Matthew F. Singh

The paper constructs dynamical models of resting-state brain networks from fMRI data, revealing anatomically reliable attractor landscapes that go beyond conventional functional connectivity analyses.

This paper develops a framework to model brain dynamics from time-series data, revealing individual differences in alpha and beta oscillations through excitation-inhibition balance.

The paper introduces the DMCC55B dataset, a collection of fMRI data from 55 healthy young adults performing cognitive control tasks, for benchmarking and analysis of task performance and individual differences.

This paper improves task fMRI preprocessing by filtering out pre-event intrinsic activity using individualized MINDy models, enhancing statistical power and behavioral prediction.

The paper presents a method to efficiently compute all fixed points of discrete systems on large networks using graph partitioning and decomposition.

This paper introduces MINDy, a method to create individualized dynamic brain models from resting state fMRI data, showing they are predictive and better at uncovering mechanisms than functional connectivity methods.

This paper introduces surrogate deconvolution to estimate hidden biological systems from indirect measurements, showing it is scalable and competitive with Kalman Filters in neuroscience applications.

This paper introduces a geometric classification method for brain network dynamics that achieves high temporal resolution and anatomical inference using conic derivative discriminants.