Desktop version

Home arrow Computer Science arrow Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016

Source

Discussion and Conclusions

This study demonstrates that with high angular and spatial resolution diffusion imaging, the amygdala can be parceled into three subflelds. The automated clustering uses only microstructural information within the amygdala and does not require prior knowledge of histology or cortical functional projections of amygdaloid subnuclei. The physical locations of the three subflelds infer three subnuclei including lateral, basolateral, and centromedial nuclei. However, further study is warranted to validate their cortical projections by incorporating dMRI tractography to link each cluster to functionally relevant cortical regions and to compare with histologically defined subnuclei.

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.

References

  • 1. Aggleton, J.P.: The Amygdala: Neurobiological Aspects of Emotion, Memory, and Mental Dysfunction, xii, 615 p. Wiley-Liss., New York; Chichester (1992)
  • 2. Barr, M.L., Kiernan, J.A.: The Human Nervous System: An Anatomical Viewpoint, 6th edn, vii, 451 p. Lippincott, Philadelphia (1993)
  • 3. 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)
  • 4. Whalen, P.J., et al.: Functional neuroimaging studies of the amygdala in depression. Semin. Clin. Neuropsychiatry. 7(4), 234-242 (2002)
  • 5. 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)
  • 6. Saygin, Z.M., et al.: Connectivity-based segmentation of human amygdala nuclei using probabilistic tractography. Neuroimage. 56(3), 1353-1361 (2011)
  • 7. Bach, D.R., et al.: Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography. J. Neurosci. 31(2), 618-623 (2011)
  • 8. 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)
  • 9. Solano-Castiella, E., et al.: Diffusion tensor imaging segments the human amygdala in vivo. Neuroimage. 49(4), 2958-2965 (2010)
  • 10. Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259-267 (1994)
  • 11. Wu, Y.C.: Diffusion MRI: Tensors and Beyond in Medical Physics, p. 150. University of Wisconsin-Madison, Madison (2006)
  • 12. Tournier, J.D., Mori, S., Leemans, A.: Diffusion tensor imaging and beyond. Magn. Reson. Med. 65(6), 1532-1556 (2011)
  • 13. Alexander, D.C.: Multiple-fiber reconstruction algorithms for diffusion MRI. Ann. N. Y. Acad. Sci. 1064, 113-133 (2005)
  • 14. Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52(6), 1358-1372 (2004)
  • 15. 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)
  • 16. 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)
  • 17. Wedeen, V.J., et al.: Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 54(6), 1377-1386 (2005)
  • 18. Rathi, Y., et al.: Directional functions for orientation distribution estimation. Med. Image Anal. 13(3), 432-444 (2009)
  • 19. Frank, L.R.: Characterization of anisotropy in high angular resolution diffusion-weighted MRI. Magn. Reson. Med. 47(6), 1083-1099 (2002)
  • 20. 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)
  • 21. von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395-416 (2007)
  • 22. 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), 858865 (2008)
 
Source
Found a mistake? Please highlight the word and press Shift + Enter  
< Prev   CONTENTS   Next >

Related topics