Task Fmri Guided Fiber Clustering Via a Deep Clustering Method

Fiber clustering is a prerequisite step towards tract-based analysis for human brain, and it is very important to explain brain structure and function relationship. Over the last decade, it has been an open and challenging question as to what a reasonable clustering of fibers is. Specifically, the purpose of fiber clustering is to cluster the whole brain?s white matter fibers extracted from tractography into similar and meaningful fiber bundles, thus how to definite the ?similar and meaningful? metric decides the performance and possible application of a fiber clustering method. In the past, researchers typically divided the fibers into anatomical or structural similar bundles, but rarely divided them according to functional meanings. In this work, we proposed a novel fiber clustering method by adopting the functional and structural information and combined them into the input of a deep convolutional autoencoder with embedded clustering, which can better extract and use the features within the data. The experimental results show that the proposed method can cluster the whole brain?s fibers into functionally and structurally meaningful bundles.