Development of Neuroimaging-Based Biomarkers in Major Depression
- Authors
- Han, Kyu-Man; Ham, Byung-Joo; Kim, Yong-Ku
- Issue Date
- 2021
- Publisher
- SPRINGER-VERLAG SINGAPORE PTE LTD
- Keywords
- Major depressive disorder; Magnetic resonance imaging (MRI); Neural circuit; Emotion regulation; Reward processing; Treatment outcome
- Citation
- MAJOR DEPRESSIVE DISORDER: RETHINKING AND UNDERSTANDING RECENT DISCOVERIES, v.1305, pp.85 - 99
- Indexed
- SCIE
SCOPUS
- Journal Title
- MAJOR DEPRESSIVE DISORDER: RETHINKING AND UNDERSTANDING RECENT DISCOVERIES
- Volume
- 1305
- Start Page
- 85
- End Page
- 99
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/130195
- DOI
- 10.1007/978-981-33-6044-0_6
- ISSN
- 0065-2598
- Abstract
- A leading goal in the field of biological psychiatry for depression is to find a promising diagnostic biomarker and selection of specific psychiatric treatment mode that is most likely to benefit patients with depression. Recent neuroimaging studies have characterized the pathophysiology of major depressive disorder (MDD) with functional and structural alterations in the neural circuitry involved in emotion or reward processing. Particularly, structural and functional magnetic resonance imaging (MRI) studies have reported that the brain structures deeply involved in emotion regulation or reward processing including the amygdala, prefrontal cortex (PFC), anterior cingulate cortex (ACC), ventral striatum, and hippocampus are key regions that provide useful information about diagnosis and treatment outcome prediction in MDD. For example, it has been consistently reported that elevated activity of the ACC is associated with better antidepressant response in patients with MDD. This chapter will discuss a growing body of evidence that suggests that diagnosis or prediction of outcome for specific treatment can be assisted by a neuroimaging-based biomarker in MDD.
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Collections - College of Medicine > Department of Medical Science > 1. Journal Articles
- Graduate School > Department of Biomedical Sciences > 1. Journal Articles
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