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 decomposition to assess microstructural similarity within amygdala subfields 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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References
Aggleton, J.P.: The Amygdala: Neurobiological Aspects of Emotion, Memory, and Mental Dysfunction, xii, 615 p. Wiley-Liss., New York; Chichester (1992)
Barr, M.L., Kiernan, J.A.: The Human Nervous System: An Anatomical Viewpoint, 6th edn, vii, 451 p. Lippincott, Philadelphia (1993)
Pitkanen, A., Savander, V., LeDoux, J.E.: Organization of intra-amygdaloid circuitries in the rat: an emerging framework for understanding functions of the amygdala. Trends Neurosci. 20(11), 517–523 (1997)
Whalen, P.J., et al.: Functional neuroimaging studies of the amygdala in depression. Semin. Clin. Neuropsychiatry. 7(4), 234–242 (2002)
Entis, J.J., et al.: A reliable protocol for the manual segmentation of the human amygdala and its subregions using ultra-high resolution MRI. Neuroimage. 60(2), 1226–1235 (2012)
Saygin, Z.M., et al.: Connectivity-based segmentation of human amygdala nuclei using probabilistic tractography. Neuroimage. 56(3), 1353–1361 (2011)
Bach, D.R., et al.: Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography. J. Neurosci. 31(2), 618–623 (2011)
Balderston, N.L., et al.: Functionally distinct amygdala subregions identified using DTI and high-resolution fMRI. Soc. Cogn. Affect. Neurosci. 10(12), 1615–1622 (2015)
Solano-Castiella, E., et al.: Diffusion tensor imaging segments the human amygdala in vivo. Neuroimage. 49(4), 2958–2965 (2010)
Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259–267 (1994)
Wu, Y.C.: Diffusion MRI: Tensors and Beyond in Medical Physics, p. 150. University of Wisconsin-Madison, Madison (2006)
Tournier, J.D., Mori, S., Leemans, A.: Diffusion tensor imaging and beyond. Magn. Reson. Med. 65(6), 1532–1556 (2011)
Alexander, D.C.: Multiple-fiber reconstruction algorithms for diffusion MRI. Ann. N. Y. Acad. Sci. 1064, 113–133 (2005)
Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52(6), 1358–1372 (2004)
Tournier, J.D., et al.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage. 23(3), 1176–1185 (2004)
Hess, C.P., et al.: Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Magn. Reson. Med. 56(1), 104–117 (2006)
Wedeen, V.J., et al.: Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 54(6), 1377–1386 (2005)
Rathi, Y., et al.: Directional functions for orientation distribution estimation. Med. Image Anal. 13(3), 432–444 (2009)
Frank, L.R.: Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magn. Reson. Med. 47(6), 1083–1099 (2002)
Alexander, D.C., Barker, G.J., Arridge, S.R.: Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med. 48(2), 331–340 (2002)
von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)
Wu, Y.C., Field, A.S., Alexander, A.L.: Computation of diffusion function measures in q-space using magnetic resonance hybrid diffusion imaging. IEEE Trans. Med. Imaging. 27(6), 858–865 (2008)
Acknowledgements
Supported in part by Dartmouth Synergy, Indiana Alzheimer Disease Center pilot grant, NIH R01 MH080716, R01 EB022574, R01 LM011360, R01 AG19771 and P30 AG10133.
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Wen, Q., Stirling, B.D., Sha, L., Shen, L., Whalen, P.J., Wu, YC. (2017). Parcellation of Human Amygdala Subfields Using Orientation Distribution Function and Spectral K-means Clustering. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds) Computational Diffusion MRI. MICCAI 2016. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-54130-3_10
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DOI: https://doi.org/10.1007/978-3-319-54130-3_10
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