Daniel B. Forger

The paper shows that realistic cortical connectivity generates more macroscopic traveling waves than artificial connectivity in the mouse cortex, particularly in theta, alpha, and beta frequency bands.

This paper improves sleep stage prediction from wearable data by combining topological data analysis and mathematical modeling, achieving better accuracy in REM and NREM classification.

The paper simulates a whole-cortex mouse brain model to show how network connectivity and intracellular dynamics generate spatiotemporal patterns observed in experiments.

The paper develops a framework to determine active connectivity in the mouse brain by combining experimental and computational methods to identify the most active neural networks during different times of the day.

The paper develops a method to infer causality in oscillatory biological systems, outperforming existing methods and providing a computational tool for analysis.

The paper uses daylight saving time as a natural experiment to study how genetic variation influences adaptation to abrupt time changes, finding differential effects based on morning or evening genetic predisposition.

The paper presents a method combining gene weights from mathematical biology models and gene ranks from GWAS to improve gene-set enrichment analysis for bipolar disorder.

The paper extracts circadian rhythms in heart rate from wearable data to study individual variations and real-world stimuli effects.