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NeuroImage v.162, 2017년, pp.226 - 248   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

A flexible graphical model for multi-modal parcellation of the cortex

Parisot, Sarah    (Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London, SW7 2AZ, UK   ); Glocker, Ben    (Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London, SW7 2AZ, UK   ); Ktena, Sofia Ira    (Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London, SW7 2AZ, UK   ); Arslan, Salim    (Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London, SW7 2AZ, UK   ); Schirmer, Markus D.    (Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA   ); Rueckert, Daniel    (Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London, SW7 2AZ, UK  );
  • 초록  

    Abstract Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal parcellation task. At each iteration, we compute a set of parcellations from different modalities and fuse them based on their local reliabilities. The fused parcellation is used to initialise the next iteration, forcing the parcellations to converge towards a set of mutually informed modality specific parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population. Highlights An iterative graphbased cortex parcellation method for multimodal data. A flexible approach to integrate different modalities and exploit their reliabilities. Grouplevel parcellations computed for two different multimodal configurations. Coarse modality specific parcellations computed for quantitative evaluations. Extensive quantitative and qualitative evaluation using a broad set of criteria. Graphical abstract [DISPLAY OMISSION]


  • 주제어

    Cortex parcellation .   functional Magnetic Resonance Imaging .   diffusion Magnetic Resonance Imaging .   Markov random fields .   Connectomics .   Brain connectivity.  

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