NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience

Contralateral medial temporal lobe damage in right but not left temporal lobe epilepsy: a (1)H magnetic resonance spectroscopy study

Background: Proton magnetic resonance spectroscopy (MRS) of the hippocampus is useful in lateralising the epileptic focus in temporal lobe epilepsy for subsequent surgical resection. Previous studies have reported abnormal contralateral MRS values in up to 50% of the patients. Objective: To identify the contributing factors to contralateral damage, as determined by MRS, and its extension in patients with temporal lobe epilepsy. Methods: Single voxel MRS was carried out in the hippocampus and lateral temporal neocortex of both hemispheres in 13 patients with left temporal lobe epilepsy (LTLE) and 16 patients with right temporal lobe epilepsy (RTLE). All patients had mesial temporal lobe epilepsy with hippocampal sclerosis. Controls were 21 healthy volunteers of comparable age. Results: Consistent with previous studies, the NAA/(Cho+Cr) ratio was abnormally low in the hippocampus ipsilateral to the focus (p < 0.0001), and there were lower values in both patient groups in the ipsilateral temporal neocortex (p < 0.0001). Patients with RTLE had left hippocampal MRS anomalies (p = 0.0018), whereas the right hippocampus seemed to be undamaged in LTLE patients. Conclusions: Unilateral mesial temporal lobe epilepsy is associated with widespread metabolic abnormalities which involve contralateral mesial and neocortical temporal lobe structures. These abnormalities appear to be more pronounced in patients with RTLE