Author
Deepa Nath
Bio: Deepa Nath is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Functional magnetic resonance imaging & Resting state fMRI. The author has co-authored 1 publications.
Papers
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21 May 2021
TL;DR: In this article, the authors explored the learning of resting-state functional magnetic resonance imaging connectivity and explained the significance of such connectivity analysis, the methods available for it, and the shortcomings associated with the data.
Abstract: Resting-state fMRI (rsfMRI) was firstly characterized by Biswal et al [1] in the year 1995 and since then it is used for studying patients with various neurosurgical, neurologic, and other brain-related disorders. Various statistical methods are used to analyze the resting-state functional magnetic resonance imaging connectivity. This paper attempts to explore more on the learning of resting-state functional magnetic resonance imaging connectivity. This study will help to understand the usage of rsfMRI to evaluate functional connectivity in the human brain and its usage for surgical decision-making for drug-resistant epilepsy. This paper explains the significance of such connectivity analysis, the methods available for it, and the shortcomings associated with the data.
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TL;DR: In this article , the authors investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the difference between patients with different surgical outcomes.