Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment
Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment
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DOI：
10.1093/bmb/65.1.193
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发表时间：
2003-01-01
影响因子：
6.7
通讯作者：
Mayberg, HS
中科院分区：
文献类型：
--
作者：
Mayberg, HS
虽然重性抑郁症发病机制的表征是神经科学研究的基本目标，但同样重要的临床目标是确定可能提高诊断准确性并指导个体患者治疗选择的生物标志物。为此，功能性神经影像学研究的综合检查区域代谢和血流变化的抑郁症的背景下，可测试的边缘皮层网络模型。“网络”功能障碍结合积极的内在代偿过程被认为是解释临床观察到的抑郁症状的异质性，以及实验描述的预处理扫描模式的变化。此外，这些功能障碍的边缘-皮质通路的同步调节被认为是疾病缓解的关键，无论治疗方式如何。测试响应特定的功能之间的关系，在这个网络中使用多变量的方法进行了讨论，目的是确定治疗无反应，复发风险和疾病的脆弱性的生物标志物的角度。这些通路之间的适应性和适应不良功能相互作用的表征被视为未来发展基于证据的算法的关键一步，该算法将优化个体抑郁症患者的诊断和治疗。
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.