Li Xue1,2, Junneng Shao1,2, Huan Wang1,2, Xinyi Wang1,2, Rongxin Zhu3,4, Zhijian Yao5,6, Qing Lu7,8. 1. School of Biological Science & Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, Jiangsu Province, 210096, China. 2. Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China. 3. Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China. 4. Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China. 5. Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China. zjyao@nju.edu.cn. 6. Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China. zjyao@nju.edu.cn. 7. School of Biological Science & Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, Jiangsu Province, 210096, China. luq@seu.edu.cn. 8. Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China. luq@seu.edu.cn.
Abstract
OBJECTIVES: Determining the clinical homogeneous and heterogeneous sets among depressive patients is the key to facilitate individual-level treatment decision. METHODS: The diffusion tensor imaging (DTI) data of 62 patients with major depressive disorder (MDD) and 39 healthy controls were used to construct a Latent Dirichlet Allocation (LDA) Bayesian model. Another 48 MDD patients were used to verify the robustness. The LDA model was employed to identify both shared and unique imaging-derived factors of two typically antidepressant-targeted depressive patients, selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Furthermore, we applied canonical correlation analysis (CCA) between each factor loading and Hamilton depression rating scale (HAMD) sub-score, to explore the potential neurophysiological significance of each factor. RESULTS: The results revealed the imaging-derived connectional fingerprint of all patients could be situated along three latent factor dimensions; such results were also verified by the out-of-sample dataset. Factor 1, uniquely expressed by SNRI-targeted patients, was associated with retardation (r = 0.4, p = 0.037) and characterized by coupling patterns between default mode network and cognitive control network. Factor 3, uniquely expressed by SSRI-targeted patients, was associated with cognitive impairment (r = 0.36, p = 0.047) and characterized by coupling patterns within cognitive control and attention network, and the connectivity between threat and reward network. Shared factor 2, characterized by coupling patterns within default mode network, was associated with anxiety (r = 0.54, p = 0.005) and sleep disturbance (r = 0.37, p = 0.032). CONCLUSIONS: Our findings suggested that quantification of both homogeneity and heterogeneity within MDD may have the potential to inform rational design of pharmacological therapies. KEY POINTS: • The shared and unique manifestations guiding pharmacotherapy of depressive patients are caused by the homogeneity and heterogeneity of underlying structural connections of the brain. • Both shared and unique factor loadings were found in different antidepressant-targeted patients. • Significant correlations between factor loading and HAMD sub-scores were found.
OBJECTIVES: Determining the clinical homogeneous and heterogeneous sets among depressive patients is the key to facilitate individual-level treatment decision. METHODS: The diffusion tensor imaging (DTI) data of 62 patients with major depressive disorder (MDD) and 39 healthy controls were used to construct a Latent Dirichlet Allocation (LDA) Bayesian model. Another 48 MDD patients were used to verify the robustness. The LDA model was employed to identify both shared and unique imaging-derived factors of two typically antidepressant-targeted depressive patients, selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Furthermore, we applied canonical correlation analysis (CCA) between each factor loading and Hamilton depression rating scale (HAMD) sub-score, to explore the potential neurophysiological significance of each factor. RESULTS: The results revealed the imaging-derived connectional fingerprint of all patients could be situated along three latent factor dimensions; such results were also verified by the out-of-sample dataset. Factor 1, uniquely expressed by SNRI-targeted patients, was associated with retardation (r = 0.4, p = 0.037) and characterized by coupling patterns between default mode network and cognitive control network. Factor 3, uniquely expressed by SSRI-targeted patients, was associated with cognitive impairment (r = 0.36, p = 0.047) and characterized by coupling patterns within cognitive control and attention network, and the connectivity between threat and reward network. Shared factor 2, characterized by coupling patterns within default mode network, was associated with anxiety (r = 0.54, p = 0.005) and sleep disturbance (r = 0.37, p = 0.032). CONCLUSIONS: Our findings suggested that quantification of both homogeneity and heterogeneity within MDD may have the potential to inform rational design of pharmacological therapies. KEY POINTS: • The shared and unique manifestations guiding pharmacotherapy of depressive patients are caused by the homogeneity and heterogeneity of underlying structural connections of the brain. • Both shared and unique factor loadings were found in different antidepressant-targeted patients. • Significant correlations between factor loading and HAMD sub-scores were found.
Authors: Xiuming Zhang; Elizabeth C Mormino; Nanbo Sun; Reisa A Sperling; Mert R Sabuncu; B T Thomas Yeo Journal: Proc Natl Acad Sci U S A Date: 2016-10-04 Impact factor: 11.205