Research and Publications

Recent publications 

(Google Scholar Webpage [link])

Sanchez-Romero, R., Ito, T., Mill, R. D., Hanson, S. J., & Cole, M. W. (2023). Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations. NeuroImage (in press). Preprint: bioRxiv [bioRxiv] 

Cocuzza, C. V., Sanchez-Romero, R., Ito, T., Mill, R. D., Keane, B. P., & Cole, M. W. (2022). Distributed network processes account for the majority of variance in localized visual category selectivity. bioRxiv [bioRxiv] [github]

Cocuzza, C. V., Sanchez-Romero, R., & Cole, M. W. (2022). Protocol for activity flow mapping of neurocognitive computations using the Brain Activity Flow Toolbox. STAR Protocols, 3(1), 101094. [link] [Actflow Toolbox github]

Cole, M. W., Ito, T., Cocuzza, C., & Sanchez-Romero, R. (2021). The functional relevance of task-state functional connectivity. Journal of Neuroscience, 41(12), 2684-2702. [link] [github]

Sanchez-Romero, R., & Cole, M. W. (2021). Combining multiple functional connectivity methods to improve causal inferences. Journal of Cognitive Neuroscience, 33(2), 180-194. [link] [CombinedFC github]

Huang, B., Zhang, K., Zhang, J., Ramsey, J., Sanchez-Romero, R., Glymour, C., & Schölkopf, B. (2020). Causal discovery from heterogeneous/nonstationary data. Journal of Machine Learning Research, 21(89), 1-53. [link] [code part of causal-learn github]

Reid, A. T., Headley, D. B., Mill, R. D., Sanchez-Romero, R., Uddin, L. Q., Marinazzo, D., ... & Calhoun, V. (2019). Advancing functional connectivity research from association to causation. Nature neuroscience, 1-10. [link] 

Sanchez-Romero, R., Ramsey, J.D., Zhang, K., & Glymour, C. (2019). Identification of Effective Connectivity Subregions. arXiv. [arXiv]

Sanchez-Romero, R., Ramsey, J. D., Zhang, K., Glymour, M. R., Huang, B., & Glymour, C. (2019). Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods . Network Neuroscience, 3(2), 274-306. [link] [data repository] [Two-Step algorithm github]

Variable definition for brain connectivity

Correctly defining brain variables for connectivity analysis, at different spatial and temporal scales, is an ongoing challenge in neuroimaging. The goal is to accurately delimit distinct functional or anatomical regions of interest (ROIs) that may be used as variables in connectivity analysis pipelines.

Sanchez-Romero, R., Ramsey, J.D., Zhang, K., & Glymour, C. (2019). Identification of Effective Connectivity Subregions. arXiv. [arXiv]

Sanchez-Romero, R., Ramsey, J. D., Liang, J. C., & Glymour, C. (2017). Identification of Mechanisms of Functional Signaling Between Human Hippocampus Regions. bioRxiv. [biorXiv]

Sanchez-Romero, R., Ramsey, J. D., Liang, J. C., Jarbo, K., & Glymour, C. (2016). Estimation of Voxelwise Effective Connectivities: Applications to High Connectivity Sub-Regions within Hippocampal and within Corticostriatal Networks. bioRxiv, 039057. [biorXiv]

*This paper was presented as a poster in the Society for Neuroscience Meeting, Chicago 2015. [poster]

Sanchez-Romero, R. (2012). Formation of Variables for Brain Connectivity. MS Thesis. Carnegie Mellon University. [pdf]

Causal inference algorithms for fMRI data

A considerable number of techniques for inferring functional and effective connectivity have been developed in the brain mapping community during the last decade. Our group has been working in tailoring Bayes Nets methods to fMRI data. We hope to extract information about statistical dependencies between brain variables and, when possible, derive causal hypotheses about the mechanisms governing functional interactions between brain regions during task and at rest.

Sanchez-Romero, R., & Cole, M. W. (2021). Combining multiple functional connectivity methods to improve causal inferences. Journal of Cognitive Neuroscience, 33(2), 180-194. [link]  [CombinedFC github]

Reid, A. T., Headley, D. B., Mill, R. D., Sanchez-Romero, R., Uddin, L. Q., Marinazzo, D., ... & Calhoun, V. (2019). Advancing functional connectivity research from association to causation. Nature neuroscience, 1-10. [link]

Sanchez-Romero, R., Ramsey, J. D., Zhang, K., Glymour, M. R., Huang, B., & Glymour, C. (2019). Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods . Network Neuroscience, 3(2), 274-306.  [link] [data repository] [Two-Step algorithm github]

*An early version of this work was presented as a poster in the 7th International Workshop on Pattern Recognition in Neuroimaging, Toronto 2017. [poster]

Huang, B., Zhang, K., Zhang, J., Sanchez-Romero, R., Glymour, C., & Scholkopf, B. (2017, November). Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. In 2017 IEEE International Conference on Data Mining (ICDM) (pp. 913-918). IEEE. [link]

Ramsey, J.D., Glymour, M., Sanchez-Romero, R., & Glymour, C. (2017). A million variables and more: The Fast Equivalence Greedy Search algorithm for learning high dimensional graphical causal models, with an Application to functional magnetic resonance images. International Journal of Data Science and Analytics. [link]

Ramsey, J. D., Sanchez-Romero, R., & Glymour, C. (2014). Non-Gaussian methods and high-pass filters in the estimation of effective connections. NeuroImage, 84, 986-1006. [pubmed]

*This paper was presented as a poster in the Fourth Biennial Conference in Resting State / Brain Connectivity, Boston 2014. [poster]

Center for Causal Discovery CCD  (University of Pittsburgh and Carnegie Mellon University)            

*Patient classification with whole-brain causal networks

As part of the Center for Causal Discovery (CCD), the Brain Functional Connectivity project seeks to discover the causal influences among small spatial regions of the human brain. The ability to identify accurately active causal pathways (“effective connections”) resolved at the voxel level for the entire cortex is within reach and offers the prospect of much finer diagnoses and classifications of disorders and improved monitoring of treatment effects.

We are performing statistical causal analysis on fMRI data from individuals with autism spectrum disorder and typical controls. We want to characterize causal network differences between these groups. We plan to perform a similar investigation on fMRI data of individuals with schizophrenia.

Access the Center for Causal Discovery CCD website here: [link]

Glymour, M., Sanchez-Romero, R., Ramsey, J.D., Zhang, K., Huang, B., Glymour C., (2016). Fusiform and Cerebellum rs-fMRI Connectivity Implicated in ASD. Poster presented in the Cognitive Neuroscience Society Meeting, New York, 2016. [poster]

(Listed as "Comparative Resting-State MRI Effective Connectivity in Austism Spectrum Disorder")

 Philosophy of Neuroscience

Glymour, C., & Sanchez-Romero, R., (2018). Helmholtz’s Vision: Underdetermination, Behavior and the Brain. In M. Sprevak & M. Colombo (Eds.), The Routledge Handbook of the Computational Mind. Routledge. [link]