Association between Scale-Free Brain Dynamics and Behavioral Performance: Functional MRI Study in Resting State and Face Processing Task

From the brain's encoding of input dynamics to its behavior: neural dynamics shape bias in decision making

The human brain is tightly connected to the individual's environment and its input dynamics. How the dynamics of periodic environmental stimuli influence neural activity and subsequent behavior via neural entrainment or alignment is not fully clear yet, though. This study explores how periodic environmental stimuli influence neural activity and behavior. EEG data was collected during a Go-NoGo task with a periodic intertrial interval (ITI) of 1.3 s (0.769 Hz). Results showed that the task's temporal structure increased power spectrum activity at 0.769 Hz, which showed high intersubject variability. Higher task-periodicity effects were linked to stronger phase-based intertrial coherence (ITC) and reduced neural complexity, as measured by lower Lempel-Ziv Complexity (LZC). Additionally, higher periodicity in the power spectrum correlated with faster reaction times and stronger response bias. We conclude that the encoding of the inputs' dynamics into the brains power spectrum shapes subsequent behavior, e.g., RT and response bias, through reducing neural complexity.

Differential associations between scale-free neural dynamics and different levels of cognitive ability

As indicators of cognitive function, scale-free neural dynamics are gaining increasing attention in cognitive neuroscience. Although the functional relevance of scale-free dynamics has been extensively reported, one fundamental question about its association with cognitive ability remains unanswered: is the association universal across a wide spectrum of cognitive abilities or confined to specific domains? Based on dual-process theory, we designed two categories of tasks to analyze two types of cognitive processes-automatic and controlled-and examined their associations with scale-free neural dynamics characterized from resting-state electroencephalography (EEG) recordings obtained from a large sample of human adults (N = 102). Our results showed that resting-state scale-free neural dynamics did not predict individuals' behavioral performance in tasks that primarily engaged the automatic process but did so in tasks that primarily engaged the controlled process. In addition, by fitting the scale-free parameters separately in different frequency bands, we found that the cognitive association of scale-free dynamics was more strongly manifested in higher-band EEG spectrum. Our findings indicate that resting-state scale-free dynamics are not universal neural indicators for all cognitive abilities but are mainly associated with high-level cognition that entails controlled processes. This finding is compatible with the widely claimed role of scale-free dynamics in reflecting properties of complex dynamic systems.

Variability and task-responsiveness of electrophysiological dynamics: scale-free stability and oscillatory flexibility

Cortical oscillations and scale-free neural activity are thought to influence a variety of cognitive functions, but their differential relationships to neural stability and flexibility has never been investigated. Based on the existing literature, we hypothesize that scale-free and oscillatory processes in the brain exhibit different trade-offs between stability and flexibility; specifically, cortical oscillations may reflect variable, task-responsive aspects of brain activity, while scale-free activity is proposed to reflect a more stable and task-unresponsive aspect. We test this hypothesis using data from two large-scale MEG studies (HCP: n = 89; CamCAN: n = 195), operationalizing stability and flexibility by task-responsiveness and spontaneous intra-subject variability in resting state. We demonstrate that the power-law exponent of scale-free activity is a highly stable parameter, which responds little to external cognitive demands and shows minimal spontaneous fluctuations over time. In contrast, oscillatory power, particularly in the alpha range (8-13 Hz), responds strongly to tasks and exhibits comparatively large spontaneous fluctuations over time. In sum, our data support differential roles for oscillatory and scale-free activity in the brain with respect to neural stability and flexibility. This result carries implications for criticality-based theories of scale-free activity, state-trait models of variability, and homeostatic views of the brain with regulated variables vs. effectors.

Power spectrum of resting-state blood-oxygen-level-dependent signal

Hemodynamic modeling is used to explore the origin, predict, and analyze the power spectrum of the resting-state blood-oxygen-level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI), which has been reported to have a power-law form, i.e., P(f)∝f^{-s}, where P(f) is the power, f is the frequency, and s>0 is the power-law exponent. However, current fMRI experimental paradigms have limited acquisition durations, affecting the spectral resolution of fMRI data at the low-frequency regime. Here, the claimed power-law spectrum is investigated by using a recent hemodynamic model to analytically derive the BOLD power spectrum, with parameters that are related to neurophysiology. The theoretical results show that, for all realistic parameter combinations, the BOLD power spectrum is flat at f≲0.01Hz, has a weak resonance originating from intrinsic oscillations of vasodilatory response, and becomes a power law for high frequencies, all of which is in agreement with an empirical data set that describes the spectrum of one subject and brain region. However, the results are contrary to studies reporting a pure power-law spectrum at f≲0.2Hz. The discrepancy is attributed largely to data averaging employed by current approaches that averages together important properties of the BOLD power spectrum, such as its resonance, that biases the spectrum to only show a power law. Data averaging also reduces the high-frequency power-law exponent relative to individual cases. Overall, this work demonstrates how the model can reproduce BOLD dynamics and further analyze its low-frequency behavior. Moreover, it also uses the model to explain the impact of procedures, such as data averaging, on the reported features of the BOLD power spectrum.

A Copernican Approach to Brain Advancement: The Paradigm of Allostatic Orchestration

There are two main paradigms for brain-related science, with different implications for brain-focused intervention or advancement. The paradigm of homeostasis (“stability through constancy,” Walter Cannon), originating from laboratory-based experimental physiology pioneered by Claude Bernard, shows that living systems tend to maintain system functionality in the direction of constancy (or similitude). The aim of physiology is to elucidate the factors that maintain homeostasis, and therapeutics aim to correct abnormal factor functions. The homeostasis paradigm does not formally recognize influences outside its controlled experimental frames and it is variable in its modeling of neural contributions. The paradigm of allostatic orchestration (PAO) extends the principle of allostasis (“stability through change”) as originally put forth by Peter Sterling. The PAO originates from an evolutionary perspective and recognizes that biological set points change in anticipation of changing environments. The brain is the organ of central command, orchestrating cross-system operations to support optimal behavior at the level of the whole organism. Alternative views of blood pressure regulation and posttraumatic stress disorder (PTSD) illustrate differences between the paradigms. For the PAO, complexities of top-down neural effects and environmental context are foundational (not to be “factored out”), and anticipatory regulation is the principle of their interface. The allostatic state represents the integrated totality of brain-body interactions. Health itself is an allostatic state of optimal anticipatory oscillation, hypothesized to relate to the state of criticality, a mathematical point of poise between phases, on the border between order and disorder (or the “edge of chaos”). Diseases are allostatic states of impaired anticipatory oscillations, demonstrated as rigidifications of set points across the brain and body (disease comorbidity). Conciliation of the paradigms is possible, with “reactive homeostasis” resolved as an illusion stemming from the anticipation of environmental monotony. Considerations are presented with respect to implications of the two paradigms for brain-focused intervention or advancement; the hypothesis that the state of criticality is a vehicle for evolutionary processes; concordance with a philosophy of freedom based on ethical individualism as well as self-creativity, non-obsolescence, empowerment, and citizenship; and concluding reflections on the science and ethics of the placebo, and the potential for virtuous cycles of brain-Anthropocene interactions.