Topics for Master Theses

Beschreibung: A multiverse analysis is a statistical approach that explores multiple analytical pathways for the same research question, making the variability of results across different analytical choices transparent. This method addresses the challenge of researcher degrees of freedom by systematically varying key assumptions and parameters to assess the robustness of findings. For EEG data sets, which are complex and sensitive to various preprocessing and analysis decisions, a multiverse analysis is particularly beneficial. It allows researchers to demonstrate how conclusions about brain activity might vary with different analytical strategies. This approach not only enhances the credibility and reproducibility of the findings but also helps to prevent publication of p-hacked results. The multiverse analysis will be performed on an EEG data set of a commonly used working memory task: the Sternberg task.

Literature
Master's Thesis Arne Hansen

Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no "fishing expedition" or "p-hacking" and the research hypothesis was posited ahead of time. Department of Statistics, Columbia University, 348(1-17), 3.

Sarma, A., Kale, A., Moon, M., Taback, N., Chevalier, F., Hullman, J., & Kay, M. (2021). multiverse: Multiplexing Alternative Data Analyses in R Notebooks [Preprint]. Open Science Framework. https://doi.org/10.31219/osf.io/yfbwm

Sternberg, S. (1966). High-Speed Scanning in Human Memory. Science, 153(3736):652-654

Kontakt: MSc Arne Hansen, E-Mail

Beschreibung: The hippocampus, a key region implicated in Alzheimer's disease (AD), comprises distinct subfields such as CA1, CA3, dentate gyrus, and subiculum. Emerging evidence suggests that specific subfields may show prominent outlier patterns in AD, reflecting varying levels of vulnerability. Furthermore, linking these patterns to cognitive decline and clinical severity could provide a deeper understanding of disease progression and heterogeneity.

This thesis focuses on deriving normative models for hippocampal subfield volumes using pre-extracted information from T1-weighted MRI scans and analyzing the deviation patterns obtained. The extensive data available from the UK Biobank will be used to establish a baseline for healthy aging, while deviations in these subfields will be examined in relation to AD using the OASIS-3 dataset. By comparing these baselines and deviations, the project will explore subfield-specific patterns of neurodegeneration and their potential links to cognitive decline and disease severity.

Further directions of the project include investigating sex differences in subfield vulnerability, examining brain asymmetry in atrophy patterns, and exploring the relationship between hippocampal subfield changes and AD subtypes. This study seeks to enhance our understanding of hippocampal subfield involvement in AD and its contribution to disease heterogeneity.

Literature:
La Joie, R., Perrotin, A., de La Sayette, V., Egret, S., Doeuvre, L., Belliard, S., et al. (2013). Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease, and semantic dementia. NeuroImage: Clinical, 3, 155-162. DOI: 10.1016/j.nicl.2013.08.007

Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016). Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies. Biological Psychiatry, 80(7), 552-61. DOI: 10.1016/j.biopsych.2015.12.023

Verdi, S., Marquand, A. F., Schott, J. M., & Cole, J. H. (2021). Beyond the average patient: How neuroimaging models can address heterogeneity in dementia. Brain, 144(10), 2946-2953. DOI: 10.1093/brain/awab165


Kontakt: MSc Camille Elleaume, E-Mail

Beschreibung: Background
Traditional diagnostic approaches in psychology (e.g., ICD-11 and DSM-V) often categorize disorders as isolated, independent constructs. In contrast, transdiagnostic approaches identify common features across different disorders, offering a more integrative perspective on mental health. These approaches create dimensions of interconnected mental disorders, which can help explain phenomena such as comorbidity, symptom overlap across diagnoses, and symptom variability within diagnoses. One example of such an approach is the Hierarchical Taxonomy of Psychopathology (HiTOP) model.

Objectives
This master's thesis seeks to apply a dimensional approach to investigate a broad range of psychopathologies in childhood and adolescence, with a focus on the relatedness of and intercorrelations among these psychopathologies. A key tool for this investigation could be the Child Behavior Checklist (CBCL), which includes various externalizing and internalizing symptoms. A thorough examination of the methodological possibilities for analyzing such data should be a core component of the thesis. A budding interest for statistical methods is favorable. The analyses will primarily focus on behavioral data (mainly questionnaire data), though the inclusion of additional measures, such as EEG or MRI data, may be possible if time allows.

