Biology subjects

Jost, J.

Publications and source records attributed to Jost, J..

12 recordsLinked to original sources

bioRxiv2026

A computational model of the two dentate gyrus blades

The Dentate Gyrus (DG) is a key part of the hippocampus, and damage to the DG produces a wide range of pathologies, including overgeneralization of contexts, affective dysregulation (Anacker et al., 2018), and epileptogenic effects (Sloviter, 1994). The canonical model of the DG focuses on pattern separation for subsequent memory storage in the hippocampal subfield CA3. Experimental results challenge the singular focus on pattern separation and extend the function of the DG to the precise binding of objects and events to space, and the integration of information across episodes. Recent studies suggest that pattern separation and integration preferentially rely on distinct DG blades, with the suprapyramidal and infrapyramidal blades biased toward separation and integration, respectively. Here, we propose the first computational model that accounts for this distinction: an exemplar-based k-WTA architecture in the suprapyramidal DG (DGSUP) supports pattern separation and episode-specific representations, whereas an architecture with gradual heterosynaptic plasticity in the infrapyramidal DG (DGINF) supports integration of patterns across episodes. Both coding regimes are tested with two datasets: MNIST and neurally plausible entorhinal cortex inputs, thus suggesting some domain generality. Using the entorhinal cortex inputs, the two blades form place fields that either remap or maintain a stable code, consistent with experimental results. Novel inputs, including novel digit classes and novel spatial episodes, are incorporated through a neurogenesis-inspired turnover and recruitment mechanism. The two processing streams allow for a comparison of ongoing experience with the generalized expectations formed through integration across episodes. This yields prediction errors that can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. The differential processing across the DG could thus aid in the iterative construction of spatial cognitive maps that encode location-dependent expectations, while at the same time preserving individual episodic memory traces. These functions are accomplished with biologically plausible learning regimes and widen the scope of DG computation beyond its well-established role in pattern separation.

neuroscience↗

bioRxiv2025

Node persistence from topological data analysis reveals changes in brain functional connectivity

Large-scale analyses of brain functional connectivity can uncover disruptions in regional activity and connectivity that are commonly associated with neurological disorders or cognitive decline associated with healthy aging. In our study, we employ persistent homology (PH), a prominent tool in topological data analysis (TDA), to investigate changes in resting-state functional connectivity in healthy aging and autism spectrum disorder (ASD). We analyze functional connectivity changes across three distinct scales: (a) global scale (brain-wide changes), (b) mesoscopic scale (resting-state network-level changes), and (c) local scale (region-level changes). At the local scale, we introduce node persistence, a novel and scalable PH-based measure that detects brain regions with significant differences in healthy aging or ASD. Notably, these regions overlap with regions whose non-invasive stimulation improves motor function in the elderly or alleviates ASD symptoms, suggesting the utility of node persistence in identifying clinically relevant brain regions affected by aging and ASD.

neuroscience↗

bioRxiv2024

Symmetry and Generalization in Local Learning of Predictive Representations

In spatial cognition, the Successor Representation (SR) from reinforcement learning provides a compelling candidate of how predictive representations are used to encode space. In particular, hippocampal place cells are hypothesized to encode the SR. Here, we investigate how varying the temporal symmetry in learning rules influences those representations. To this end, we use a simple local learning rule which can be made insensitive to the temporal order. We analytically find that a symmetric learning rule results in a successor representation under a symmetrized version of the experienced transition structure. We then apply this rule to a two-layer neural network model loosely resembling hippocampal subfields CA3 - with a symmetric learning rule and recurrent weights - and CA1 - with an asymmetric learning rule and no recurrent weights. Here, when exposed repeatedly to a linear track, neurons in our model in CA3 show less shift of the centre of mass than those in CA1, in line with existing empirical findings. Investigating the functional benefits of such symmetry, we employ a simple reinforcement learning agent which may learn symmetric or classical successor representations. Here, we find that using a symmetric learning rule yields representations which afford better generalization, when the agent is probed to navigate to a new target without relearning the SR. This effect is reversed when the state space is not symmetric anymore. Thus, our results hint at a potential benefit of the inductive bias afforded by symmetric learning rules in areas employed in spatial navigation, where there naturally is a symmetry in the state space. Author summaryThe hippocampus is a brain region which plays a crucial role in spatial navigation for both animals and humans. Contemporarily, its thought to store predictive representations of the environment, functioning like maps that indicate the likelihood of visiting certain locations in the future. In our study, we used an artificial neural network model to learn these predictive representations by adjusting synaptic connections between neurons according to local learning rules. Unlike previous research, our model includes learning rules that are invariant to the temporal order of events, meaning they are symmetric with respect to the reversal of input timings. This approach produces predictive representations particularly useful for understanding spatial relationships, as navigating from one point to another is often equivalent to the reverse. Our model offers additional insights: it replicates observed properties of hippocampal cells and helps an artificial agent solve navigation tasks. The agent trained with our model not only learns to navigate but also generalizes better to new targets compared to traditional models. Our findings suggest that symmetric learning rules enhance the brains ability to create useful predictive maps for problems which are inherently symmetric, as is navigation.

