Elsevier

Biological Psychiatry

Volume 63, Issue 7, 1 April 2008, Pages 656-662
Biological Psychiatry

Original Article
Pattern Classification of Sad Facial Processing: Toward the Development of Neurobiological Markers in Depression

https://doi.org/10.1016/j.biopsych.2007.08.020Get rights and content

Background

Methods of analysis that examine the pattern of cerebral activity over the whole brain have been used to identify and predict neurocognitive states in healthy individuals. Such methods may be applied to functional neuroimaging data in patient groups to aid in the diagnosis of psychiatric disorders and the prediction of treatment response. We sought to examine the sensitivity and specificity of whole brain pattern classification of implicit processing of sad facial expressions in depression.

Methods

Nineteen medication-free patients with depression and 19 healthy volunteers had been recruited for a functional magnetic resonance imaging (fMRI) study involving serial scans. The fMRI paradigm entailed incidental affective processing of sad facial stimuli with modulation of the intensity of the emotional expression (low, medium, and high intensity). The fMRI data were analyzed at each level of affective intensity with a support vector machine pattern classification method.

Results

The pattern of brain activity during sad facial processing correctly classified up to 84% of patients (sensitivity) and 89% of control subjects (specificity), corresponding to an accuracy of 86% (p < .0001). Classification of patients’ clinical response at baseline, prior to the initiation of treatment, showed a trend toward significance.

Conclusions

Significant classification of patients in an acute depressive episode was achieved with whole brain pattern analysis of fMRI data. The prediction of treatment response showed a trend toward significance due to the reduced power of the subsample. Such methods may provide the first steps toward developing neurobiological markers in psychiatry.

Section snippets

Subjects

Nineteen participants (13 women; age range: 29–58 years) meeting DSM-IV criteria for major depressive disorder (1) according to the Structured Clinical Interview for DSM-IV Axis I Disorders (22) and clinical interview with a psychiatrist were recruited through local newspaper advertisements. Inclusion criteria were an acute episode of major depressive disorder of the unipolar subtype and a score of at least 18 on the 17-item Hamilton Rating Scale for Depression (HRSD) (23). Exclusion criteria

Group Classification of Diagnosis

From the whole brain analysis, training with the individual scans for each affective intensity, at the highest intensity, 74% of the patients were correctly classified as belonging to the patient group and 63% of healthy comparison subjects were correctly assigned to the control group. The accuracy was 68% with a sensitivity of 74% and a specificity of 63% (Figure 1, Table 2). The probability of reaching such a level of accuracy by allocating subjects at random to either group is p = .017.

Discussion

The whole brain pattern of neural activity to implicit processing of sad facial expressions significantly distinguished individuals in an acute episode of depression from healthy individuals. Moreover, the prediction of those patients who achieved a full clinical remission following antidepressant therapy from those who had persistent symptoms reached a trend toward significance. The development of neurobiological diagnostic markers for depression and predictors of treatment response requires

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