Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders

Echinacoside: A promising active natural products and pharmacological agents

Echinacoside, a natural phenylethanoid glycoside, was discovered and isolated from the garden plant Echinacea angustifolia DC., belonging to the Compositae family, approximately sixty years ago. Extensive investigations have revealed that it possesses a wide array of pharmacologically beneficial activities for human health, particularly notable for its neuroprotective and anticancer activity. Several crucial concerns surfaced, encompassing the recognition of active metabolites that exhibited inadequate bioavailability in their prototype form, the establishment of precise molecular signal pathways or targets associated with the aforementioned effects of echinacoside, and the scarcity of dependable clinical trials. Hence, the question remains unanswered as to whether scientific research can effectively utilize this natural compound. To support future studies on this natural product, it is imperative to provide a systematic overview and insights into potential future prospects. The current review provides a comprehensive analysis of the existing knowledge on echinacoside, encompassing its wide distribution, structural diversity and metabolism, diverse therapeutic applications, and improvement of echinacoside bioavailability for its potential utilization.

Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer

A central hypothesis on brain functioning is that long-term potentiation (LTP) and depression (LTD) regulate the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granule cells have been shown to control the gain of signals transmitted through the mossy fiber pathway by exploiting synaptic inhibition in the glomeruli. However, the way LTP and LTD control signal transformation at the single-cell level in the space, time and frequency domains remains unclear. Here, the impact of LTP and LTD on incoming activity patterns was analyzed by combining patch-clamp recordings in acute cerebellar slices and mathematical modeling. LTP reduced the delay, increased the gain and broadened the frequency bandwidth of mossy fiber burst transmission, while LTD caused opposite changes. These properties, by exploiting NMDA subthreshold integration, emerged from microscopic changes in spike generation in individual granule cells such that LTP anticipated the emission of spikes and increased their number and precision, while LTD sorted the opposite effects. Thus, akin with the expansion recoding process theoretically attributed to the cerebellum granular layer, LTP and LTD could implement selective filtering lines channeling information toward the molecular and Purkinje cell layers for further processing.

A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits

The modeling of extended microcircuits is emerging as an effective tool to simulate the neurophysiological correlates of brain activity and to investigate brain dysfunctions. However, for specific networks, a realistic modeling approach based on the combination of available physiological, morphological and anatomical data is still an open issue. One of the main problems in the generation of realistic networks lies in the strategy adopted to build network connectivity. Here we propose a method to implement a neuronal network at single cell resolution by using the geometrical probability volumes associated with pre- and postsynaptic neurites. This allows us to build a network with plausible connectivity properties without the explicit use of computationally intensive touch detection algorithms using full 3D neuron reconstructions. The method has been benchmarked for the mouse hippocampus CA1 area, and the results show that this approach is able to generate full-scale brain networks at single cell resolution that are in good agreement with experimental findings. This geometric reconstruction of axonal and dendritic occupancy, by effectively reflecting morphological and anatomical constraints, could be integrated into structured simulators generating entire circuits of different brain areas facilitating the simulation of different brain regions with realistic models.