A network engineering perspective on probing and perturbing cognition with neurofeedback

Abstract

Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition.

Keywords: cognition; control theory; graph theory; network neuroscience; neurofeedback.

Figure 1

Open‐loop perturbation of brain and behavior. Neuroscience has long exercised an open‐loop approach to probe the characteristics of neural systems. By perturbing behavior and measuring change in neural dynamics or—in reverse—perturbing neural dynamics and measuring change in behavior, this method is used to generate a map between neural dynamics and behavior. For example, consider the following set of experiments investigating the neural basis behind the perception of shapes: to generate a forward mapping between stimulus and neural response, one might measure the change in neural activity of specific brain regions to visual perturbations of object shape; to generate a reverse mapping between neural response and stimulus, one might measure the change in perception of shape due to perturbation of neural activity (perhaps through lesioning or neurostimulation). The forward and reverse mapping are limited in their ability to describe how neural activity and behavior change together on a dynamical continuum. Neurofeedback enables investigators to close the loop around forward and reverse mapping approaches—in real time.

Figure 2

Probing cognitive state with neurofeedback. Indirect perturbation of behavioral state using neurofeedback is a powerful technique to probe the cognitive state space in individuals. Suppose that one wanted to learn the neural basis underlying spatial reasoning ability using a mental manipulation task. (A) The experimenter could present the subject with a circle and a star shape, and ask the subject to observe the stimuli and mentally imagine the circle morphing into a star. Meanwhile, a statistical model could be trained to capture the voxel activation pattern pertaining to the imagined shapes. (B) To probe and quantify the subject's thought process associated with mental manipulation, the experimenter might display the circle to the subject and ask the subject to use his or her mind to manipulate the object into a star. Using the shape‐based model of voxel activation, the experiment could decode the mentally visualized shape from the voxel activation pattern—in real time—and feed the current state of the imagined shape back to the subject. By tracking how subjects explore the cognitive state space while learning how to perform mental operations during a task, investigators could map how individuals use different cognitive strategies to accomplish the task and could further map the distinct neural drivers of these strategies.

Figure 3

Multiscale topology in brain networks. Brain networks have unique organizing principles at local, meso‐, and global scales that provide information about how neural information is represented, processed, and communicated between brain regions. Brain networks are modeled as a collection of nodes—representing regions of interest with presumably coherent functional responsibilities —and edges—structural connections or functional interactions between brain regions. (A) Node centrality describes the importance of individual nodes in terms of their connectivity relative to other nodes in the network. Nodes with more connections or stronger edges tend to be hubs (red), while nodes with fewer connections tend to be isolated (blue). (B) Clustering coefficient, a measure of connectivity between the neighbors of a node, is another local measure of network topology. Unlike network topologies with strong hubness qualities, as in (A), networks with strong clustering coefficient demonstrate a high density of triangles that is believed to facilitate local information processing. (C) Modularity is a mesoscale topological property that captures communities of nodes that are tightly connected to one another and weakly connected to nodes in other communities. Modular organization underlies a rich functional specialization within individual communities. Here, nodes of different communities are colored red, blue, or pink. (D) Networks with core–periphery structure exhibit a set of tightly connected nodes (core; red) sparsely connected to a set of isolated nodes (periphery; blue). This organization is in stark contrast to the modular organization in (C). The core–periphery architecture is a characteristic of networks that integrate information from isolated regions in a central area. Adapted with permission from Ref. 127.

Figure 4

Brain network controllers drive transitions between dynamical states. To accomplish behavioral and cognitive goals, brain networks internally navigate a complex space of dynamical states. Stable brain states may lie in basins of local minimum energy—requiring the brain to expend metabolic energy to move over high‐energy peaks when transitioning from the current state to the next state. Within the space of possible dynamical states, there are easily accessible states and harder‐to‐reach states; in some cases, the accessible states are healthy, while in other cases they may contribute to dysfunction, and similarly for the harder‐to‐reach states. Two commonly observed control strategies in complex systems are average control and modal control. In average control, highly central nodes navigate the brain toward easy‐to‐reach states. In contrast, modal control nodes tend to be isolated brain regions that navigate the brain toward hard‐to‐reach states that may require additional energy expenditure.142 Adapted with permission from Ref. 127.

Figure 5

Self‐regulating brain network controllers for cognition. Neurofeedback could be used to teach individuals how to modulate brain activity in important control points that drive changes in dynamical brain state—an experimental tool that would offer tremendous opportunities for studying “cognition dynamics,” or the ability to perform specific tasks based on the current brain state. Furthermore, this approach might be used to train individuals with specific cognitive deficits to better manage their ability to perform certain types of tasks. (A) Suppose that individuals could be trained to upregulate the brain's average controller (red node) to assist in navigating different brain states associated with a specific task, such as opening up and reading a book. (B) If the subject has difficulty with switching between tasks—such as reading and doing math—he/she might be trained to upregulate his/her brain's modal controller (blue node) to switch more efficiently. (C) If the subject has difficulty with comprehension or reading aloud, he/she might be trained to upregulate his/her brain boundary controllers between functional modules associated with language and speech.