Biology subjects

Loeffler, C.

Publications and source records attributed to Loeffler, C..

5 recordsLinked to original sources

bioRxiv2025

Task-specific theta enhancement and domain-general alpha/beta suppression as oscillatory signatures of individual differences in cognitive flexibility

Cognitive flexibility--the ability to adapt to changing situational or task demands--is a fundamental aspect of human cognition and a potential source of individual differences in higher-order cognitive abilities. To further understand the neural mechanisms underlying this ability, we examined event-related spectral perturbations (ERSP) in three cued switching tasks (parity/magnitude, global/local, number/letter) in a sample of 148 adults. Employing a data-driven approach that combined massunivariate time-frequency analysis, cluster-based permutation testing, and latent change structural equation modeling, we identified two robust neural signatures: a transient frontal theta increase and a sustained alpha/beta suppression. Both effects were more pronounced in switch compared to repeat trials. Frontal theta reflected task-specific control processes, whereas parietal alpha/beta indexed domaingeneral attentional processes that persisted from cue onset through target processing--consistent with two stage-models of task switching. Notably, flexibility-related alpha/beta dynamics did not correlate with measures of intelligence or working memory capacity (WMC), underscoring the distinctiveness of cognitive flexibility from general cognitive abilities. These findings provide the first direct evidence that alpha/beta suppression constitutes a reliable, generalizable neural signature of individual differences in cognitive flexibility.

neuroscience↗

bioRxiv2019

Pan-cancer image-based detection of clinically actionable genetic alterations

Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular biology assays.1 These tests can be a bottleneck in oncology workflows because of high turnaround time, tissue usage and costs.2 Here, we show that deep learning can predict point mutations, molecular tumor subtypes and immune-related gene expression signatures3,4 directly from routine histological images of tumor tissue. We developed and systematically optimized a one-stop-shop workflow and applied it to more than 4000 patients with breast5, colon and rectal6, head and neck7, lung8,9, pancreatic10, prostate11 cancer, melanoma12 and gastric13 cancer. Together, our findings show that a single deep learning algorithm can predict clinically actionable alterations from routine histology data. Our method can be implemented on mobile hardware14, potentially enabling point-of-care diagnostics for personalized cancer treatment in individual patients.

genetics↗

bioRxiv2019

The Electrophysiological Signature of Mind Wandering

Mind wandering during ongoing tasks can impede task performance and increase the risk of failure in laboratory as well as in daily-life tasks and work environments. Neurocognitive measures like the electroencephalography (EEG) offer the opportunity to assess mind wandering non-invasively without interfering with the primary task. However, the literature on electrophysiological correlates of mind wandering is rather inconsistent. The present study aims towards clarifying this picture by breaking down the temporal dynamics of mind-wandering encounters using a cluster-based permutation approach. Participants performed a switching task during which mind wandering was occasionally assessed via thought probes applied after trial completion at random time points. In line with previous studies, response accuracy was reduced during mind wandering. Moreover, alpha power during the inter-trial interval was significantly increased on those trials on which participants reported that they had been mind-wandering. This spatially widely distributed effect is theoretically well in line with recent findings linking an increased alpha power to an internally oriented state of attention. Measurements of alpha power may therefore be used to detect mind wandering online during critical tasks in traffic and industry in order to prevent failures.

neuroscience↗

bioRxiv2019

Deep learning detects virus presence in cancer histology

Oncogenic viruses like human papilloma virus (HPV) or Epstein Barr virus (EBV) are a major cause of human cancer. Viral oncogenesis has a direct impact on treatment decisions because virus-associated tumors can demand a lower intensity of chemotherapy and radiation or can be more susceptible to immune check-point inhibition. However, molecular tests for HPV and EBV are not ubiquitously available.\n\nWe hypothesized that the histopathological features of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial intelligence (AI) through deep learning-based analysis of images from routine hematoxylin and eosin (HE) stained slides. We show that deep transfer learning can predict presence of HPV in head and neck cancer with a patient-level 3-fold cross validated area-under-the-curve (AUC) of 0.89 [0.82; 0.94]. The same workflow was used for Epstein-Barr virus (EBV) driven gastric cancer achieving a cross-validated AUC of 0.80 [0.70; 0.92] and a similar performance in external validation sets. Reverse-engineering our deep neural networks, we show that the key morphological features can be made understandable to humans.\n\nThis workflow could enable a fast and low-cost method to identify virus-induced cancer in clinical trials or clinical routine. At the same time, our approach for feature visualization allows pathologists to look into the black box of deep learning, enabling them to check the plausibility of computer-based image classification.

cancer biology↗

bioRxiv2018

Analysis of multiple fungal sequence repositories highlights shortcomings in microbial databases

Metagenomics studies leverage genomic reference databases to generate discoveries in basic science and translational research. However, current microbial studies use disparate reference databases that lack consistent standards of specimen inclusion, data preparation, taxon labelling and accessibility, hindering their quality and comprehensiveness, and calling for the establishment of recommendations for reference genome database assembly. Here, we analyze existing fungal and bacterial databases and discuss guidelines for the development of a master reference database that promises to improve the quality and quantity of omics research.

bioinformatics↗