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11-02 Identifying affective markers within an integrative neuroscience model of depression

Published online by Cambridge University Press:  24 June 2014

D Mathersul,
A Kemp,
P Hopkinson and
E Gordon
D Mathersul
Affiliation:
The Brain Dynamics Centre, Westmead Millennium Institute, Westmead Hospital and Western Clinical School, University of Sydney, Australia
A Kemp
Affiliation:
The Brain Dynamics Centre, Westmead Millennium Institute, Westmead Hospital and Western Clinical School, University of Sydney, AustraliaPsychological Medicine, University of Sydney
P Hopkinson
Affiliation:
The Brain Dynamics Centre, Westmead Millennium Institute, Westmead Hospital and Western Clinical School, University of Sydney, AustraliaBrain Resource International Database, Brain Resource Company, Sydney, Australia
E Gordon
Affiliation:
The Brain Dynamics Centre, Westmead Millennium Institute, Westmead Hospital and Western Clinical School, University of Sydney, AustraliaBrain Resource International Database, Brain Resource Company, Sydney, Australia
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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. 338 - 339
Copyright
Copyright © 2006 Blackwell Munksgaard

Background:

Our integrative model of depression focuses on disturbances in affective and cognitive function, which contribute to clinical depression. Endophenotypes for depression include disturbances in emotion processing and in inhibitory executive functions. If these disturbances are trait like in nature, consistent with endophenotype status, we might expect them to be present in subclinical depression. Following a dimensional view of depression, our objective was to identify emotion perception and cognitive markers of subclinical depression while controlling for the effects of age and gender.

Method:

Subjects from the Brain Resource International Database (BRID) were tested on the standardized BRID protocols, which included assessment for level of depressed mood and performance on the computerized tests of social (facial emotion recognition) and general cognition.

Results:

Regression analyses showed that higher subclinical depression was significantly predicted by both social cognition (lower accuracy for recognizing fearful expressions, as well as slower reaction time for recognizing expressions of happiness and anger) and general cognition (reduced inhibition on the go-no-go test). These findings were observed over and above the effects of age and gender. When considered together, the combination of poor fear recognition and poor inhibition made the greatest contribution to level of depression.

Conclusions:

Our results provide support for an integrated model of depression, in which difficulties in both emotion processing and inhibition are defining features. Dysregulation of frontolimbic circuits may contribute to disturbed processing of salient signals of emotion and the inability to inhibit automatic responses.