Patients with major depressive disorder alters dorsal medial prefrontal cortex response to anticipation with different saliences

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Abstract

Patients with major depressive disorder (MDD) show an impaired ability to modulate emotional states and deficits in processing emotional information. Many studies in healthy individuals showed a major involvement of the medial prefrontal cortex (MPFC) in the modulation of emotional processing. Recently, we used emotional expected pictures with high or low salience in a functional MRI (fMRI) paradigm to study altered modulation of MPFC in MDD patients and to explore the neural correlates of pathological cognitive bias, and investigated the effect of symptom severity on this functional impairment. Data were obtained from 18 healthy subjects and 18 MDD patients, diagnosed according to the ICD-10 criteria. Subjects lay in a 3 T Siemens Trio scanner while viewing emotional pictures, which were randomly preceded by an expectancy cue in 50% of the cases. Our study showed a lower effect of salience on dorsal MPFC (DMPFC) activation during anticipatory preparation in depressed subjects. Differential effects for high salient versus low salient pictures viewing were also found in DMPFC in depressed and healthy subjects, and this effect was significantly higher for depressed subjects and correlated positively with Hamilton rating scale for depression (HAMD) scores. Comparing the anticipation effect on subsequent picture viewing between high and low salient pictures, differential effects were higher for depressed subjects during high salient picture viewing and lower during low salient picture viewing. Therefore, we could shed light on DMPFC functioning in depressive disorder by separating cognitive and consumatory effects in this region.

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Zhang, B., & Wang, J. (2017). Patients with major depressive disorder alters dorsal medial prefrontal cortex response to anticipation with different saliences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10654 LNAI, pp. 171–180). Springer Verlag. https://doi.org/10.1007/978-3-319-70772-3_16

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