Biology subjects

Becker, M.

Publications and source records attributed to Becker, M..

6 recordsLinked to original sources

bioRxiv2018

3-D navigation: A core driver of brain plasticity in video game interventions

Recent evidence has repeatedly shown that 3D platform video game training leads to substantial brain structural plasticity in hippocampus, prefrontal cortex and cerebellum. However, a great disadvantage of using complex video game interventions is the difficulty to attribute the observed effects to specific game mechanics.\n\nIn order to address this caveat, we conducted a longitudinal training study in which 40 participants were randomly assigned to train with a 3D platformer game or a 2D platformer game. The main difference between the two games lies within their affordance for spatial exploration. After a training phase of two months, we observed extended brain structural increases in the 3D in comparison to the 2D condition in bilateral prefrontal areas, hippocampus/ entorhinal cortex as well as precuneus and the temporal lobe. In the reverse contrast an increase in bilateral caudate nucleus was observed.\n\nThe results demonstrate a crucial role of 3D spatial navigation for widespread brain plasticity effects within a two-months training setup. Given the vast complexity of video games, spatial navigation seems to play an outstanding role in structural plasticity. Since prefrontal, temporal and hippocampal volume deficits are prominent risk factors for several psychiatric disorders, daily navigation habits (outdoor movement, using GPS devices etc.) have to be considered in future mental disorders prevention research.

neuroscience↗

bioRxiv2018

Influence of nutritional tyrosine on cognition and functional connectivity in healthy old humans

Tyrosine is precursor for monoamine neurotransmitters such as dopamine (DA), which is one of the key neurotransmitters in the frontostriatal network and of crucial relevance for mental disorders. Recent research reported that high dose tyrosine application resulted in increased brain DA synthesis, which is consistent with the observation of positive associations between daily tyrosine intake and cognitive test performance. In the present study, we investigated the associations between working memory (WM) dependent tasks and self-reported nutritional tyrosine intake within a large group of healthy elderly humans (286 subjects) by additionally including brain functional data. We observed a negative correlation between tyrosine intake and resting-state functional connectivity (rsFC) between the striatum (putamen) and the prefrontal cortex. That is to say, we found higher rsFC in individuals consuming less tyrosine per day. At the same time, this increased rsFC or hyperconnectivity was associated with lower WM performance. These findings suggest that lower or insufficient supply of tyrosine might result in dysfunctional connectivity between striatal and frontal regions leading to lower WM capacity in healthy elderly humans.

neuroscience↗

bioRxiv2018

Diagnostic value of blood gene expression-based classifiers as exemplified for acute myeloid leukemia

ABSTRACTAcute Myeloid Leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. Despite nearly two decades of promising results using gene expression profiling, international recommendations for diagnosis and differential diagnosis of AML remain based on classical approaches including assessment of morphology, immunophenotyping, cytochemistry, and cytogenetics. Concerns about the translation of whole transcriptome profiling include the robustness of derived predictors when taking into account factors such as study- and site-specific effects and whether achievable levels of accuracy are sufficient for practical use. In the present study, we sought to shed light on these issues via a large-scale analysis using machine learning methods applied to a total of 12,029 samples from 105 different studies. Taking advantage of the breadth of data and the now much improved understanding of high-dimensional modeling, we show that AML can be predicted with high accuracy. High-dimensional approaches - in which multivariate signatures are learned directly from genome-wide data with no prior biological knowledge - are highly effective and robust. We explore also the relationship between predictive signatures, differential expression and known AML-related genes. Taken together, our results support the notion that transcriptome assessment could be used as part of an integrated genomic approach in cancer diagnosis and treatment to be implemented early on for diagnosis and differential diagnosis of AML.\n\nOne Sentence SummaryBlood gene expression data and machine learning were used to develop robust and accurate classifiers for diagnosis and differential diagnosis of acute myeloid leukemia based on analysis of more than 12,000 samples derived from more than 100 individual studies

genomics↗

bioRxiv2018

Association Between Rare Copy Number Variation and Response to Social Skills Training in Autism Spectrum Disorder

