Biology subjects

Schneider, J.

Publications and source records attributed to Schneider, J..

7 recordsLinked to original sources

bioRxiv2018

Thought experiment: Decoding cognitive processes from the fMRI data of one individual

Cognitive processes, such as the generation of language, can be mapped onto the brain using fMRI. These maps can in turn be used for decoding the respective processes from the brain activation patterns. Given individual variations in brain anatomy and organization, analyzes on the level of the single person are important to improve our understanding of how cognitive processes correspond to patterns of brain activity. They also allow to advance clinical applications of fMRI, because in the clinical setting making diagnoses for single cases is imperative. In the present study, we used mental imagery tasks to investigate language production, motor functions, visuo-spatial memory, face processing, and resting-state activity in a single person. Analysis methods were based on similarity metrics, including correlations between training and test data, as well as correlations with maps from the NeuroSynth meta-analysis. The goal was to make accurate predictions regarding the cognitive domain (e.g. language) and the specific content (e.g. animal names) of single 30-second blocks. Four teams used the dataset, each blinded regarding the true labels of the test data. Results showed that the similarity metrics allowed to reach the highest degrees of accuracy when predicting the cognitive domain of a block. Overall, 23 of the 25 test blocks could be correctly predicted by three of the four teams. Excluding the unspecific rest condition, up to 10 out of 20 blocks could be successfully decoded regarding their specific content. The study shows how the information contained in a single fMRI session and in each of its single blocks can allow to draw inferences about the cognitive processes an individual engaged in. Simple methods like correlations between blocks of fMRI data can serve as highly reliable approaches for cognitive decoding. We discuss the implications of our results in the context of clinical fMRI applications, with a focus on how decoding can support functional localization.

neuroscience↗

bioRxiv2018

Can Drosophila melanogaster tell who’s who?

Drosophila melanogaster are known to live in a social but cryptic world of touch and odours, but the extent to which they can perceive and integrate visual information is a hotly debated topic. Some researchers fixate on the limited resolution of D. melanogasters optics, others on their seemingly identical appearance; yet there is evidence of individual recognition and surprising visual learning in flies. Here, we apply machine learning and show that individual D. melanogaster are visually distinct. We also use the striking similarity of Drosophilas visual system to current convolutional neural networks to theoretically investigate D. melanogasters capacity for visual understanding. We find that, despite their limited optical resolution, D. melanogasters neuronal architecture has the capability to extract and encode a rich feature set that allows flies to re-identify individual conspecifics with surprising accuracy. These experiments provide a proof of principle that Drosophila inhabit in a much more complex visual world than previously appreciated.\n\nAuthor summaryIn this paper, we determine a proof of principle for inter-individual recognition in two parts; is there enough information contained in low resolution pictures for inter-fly discrimination, and if so does Drosophilas visual system have enough capacity to use it. We show that the information contained in a 29x29 pixel image (number of ommatidia in a fly eye) is sufficient to achieve 94% accuracy in fly re-identification. Further, we show that the fly eye has the theoretical capacity to identify another fly with about 75% accuracy. Although it is unlikely that flies use the exact algorithm we tested, our results show that, in principle, flies may be using visual perception in ways that are not usually appreciated.

animal behavior and cognition↗

bioRxiv2018

Magnetic resonance imaging of the regenerating neonatal mouse heart

We present longitudinal magnetic resonance imaging (MRI) of neonatal mouse hearts during the first three weeks following coronary artery ligation to mimic heart attack. We confirm heart regeneration in individual animals injured on post-natal day 1 (P1) while those injured on P7 show the adult response of fibrosis, scarring and impaired heart performance. We document heart growth and development of the principal functional cardiac parameters, and also remodeling during tissue regeneration as compared to fibrosis when imaging repeatedly up to 21 days after myocardial infarction (MI). We reveal compensatory changes in cardiac function with the restoration of tissue and resolution of injury for the P1 cohort and sustained injury responses for the P7 cohort. This study resolves the controversy surrounding neonatal mouse heart regeneration and establishes a functional platform for live capture of the regenerative process and for the future testing of genetic or therapeutic interventions.

