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Non-invasive treatment of neurodegenerative diseases is particularly challenging in Western countries, where the population age is increasing. In this work, magnetic propagation in human head is modelled by Finite-Difference Time-Domain (FDTD) method, taking into account specific characteristics of Transcranial Magnetic Stimulation (TMS) in neurodegenerative diseases. It uses a realistic high-resolution three-dimensional human head mesh. The numerical method is applied to the analysis of magnetic radiation distribution in the brain using two realistic magnetic source models: a circular coil and a figure-8 coil commonly employed in TMS. The complete model was applied to the study of magnetic stimulation in Alzheimer and Parkinson Diseases (AD, PD). The results show the electrical field distribution when magnetic stimulation is supplied to those brain areas of specific interest for each particular disease. Thereby the current approach entails a high potential for the establishment of the current underdeveloped TMS dosimetry in its emerging application to AD and PD., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Transcranial magnetic stimulation (TMS) is the most important technique currently available to study cortical excitability. Additionally, TMS can be used for therapeutic and rehabilitation purposes, replacing the more painful transcranial electric stimulation (TES). In this paper we present an innovative and easy-to-use tool that enables neuroscientists to design, carry out and analyze scientific studies based on TMS experiments for both diagnostic and research purposes, assisting them not only in the practicalities of administering the TMS but also in each step of the entire study's workflow. One important aspect of this tool is that it allows neuroscientists to specify research designs at will, enabling them to define any parameter of a TMS study starting from data acquisition and sample group definition to automated statistical data analysis and RDF data storage. It also supports the diagnosing process by using on-line support vector machines able to learn incrementally from the diseases instances that are continuously added into the system. The proposed system is a neuroscientist-centred tool where the protocols being followed in TMS studies are made explicit, leaving to the users flexibility in exploring and sharing the results, and providing assistance in managing the complexity of the final diagnosis. This type of tool can make the results of medical experiments more easily exploitable, thus accelerating scientific progress., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)