Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework

Tomas Ros, Bernard J Baars, Ruth A Lanius, Patrik Vuilleumier, Tomas Ros, Bernard J Baars, Ruth A Lanius, Patrik Vuilleumier

Abstract

Neurofeedback (NFB) is emerging as a promising technique that enables self-regulation of ongoing brain oscillations. However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on the manner in which NFB is able to achieve these outcomes. The present work attempts to bring together various concepts from neurobiology, engineering, and dynamical systems so as to propose a contemporary theoretical framework for the mechanistic effects of NFB. The objective is to provide a firmly neurophysiological account of NFB, which goes beyond traditional behaviorist interpretations that attempt to explain psychological processes solely from a descriptive standpoint whilst treating the brain as a "black box". To this end, we interlink evidence from experimental findings that encompass a broad range of intrinsic brain phenomena: starting from "bottom-up" mechanisms of neural synchronization, followed by "top-down" regulation of internal brain states, moving to dynamical systems plus control-theoretic principles, and concluding with activity-dependent as well as homeostatic forms of brain plasticity. In support of our framework, we examine the effects of NFB in several brain disorders, including attention-deficit hyperactivity (ADHD) and post-traumatic stress disorder (PTSD). In sum, it is argued that pathological oscillations emerge from an abnormal formation of brain-state attractor landscape(s). The central thesis put forward is that NFB tunes brain oscillations toward a homeostatic set-point which affords an optimal balance between network flexibility and stability (i.e., self-organised criticality (SOC)).

Keywords: brain computer interface (BCI); brain disorders; brain plasticity; criticality; electroencephalography (EEG); magnetoencephalography (MEG); neurofeedback; neuromodulation.

Figures

Figure 1
Figure 1
The generation of electroencephalogram (EEG) network oscillations. EEG signals are generated by the integration of neural activity at multiple spatial (A) and temporal (B) scales. After Le Van Quyen (2011).
Figure 2
Figure 2
Control of EEG (de)synchronization via shifts in intrinsic brain state. Here, a recurrent functional circuit subserving top-down attention is triggered by neocortical structures (black lines; frontal-eye fields (FEF); visual cortices (V1/V4), and reinforced by ascending neuromodulatory pathways (blue/green/red). Adapted with permission from Harris and Thiele (2011).
Figure 3
Figure 3
EEG spectral signatures of healthy and psychiatric populations. (Panel A) Mean (± SEM) EEG power spectra of healthy control subjects (red) and psychiatric patients (blue). (Panel B) Mean subgroup spectra for controls (CON) (red, n = 18), schizophrenia spectrum disorder (SSD) (purple, n = 14), obsessive-compulsive disorder (OCD) (green, n = 10), depression disorder (DD) (light blue, n = 5). From Schulman et al. (2011).
Figure 4
Figure 4
A visual portrayal of state-space landscapes.(A) A hill and valley representation of a repellor (left) and an attractor (right); (B) the shallow attractor (left) has a shorter dwell-time than the deeper attractor (right); (C) a multi-attractor landscape exhibiting multistability; (D) EEG state transitions during sleep-wake activity in the rat, comprising of whisker twitching (WT), active exploration (AE), quiet wake (QW), rapid-eye movement (REM), slow-wave sleep (SWS), intermediate stage (IS). From Gervasoni et al. (2004).
Figure 5
Figure 5
Phase-space dynamical plots of EEG rhythms during sleep. Attractor-like (limit cycle) shapes are more pronounced for alpha (A) and delta rhythms (C), compared to the beta rhythm (B). From Pradhan et al. (1995).
Figure 6
Figure 6
A prototypical closed-loop control circuit. The circuit consists of a Controller (green) which regulates the control parameter until the output of the System (blue), measured by the Sensor (red), matches the internal reference value, or set-point (±).
Figure 7
Figure 7
Attractor landscape pre-post therapeutic stimulation. (A) Before stimulation, both the pathological state (strong weights, high neuronal synchronization) and the healthy state (weak weights, low neuronal synchronization) are stable, i.e., they are local minima of an abstract energy function. (B) During stimulation, the pathological state becomes unstable and the network is driven towards the healthy state. After stimulation has stopped, the network stays in the healthy state. From Pfister and Tass (2010).
Figure 8
Figure 8
Short-term Hebbian plasticity following neurofeedback (NFB). Scatter-plot of mean alpha amplitude change across electrodes during feedback vs. resting state (post-feedback), for NFB (A) and SHAM (B) groups. The anatomical location of each subgroup of electrodes is represented by a different color (see legend). *p < 0.05, **p < 0.01. From Ros et al. (2013).
Figure 9
Figure 9
Long-term Hebbian plasticity following neurofeedback (NFB). Theta oscillation amplitude vs. ADHD clinical score change. (A) Change of total ADHD score vs. post-NFB change of theta activity. (B) Change of total ADHD score vs. pre-NFB theta activity (baseline). From Gevensleben et al. (2009).
Figure 10
Figure 10
Homeostatic “rebound” following desynchronizing neurofeedback (NFB). Left: mean (±SEM) global alpha amplitude in PTSD patients: before (Baseline 1), during (Neurofeedback), and right after neurofeedback (Baseline 2). Right: Topographic plot of mean alpha amplitude change during neurofeedback (NFB), relative to resting-state (Baseline 1). *p < 0.05, ** P < 0.005. From Kluetsch et al. (2014).

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