18 citations · 31 across the 7 of their papers we have counts for
7 papers
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures
Jessica Dafflon, Walter H. L Pinaya, Federico Turkheimer +7
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to…
Chaos in Homeostatically Regulated Neural Systems
Wilten Nicola, Peter Hellyer, Sue Ann Campbell +1
Low-dimensional yet rich dynamics often emerge in the brain. Examples include oscillations and chaotic dynamics during sleep, epilepsy, and voluntary movement. However, a general m…
Balanced activation in a simple embodied neural simulation
Peter J. Hellyer, Claudia Clopath, Angie A. Kehagia +2
In recent years, there have been many computational simulations of spontaneous neural dynamics. Here, we explore a model of spontaneous neural dynamics and allow it to control a vi…
Graph embeddings of dynamic functional connectivity reveal discriminative patterns of task engagement in HCP data
Ricardo Pio Monti, Romy Lorenz, Peter Hellyer +3
There is increasing evidence to suggest functional connectivity networks are non-stationary. This has lead to the development of novel methodologies with which to accurately estima…
Local inhibitory plasticity tunes global brain dynamics and allows the emergence of functional brain networks
Peter J. Hellyer, Barbara Jachs, Robert Leech +1
Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important t for efficient neural functi…
Exploring spatiotemporal network transitions in task functional MRI
Gregory Scott, Peter J. Hellyer, Adam Hampshire +1
A critical question for cognitive neuroscience regards how transitions between cognitive states emerge from the dynamic activity of functional brain networks. However, current meth…
Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series
Ricardo Pio Monti, Peter Hellyer, David Sharp +3
Understanding the functional architecture of the brain in terms of networks is becoming increasingly common. In most fMRI applications functional networks are assumed to be station…