometric Triangle to Illustrate the Effectiveness of T-PEMF Concomitant with Antidepressants in Treatment Resistant Patients: A Double-Blind, Randomised, Sham-Controlled Trial Revisited with Focus on the Patient-Reported Outcomes. Depression Research and Treatment 2011, 806298.Google ScholarPubMed
Bech, P, Lauritzen, L, Lunde, M, Unden, M, Hellstrom, LC, Csillag, C and Martiny, K (2014) Psychometric analysis of the Melancholia Scale in trials with non-pharmacological augmentation of patients with therapy-resistant depression. Acta Neuropsychiatrica 26, 155–160.CrossRefGoogle ScholarPubMed
Bech, P, Timmerby, N, Martiny, K, Lunde, M and Soendergaard, S (2015) Psychometric evaluation of the Major Depression Inventory (MDI) as depression severity scale using the LEAD (Longitudinal Expert Assessment of All Data) as index of validity. BMC Psychiatry 15, 190.CrossRefGoogle ScholarPubMed
Berryhill, ME, Chein, J and Olson, IR (2011) At the intersection of attention and memory: the mechanistic role of the posterior parietal lobe in working memory. Neuropsychologia 49, 1306–1315.CrossRefGoogle ScholarPubMed
Cohen, J (2009) Statistical Power Analysis for the Behavioral Sciences. USA: Psychology Press.Google Scholar
Downing, PE (2000) Interactions between visual working memory and selective attention. Psychological Science 11, 467–473.CrossRefGoogle ScholarPubMed
Esterman, M, Chiu, YC, Tamber-Rosenau, BJ and Yantis, S (2009) Decoding cognitive control in human parietal cortex. Proceedings of the National Academy of Sciences of the United States of America 106, 17974–17979.CrossRefGoogle ScholarPubMed
Fleck, C, Wiig, EH and Corwin, M (2015) Stroop interference and AQT cognitive speed may play complementary roles in differentiating dementias with frontal and posterior lesions. Community Mental Health Journal 51, 315–320.CrossRefGoogle ScholarPubMed
Gonda, X, Pompili, M, Serafini, G, Carvalho, AF, Rihmer, Z and Dome, P (2015) The role of cognitive dysfunction in the symptoms and remission from depression. Annals of General Psychiatry 14, 27.CrossRefGoogle ScholarPubMed
Greenberg, LM and Waldman, ID (1993) Developmental normative data on the test of variables of attention (T.O.V.A.). Journal of Child Psychology and Psychiatry 34, 1019–1030.CrossRefGoogle Scholar
Hamilton, M (1960) A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry 23, 56–62.CrossRefGoogle ScholarPubMed
Jacobson, JM, Nielsen, NP, Minthon, L, Warkentin, S and Wiig, EH (2004) Multiple rapid automatic naming measures of cognition: normal performance and effects of aging. Perceptual and Motor Skills 98, 739–753.CrossRefGoogle ScholarPubMed
Katzman, MA, Bilkey, TS, Chokka, PR, Fallu, A and Klassen, LJ (2017) Adult ADHD and comorbid disorders: clinical implications of a dimensional approach. BMC Psychiatry 17, 302.CrossRefGoogle ScholarPubMed
Larochette, AC, Harrison, AG, Rosenblum, Y and Bowie, CR (2011) Additive neurocognitive deficits in adults with attention-deficit/hyperactivity disorder and depressive symptoms. Archives of Clinical Neuropsychology 26, 385–395.CrossRefGoogle ScholarPubMed
Lingjaerde, O, Ahlfors, UG, Bech, P, Dencker, SJ and Elgen, K (1987) The UKU side effect rating scale. A new comprehensive rating scale for psychotropic drugs and a cross-sectional study of side effects in neuroleptic-treated patients. Acta Psychiatrica Scandinavica 334, 1–100.CrossRefGoogle Scholar
Magell, G, Gustavsson, J, Wiig, E and Nielsen, N (2018) Monitoring methylphenidate dose effects in adults with attention deficit disorder with hyperactivity: a validation study. Journal of Neuroscience and Neuropsychology 2, 1–7.Google Scholar
Martiny, K, Lunde, M and Bech, P (2010) Transcranial low voltage pulsed electromagnetic fields in patients with treatment-resistant depression. Biological Psychiatry 68, 163–169.CrossRefGoogle ScholarPubMed
Martiny, K, Refsgaard, E, Lund, V, Lunde, M, Sorensen, L, Thougaard, B, Lindberg, L and Bech, P (2013) The day-to-day acute effect of wake therapy in patients with major depression using the HAM-D6 as primary outcome measure: results from a randomised controlled trial. PLoS One 8, e67264.CrossRefGoogle ScholarPubMed
Mcintosh, D, Kutcher, S, Binder, C, Levitt, A, Fallu, A and Rosenbluth, M (2009) Adult ADHD and comorbid depression: a consensus-derived diagnostic algorithm for ADHD. Neuropsychiatric Disease and Treatment 5, 137–150.Google ScholarPubMed
