Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study.

Advancements in machine learning hold great promise for the analysis of multimodal neuroimaging data. They can help identify biomarkers and improve diagnosis for various neurological disorders. However, the application of such techniques for rare and heterogeneous diseases remains challenging due to small-cohorts available for acquiring data.

A faster way to model neuronal circuitry.

Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.

Reproducing and quantitatively validating a biologically-constrained point-neuron model of CA1 pyramidal cells.

We have attempted to reproduce a biologically-constrained point-neuron model of CA1 pyramidal cells. The original models, developed for the Brian simulator, captured the frequency-current profiles of both strongly and weakly adapting cells. As part of the present study, we reproduced the model for different simulators, namely Brian2 and NEURON.

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models.

In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires the availability of experimental data and analysis tools as well as neural simulation environments and, often, large scale computing facilities. All these components are rarely found in a comprehensive framework and usually require programming.

Difference in axon diameter and myelin thickness between excitatory and inhibitory callosally projecting axons in mice.

Synchronization of network oscillation in spatially distant cortical areas is essential for normal brain activity. Precision in synchronization between hemispheres depends on the axonal conduction velocity, which is determined by physical parameters of the axons involved, including diameter, and extent of myelination. To compare these parameters in long-projecting excitatory and inhibitory axons in the corpus callosum, we used genetically modified mice and virus tracing to separately label CaMKIIα expressing excitatory and GABAergic inhibitory axons.

EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience.

We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easily upload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is a prominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in the corresponding publications.

Effect of Variations in Gap Junctional Coupling on the Frequency of Oscillatory Action Potentials in a Smooth Muscle Syncytium.

Gap junctions provide pathways for intercellular communication between adjacent cells, allowing exchange of ions and small molecules. Based on the constituent protein subunits, gap junctions are classified into different subtypes varying in their properties such as unitary conductances, sensitivity to transjunctional voltage, and gating kinetics. Gap junctions couple cells electrically, and therefore the electrical activity originating in one cell can affect and modulate the electrical activity in adjacent cells.

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data.

Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context.

Electrophysiology of Syncytial Smooth Muscle.

As in other excitable tissues, two classes of electrical signals are of fundamental importance to the functioning of smooth muscles: junction potentials, which arise from neurotransmission and represent the initiation of excitation (or in some instances inhibition) of the tissue, and spikes or action potentials, which represent the accomplishment of excitation and lead on to contractile activity. Unlike the case in skeletal muscle and in neurons, junction potentials and spikes in smooth muscle have been poorly understood in relation to the electrical properties of the tissue and in terms of their spatiotemporal spread within it. This owes principally to the experimental difficulties involved in making precise electrical recordings from smooth muscles and also to two inherent features of this class of muscle, ie, the syncytial organization of its cells and the distributed innervation they receive, which renders their biophysical analysis problematic.

Investigation of the Syncytial Nature of Detrusor Smooth Muscle as a Determinant of Action Potential Shape.

Unlike most excitable cells, certain syncytial smooth muscle cells are known to exhibit spontaneous action potentials of varying shapes and sizes. These differences in shape are observed even in electrophysiological recordings obtained from a single cell. The origin and physiological relevance of this phenomenon are currently unclear.

A Method for the Analysis of AP Foot Convexity: Insights into Smooth Muscle Biophysics.

Action potential (AP) profiles vary based on the cell type, with cells of the same type typically producing APs with similar shapes. But in certain syncytial tissues, such as the smooth muscle of the urinary bladder wall, even a single cell is known to exhibit APs with diverse profiles. The origin of this diversity is not currently understood, but is often attributed to factors such as syncytial interactions and the spatial distribution of parasympathetic nerve terminals.

Modeling extracellular fields for a three-dimensional network of cells using NEURON.

Background: Computational modeling of biological cells usually ignores their extracellular fields, assuming them to be inconsequential. Though such an assumption might be justified in certain cases, it is debatable for networks of tightly packed cells, such as in the central nervous system and the syncytial tissues of cardiac and smooth muscle.

New Method: In the present work, we demonstrate a technique to couple the extracellular fields of individual cells within the NEURON simulation environment.

A computational model of urinary bladder smooth muscle syncytium : validation and investigation of electrical properties.

Certain smooth muscles, such as the detrusor of the urinary bladder, exhibit a variety of spikes that differ markedly in their amplitudes and time courses. The origin of this diversity is poorly understood but is often attributed to the syncytial nature of smooth muscle and its distributed innervation. In order to help clarify such issues, we present here a three-dimensional electrical model of syncytial smooth muscle developed using the compartmental modeling technique, with special reference to the bladder detrusor.