neuroscience↗

bioRxiv2023

Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning

Predicting neuromarkers for cognitive abilities using fMRI has been a major focus of research in the past few years. However, it has recently been reported that many thousands of participants are required to obtain reproducible results (Marek et al (2022)). This appears to be a major impediment to obtaining neuromarkers from fMRI because large sample sizes are typically not available in neuroimaging studies. Here we show that the out-of-sample prediction accuracy can be dramatically improved by supplementing fMRI with readily available non-imaging information so that reliable predictive modeling becomes feasible even for small sample sizes. Specifically, we introduce a novel machine learning method that predicts intelligence from resting-state fMRI data, leveraging educational level as supplementary information. We refer to our approach as "semi-blind machine learning (SML)" because it operates under the assumption that supplementary information, such as educational level, is available for subjects in both the training and test sets. This setup closely mirrors real-world scenarios, especially in clinical contexts, where patient background information typically exists and can be utilized to boost prediction accuracy. However, guarding against bias is crucial. Subjects should not be categorized as more intelligent simply based on their higher education levels. Therefore, our approach contains a component explicitly designed for bias control. We have applied our method to three different data collections and observed marked improvements in prediction accuracies across a wide range of sample sizes. We anticipate that semi-blind machine learning provides a promising approach to fMRI-based predictive modelling with the potential for a wide range of future applications.

neuroscience↗

bioRxiv2022

Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks

Geometry-inspired notions of discrete Ricci curvature have been successfully used as markers of disrupted brain connectivity in neuropsychiatric disorders, but their ability to characterize age-related changes in functional connectivity is unexplored. Here, we apply Forman-Ricci curvature and Ollivier-Ricci curvature to compare functional connectivity networks of healthy young and older subjects from the Max Planck Institute Leipzig Study for Mind-Body-Emotion Interactions (MPI-LEMON) dataset (N = 225). We found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity. Meta-analysis decoding demonstrated that those brain regions with age-related curvature differences were associated with cognitive domains known to manifest age-related changes - movement, affective processing and somatosensory processing. Moreover, the curvature values of some brain regions showing age-related differences exhibited correlations with behavioral scores of affective processing. Finally, we found an overlap between brain regions showing age-related curvature differences and those brain regions whose non-invasive stimulation resulted in improved movement performance in older adults. These results suggest that both Forman-Ricci curvature and Ollivier-Ricci curvature correctly identify brain regions that are known to be functionally or clinically relevant. Our results add to a growing body of evidence demonstrating the sensitivity of discrete Ricci curvature measures to changes in the organisation of functional connectivity networks, both in health and disease.

neuroscience↗

bioRxiv2021

Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

While standard graph-theoretic measures have been widely used to characterize atypical resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired network measures have not been applied. In this study, we apply Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and typically developing individuals (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We find brain-wide and region-specific ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures, with region-specific differences concentrated in Default Mode, Somatomotor and Ventral Attention networks for Forman-Ricci curvature. We use meta-analysis decoding to demonstrate that brain regions with curvature differences are associated to those cognitive domains known to be impaired in ASD. Further, we show that brain regions with curvature differences overlap with those brain regions whose non-invasive stimulation improves ASD-related symptoms. These results suggest the utility of graph Ricci curvatures in characterizing atypical connectivity of clinically relevant regions in ASD and other neurodevelopmental disorders.

