Abstract
The objective of our study was to elucidate distinct paths to depression in a model that incorporates age, measures of medical comorbidity, neuroanatomical compromise, and cognitive status in a sample of patients with late-life major depressive disorder (MDD) and nondepressed controls. Our study was cross-sectional in nature and utilized magnetic resonance imaging (MRI) estimates of brain and high-intensity lesion volumes together with clinical indices of cerebrovascular and nonvascular medical comorbidity. Neuroanatomic and clinical measures were incorporated into a structural covariance model in order to test pathways to MDD. Our data indicate that there are two paths to MDD; one path is represented by vascular and nonvascular medical comorbidity that contribute to high-intensity lesions that lead to depression. Smaller brain volumes represent a distinct path to the mood disorder. Age influences depression by increasing atrophy and overall medical comorbidity but has no direct impact on MDD. These findings demonstrate that there are distinct biological substrates to the neuroanatomical changes captured on MRI. These observations further suggest that neurobiological mechanisms acting in parallel may compromise brain structure/function, thereby predisposing individuals to clinical brain disorders such as depression.
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Acknowledgements
Supported by NIMH grants MH 55115 and MH61567 (AK) and NIMH grant MH 52129 (Clinical Research Center for Depression in Late Life). Presented in Part at the Annual Meeting of the Society for Neuroscience, New Orleans, Oct 2000
STATISTICAL ENDNOTE
The statistical methodology of path analysis remains relatively unfamiliar in the neurosciences. It is an extension of regression analysis that attempts to separate causal influences among a set of variables into direct and indirect (or mediating) pathways. Path analysis makes it possible to model more complex systems than is possible with usual multiple regression models that include only a single dependent variable. Path models typically include intervening variables, often at multiple causal levels, and may omit paths presumed to be irrelevant. Using the methods of structural equation modeling (SEM), overall evaluation of an entire postulated system of causal relations is possible.
Multiple logistic regression analysis, as employed in a previous report of this study (Kumar et al. 2000), evaluates the significance of direct associations from a set of predictor variables to the occurrence of depression. Path analysis, as employed in this report, makes it possible to simultaneously evaluate the statistical plausibility of the entire set of posited causal relationships. As depicted in the main Figure 1, this is a multilevel model that includes both direct and indirect causal paths. The goodness-of-fit χ2 tests the deviation of the data from the model. When significant, it indicates that the data do not fit the posited causal model. Thus, a nonsignificant goodness-of-fit χ2 is desirable, in that it indicates that the posited causal model is statistically plausible in light of the observed data. Of course, the fact that a model is plausible does not guarantee that it is correct. It is not at all uncommon for several alternative models to “fit” the data (as occurred in this case).
The Mplus software makes it possible to simultaneously evaluate multistage models including both continuous and dichotomous dependent variables. In the former case, the path (regression) coefficients are conventional linear regression coefficients, indicating the expected change in the dependent variable for a unit change in the independent variable. In the case of dichotomous dependent variables (e.g., depression in the current analyses), the coefficients are probit coefficients. As such, they represent the change in the probability of “caseness” associated with a unit change in the independent variable. The model underlying the probit portion of the analysis is that the likelihood of observing the outcome (depression in this case) is based on one's position on a hypothetical, unobserved normal curve. The probit equation estimates a linear combination of predictors to predict the z-score representing one's position on that curve and thus the probability of being a case.
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Kumar, A., Mintz, J., Bilker, W. et al. Autonomous Neurobiological Pathways to Late-Life Major Depressive Disorder: Clinical and Pathophysiological Implications. Neuropsychopharmacol 26, 229–236 (2002). https://doi.org/10.1016/S0893-133X(01)00331-1
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DOI: https://doi.org/10.1016/S0893-133X(01)00331-1
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