Sample
The data are already available and must not be collected. The sample includes approximately 4500 participants from the United States, aged 5 to 21 years. Behavioral, psychiatric, neuroanatomical, demographic, and further data were collected. Target age group: childhood and adolescence.

Formalities
This Master's thesis must be written in English ? strong English language skills are a prerequisite. The project is designed to be completed within two semesters.

Further Reading
Alexander, L. M., Escalera, J., Ai, L., Andreotti, C., Febre, K., Mangone, A., Vega-Potler, N., Langer, N., Alexander, A., Kovacs, M., Litke, S., O'Hagan, B., Andersen, J., Bronstein, B., Bui, A., Bushey, M., Butler, H., Castagna, V., Camacho, N., ... Milham, M. P. (2017). 92 An open resource for transdiagnostic research in pediatric mental health and learning disorders. Scientific Data, 4(1), 170181. https://doi.org/10.1038/sdata.2017.181

Forbes, M. K., Ringwald, W. R., Allen, T., Cicero, D. C., Clark, L. A., DeYoung, C. G., Eaton, N., Kotov, R., Krueger, R. F., Latzman, R. D., Martin, E. A., Naragon-Gainey, K., Ruggero, C. J., Waldman, I. D., Brandes, C., Fried, E. I., Goghari, V. M., Hankin, B., Sperry, S., ... Wright, A. G. C. (2024a). Principles and procedures for revising the hierarchical taxonomy of psychopathology. Journal of Psychopathology and Clinical Science, 133(1), 4-19. https://doi.org/10.1037/abn0000886

Forbes, M. K., Watts, A. L., Twose, M., Barrett, A., Hudson, J. L., Lyneham, H. J., McLellan, L., Newton, N. C., Sicouri, G., Chapman, C., McKinnon, A., Rapee, R. M., Slade, T., Teesson, M., Markon, K., & Sunderland, M. (2024b). A hierarchical model of the symptom-level structure of psychopathology in youth. Clinical Psychological Science. Advance online publication. https://doi.org/10.1177/21677026241257852

Kotov, R., Krueger, R. F., Watson, D., Cicero, D. C., Conway, C. C., DeYoung, C. G., Eaton, N. R., Forbes, M. K., Hallquist, M. N., Latzman, R. D., Mullins-Sweatt, S. N., Ruggero, C. J., Simms, L. J., Waldman, I. D., Waszczuk, M. A., & Wright, A. G. C. (2021). The Hierarchical Taxonomy of Psychopathology (HiTOP): A Quantitative Nosology Based 108 on Consensus of Evidence. Annual Review of Clinical Psychology, 17(1), 83-108. https://doi.org/10.1146/annurev-clinpsy-081219-093304

Ringwald, W. R., Forbes, M. K., & Wright, A. G. C. (2023). Meta-analysis of structural evidence for the Hierarchical Taxonomy of Psychopathology (HiTOP) model. Psychological Medicine, 1-14. https://doi.org/10.1017/S0033291721001902
https://www.apa.org/depression-guideline/child-behavior-checklist.pdf

Kontakt: MSc Ines Engler, E-Mail

Beschreibung: There are many neuroimaging studies in autism and ADHD that characterize different biological aspects of the conditions. Combining simultaneously different neuroanatomical or neurofunctional measures is still limited and can provide a more complementary, informative picture of their underlying neurobiology. In this project, we will employ linked independent component analysis to study different neuroanatomical or neurofunctional aspects of brain organization and study the added value of such multimodal analyses.

Datasets: ABIDE, HBN, ADHD200, UKB, (EU-AIMS)

Methods: linked ICA on different Freesurfer derivatives (CT, SA, volume, ...) / resting-state fMRI metrics (SCA, ReHo, VMHC, fALFF, ...)

Literature:
1. https://elifesciences.org/articles/44443
2. https://www.nature.com/articles/s44220-024-00349-4
3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662460/

Kontakt: Dr. Dorothea Floris, E-Mail