neuroscience↗

bioRxiv2021

Predicting intelligence from fMRI data of the human brain in a few minutes of scan time

In recent years, the prediction of individual behaviour from the fMRI-based functional connectome has become a major focus of research. The motivation behind this research is to find generalizable neuromarkers of cognitive functions. However, insufficient prediction accuracies and long scan time requirements are still unsolved issues. Here we propose a new machine learning algorithm for predicting intelligence scores of healthy human subjects from resting state (rsfMRI) or task-based fMRI (tfMRI). In a cohort of 390 unrelated test subjects of the Human Connectome Project, we found correlations between the observed and the predicted general intelligence of more than 50 percent in tfMRI, and of around 59 percent when results from two tasks are combined. Surprisingly, we found that the tfMRI data were significantly more predictive of intelligence than rsfMRI even though they were acquired at much shorter scan times (approximately 10 minutes versus 1 hour). Existing methods that we investigated in a benchmark comparison underperformed on tfMRI data and produced prediction accuracies well below our results. Our proposed algorithm differs from existing methods in that it achieves dimensionality reduction via ensemble learning and partial least squares regression rather than via brain parcellations or ICA decompositions. In addition, it introduces Ricci-Forman curvature as a novel type of edge weight.

neuroscience↗

bioRxiv2020

Decoupling of resistance and tolerance against one of two related parasites (Eimeria) in mice

Resistance (host capacity to reduce parasite burden) and tolerance (host capacity to reduce impact on its health for a given parasite burden) manifest two different lines of defence. Tolerance can be independent from resistance, traded-off against it, or the two can be positively correlated because of redundancy in underlying (immune) processes. We here tested whether this coupling between tolerance and resistance could differ upon infection with closely related parasite species. We tested this in experimental infections with two parasite species of the genus Eimeria. We measured proxies for resistance (the (inverse of) number of parasite transmission stages (oocysts) per gram of feces at the day of maximal shedding) and tolerance (the slope of maximum relative weight loss compared to day of infection on number of oocysts per gram of feces at the day of maximal shedding for each host strain) in four inbred mouse strains and four groups of F1 hybrids belonging to two mouse subspecies, Mus musculus domesticus and M. m. musculus. We found a negative correlation between resistance and tolerance against E. falciformis, while the two are uncoupled against E. ferrisi. We conclude that resistance and tolerance against the first parasite species might be traded off, but evolve more independently in different mouse genotypes against the latter. We argue that evolution of the host immune defences can be studied largely irrespective of parasite isolates if resistance-tolerance coupling is absent or weak (E. ferrisi) but host-parasite coevolution is more likely observable and best studied in a system with negatively correlated tolerance and resistance (E. falciformis).

evolutionary biology↗

bioRxiv2019

Generalist Eimeria species in rodents: multilocus analyses indicate inadequate resolution of established markers

Intracellular parasites of the genus Eimeria are described as tissue/host specific. Phylogenetic classification of rodent Eimeria suggested that some species have a broader host range than previously assumed. We explore if Eimeria spp. infecting house mice are misclassified by the most widely used molecular markers due to a lack of resolution, or if, instead, these parasite species are indeed infecting multiple host species.\n\nWith the commonly used markers (18S/COI), we recovered monophyletic clades of E. falciformis and E. vermiformis from Mus that included E. apionodes identified in other rodent host species (Apodemus spp., Myodes glareolus, and Microtus arvalis). A lack of internal resolution in these clades could suggest the existence of a species complex with a wide host range infecting murid and cricetid rodents. We question, however, the power of COI and 18S markers to provide adequate resolution for assessing host specificity. In addition to the rarely used marker ORF470 from the apicoplast genome, we present multilocus genotyping as an alternative approach. Phylogenetic analysis of 35 nuclear markers differentiated E. falciformis from house mice from isolates from Apodemus hosts. Isolates of E. vermiformis from Mus are still found in clusters interleaved with non-Mus isolates, even with this high resolution data.\n\nIn conclusion, we show that species-level resolution should not be assumed for COI and 18S markers in Coccidia. Host-parasite co-speciation at shallow phylogenetic nodes, as well as contemporary coccidian host ranges more generally, are still open questions that need to be addressed using novel genetic markers with higher resolution.

