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Current Projects & Manuscripts

For a complete list of publications and collaborations, visit ResearchGate.

Rudolph M. D., Muscatell K. A., & Cohen J. R. (2021). Utilizing Cognitive Neuroscience and Machine Learning to Advance Prediction of Cognitive Impairment in Breast Cancer Survivors. Synopsis: This initial small-sample study sought to assess the complexity of cancer-related cognitive impairment (CRCI) and determine the probability that BCS, within 1-12 months of treatment with chemotherapy, could be accurately distinguished from healthy control individuals based on a combination of self-report measures, neuropsychological assessments commonly used in CRCI-related studies, and three experimental cognitive paradigms that decompose specific components of attention, memory, and executive functioning. Analytical Methods: Random forest classification, cross-validation.

Rudolph M. D., Cohen J. R., & Madden D. J. (2021). Global & local impacts of white matter hyperintensities (WMH) on structural brain network organization and communication capacity in cognitive aging (In Prep).  Synopsis: Multivariate prediction analyses assess the global and local impact of white matter lesions (FLAIR) on age-dependent structural brain (diffusion MRI) connectivity, network topology, and cognitive functioning. WMHs, particularly lesions in close proximity to the lateral ventricles, increase exponentially with age. A key focus of this work is to establish how WMHs impact local and global aspects of brain functioning via disruption of cortical-subcortical connections. Collaborator: Dr. David J. Madden at Duke University. Analytical Methods: DWI, graph theory, partial-least squares regression, cross-validation, statistical mediation.

Rudolph M. D., Cohen J. R., & Dayan A. (2022). A comparison of functional brain network redundancy measures in individuals with and without Alzheimer’s disease (In Prep). Synopsis: A key aspect of this work is to identify early neural markers of cognitive aging and degeneration in individuals at-risk for developing AD. Three graph theoretical metrics (clustering coefficient, redundancy, and communicability) may be used to quantify the brains resilience to normative and pathological aging processes. Each metric may uniquely capture the presence of duplicate elements and alternate routing strategies in (functional or structural) brain networks that support normative brain functioning in the event of focal attack, or in response to accumulation of adverse events over the lifespan. Here, metrics assessing network redundancy are used to compare functional brain networks in a large sample of typically and atypically aging individuals (cognitively normal, AD-converters, and current-AD) from the Open Access Series of Imaging Studies (OASIS) initiative. The effect of network thresholding on metrics computed using binary and weighted graphs is assessed. Analytical Methods: Intrinsic fMRI, graph theory, permutation testing.

Rudolph M. D. (2020). Aging, Cancer, & Dementia: A Review of Shared & Unique Mechanisms Underlying Cognitive Dysfunction & Neuropathology to Inform Data-Driven Network-Based Models of Risk & Resilience (Accepted). Synopsis: Comprehensive review submitted to the Department of Psychology & Neuroscience at UNC Chapel Hill to fulfill PhD requirements.

Recent Projects & Collaborations

  • Alvarez G. M., Rudolph M. D., Cohen J. R., & Muscatell K. A. (2021). Lower Socioeconomic Position is Associated with Greater Activity and Integration within an Allostatic-Interoceptive Brain System in Response to Affective Stimuli (Submitted). Synopsis: This study examined the association between socioeconomic position (i.e., education, income) and affective reactivity among 122 mid-to-late-life adults. Methods: Dynamic functional connectivity, task-based fMRI, graph theory.

Selected Publications


Featured Talks


  • Impact of Motion on Functional Connectivity in Development (2018). Objective: Discuss the use of global signal regression and the deleterious effects of in-scanner motion on scan quality and implications for conducting network analyses.

  • Society for Research & Child Development (2017). Objective: Discuss the negative impacts of pre and postnatal stress on newborn functional brain [network] development. Invited to speak on early-life adversity and socioeconomic status or position.

Selected Posters & Presentations


  • Rudolph M. D., Muscatell K. A., Cohen J. R. (2019). Assessing Cognitive Impairment in Specific Components of Cognition in Women Breast Cancer Survivors. Aging Conference

  • Rudolph M. D., Fair D. A. (2018). Maternal IL-6 during pregnancy can be estimated from newborn brain connectivity and predicts future working memory in offspring. FLUX Satellite Conference

  • Rudolph M. D., Fair D. A. (2014). Community Detection & Network Topology During the First Year of Life – A Resting-State Functional Connectivity Study. FLUX Conference

  • Rudolph M. D., Nagel B. J. (2013). Segmenting the insular cortex in adolescents – A resting state functional connectivity study. Society for Neuroscience Conference

  • Griesar W., Rudolph M. D., et al. (2013). Cross-institutional collaboration in neuroscience outreach: Undergraduates, graduates, middle and high schoolers get together to learn about the brain. Society for Neuroscience Conference

Mentored Projects & Presentations


  • North Carolina School of Science & Mathematics (High-School)
    • Gonzalez I., Cohen J. R., Rudolph M. D. (2021-2022). Structural Brain Network Alterations in Aging

    • Emehel C., Cohen J. R., Rudolph M. D. (2019-2020). Assessing the Effects of Depression and Stress on Cognition in Breast Cancer Survivors

    • Liu C., Cohen J. R., Rudolph M. D. (2018-2019). Fluctuation Rates within the D2 Test of Attention in Relation to Sustained Attention

  • Oregon Health & Science University (Undergraduate)
    • Zhu J., Fair D. A., Rudolph M. D. (2014-2015). Classification and Prediction of Neural Networks Influenced by Emotional Contexts Using Resting State Functional Connectivity and Machine-Learning.