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GABAergic dysfunction in mood disorders.

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Title: GABAergic dysfunction in mood disorders.
Authors: Brambilla, P, Perez, J, Barale, F, Schettini, G, Soares, J C
Source: Molecular Psychiatry. 2003, Vol. 8 Issue 8, p721-737. 17p.
Subjects: Affective disorders, GABA, Neural transmission, Central nervous system, Electroconvulsive therapy
Abstract: The authors review the available literature on the preclinical and clinical studies involving GABAergic neurotransmission in mood disorders. γ-Aminobutyric acid (GABA) is an inhibitory neurotransmitter present almost exclusively in the central nervous system (CNS), distributed across almost all brain regions, and expressed in interneurons modulating local circuits. The role of GABAergic dysfunction in mood disorders was first proposed 20 years ago. Preclinical studies have suggested that GABA levels may be decreased in animal models of depression, and clinical studies reported low plasma and CSF GABA levels in mood disorder patients. Also, antidepressants, mood stabilizers, electroconvulsive therapy, and GABA agonists have been shown to reverse the depression-like behavior in animal models and to be effective in unipolar and bipolar patients by increasing brain GABAergic activity. The hypothesis of reduced GABAergic activity in mood disorders may complement the monoaminergic and serotonergic theories, proposing that the balance between multiple neurotransmitter systems may be altered in these disorders. However, low GABAergic cortical function may probably be a feature of a subset of mood disorder patients, representing a genetic susceptibility. In this paper, we discuss the status of GABAergic hypothesis of mood disorders and suggest possible directions for future preclinical and clinical research in this area. [ABSTRACT FROM AUTHOR]
Copyright of Molecular Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: The authors review the available literature on the preclinical and clinical studies involving GABAergic neurotransmission in mood disorders. γ-Aminobutyric acid (GABA) is an inhibitory neurotransmitter present almost exclusively in the central nervous system (CNS), distributed across almost all brain regions, and expressed in interneurons modulating local circuits. The role of GABAergic dysfunction in mood disorders was first proposed 20 years ago. Preclinical studies have suggested that GABA levels may be decreased in animal models of depression, and clinical studies reported low plasma and CSF GABA levels in mood disorder patients. Also, antidepressants, mood stabilizers, electroconvulsive therapy, and GABA agonists have been shown to reverse the depression-like behavior in animal models and to be effective in unipolar and bipolar patients by increasing brain GABAergic activity. The hypothesis of reduced GABAergic activity in mood disorders may complement the monoaminergic and serotonergic theories, proposing that the balance between multiple neurotransmitter systems may be altered in these disorders. However, low GABAergic cortical function may probably be a feature of a subset of mood disorder patients, representing a genetic susceptibility. In this paper, we discuss the status of GABAergic hypothesis of mood disorders and suggest possible directions for future preclinical and clinical research in this area. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Molecular Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 2003
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