microbiology↗

bioRxiv2019

Intensity of infection with intracellular Eimeria spp. and pinworms is reduced in hybrid mice compared to parental subspecies

The longstanding impression that hybrid mice are more highly parasitized and therefore less fit than parentals persists despite the findings of recent studies. Working across a novel transect of the European House Mouse hybrid zone we assessed intracellular infections by Eimeria, a parasite of high pathogenicity, and infections by pinworms, assumed to be less pathogenic. For Eimeria we found lower intensities in hybrid hosts than in parental mice but no evidence of lowered probability of infection in the centre of the hybrid zone. This means ecological and epidemiological factors are very unlikely to be responsible for the reduced load of infected hybrids. Focussing on parasite intensity (load in infected hosts) we also corroborated reduced pinworm loads reported for hybrid mice in previous studies. In addition we questioned whether differences in body condition during infection would indicate different impacts on hybrid vs. parental hosts health. We couldnt show such an effect. We conclude that intensity of diverse parasites, including the previously unstudied Eimeria, is reduced in hybrid mice compared to parental subspecies. We suggest caution in extrapolating this to differences in hybrid host fitness in the absence of, for example, evidence for a link between parasitemia and health.

evolutionary biology↗

bioRxiv2019

Detection and quantification of house mouse Eimeria at the species level - challenges and solutions for the assessment of Coccidia in wildlife

Detection and quantification of coccidia in studies of wildlife can be challenging. Therefore, the prevalence of coccidia is often not assessed at the parasite species level in non-livestock animals. Parasite species-specific prevalences are especially important when studying evolutionary questions in wild populations. We tested whether increased host population density increases the prevalence of individual Eimeria species at the farm level, as predicted by epidemiological theory. We studied free-living commensal populations of the house mouse (Mus musculus) in Germany and established a strategy to detect and quantify Eimeria infections. We show that a novel diagnostic primer targeting the apicoplast genome (Ap5) and coprological assessment after flotation provide complementary detection results increasing sensitivity. Genotyping PCRs confirm detection in a subset of samples and cross-validation of different PCR markers does not indicate a bias towards a particular parasite species in genotyping. We were able to detect double infections and to determine the preferred niche of each parasite species along the distal-proximal axis of the intestine. Parasite genotyping from tissue samples provides an additional indication for the absence of species bias in genotyping amplifications. Three Eimeria species were found infecting house mice at different prevalences: Eimeria ferrisi (16.7%; 95% CI 13.2 - 20.7), E. falciformis (4.2%; 95% CI 2.6 - 6.8) and E. vermiformis (1.9%; 95% CI 0.9 - 3.8). We also find that mice in dense populations are more likely to be infected with E. falciformis and E. ferrisi.\n\nWe provide methods for the assessment of prevalences of coccidia at the species level in rodent systems. We show and discuss how such data can help to test hypotheses in ecology, evolution and epidemiology on a species level.

microbiology↗

bioRxiv2018

A novel non-invasive method to detect gut barrier related changes during a gastrointestinal nematode infection.

Currently, methods for monitoring changes of gut barrier integrity and the associated immune response via non-invasive means are limited. Therefore, we aimed to develop a novel non-invasive technique to investigate immunological host responses representing gut barrier changes in response to infection. We identified the mucous layer on feces from mice to be mainly composed of exfoliated intestinal epithelial cells. Expression of RELM-{beta}, a gene prominently expressed in intestinal nematode infections, was used as an indicator of intestinal cellular barrier changes to infection. RELM-{beta} was detected as early as 6 days post-infection (dpi) in exfoliated epithelial cells. Interestingly, RELM-{beta} expression also mirrored the quality of the immune response, with higher amounts being detectable in a secondary infection and in high dose nematode infection in laboratory mice. This technique was also applicable to captured worm-infected wild house mice. We have therefore developed a novel non-invasive method reflecting gut barrier changes associated with alterations in cellular responses to a gastrointestinal nematode infection.

immunology↗