Research Interests

My interests are in digital signal and image processing, machine learning, artificial intelligence, data science, and generally any problem that can be solved using a computer. As a graduate research assistant at TReNDS, I analyze high dimensional brain imaging data to identify predictive patterns of function in the healthy and diseased brain.

Google Scholar profile:

Journal Articles

  1. Salman, M. S., Wager, T., Damaraju, E., Abrol, A., Vergara, V., Fu, Z., & Calhoun, V. (2021). An Approach to Automatically Label & Order Brain Activity/Component Maps. Brain Connectivity, brain.2020.0950.
  2. Abrol, A., Fu, Z., Salman, M., Silva, R., Du, Y., Plis, S., & Calhoun, V. (2021). Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nature Communications, 12(1), 353.
  3. Du, Y., Fu, Z., Sui, J., Gao, S., Xing, Y., Lin, D., Salman, M., Abrol, A., Rahaman, M. A., Chen, J., Hong, L. E., Kochunov, P., Osuch, E. A., & Calhoun, V. D. (2020). NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage: Clinical, 102375.
  4. Vergara, V. M., Salman, M., Abrol, A., Espinoza, F. A., & Calhoun, V. D. (2020). Determining the Number of States in Dynamic Functional Connectivity Using Cluster Validity Indexes. Journal of Neuroscience Methods, 108651.
  5. Qi, S., Sui, J., Chen, J., Liu, J., Jiang, R., Silva, R., Iraji, A., Damaraju, E., Salman, M., Lin, D., Fu, Z., Zhi, D., Turner, J. A., Bustillo, J., Ford, J. M., Mathalon, D. H., Voyvodic, J., McEwen, S., Preda, A., … Calhoun, V. D. (2019). Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia. Human Brain Mapping, hbm.24632.
  6. Salman, M. S., Du, Y., Lin, D., Fu, Z., Fedorov, A., Damaraju, E., Sui, J., Chen, J., Mayer, A., Posse, S., Mathalon, D., Ford, J. M., Van Erp, T., & Calhoun, V. D. (2019). Group ICA for identifying biomarkers in schizophrenia: ‘Adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression. NeuroImage: Clinical, 101747.
  7. Salman, M. S., Vergara, V. M., Damaraju, E., & Calhoun, V. D. (2019). Decreased Cross-Domain Mutual Information in Schizophrenia From Dynamic Connectivity States. Frontiers in Neuroscience, 13, 873.
  8. Du, Y., Pearlson, G. D., Lin, D., Sui, J., Chen, J., Salman, M., Tamminga, C. A., Ivleva, E. I., Sweeney, J. A., Keshavan, M. S., Clementz, B. A., Bustillo, J., & Calhoun, V. D. (2017). Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder. Human Brain Mapping, 38(5), 2683–2708.

Conference Proceedings

  1. Salman, M., Du, Y., Damaraju, E., Lin, Q., & Calhoun, V. D. (2017). Group information guided ICA shows more sensitivity to group differences than dual-regression. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 362–365.
  2. Salman, M. S., Vergara, V. M., Damaraju, E., & Calhoun, V. D. (2018). Weak Mutual Information Between Functional Domains in Schizophrenia. 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 1362–1366.
  3. Salman, M. S., Wager, T. D., Damaraju, E., Abrol, A., & Calhoun, V. D. (2020). Fully automated ordering and labeling of ICA components. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1110–1114.
  4. Salman, Mustafa S., Du, Y., & Calhoun, V. D. (2017). Identifying FMRI dynamic connectivity states using affinity propagation clustering method: Application to schizophrenia. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 904–908. 1109/ICASSP.2017.7952287

Reviewing Services

Frontiers in Neuroscience, Human Brain Mapping