Format

Send to

Choose Destination
Neurosci Biobehav Rev. 2018 Jan;84:272-288. doi: 10.1016/j.neubiorev.2017.08.019. Epub 2017 Aug 30.

Treatment resistant depression: A multi-scale, systems biology approach.

Author information

1
Depression Task Force, Hope for Depression Research Foundation, New York, NY 10019, United States; University of Michigan, United States.
2
Depression Task Force, Hope for Depression Research Foundation, New York, NY 10019, United States; Columbia University, United States; New York State Psychiatric Institute, United States.
3
Depression Task Force, Hope for Depression Research Foundation, New York, NY 10019, United States; Emory University, United States.
4
Depression Task Force, Hope for Depression Research Foundation, New York, NY 10019, United States; Rockefeller University, United States.
5
Depression Task Force, Hope for Depression Research Foundation, New York, NY 10019, United States; McGill University, United States; Singapore Institute for Clinical Science, Singapore.
6
Depression Task Force, Hope for Depression Research Foundation, New York, NY 10019, United States; Icahn School of Medicine at Mount Sinai, United States. Electronic address: eric.nestler@mssm.edu.

Abstract

An estimated 50% of depressed patients are inadequately treated by available interventions. Even with an eventual recovery, many patients require a trial and error approach, as there are no reliable guidelines to match patients to optimal treatments and many patients develop treatment resistance over time. This situation derives from the heterogeneity of depression and the lack of biomarkers for stratification by distinct depression subtypes. There is thus a dire need for novel therapies. To address these known challenges, we propose a multi-scale framework for fundamental research on depression, aimed at identifying the brain circuits that are dysfunctional in several animal models of depression as well the changes in gene expression that are associated with these models. When combined with human genetic and imaging studies, our preclinical studies are starting to identify candidate circuits and molecules that are altered both in models of disease and in patient populations. Targeting these circuits and mechanisms can lead to novel generations of antidepressants tailored to specific patient populations with distinctive types of molecular and circuit dysfunction.

KEYWORDS:

Amygdala; ChIP-sequencing; Epigenetics; GWAS; Gene expression; Hippocampus; Major depressive disorder; Neural circuits; Nucleus accumbens; Prefrontal cortex; RNA-sequencing

PMID:
28859997
PMCID:
PMC5729118
DOI:
10.1016/j.neubiorev.2017.08.019
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center