Nielsen, NP, Ringstrom, R, Wiig, EH and Minthon, L (2007) Associations between AQT processing speed and neuropsychological tests in neuropsychiatric patients. American Journal of Alzheimer’s Disease & Other Dementias 22, 202–210.CrossRefGoogle ScholarPubMed
Nielsen, NP and Wiig, EH (2011a) An additive model for relations between AQT single- and dual-dimension naming speed. Perceptual and Motor Skills 112, 499–508.CrossRefGoogle ScholarPubMed
Nielsen, NP and Wiig, EH (2011b) AQT cognitive speed and processing efficiency differentiate adults with and without ADHD: a preliminary study. International Journal of Psychiatry in Clinical Practice 15, 219–227.CrossRefGoogle ScholarPubMed
Nielsen, NP and Wiig, EH (2013) Validation of the AQT color-form additive model for screening and monitoring pharmacological treatment of ADHD. Journal of Attention Disorders 17, 187–193.CrossRefGoogle ScholarPubMed
Nielsen, NP, Wiig, EH, Back, S and Gustafsson, J (2017) Processing speed can monitor stimulant-medication effects in adults with attention deficit disorder with hyperactivity. Nordic Journal of Psychiatry 71, 296–303.CrossRefGoogle ScholarPubMed
Nielsen, NP, Wiig, EH, Warkentin, S and Minthon, L (2004) Clinical utility of color-form naming in Alzheimer’s disease: preliminary evidence. Perceptual and Motor Skills 99, 1201–1204.Google ScholarPubMed
Palmqvist, S, Minthon, L, Wattmo, C, Londos, E and Hansson, O (2010) A Quick Test of cognitive speed is sensitive in detecting early treatment response in Alzheimer’s disease. Alzheimer’s Research & Therapy 2, 29.CrossRefGoogle ScholarPubMed
Rucklidge, JJ and Tannock, R (2002) Validity of the Brown ADD scales: an investigation in a predominantly inattentive ADHD adolescent sample with and without reading disabilities. Journal of Attention Disorders 5, 155–164.Google Scholar
Ryan, M, Jacobson, LA, Hague, C, Bellows, A, Denckla, MB and Mahone, EM (2017) Rapid automatized naming (RAN) in children with ADHD: An ex-Gaussian analysis. Child Neuropsychology 23, 571–587.CrossRefGoogle ScholarPubMed
Sackeim, HA (2001) The definition and meaning of treatment-resistant depression. The Journal of Clinical Psychiatry 62 (Suppl 16), 10–17.Google ScholarPubMed
Sheehan, DV, Lecrubier, Y, Sheehan, KH, Amorim, P, Janavs, J, Weiller, E, Hergueta, T, Baker, R and 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. The Journal of Clinical Psychiatry 59(Suppl 20), 22–33;quiz 34–57.Google ScholarPubMed
Silverstein, MJ, Alperin, S, Faraone, SV, Kessler, RC and Adler, LA (2018) Test-retest reliability of the adult ADHD Self-Report Scale (ASRS) v1.1 Screener in non-ADHD controls from a primary care physician practice. Family Practice 35, 336–341.Google ScholarPubMed
Theiling, J and Petermann, F (2016) Neuropsychological profiles on the WAIS-IV of adults with ADHD. Journal of Attention Disorders 20, 913–924.CrossRefGoogle ScholarPubMed
Weigard, A and Huang-Pollock, C (2017) The role of speed in ADHD-related working memory deficits: A time-based resource-sharing and diffusion model account. Clinical Psychological Science 5, 195–211.CrossRefGoogle ScholarPubMed
Wiig, E, Nielsen, N, Minthon, L, Londos, E and Jacobson, J (2009) AQT Cognitive Speed: A Review of Behavioral Correlates and Temporal-Parietal Perfusion in Normal Adults and Patients with Dementia. Kerala, India: Research Signpost.Google Scholar
Wiig, E, Nielsen, N, Minthon, L and Warkentin, S (2002) A Quick Test of Cognitive Speed (AQT): Assessment of Parietal Function. San Antonio, TX: Psychological Cooperation.Google Scholar
Wiig, E, Nielsen, N, Minthon, L and Warkentin, S (2005) A Quick Test of Cognitive Speed (AQT). Et Kort Manual. Stockholm: Hartcourt/PsychCorp.Google Scholar
Wiig, EH, Annas, P, Basun, H, Andreasen, N, Lannfelt, L, Zetterberg, H, Blennow, K and Minthon, L (2010) The stability of AQT processing speed, ADAS-Cog and MMSE during acetylcholinesterase inhibitor treatment in Alzheimer’s disease. Acta Neurologica Scandinavica 121, 186–193.CrossRefGoogle ScholarPubMed
Wiig, EH and Nielsen, NP (2012) A quick test of cognitive speed for comparing processing speed to differentiate adult psychiatric referrals with and without attention-deficit/hyperactivity disorders. The Primary Care Companion for CNS Disorders 14, PCC.11m01273. doi: 10.4088/PCC.11m01273.Google ScholarPubMed
Wiig, EH, Nielsen, NP and Jacobson, JM (2007) A Quick Test of Cognitive Speed: patterns of age groups 15 to 95 years. Perceptual and Motor Skills 104, 1067–1075.CrossRefGoogle ScholarPubMed