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Parcellation of Human Amygdala Subfields Using Orientation Distribution Function and Spectral K-means Clustering

Qiuting Wen, Brian D. Stirling, Long Sha, Li Shen, Paul J. Whalen, and Yu-Chien Wu

Abstract Amygdala plays an important role in fear and emotional learning, which are critical for human survival. Despite the functional relevance and unique circuitry of each human amygdaloid subnuclei, there has yet to be an efficient imaging method for identifying these regions in vivo. A data-driven approach without prior knowledge provides advantages of efficient and objective assessments. The present study uses high angular and high spatial resolution diffusion magnetic resonance imaging to generate orientation distribution function, which bears distinctive microstructural features. The features were extracted using spherical harmonic

Q. Wen ? L. Shen

Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Goodman Hall, 355 West 16th Street, Suite 4100, Indianapolis, IN, 46202, USA

e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it B.D. Stirling

Division of Biokinesiology and Physical Therapy, University of Southern California,

Los Angeles, CA, 90089, USA

Department of Psychological and Brain Sciences and Dartmouth Brain Imaging Center, Dartmouth College, 6207, Moore Hall, Hanover, NH, 03755, USA e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it

L. Sha

Neuroscience Institute, New York University, New York, NY, 10016, USA

Department of Psychological and Brain Sciences and Dartmouth Brain Imaging Center, Dartmouth College, 6207, Moore Hall, Hanover, NH, 03755, USA

P.J. Whalen

Department of Psychological and Brain Sciences and Dartmouth Brain Imaging Center, Dartmouth College, 6207, Moore Hall, Hanover, NH, 03755, USA

Y.-C. Wu (H)

Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, 90089, USA

Department of Psychological and Brain Sciences and Dartmouth Brain Imaging Center, Dartmouth College, 6207, Moore Hall, Hanover, NH, 03755, USA e-mail: This email address is being protected from spam bots, you need Javascript enabled to view it © Springer International Publishing AG 2017

A. Fuster et al. (eds.), Computational Diffusion MRI, Mathematics

and Visualization, DOI 10.1007/978-3-319-54130-3_10

decomposition to assess microstructural similarity within amygdala subflelds that are identified via similarity matrices using spectral k-mean clustering. The approach was tested on 32 healthy volunteers and three distinct amygdala subfields were identified including medial, posterior-superior lateral, and anterior-inferior lateral.

 
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