Hasler, G; Northoff, G (2011). Discovering imaging endophenotypes for major depression. Molecular psychiatry, 16(6), pp. 604-19. Basingstoke: Nature Publishing Group 10.1038/mp.2011.23
Full text not available from this repository. (Request a copy)Psychiatry research lacks an in-depth understanding of mood disorders phenotypes, leading to limited success of genetics studies of major depressive disorder (MDD). The dramatic progress in safe and affordable magnetic resonance-based imaging methods has the potential to identify subtle abnormalities of neural structures, connectivity and function in mood disordered subjects. This review paper presents strategies to improve the phenotypic definition of MDD by proposing imaging endophenotypes derived from magnetic resonance spectroscopy measures, such as cortical gamma-amino butyric acid (GABA) and glutamate/glutamine concentrations, and from measures of resting-state activity and functional connectivity. The proposed endophenotypes are discussed regarding specificity, mood state-independence, heritability, familiarity, clinical relevance and possible associations with candidate genes. By improving phenotypic definitions, the discovery of new imaging endophenotypes will increase the power of candidate gene and genome-wide associations studies. It will also help to develop and evaluate novel therapeutic treatments and enable clinicians to apply individually tailored therapeutic approaches. Finally, improvements of the phenotypic definition of MDD based on neuroimaging measures will contribute to a new classification system of mood disorders based on etiology and pathophysiology.
Item Type: |
Journal Article (Further Contribution) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > University Psychiatric Services > University Hospital of Psychiatry and Psychotherapy > Healthcare Research |
UniBE Contributor: |
Hasler, Gregor |
ISSN: |
1359-4184 |
Publisher: |
Nature Publishing Group |
Language: |
English |
Submitter: |
Factscience Import |
Date Deposited: |
04 Oct 2013 14:22 |
Last Modified: |
05 Dec 2022 14:06 |
Publisher DOI: |
10.1038/mp.2011.23 |
PubMed ID: |
21602829 |
Web of Science ID: |
000290851800009 |
URI: |
https://boris.unibe.ch/id/eprint/7703 (FactScience: 213020) |
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