2 CGS-M

2.1 Abstract

Current efforts to understand how the human brain causes individuals to behave differently have focused on neuroimaging methods that treat brain activity as static in time. However, brain activity is inherently variable from moment to moment, and this variability appears to provide the neural substrate for optimizing behaviour in our often unpredictable environment. This suggests that paradigms involving variable tasks could be used as neural “stress tests” that emphasize individual-specific features of brain activity that relate to differences in behaviour. Using electroencephalographic data from the Healthy Brain Network (n = 2,630), the proposed research will compare static (functional connectivity) and variable (dynamic functional connectivity) metrics of brain activity to test (1) how these metrics capture individual-specific features of intrinsic and task-evoked brain states during naturalistic and cognitive tasks, and (2) how each metric performs at predicting individual differences in cognitive ability and behaviour. I expect that each metric will capture different aspects of individual-specific features of brain activity. I also expect dynamic functional connectivity to be better at predicting individual differences in cognitive ability and behaviour, due to it capturing richer information than functional connectivity. The proposed research accomplishes two important goals: First, it addresses several calls in the literature to explore alternative metrics and paradigms that may move us closer to understanding the neural causes of differences in behaviour. Second it lays the empirical groundwork for future neuroscience research seeking to develop neuroimaging-based biomarkers with real-world utility in areas such as biometrics and human lifespan development.

Keywords: Brain-Behaviour Relationship, Intrinsic Brain Activity, Task-evoked Brain Activity, Brain Dynamics, Brain Signal Variability, State-to-State Transitions, Individual Differences, Behaviour Prediction, Functional Connectivity, Electroencephalography (EEG)

2.2 Research Proposal

A central goal of human neuroscience is to understand how the brain causes behaviour by measuring and interpreting brain activity under a variety of conditions (Adolphs, 2015). The common form to this approach involves averaging brain and behavioural data across individuals to infer how general patterns of brain activity relate to some aspect of behaviour. However, this approach has two shortcomings: First, it encourages a reactive view of brain function, overemphasizing the role of task-evoked factors despite the strongly intrinsic and only moderately state-dependent nature of brain activity (Gratton et al., 2018). Second, it does not address how individual brains are linked to individual differences in behaviour, as averaging data obscures individual-specific features of brain activity and behaviour (Speelman & McGann, 2013). Nonetheless, emerging research has revealed that differences in individual-specific features of intrinsic and task-evoked brain activity are stable (Gratton et al., 2018) and predictive of individual differences in cognitive ability (Finn et al., 2015) and behaviour (Seitzman et al., 2019). Accordingly, these differences should no longer be ignored when studying how the human brain causes behaviour.

Current efforts to address this gap have used neuroimaging methods that treat intrinsic and task-evoked activity in brain areas (Elliott et al., 2020) or networks (Marek et al., 2020) as static in time; however, brain activity is inherently variable from moment to moment (Faisal, Selen, & Wolpert, 2008), and this variability appears to provide the substrate for effective brain function, permitting the adaptability and efficiency needed to optimize responses to our often unpredictable environment (Garrett et al., 2013). Intriguingly, this suggests that paradigms involving transitions from intrinsic to task-evoked states could be used as “stress tests” that emphasize individual differences in brain function optimization in response to changing conditions (see Finn et al., 2017). Therefore, the objective of the proposed research is to compare static and variable metrics of brain activity in order to test (1) how these metrics capture individual-specific features of intrinsic and task-evoked brain states, and (2) how each metric performs at predicting individual differences in cognitive ability and behaviour.

I will use data from the Healthy Brain Network (Alexander et al., 2017) to test the relationship between intrinsic and task-evoked brain states, cognitive ability, and behaviour. The Healthy Brain Network is a high-quality open data set containing cognitive ability data measured with the Wechsler Intelligence Scale (Wechsler, 2008, 2014), as well as electroencephalographic (EEG) data collected during intrinsic (resting, natural movie watching) and task-evoked (learning, decision-making, attention) brain states, for 2,630 participants (ages 5-21). Within each participant, I will use functional connectivity (Bastos & Schoffelen, 2016) and dynamic functional connectivity (Hutchison et al., 2013) to capture time-averaged and time-variable connectivity between brain regions that share functional properties during intrinsic and task-evoked states. I will then use a two-fold cross-validation approach (see Kriegeskorte, 2015) to (1) select connectivity features strongly correlated with cognitive ability and behaviour, (2) build a model based on half the participants, and (3) use the model to predict cognitive ability and behaviour of the remaining participants. I expect that the connectivity metrics will provide orthogonal but complimentary information about individual-specific features of intrinsic and task-evoked states, reflecting each metric’s ability to capture a different aspect of brain function (Hutchison et al., 2013). I also expect dynamic functional connectivity to be better at predicting individual differences in cognitive ability and behaviour, due to it capturing richer information about brain function than functional connectivity (Hutchison et al., 2013).

By capturing individual-specific features of intrinsic and task-evoked brain states and using these to predict cognitive ability and behaviour within a heterogeneous population, the proposed research accomplishes two important goals: First, it addresses several calls in the literature (Elliott et al., 2020; Finn et al., 2017, 2015; Garrett et al., 2013; Gratton et al., 2018; Marek et al., 2020; Seitzman et al., 2019; Speelman & McGann, 2013) to explore alternative metrics and paradigms that may move us closer to understanding the neurological causes of differences in behaviour. Second it lays the empirical groundwork for future neuroscience research seeking to develop neuroimaging-based biomarkers with real-world utility in areas such as biometrics (Chan, Kuo, Cheng, & Chen, 2018) and human lifespan development (Garrett et al., 2013). An in-depth version of this research proposal will be preregistered on the Open Science Framework (Center for Open Science, 2020) prior to analyses, and all EEG preprocessing scripts, analysis scripts, and written work will be shared openly.

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