Discovering dynamic brain networks from big data in rest and task

Abstract

Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network

“…The HMM has recently been applied to model neural dynamics inferred from magnetoencephalography (MEG) (Baker et al, 2014;Quinn et al, 2018;Vidaurre et al, 2016) as well as task-based and resting-state functional MRI data (Ryali et al, 2016;Vidaurre et al, 2017a;Vidaurre et al, 2017b;Taghia et al, 2018). These seminal studies provide evidence for the utility of the HMM in characterizing dynamic interactions between brain networks in healthy individuals and show that the frequency of state transitions increases with age (Ryali et al, 2016) and transitions can be organized hierarchically into cognitive and sensorimotor metastates, with dwell times in these putative metastases being heritable and correlating with cognitive traits (Vidaurre et al, 2017a).…”

Functional network dynamics in a neurodevelopmental disorder of known genetic origin

“…In recent years, whole-brain imaging methods have advanced our ability to characterise functional brain connectivity, and have revealed that distributed neural systems support cognition and behaviour (Astle, Barnes, Baker, Colclough, & Woolrich, 2015;Barnes, Woolrich, Baker, Colclough, & Astle, 2016;Smith et al, 2015;Vidaurre et al, 2017). Disruptions to functional connectivity are considered a characteristic feature of multiple developmental disorders, but surprisingly little is known about the mechanisms that drive this variability.…”

A generative model of whole-brain effective connectivity

“…Particularly, functional connectivity has been used frequently for studying the functional organization of large (whole-brain) networks both in tasks and in the "resting state" (i.e., unconstrained cognition in the absence of external perturbations). Various methods have been proposed (for a comprehensive review, see Karahanoglu and Van De Ville, 2017), ranging from conventional correlation or coherence analyses which assume stationarity (Fox et al, 2005) to sliding-window correlation analyses that can capture dynamic fluctuations in functional connectivity (Chang and Glover, 2010 NeuroImage 179 (2018) 505-529 coupled) Hidden Markov models (HMM;Bolton et al, 2018;Karahanoglu and Van De Ville, 2015;Vidaurre et al, 2017), and approaches from statistical mechanics that rely on entropy maximization (Ashourvan et al, 2017). Other functional connectivity methods have focused on sparsity (Bielczyk et al, 2018;Eavani et al, 2015;Ryali et al, 2012), including generative models that can exploit anatomical information (Hinne et al, 2014).…”