Respiration modulates oscillatory neural network activity at rest

Abstract

Despite recent advances in understanding how respiration affects neural signalling to influence perception, cognition, and behaviour, it is yet unclear to what extent breathing modulates brain oscillations at rest. We acquired respiration and resting state magnetoencephalography (MEG) data from human participants to investigate if, where, and how respiration cyclically modulates oscillatory amplitudes (2 -150 Hz). Using measures of phase-amplitude coupling, we show respiration-modulated brain oscillations (RMBOs) across all major frequency bands. Sources of these modulations spanned a widespread network of cortical and subcortical brain areas which formed clusters with distinct spectro-temporal modulation profiles. Globally, gamma modulation increased with distance to the head centre, whereas delta and theta modulation decreased with height in the sagittal plane. Overall, we provide the first comprehensive mapping of RMBOs across the entire brain, highlighting respiration-brain coupling as a fundamental mechanism to shape neural processing within canonical resting-state and respiratory control networks.

The influence of the respiratory cycle on reaction times in sensory-cognitive paradigms

“…In line with the notion that respiration may influence neocortical function, brain activity in multiple brain regions apparently co-modulates with the respiratory cycle, possibly reflecting the propagation of feedback signals about the respiratory state into sensory-cognitive processes (Perl et al 2019; Kluger & Gross 2020; Zelano et al 2016; Herrero et al 2018). Behavioural studies have shown that animal or human participant’s performance can covary with the respiratory cycle in a variety of tasks, ranging from sensory detection (Flexman et al 1974; Gallego et al 1991) to emotion recognition (Arch & Craske 2006; Homma & Masaoka 2008; Vlemincx et al 2010; Zelano et al 2016), memory recall (Arshamian et al 2018; Huijbers et al 2014; Nakamura et al 2018; Zelano et al 2016), or more complex mental tasks (Perl et al 2019).…”

“…Thereby, information about the respiratory state is potentially widely available in subcortical and cortical brain regions (Heck et al 2016). This gives us the potential to consciously adjust breathing such as during singing but may also allow respiratory-related signals to continuously and subconsciously influence perceptual or cognitive functions (Kluger & Gross 2020; Tort et al 2018;Varga & Heck 2017).…”

Respiration aligns perception with neural excitability

“…While there is a rather extensive body of literature on the interaction of respiratory and neural rhythms in animals, these links are only beginning to be addressed in the human brain. Recently, both invasive 7 and non-invasive work 8 has shown respiration to modulate neural oscillations across a wide network of cortical and subcortical areas, including those not typically associated with olfaction. Moreover, modulatory effects of respiration have been demonstrated in motor 9,10 as well as in cognitive tasks 7,11 .…”

“…Recently, both invasive 7 and non-invasive work 8 has shown respiration to modulate neural oscillations across a wide network of cortical and subcortical areas, including those not typically associated with olfaction. Moreover, modulatory effects of respiration have been demonstrated in motor 9,10 as well as in cognitive tasks 7,11 . Taken together, these studies provide strong evidence for breathing-related changes in neural signalling that, particularly in task contexts, translate into changes in behaviour.…”

“…deviation from a uniform distribution) is quantified by the entropy of this distribution. To account for frequencydependant biases, we followed previously validated approaches7 and computed 200 surrogate MIs using random shifts of respiratory phase time series with concatenation across the edges. The normalised MI was computed by subtracting, for each frequency, the mean of all surrogate MIs and dividing by their standard deviation leading to MI values in units of standard deviation of the surrogate distribution.…”