Challenges in social communication and interaction are core symptoms in autism spectrum disorder (ASD) for which social skills group training (SSGT) is a commonly used intervention. SSGT has shown modest but heterogeneous effects in clinical trials, and therefore identification of effect moderators could enable more precise intervention decisions. One of the major genetic risk factors in ASD are rare copy number variation (CNV). However, limited information exists whether rare CNVs profiles can be used to aid in intervention decisions. Therefore, we conducted the first study to date analyzing rare CNVs as genetic moderators in the outcome of SSGT in ASD. For this, we analyzed rare genic CNV carrier status of 207 children of which 105 received SSGT and 102 standard care as part of a recent randomized clinical trial for 12-weeks SSGT. We used mixed linear models to assess the association of being a CNV carrier, grouped by the effect and size of the CNVs and the primary response to SSGT, the parent-report Social Responsiveness Scale (SRS) measured at post-intervention and 3-months follow-up. Additionally, we analyzed the secondary outcome assessments included parent-rated adaptive behaviors (ABAS-II) and trainer-rated clinical global impression (CGI). We show that being a carrier of any size rare genic CNV did not impact on the SSGT outcome. However, when stratifying the groups by size of the CNVs, we identified that carriers of large CNVs (>500 kb) showed inferior SRS outcomes at post-intervention ({beta} = 15.35, 95% CI 2.86-27.84, P=0.017) and follow-up ({beta} = 14.19, 95% CI 1.68-26.70, P=0.028). Similar results were shown for the parent-rated secondary outcome. In contrast, the carriers of small CNVs had better outcome at post-intervention ({beta} = -1.20, 95 % CI - 2.0 - -0.4 P = 0.003) but not at follow-up for the trainer-rated secondary outcome CGI. These results remained when we tested the specificity of the effect by including the standard care group and adjusting for IQ levels. While our study suggests that being a carrier of any size rare genic CNV did not impact the outcome, it provides preliminary evidence that carriers of high-risk CNVs might not benefit on SSGT as much as non-carriers. Our results indicate that genetic information eventually might help guide personalized intervention planning in ASD. We additionally highlight that more research is needed to understand the intervention needs of autistic individuals with specified molecular alterations.

genetics↗

bioRxiv2018

FASTGenomics: An analytical ecosystem for single-cell RNA sequencing data

Recent technological advances enable genomics of individual cells, the building blocks of all living organisms. Single cell data characteristics differ from those of bulk data, which led to a plethora of new analytical strategies. However, solutions are only useful for experts and currently, there are no widely accepted gold standards for single cell data analysis. To meet the requirements of analytical flexibility, ease of use and data security, we developed FASTGenomics (https://fastgenomics.org) as a powerful, efficient, versatile, robust, safe and intuitive analytical ecosystem for single-cell transcriptomics.

bioinformatics↗

bioRxiv2016

Resting-state fMRI correlations: from link-wiseunreliability to whole brain stability

The functional architecture of spontaneous BOLD fluctuations has been characterized in detail by numerous studies, demonstrating its potential relevance as a biomarker. However, the systematic investigation of its consistency is still in its infancy. Here, we analyze both the within- and between-subject variability as well as the test-retest reliability of resting-state functional connectivity (FC) estimates in a unique data set comprising multiple fMRI scans (42) from 5 subjects, and 50 single scans from 50 subjects. To this aim we adopted a statistical framework enabling us to disentangle the contribution of different sources of variability and their dependence on scan duration, and showed that the low reliability of single links can be largely improved using multiple scans per subject. Moreover, we show that practically all observed inter-region variability (at the link-level) is not significant and due to the statistical uncertainty of the estimator itself rather than to genuine variability among areas. Finally, we use the proposed statistical framework to demonstrate that, despite the poor consistency of single links, the information carried by the whole-brain spontaneous correlation structure is indeed robust, and can in fact be used as a functional fingerprint.

neuroscience↗