Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  


BibTex format

author = {Hancock, F and Cabral, J and Luppi, AI and Rosas, FE and Mediano, PAM and Dipasquale, O and Turkheimer, FE},
doi = {10.1016/j.neuroimage.2022.119433},
journal = {NeuroImage},
pages = {1--16},
title = {Metastability, fractal scaling, and synergistic information processing: what phase relationships reveal about intrinsic brain activity},
url = {},
volume = {259},
year = {2022}

RIS format (EndNote, RefMan)

AB - Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.
AU - Hancock,F
AU - Cabral,J
AU - Luppi,AI
AU - Rosas,FE
AU - Mediano,PAM
AU - Dipasquale,O
AU - Turkheimer,FE
DO - 10.1016/j.neuroimage.2022.119433
EP - 16
PY - 2022///
SN - 1053-8119
SP - 1
TI - Metastability, fractal scaling, and synergistic information processing: what phase relationships reveal about intrinsic brain activity
T2 - NeuroImage
UR -
UR -
UR -
UR -
VL - 259
ER -