Spatial alignment of human cortex by matching hierarchical patterns of functional connectivity

2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Volumn , Issue , 2014, Pages 329-332

  Li, Hongming ^a   Fan, Yong ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

Human cortex; Inter subject alignment; Local functional connectivity pattern

Indexed keywords

MEDICAL IMAGING;

FUNCTIONAL CONNECTIVITY; FUNCTIONAL SIGNALS; HIERARCHICAL PATTERNS; HIERARCHICAL STRUCTURES; HUMAN CORTEX; LOCAL FUNCTIONAL CONNECTIVITY PATTERNS; RESTING-STATE FMRI; SPATIAL ALIGNMENT;

PROBABILITY DISTRIBUTIONS;

EID: 84921723258     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/isbi.2014.6867875     Document Type: Conference Paper

References (13)

1. Ng, B., G. Hamarneh, and R. Abugharbieh, Modeling brain activation in fMRI using group MRF. IEEE Trans Med Imaging, 2012. 31(5): p. 1113-23.
2. Sabuncu, M.R., et al., Function-based intersubject alignment of human cortical anatomy. Cereb Cortex, 2010. 20(1): p. 130-40.
3. Conroy, B.R., et al., Inter-subject alignment of human cortical anatomy using functional connectivity. Neuroimage, 2013. 81: p. 400-11.
4. Langs, G., et al. Functional Geometry Alignment and Localization of Brain Areas. in Neural Information Processing Systems. 2010.
5. Jiang, D., et al. fMRI alignment based on local functional connectivity patterns. in SPIE Medical Imaging. 2012.
6. Jiang, D., et al., Groupwise spatial normalization of fMRI data based on multi-range functional connectivity patterns. Neuroimage, 2013. 82: p. 355-72.
7. Yeo, B.T., et al., Spherical demons: fast diffeomorphic landmark-free surface registration. IEEE Trans Med Imaging, 2010. 29(3): p. 650-68.
8. Fischl, B. Freesurfer. Available from: http://surfer.nmr.mgh.harvard.edu.
9. Bishop, C., Pattern recognition and machine learning. 2006: Springer, Newyork.
10. Cox, R. AFNI. Available from: http://afni.nimh.nih.gov/.
11. Laird, A.R., et al., Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J Neurosci, 2009. 29(46): p. 14496-505.
12. Calhoun, V.D., et al., A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp, 2001. 14(3): p. 140-51.
13. Du, Y. and Y. Fan, Group information guided ICA for fMRI data analysis. Neuroimage, 2013. 69: p. 157-97.