biophysics↗

bioRxiv2018

GRIMM: GRaph IMputation and Matching for HLA Genotypes

Motivation: For over 10 years allele-level HLA matching for bone marrow registries has been performed in a probabilistic context. HLA typing technologies provide ambiguous results in that they could not distinguish among all known HLA allele sequences, therefore registries have implemented matching algorithms that provide lists of donor and cord blood units ordered in terms of the likelihood of allele-level matching at specific HLA loci. With the growth of registry sizes, current match algorithm implementations are unable to provide match results in real time.\n\nResults: We present here novel computationally-efficient open source implementation of an HLA imputation and match algorithm using a graph database platform. Using graph traversal, our algorithm runtime grows slowly with registry size. This implementation generates results that agree with consensus output on a publicly-available match algorithm crossvalidation dataset.\n\nAvailability: The Python, Perl and Neo4jJcode is available at https://git.com/nmdp-bioinformatics/grimm\n\nSupplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

A molecular network of the aging brain implicates INPPL1 and PLXNB1 in Alzheimer’s disease

The fact that only symptomatic therapies of small effect are available for Alzheimers disease (AD) today highlights the need for new therapeutic targets with which to prevent a major contributor to aging-related cognitive decline. Here, we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first identify the role of modules of coexpressed genes, and then confirm them in independent AD datasets. Then, we prioritize influential genes in AD-related modules and test our predictions in human model systems. We functionally validate two putative regulator genes in human astrocytes: INPPL1 and PLXNB1, whose activity in AD may be related to semaphorin signalling and type II diabetes, which have both been implicated in AD. This arc of network identification followed by statistical and experimental validation provides specific new targets for therapeutic development and illustrates a network approach to a complex disease.\n\nOne sentence summaryMolecular network analysis of RNA sequencing data from the aging human cortex identifies new Alzheimers and cognitive decline genes.

systems biology↗

bioRxiv2017

Identification of Memory Reactivation during Sleep by EEG Classification

Memory reactivation during sleep is critical for consolidation, but also extremely difficult to measure as it is subtle, distributed and temporally unpredictable. This article reports a novel method for detecting such reactivation in standard sleep recordings. During learning, participants produced a complex sequence of finger presses, with each finger cued by a distinct audio-visual stimulus. Auditory cues were then re-played during subsequent sleep to trigger neural reactivation through a method known as targeted memory reactivation (TMR). Next, we used electroencephalography data from the learning session to train a machine learning classifier, and then applied this classifier to sleep data to determine how successfully each tone had elicited memory reactivation. Above chance classification was significantly higher in slow wave sleep than in stage 2, suggesting differential efficacy of TMR in these two sleep stages. Interestingly, classification success reduced across numerous repetitions of the tone cue, suggesting either a gradually reducing responsiveness to such cues or a plasticity-related change in the neural signature as a result of cueing. We believe this method will be invaluable for future investigations of memory consolidation.

neuroscience↗

bioRxiv2017

Dynamic genome wide expression profiling of Drosophila head development reveals a novel role of Hunchback in retinal glia cell development and blood-brain barrier integrity

The development of different cell types must be tightly coordinated in different organs. The developing head of Drosophila melanogaster represents an excellent model to study the molecular mechanisms underlying this coordination because the eye-antennal imaginal discs contain the organ anlagen of nearly all adult head structures, such as the compound eyes or the antennae. We studied the genome wide gene expression dynamics during eye-antennal disc development in D. melanogaster to identify new central regulators of the underlying gene regulatory network. Expression based gene clustering and transcription factor motif enrichment analyses revealed a central regulatory role of the transcription factor Hunchback (Hb). We confirmed that hb is expressed in two polyploid retinal subperineurial glia cells (carpet cells). Our functional analysis shows that Hb is necessary for carpet cell development and loss of Hb function results in abnormal glia cell migration and photoreceptor axon guidance patterns. Additionally, we show for the first time that the carpet cells are an integral part of the blood-brain barrier.

developmental biology↗