Mark A. Kramer, PhD

Professor, Department of Mathematics and Statistics

Boston University, Boston, MA, 02215

Research

We build tools to understand the brain through analysis and modeling of neural data. Topics of our recent work include:

What features characterize the dynamics of the seizing human brain?

Example publication: Schlafly, et al., Multiple sources of fast traveling waves during human seizures: resolving a controversy. J Neurosci. 42(36):6966-698, 2022.
Example publication: Spencer, et al., Source EEG reveals that Rolandic epilepsy is a regional epileptic encephalopathy. Neuroimage Clin. 33:102956, 2022.
Example publication: Kramer, et al., Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes, Brain, 142(5):1296-1309, 2019.

What fundamental principles govern the organization of brain rhythms?

Example publication: Kramer, Golden rhythms as a theoretical framework for cross-frequency organization. Neurons, Behavior, Data Analysis and Theory, 2022.

How do we track brain rhythms in real time?

Example publication: Wodeyar et al., A state space modeling approach to real-time phase estimation. eLife, 10:e68803, 2021.

How do we characterize interacting brain rhythms?

Example publication: Nadalin et al., A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects. eLife, 8:e44287, 2019.

We also build educational resources to help make sense of neural data.

Example publication: Kramer and Eden, Case Studies in Neural Data Analysis: A Guide for the Practicing Neuroscientist, MIT Press, 2016.

Case-Studies-Python: Are you a neuroscientist interested in using Python to analyze your data or teach your class? If so, check out this online resource.