Biology subjects

Eberhardt, F.

Publications and source records attributed to Eberhardt, F..

6 recordsLinked to original sources

bioRxiv2023

Unsupervised mapping of causal relations between brain and behavior

Human lesion studies offer one of the most direct routes to investigating the relations between brain regions and behavioral outcomes in circumstances where experimental interventions are highly restricted. However, these studies face a major challenge in identifying the right level of granularity at which brain regions and behavioral outcomes should be analyzed to identify the relation between lesions to specific brain regions and specific behavioral outcomes. Here we showcase a novel data-driven approach, Causal Feature Learning (CFL), that learns the appropriate level of analysis and the relation between lesion and cognitive impairment at the same time. The method avoids specifying brain regions and specific outcome measures a priori, allowing for the discovery of new cross-cutting lesion-behavior maps. We show that CFL robustly recovers lesion behavior maps in a simulated dataset where Canonical Correlation Analysis fails to provide interpretable results. We then show that CFL recovers known lesion-behavior maps for language deficits and visuospatial processing using a large dataset of lesion subjects, and we illustrate how CFL can be used to identify new groupings of outcomes when mapping lesions to depression symptoms.

neuroscience↗

bioRxiv2023

Quantifying the Influence of the Actin Cytoskeleton on Ion Transport in Dendritic Spines by Homogenization of the Poisson-Nernst-Planck Equations

Dendritic spines are filled with a very dense actin cytoskeleton. However, due to their small size, the impact of this mesh on biophysical parameters has not been studied so far, and it remains unclear to what extent it might affect ion flow in dendritic spines. Only recently has the three-dimensional internal structure of dendritic spines been quantified in great detail in electron microscopic tomography data. Based on these results, we estimate the effect of the spine actin cytoskeleton on diffusion and permittivity. We apply a method called homogenization to estimate effective diffusion tensors and permittivity tensors in Poisson-Nernst-Planck (PNP) equations. We find that the volume taken up by the intracellular structure alone cannot explain the changes in these biophysical parameters. The characteristic architecture of the intracellular space in dendritic spines will reduce the diffusion of ions by 33% to 46% and the permittivity by 30% to 42%, compared to values found for the cytosol free of intracellular structures. These results can be used to improve computational studies using PNP equations and help to better interpret experimental results of electrical and chemical compartmentalization.

biophysics↗

bioRxiv2023

Parameters of Cable Theory Are Mostly Unaffected by the Geometry of Dendritic Spines

Dendritic spines are extremely small and experimentally difficult to access. Therefore, it is still uncertain whether all assumptions of basic neuroscientific theories, such as cable theory, are valid there. Previous theoretical work suggests that electroneutrality could be violated in dendritic spines. If this were true, new theories would be required. Unfortunately, these results were based on a greatly simplified model system with unrealistic ion concentrations. Inspired by these studies, we apply Poison-Nernst-Planck (PNP) equations to study the profiles of ion concentrations and the membrane potential in dendritic spines in a physiologically relevant regime. We find that, for realistic ion concentrations and in contrast to previous results, electroneutrality is a valid assumption for all tested geometries, irrespective of size and shape. However, the surface charge causes an accumulation of counter ions and a strong electric field near the surface of the membrane in the intra- and extracellular space. Still, a plate capacitor model accurately describes the capacitance of the membrane. Most importantly, the two cable parameters - the specific capacitance and the intracellular resistivity - are constants over a wide range of parameters. These results justify the application of models based on cable theory to dendritic spines.

neuroscience↗

bioRxiv2023

Ion-Concentration Gradients During Synaptic Input Increase the Voltage Depolarization in Dendritic Spines

The cable equation is key for understanding the electrical potential along dendrites or axons, but its application to dendritic spines remains limited. Their volume is extremely small so that moderate ionic currents suffice to alter ionic concentrations. The resulting chemical-potential gradients between dendrite and spine head lead to measurable electrical currents. The cable equation, however, considers electrical currents only as result of gradients in the electrical potential. The Poisson-Nernst-Planck (PNP) equations allow a more accurate description, as they include both types of currents. Previous PNP simulations predict a considerable change of ionic concentrations in spines during an excitatory postsynaptic potential (EPSP). However, solving PNP-equations is computationally expensive, limiting their applicability for complex structures. Here, we present a system of equations that generalizes the cable equation and considers both, electrical potentials and time-dependent concentrations of ion species with individual diffusion constants. Still, basic numerical algorithms can be employed to solve such systems. Based on simulations, we confirm that ion concentrations in dendritic spines are changing significantly during current injections that are comparable to synaptic events. Electrical currents reflecting ion diffusion through the spine neck increase voltage depolarizations in the spine head. Based on this effect, we identify a mechanism that affects the influx of Ca2+ in sequences of pre- and postsynaptic action potentials. Taken together, the diffusion of individual ion species need to be taken into account to accurately model electrical currents in dendritic spines. In the future the presented equations can be used to accurately integrate dendritic spines into multicompartment models to study synatptic integration.

neuroscience↗

bioRxiv2021

Intracranial electrical stimulation alters meso-scale network integration as a function of network topology

Human brain dynamics are organized into a multi-scale network structure that contains multiple tight-knit, meso-scale communities. Recent work has demonstrated that many psychological capacities, as well as impairments in cognitive function secondary to damage, can be mapped onto organizing principles at this mesoscopic scale. However, we still dont know the rules that govern the dynamic interactions between regions that are constrained by the topology of the broader network. In this preregistered study, we utilized a unique human dataset in which whole brain BOLD-fMRI activity was recorded simultaneously with intracranial electrical stimulation, to characterize the effects of direct neural stimulation on the dynamic reconfiguration of the broader network. Direct neural stimulation increased the extent to which the stimulation sites own mesoscale community integrated with the rest of the brain. Further, we found that these network changes depended on the topological role of the stimulation site itself: stimulating regions with high participation coefficients led to global integration, whereas stimulating sites with low participation coefficients integrated that regions own community with the rest of the brain. These findings provide direct causal evidence for how network topology shapes and constrains inter-regional coordination, and suggest applications for targeted therapeutic interventions in patients with deep-brain stimulation.

neuroscience↗

bioRxiv2017

Causal Mapping of Emotion Networks in the Human Brain: Framework and Preliminary Findings

Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI.

neuroscience↗