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Wagging the black dog: predicting depression severity using neuropsychological measures

Published online by Cambridge University Press:  24 June 2014

P Hopkinson,
A Kemp,
E Gordon,
R Clarke,
P Boyce and
L Williams
P Hopkinson
Affiliation:
Brain Dynamics CentreBrain Resource Company
A Kemp
Affiliation:
Brain Dynamics Centre
E Gordon
Affiliation:
Brain Resource Company
R Clarke
Affiliation:
Flinders University of South Australia
P Boyce
Affiliation:
Psychological Medicine, Westmead Hospital, Australia
L Williams
Affiliation:
Brain Dynamics Centre
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Abstract

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Information

Type
Abstracts from ‘Brainwaves’— The Australasian Society for Psychiatric Research Annual Meeting 2006, 6–8 December, Sydney, Australia
Information
Acta Neuropsychiatrica , Volume 18 , Issue 6 , December 2006, pp. 255
Copyright
Copyright © 2006 Blackwell Munksgaard

Background:

Depression poses an enormous burden on both the individual and the community. However, relatively little is known about the mechanisms that underpin the disorder. Core neuropsychological domains include memory, executive, sensorimotor, attention and verbal functions. However, the conceptualization of depression usually involves the implementation of discrete variables. We decided to integrate these core neuropsy-chological domains to predict depression severity.

Methods:

Fifty patients clinically diagnosed with major depressive disorder and 200 age- and sex-matched controls undertook a neuropsychological test battery. A regression analysis was carried out to predict depression severity, as indexed by scores on the Hamilton Rating Scale for Depression-17 and Depression, Anxiety and Stress Scales.

Results:

Preliminary regression analyses show that an integration of neuropsychological indexes from the core domains predicted depression severity. Statistically significant interactions between these variables also predicted depression severity.

Conclusions:

We showed that integrating theoretically relevant neuropsychological variables such as senso-rimotor and verbal functions provided valuable insight into the understanding and prediction of depression severity. These findings offer insight into the endo-phenotypic nature of major depressive disorder. Future studies could implement similar methodology for the prediction of treatment response in depression.