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E. Caruyer and Verma, R., On facilitating the use of HARDI in population studies by creating rotation-invariant markers., Med Image Anal, vol. 20, no. 1, pp. 87-96, 2015.
F. Zhang, Savadjiev, P., Caí, W., Song, Y., Verma, R., Westin, C. - F., and O'Donnell, L. J., Fiber clustering based white matter connectivity analysis for prediction of Autism Spectrum Disorder using diffusion tensor imaging, in Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016.
T. D. Satterthwaite, Wolf, D. H., Erus, G., Ruparel, K., Elliott, M. A., Gennatas, E. D., Hopson, R., Jackson, C., Prabhakaran, K., Bilker, W. B., Calkins, M. E., Loughead, J., Smith, A., Roalf, D. R., Hakonarson, H., Verma, R., Davatzikos, C., Gur, R. C., and Gur, R. E., Functional maturation of the executive system during adolescence., J Neurosci, vol. 33, no. 41, pp. 16249-61, 2013.
Y. Ghanbari, Bloy, L., Shankar, V., J Edgar, C., Roberts, T. P. L., Schultz, R. T., and Verma, R., Functionally driven brain networks using multi-layer graph clustering., Med Image Comput Comput Assist Interv, vol. 17, no. Pt 3, pp. 113-20, 2014.
P. Savadjiev, Rathi, Y., Bouix, S., Smith, A. R., Schultz, R. T., Verma, R., and Westin, C. - F., Fusion of white and gray matter geometry: a framework for investigating brain development., Med Image Anal, vol. 18, no. 8, pp. 1349-60, 2014.
Y. Ghanbari, Smith, A. R., Schultz, R. T., and Verma, R., Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding., Med Image Anal, vol. 18, no. 8, pp. 1337-48, 2014.
M. Ingalhalikar, Smith, A. R., Bloy, L., Gur, R., Roberts, T. P. L., and Verma, R., Identifying sub-populations via unsupervised cluster analysis on multi-edge similarity graphs., Med Image Comput Comput Assist Interv, vol. 15, no. Pt 2, pp. 254-61, 2012.
G. L. Baum, Roalf, D. R., Cook, P. A., Ciric, R., Rosen, A. F. G., Xia, C., Elliott, M. A., Ruparel, K., Verma, R., Tunc, B., Gur, R. C., Gur, R. E., Bassett, D. S., and Satterthwaite, T. D., The impact of in-scanner head motion on structural connectivity derived from diffusion MRI., Neuroimage, vol. 173, pp. 275-286, 2018.
D. R. Roalf, Quarmley, M., Elliott, M. A., Satterthwaite, T. D., Vandekar, S. N., Ruparel, K., Gennatas, E. D., Calkins, M. E., Moore, T. M., Hopson, R., Prabhakaran, K., Jackson, C. T., Verma, R., Hakonarson, H., Gur, R. C., and Gur, R. E., The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort., Neuroimage, vol. 125, pp. 903-919, 2016.
B. Tunc, Ingalhalikar, M., Parker, D., Lecoeur, J., Singh, N., Wolf, R. L., Macyszyn, L., Brem, S., and Verma, R., Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning., Neurosurgery, vol. 79, no. 4, pp. 568-77, 2016.
L. Bloy, Ingalhalikar, M., Batmanghelich, N. K., Schultz, R. T., Roberts, T. P. L., and Verma, R., An integrated framework for high angular resolution diffusion imaging-based investigation of structural connectivity., Brain Connect, vol. 2, no. 2, pp. 69-79, 2012.