Brain and cognitive reserve: Translation via network control theory

Abstract

Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive "reserve," associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention.

Keywords: Control theory; Graph theory; Network science; Neurology; Neuropsychology.

Figure 1A–D

Schematic representation of dynamic network theory in the human brain. (a) The brain can be separated into differentiable regions based upon cellular architecture (b) or systems containing functionally similar neurons. (c) Neural activities can be represented in a network representation consisting of nodes (spheres) and edges (connectors). Dynamics (τ) are represented along edges and are the activities important to healthy functioning. Nodes and edges that support dynamics can be represented at a coarse “macro” scale of brain organization (d), which is our current focus. Reproduced with permission and modified from (Leon et al., 2013).

Figure 2

A traditional threshold model of reserve (cf. Stern, 2002). Brain and cognitive reserve are represented by measured quantities that cumulatively protect against disease. Patients with greater reserve remain above the impairment threshold following the onset of neuropathology. Patient 1 shows greater resilience to brain pathology than Patient 2 due to greater brain reserve with equivalent cognitive reserve. Patient 3 shows greater resilience to brain pathology than Patient 2 due to greater cognitive reserve with equivalent brain reserve. Patient 4 displays heightened neuroprotection due to the cumulative effects of (i) brain reserve equivalent in magnitude to that observed in Patient 1 and (ii) cognitive reserve equivalent in magnitude to that observed in Patient 3.

Figure 3

A generic classical feedback control scheme. The system is designed to track the reference value r. The output of the system y(t) is fed back through a sensor measurement F to compare to the reference value r(t). The controller C then takes the error e (difference) between the reference and the output to change the inputs u to the system under control P.

Figure 4

A schematic control scheme using a noninvasive brain stimulation technique, transcranial magnetic stimulation. TMS involves the application of a magnetic field in vivo to manipulate neural function via induced current in the cortex (Hallett, 2000). In principle this is one of a broad class of possible stimulation approaches (e.g., microstimulation, deep brain stimulation) in which a control theoretic view can be applied for translational goals. Here the reference r(t) is a particular brain state of interest, which could be an empirically determined state known to be adaptive for a target cognitive process. The controller (C) is a TMS coil that exerts a magnetic field u on a target site in the plant, which is the human brain (P). The state output y(t) is read by a feedback sensor (F) such as that sampled by continuous electroencephalographic monitoring. The sensed state is compared to the reference state for the next control iteration. Note that the controller and feedback sensor could be represented by other technologies.

Figure 5

Network Control Theory and the Connectome. Network control theory is a means for expressing the dynamic properties of the brain that mediate between structural networks and the functional properties they afford. Traditional reserve measures such as global brain size, regional size, and developmental variables (left panel) have influences on cognition via their underlying structural network configurations. In turn, structural network configurations plus local signaling dynamics establish the controllability of the brain system (center panel). Variance in network controllability implies variance in functional topologies such as those computed from functional magnetic resonance imaging (fMRI), electroencephalography, or electrocorticography. It also implies qualitatively different dynamics that support cognition. The controllability of regions results in neuroplastic changes due to controlling the flow of neural signaling cascades in addition to momentary maintenance and switching of cognition and cognitive modes. Neuroplasticity in turn exhibits feedforward influences on controllability over time.

Figure 6

A novel scientific intersection. Computational, theoretical, and practical approaches can contribute to a network control based approach to brain and cognitive research for translational benefits. The strengths of each discipline complement the limitations of the others. Cognitive neuroscience and neuropsychology provide models for cognition and examine associations between the brain and behavior. Network science allows us to describe and explain nonlocal complexity in neural systems. Control theory gives us the means to identify control roles and strategies in the neural data. The overlapping areas between each pair of fields represents potential subdisciplines in early stages of development, and the overlap among all three identifies a novel domain focused on control theory-based translational neuroscience.