Robust pipelines for extensive analyses of large genetic and brain imaging datasets linked to complex human behavior

The rapid technological and methodological advances in genetics, molecular biology and brain imaging in the last decades have enabled the current wide-spread application of brain-wide and genome-wide analyses of potential biological substrates of complex behavioral traits, such as psychological processes and psychiatric disorders. This thesis addresses methodological and statistical issues emerging from the large scale, complexity and explorative nature of brain-wide and genome-wide analyses. Furthermore, it points out additional steps for increasing the confidence in findings resulting from such extensive analyses. It does so by introducing two studies investigating the genetic bases of depressive symptoms and the brain imaging underpinnings of recognition memory performance, respectively. In the first study we aggregated genome-wide data of genetic variation to groups of genes and used inferential statistics to associate them with depressive symptoms. We also replicated the results in an independent sample and used imagining genetics to validate and extend our findings. In the second study, we decomposed the voxel-wise brain activation contrast of looking at previously seen vs. new pictures into 12 brain networks, based of which we evaluated recognition memory performance using prediction analysis. We used stable and reproducible data-driven decomposition and we trained and tested our prediction model in different samples, insuring higher generalizability of our findings. These two studies offer additional insight into the biological underpinnings of complex behavioral traits. Importantly, the applied analyses were carefully tailored to the specific research questions and integrated into robust pipelines for replication and validation of the initial results

Recognition memory performance can be estimated based on brain activation networks

Recognition memory is an essential ability for functioning in everyday life. Establishing robust brain networks linked to recognition memory performance can help to understand the neural basis of recognition memory itself and the interindividual differences in recognition memory performance.; We analysed behavioural and whole-brain fMRI data from 1'410 healthy young adults during the testing phase of a picture-recognition task. Using independent component analysis (ICA), we decomposed the fMRI contrast for previously seen vs. new (old-new) pictures into networks of brain activity. This was done in two independent samples (training sample: N = 645, replication sample: N = 665). Next, we investigated the relationship between the identified brain networks and interindividual differences in recognition memory performance by conducting a prediction analysis. We estimated the prediction accuracy in a third independent sample (test sample: N = 100).; We identified 12 robust and replicable brain networks using two independent samples. Based on the activity of those networks we could successfully estimate interindividual differences in recognition memory performance with high accuracy in a third independent sample (r = 0.5, p = 1.29 × 10; -07; ).; Given the robustness of the ICA decomposition as well as the high prediction estimate, the identified brain networks may be considered as potential biomarkers of recognition memory performance in healthy young adults and can be further investigated in the context of health and disease

The NCAM1 gene set is linked to depressive symptoms and their brain structural correlates in healthy individuals

Depressive symptoms exist on a continuum, the far end of which is found in depressive disorders. Utilizing the continuous spectrum of depressive symptoms may therefore contribute to the understanding of the biological underpinnings of depression. Gene set enrichment analysis (GSEA) is an important tool for the identification of gene groups linked to complex traits, and was applied in the present study on genome-wide association study (GWAS) data of depression scores and their brain-level structural correlates in healthy young individuals. On symptom level (i.e. depression scores), robust enrichment was identified for two gene sets: NCAM1 Interactions and Collagen Formation. Depression scores were also associated with decreased fractional anisotropy (FA) - a brain white matter property - within the forceps minor and the left superior temporal longitudinal fasciculus. Within each of these tracts, mean FA value of depression score-associated voxels was used as a phenotype in a subsequent GSEA. The NCAM1 Interactions gene set was significantly enriched in these tracts. By linking the NCAM1 Interactions gene set to depression scores and their structural brain correlates in healthy participants, the current study contributes to the understanding of the molecular underpinnings of depressive symptomatology

Exome sequencing of healthy phenotypic extremes links TROVE2 to emotional memory and PTSD

Heck A, Milnik A, Vukojevic V, et al. Exome sequencing of healthy phenotypic extremes links TROVE2 to emotional memory and PTSD. Nature Human Behaviour. 2017;1(4): 0081

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Abstract Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic