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Depressive disorders represent one of the main sources for the loss of healthy years of life. One of the reasons for this circumstance is the recurrent course of these disorders, which can be interrupted by current therapeutic approaches, especially in the shortterm, but seem to be maintained at least in part in the long-term. Subsequently, on one hand, this thesis deals with methodological measurement issues in the longitudinal prediction of depressive courses. On the other hand, it addresses two currently discussed neuroscience-based treatment approaches, which are investigated experimentally in a basic-psychological manner and reviewed in the light of their potential to translate results to the application in patient care. These two approaches each address potential mechanisms that may negatively impact long-term disease trajectories: First, stable endophenotypes for vulnerability factors that could regain control over the organism and reactivate maladaptive experiences, or behaviors with increasing temporal distance from therapeutic methods are focused on. In the studies presented, these were influenced by a recently rediscovered method of neuromodulation (transcranial low-intensity focused ultrasound) which is discussed in light of its unique capability to address even deepest, subcortical regions at a high spatial resolution. Lastly, as a second approach, an experimental design for the use of reconsolidation interference is presented, which could provide a first insight into the applicability of corresponding protocols in the field of depressive disorders and thus contribute to the modification, instead of inhibition, of already mentioned endophenotypes. In sum, methodological considerations for monitoring and predicting long-term courses of depression are deducted before two approaches are discussed that could potentially exert positive influences on the recurrent nature of depressive symptoms on their own, in combination with each other, or as augmentation for existing therapeutic procedures.
Objective
Alzheimer’s disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of composite variables to assess properties of cognitive domains, compensation mechanisms, and their predictability to establish a method for a more comprehensive understanding of pathological cognitive decline.
Methods
An exploratory factor analysis (EFA) and a set of increasingly restricted confirmatory factor analyses (CFAs) were conducted to find latent factors, compared them with the composite approach, and to test for longitudinal (partial-)MI in a neuropsychiatric test battery, consisting of 14 test variables. A total of 330 elderly (mean age: 73.78 ± 1.52 years at baseline) were analyzed two times (3 years apart).
Results
EFA revealed a four-factor model representing declarative memory, attention, working memory, and visual–spatial processing. Based on CFA, an accurate model was estimated across both measurement timepoints. Partial non-MI was found for parameters such as loadings, test- and latent factor intercepts as well as latent factor variances. The latent factor approach was preferable to the composite approach.
Conclusion
The overall assessment of non-MI latent factors may pose a possible target for this field of research. Hence, the non-MI of variances indicated variables that are especially suited for the prediction of pathological cognitive decline, while non-MI of intercepts indicated general aging-related decline. As a result, the sole assessment of MI may help distinguish pathological from normative aging processes and additionally may reveal compensatory neuropsychological mechanisms.