Venkateswaran Nagarajan, Sekhar Sudarshan, Thirupatchur Sanjayasarathy Thiagarajan, Krishnan Sharath Navalpakkam, Kabaleeswaran Dinesh Kannan, Ramanathan Subbu, Narayanasamy Narendran, Jagathrakshakan Sharan Srinivas, Vignesh S R
WAran Research FoundaTion Chennai, India.
Front Neuroenergetics. 2012 Feb 1;4:2. doi: 10.3389/fnene.2012.00002. eCollection 2012.
Existing current based models that capture spike activity, though useful in studying information processing capabilities of neurons, fail to throw light on their internal functioning. It is imperative to develop a model that captures the spike train of a neuron as a function of its intracellular parameters for non-invasive diagnosis of diseased neurons. This is the first ever article to present such an integrated model that quantifies the inter-dependency between spike activity and intracellular energetics. The generated spike trains from our integrated model will throw greater light on the intracellular energetics than existing current models. Now, an abnormality in the spike of a diseased neuron can be linked and hence effectively analyzed at the energetics level. The spectral analysis of the generated spike trains in a time-frequency domain will help identify abnormalities in the internals of a neuron. As a case study, the parameters of our model are tuned for Alzheimer's disease and its resultant spike trains are studied and presented. This massive initiative ultimately aims to encompass the entire molecular signaling pathways of the neuronal bioenergetics linking it to the voltage spike initiation and propagation; due to the lack of experimental data quantifying the inter dependencies among the parameters, the model at this stage adopts a particular level of functionality and is shown as an approach to study and perform disease modeling at the spike train and the mitochondrial bioenergetics level.
现有的基于电流的捕捉尖峰活动的模型，虽然在研究神经元的信息处理能力方面很有用，但未能揭示其内部功能。开发一个能将神经元的尖峰序列作为其细胞内参数的函数进行捕捉的模型，对于患病神经元的非侵入性诊断至关重要。这是有史以来第一篇提出这样一个综合模型的文章，该模型量化了尖峰活动与细胞内能量学之间的相互依存关系。与现有的电流模型相比，我们的综合模型生成的尖峰序列将更有助于揭示细胞内能量学。现在，患病神经元尖峰的异常可以在能量学层面上建立联系并因此得到有效分析。在时频域中对生成的尖峰序列进行频谱分析将有助于识别神经元内部的异常。作为一个案例研究，我们对模型的参数进行了调整以用于阿尔茨海默病，并对其产生的尖峰序列进行了研究和展示。这项大规模的举措最终旨在涵盖神经元生物能量学的整个分子信号通路，将其与电压尖峰的起始和传播联系起来；由于缺乏量化参数之间相互依存关系的实验数据，该模型在现阶段采用了特定水平的功能，并被展示为一种在尖峰序列和线粒体生物能量学层面进行疾病建模的方法。
