Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: K-means clustering has become a popular tool for connectivity-based cortical segmentation using Diffusion ighted Imaging (DWI) data. A sometimes ignored issue is, hover, that the output of the algorithm depends on the initial placement of starting points, and that different sets of starting points therefore could lead to different solutions. In this study  explore this issue.  apply k-means clustering a thousand times to the same DWI dataset collected in 10 individuals to segment two brain regions: the SMA–preSMA on the medial wall, and the insula. At the level of single subjects,  found that in both brain regions, repeatedly applying k-means indeed often leads to a variety of rather different cortical based parcellations. By assessing the similarity and frequency of these different solutions,  show that ∼ 256 k-means repetitions are needed to accurately estimate the distribution of possible solutions. Using nonparametric group statistics,  then propose a method to employ the variability of clustering solutions to assess the reliability with which certain voxels can be attributed to a particular cluster. In addition,  show that the proportion of voxels that can be attributed significantly to either cluster in the SMA and preSMA is relatively higher than in the insula and discuss how this difference may relate to differences in the anatomy of these regions.
['Luca Nanetti', 'Leonardo Cerliani', 'Valeria Gazzola', 'Remco Renken', 'Christian Keysers']
I am writing a research paper on a idea Group analyses of connectivity-based cortical parcellation using repeated k-means clustering
[{"from": "human", "value": "I am writing a research paper on a idea Group analyses of connectivity-based cortical parcellation using repeated k-means clustering"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: K-means clustering has become a popular tool for connectivity-based cortical segmentation using Diffusion ighted Imaging (DWI) data. A sometimes ignored issue is, hover, that the output of the algorithm depends on the initial placement of starting points, and that different sets of starting points therefore could lead to different solutions. In this study  explore this issue.  apply k-means clustering a thousand times to the same DWI dataset collected in 10 individuals to segment two brain regions: the SMA\u2013preSMA on the medial wall, and the insula. At the level of single subjects,  found that in both brain regions, repeatedly applying k-means indeed often leads to a variety of rather different cortical based parcellations. By assessing the similarity and frequency of these different solutions,  show that \u223c\u00a0256 k-means repetitions are needed to accurately estimate the distribution of possible solutions. Using nonparametric group statistics,  then propose a method to employ the variability of clustering solutions to assess the reliability with which certain voxels can be attributed to a particular cluster. In addition,  show that the proportion of voxels that can be attributed significantly to either cluster in the SMA and preSMA is relatively higher than in the insula and discuss how this difference may relate to differences in the anatomy of these regions."}]