Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment

• Published in: British medical bulletin Vol. 65; no. 1; pp. 193 - 207
• Main Author: Mayberg, Helen S
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.03.2003; Oxford Publishing Limited (England)
• Subjects: Algorithms; Brain - diagnostic imaging; Brain - metabolism; Brain - pathology; Cerebrovascular Circulation; Depression - diagnosis; Depression - metabolism; Depression - therapy; Humans; Magnetic Resonance Imaging; Radiography; Tomography, Emission-Computed; Tomography, Emission-Computed, Single-Photon
• ISSN: 0007-1420; 1471-8391; 1471-8391
• DOI: 10.1093/bmb/65.1.193

While characterization of pathogenetic mechanisms underlying major depression is a fundamental aim of neuroscience research, an equally critical clinical goal is to identify biomarkers that might improve diagnostic accuracy and guide treatment selection for individual patients. To this end, a synthesis of functional neuroimaging studies examining regional metabolic and blood flow changes in depression is presented in the context of a testable limbic-cortical network model. ‘Network’ dysfunction combined with active intrinsic compensatory processes is seen to explain the heterogeneity of depressive symptoms observed clinically, as well as variations in pretreatment scan patterns described experimentally. Furthermore, the synchronized modulation of these dysfunctional limbic-cortical pathways is considered critical for illness remission, regardless of treatment modality. Testing of response-specific functional relationships among regional ‘nodes’ within this network using multivariate approaches is discussed, with a perspective aimed at identifying biomarkers of treatment non-response, relapse risk and disease vulnerability. Characterization of adaptive and maladaptive functional interactions among these pathways is seen as a critical step towards future development of evidenced-based algorithms that will optimize the diagnosis and treatment of individual depressed patients.