@phdthesis{EttlingergebHaberstumpf2023,
  author    = {Ettlinger [geb. Haberstumpf], Sophia},
  title     = {Pathological cognitive decline in the elderly participants of the Vogel Study},
  doi       = {10.25972/OPUS-26558},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-265582},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Due to the global aging society and the enormous global incidence and prevalence rates that will result in the coming years, Alzheimer's Dementia (AD) represents a growing challenge for the health care system. The pathogenesis, which is unclear in parts, the chronic progression of AD, which often lasts for years, as well as insufficient diagnostic and therapeutic options complicate an adequate psychotherapeutic and medical approach to the disease. To date, AD is also considered an incurable disease. Therefore, it is essential to gain deeper insights into the early detection or even prevention of AD. Consideration of prodromal syndromes such as Mild Cognitive Impairment (MCI) can provide significant evidence about high-risk groups for AD progression and differentiate cognitively "normal" aging individuals from those with pathological cognitive decline. Thus, for example, functional Near-Infrared Spectroscopy (fNIRS) imaging helps identify early neurodegenerative processes. In contrast, potential risk factors and predictors of later-onset clinical symptoms of MCI and AD can most often be revealed and quantified via the use of neuropsychiatric test batteries. The present thesis consists of four studies and aimed to assess and describe the pathological cognitive decline in a sample of elderly study participants (age: ≥ 70 years; N = 604 at baseline) of the longitudinal, observational, and prospective "Vogel Study" from W{\"u}rzburg, Germany, who were primarily healthy at baseline, over two measurement time points approximately 3 years apart, to differentiate between healthy and diseased study participants and to define predictors of MCI/AD and longitudinal study dropout. Studies 1 and 2 differentiated healthy study participants from MCI patients based on the baseline hemodynamic response of the parietal cortex recorded by fNIRS during the processing of a paradigm (here: Angle Discrimination Task [ADT]) for visual-spatial processing performance. Neuronal hypoactivity was found in the MCI patients, with both healthy study participants and MCI patients showing higher superior and right hemispheric activation. MCI patients had more difficulty resolving the paradigm. Thus, no evidence of possible compensatory mechanisms was uncovered in the MCI patients. Study 3 first defined the four latent factors declarative memory, working memory, attention, and visual-spatial processing based on structural equation model (SEM) calculations of the sample using adequate measurement (in-)variant confirmatory factor models from the baseline assessment to the first of a total of two follow-up assessments after approximately 3 years. This allowed a dimensional assessment of pathological cognitive decline versus classificatory-categorical assignment (healthy/diseased) of the sample. In addition, the superiority of the latent factor approach over a composite approach was demonstrated. Next, using a mixed-model approach, predictive analyses were calculated for the prediction of latent factors at first follow-up by baseline risk factors. The sex of study participants proved to be the best predictor of cognitive change in all the cognitive domains, with females performing better than men in the memory domains. Specifically, for declarative memory, older age predicted lower performance regardless of sex. Additional predictive evidence emerged for low serum levels of Brain-Derived Neurotrophic Factor (BDNF) on lower attention performance and higher depression symptoms on lower visual-spatial processing performance. Study 4 further reported baseline predictors of study dropout at first follow-up. Cognitive performance, as defined in Study 3 using the four latent cognitive factors, was a predictor of study dropout for cognitive decline in the domains of declarative memory, attention, and visual-spatial processing. Conspicuous dementia screening on the Mini-Mental Status Examination (MMSE) also predicted dropout. Overall, both the use of fNIRS imaging to detect visual-spatial processing performance in the parietal cortex during applying ADT and the dimensional perspective of the neuropsychiatric test battery in the context of prediction and dropout analyses were found to be suitable for early detection research of MCI and AD. Finally, the results will be interpreted in the overall context and implications, limitations, and perspectives will be discussed.},
  language  = {en}
}
@article{HaberstumpfForsterLeinweberetal.2022,
  author    = {Haberstumpf, Sophia and Forster, Andr{\´e} and Leinweber, Jonas and Rauskolb, Stefanie and Hewig, Johannes and Sendtner, Michael and Lauer, Martin and Polak, Thomas and Deckert, J{\"u}rgen and Herrmann, Martin J.},
  title     = {Measurement invariance testing of longitudinal neuropsychiatric test scores distinguishes pathological from normative cognitive decline and highlights its potential in early detection research},
  series = {Journal of Neuropsychology},
  volume    = {16},
  journal   = {Journal of Neuropsychology},
  number    = {2},
  doi       = {10.1111/jnp.12269},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-318932},
  pages     = {324 -- 352},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{HaberstumpfLeinweberLaueretal.2022,
  author    = {Haberstumpf, Sophia and Leinweber, Jonas and Lauer, Martin and Polak, Thomas and Deckert, J{\"u}rgen and Herrmann, Martin J.},
  title     = {Factors associated with dropout in the longitudinal Vogel study of cognitive decline},
  series = {The European Journal of Neuroscience},
  volume    = {56},
  journal   = {The European Journal of Neuroscience},
  number    = {9},
  doi       = {10.1111/ejn.15446},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-318945},
  pages     = {5587 -- 5600},
  year      = {2022},
  abstract  = {Dementia, including Alzheimer's disease, is a growing problem worldwide. Prevention or early detection of the disease or a prodromal cognitive decline is necessary. By means of our long-term follow-up 'Vogel study', we aim to predict the pathological cognitive decline of a German cohort (mean age was 73.9 ± 1.55 years at first visit) with three measurement time points within 6 years per participant. Especially in samples of the elderly and subjects with chronic or co-morbid diseases, dropouts are one of the biggest problems of long-term studies. In contrast to the large number of research articles conducted on the course of dementia, little research has been done on the completion of treatment. To ensure unbiased and reliable predictors of cognitive decline from study completers, our objective was to determine predictors of dropout. We conducted multivariate analyses of covariance and multinomial logistic regression analyses to compare and predict the subject's dropout behaviour at the second visit 3 years after baseline (full participation, partial participation and no participation/dropout) with neuropsychiatric, cognitive, blood and lifestyle variables. Lower performance in declarative memory, attention and visual-spatial processing predicted dropout rather than full participation. Lower performance in visual-spatial processing predicted partial participation as opposed to full participation. Furthermore, lower performance in mini-mental status examination predicted whether subjects dropped out or participated partially instead of full participation. Baseline cognitive parameters are associated with dropouts at follow-up with a loss of impaired participants. We expect a bias into a healthier sample over time.},
  language  = {en}
}