Current insights into physiologic signals to measure body-brain interactions in neuroimaging.

Cesar Caballero-Gaudes Organizer
Basque Center of Cognition, Brain and Language
San Sebastián
Molly Bright Co Organizer
Northwestern University
Chicago, IL 
United States
Merel van der Thiel Co Organizer
Maastricht University Medical Center
Maastricht, Limburg 
Kristina Zvolanek Co Organizer
Northwestern University
Chicago, IL 
United States
Sunday, Jun 23: 1:30 PM - 5:30 PM
Educational Course - Half Day (4 hours) 
Room: ASEM Ballroom 203 
The brain does not exist in isolation. The field of neuroscience is increasingly interested in the intricate relationships between brain function and diverse concurrent physiological processes throughout the body. For many neuroscientists, this is not a new concern - strategies for removing physiological artifacts (e.g., cardiac, respiratory, myogenic) from neuroimaging data have been explored for decades. However, there is a resurgence of interest in physiologic signals, particularly in conjunction with fMRI data not only for denoising purposes but also for data interpretation. Improvements in data quality facilitate the isolation and quantification of these physiologic signals, resulting in promising new insights about brain health, physiology and function that can complement our understanding of neurobiological activity. Furthermore, it allows for the simultaneous exploration of novel physiological systems in the brain, such as the cerebral waste clearance system (i.e., the glymphatic system) and to investigate the effects of systemic arousal states driven by the autonomic nervous system on brain health and cognition. With advanced monitoring techniques, the diversity of physiological processes that can be characterized during neuroimaging experiments is expanding.

This course introduces the physiologic signals that influence typical haemodynamic-based neuroimaging datasets. We present methods for recording physiological processes during scanning and modeling the associated signal variance, with an overview of data-driven methods for achieving similar outcomes to bring new insight into existing data resources that lack physiological recordings. In addition, we will introduce cutting-edge techniques to monitor electrogastrography and introduce state-of-the-art research on the cerebral waste clearance system and autonomic nervous system, along with methods to investigate these systems simultaneously with fMRI. To complement theoretical content, we will host a hands-on session to show how to collect physiological data and incorporate them into neuroimaging analysis pipelines, either as confounding factors or signals of interest using available data and software resources.


Learn how to set up physiological data collection in a neuroimaging setting, and how to model and remove physiological fluctuations from functional MRI timeseries, with or without physiological recordings.
Learn about the contributions of the autonomic nervous system and gastric rhythms to brain fluctuations
Learn how CSF flow can be measured with MRI and what its impact is on the waste clearance system.

Target Audience

Researchers working with functional neuroimaging (fMRI, M/EEG), from cognitive to clinical research, who are interested in knowing how to improve data denoising and how to add physiological-related data to their research repertoire. In addition, researchers interested in exploring the use of fMRI to investigate autonomic nervous system functioning and cerebral waste clearance.


1. That brain you are scanning is part of a living, breathing system, and it shows

Although fMRI is a powerful tool for studying neural activity, the signals we measure reflect a diverse range of physiologic processes that intrinsically couple the brain to the body. We do not scan a brain in isolation from the human being that hosts it, and it is nearly impossible to “turn off” a beating heart or breathing lungs when we probe brain function. In this educational session, I will discuss the physics and physiology of how cardiac, respiratory and arterial CO2 factors influence the fMRI timeseries, and demonstrate best practices for recording and interpreting physiologic signals during an fMRI scan. I will also introduce less well-established physiologic signals that can also be explored in fMRI, to better understand arousal, cerebral spinal fluid flow in relation to waste clearance, gut-brain interactions, and how all these aspects of human physiology may interact with our understanding of brain function when using neuroimaging. 


Merel van der Thiel, Maastricht University Medical Center Maastricht, Limburg 

2. You have a physiological monitoring system, now what? A practical guide to acquiring and quality checking your own data

During this session, we will provide attendees with the opportunity to assess good-quality physiological measurements and prepare the data for use in neuroimaging workflows. A demonstration of photoplethysmography (PPG) and respiratory belt acquisition will allow attendees to learn current best practices during data collection in an MR scanner environment as well as some open-source tools (e.g., Physiopy:phys2bids) that are available to them. While observing first-hand physiological data acquisition, attendees will learn how to assess and improve the quality of their data. After becoming familiar with how to assess physiological measurements, we will go through a tutorial on how to use these data for denoising fMRI imaging and physiological imaging. Attendees will leave this session with 1) an improved ability to assess the quality of their physiological measurements and 2) a better understanding of how physiological data can be leveraged in fMRI data.


Sarah Goodale, Vanderbilt University Nashville, TN 
United States

3. Model-based and data-driven methods for modelling and removing physiologically-driven signals from BOLD fluctuations

Modelling physiological effects in fMRI data can increase the precision of fMRI for measuring local neural activity, as well as open new avenues for studying brain physiology and brain-body interactions in health and disease. In this educational talk, I will describe existing techniques for extracting systemic physiological information (cardiac, respiration, CO2) from concurrent physiological recordings (i.e. model-based) or directly from fMRI data (i.e. data-driven). I will also explain how to use this information for accounting for physiological confounding signals in fMRI data analysis (i.e. denoising), including both faster-scale oscillations time-locked to the respiratory/cardiac cycles and slower-scale variations (< 0.1 Hz) that overlap with the spectrum of intrinsic neuronal hemodynamic activity, and gaining further insights in the interpretation of fMRI findings for broad cognitive and clinical applications.


Stefano Moia, Maastricht University Maastricht, Linburg 

4. Using electrogastrography to measure signals from the stomach non-invasively in the fMRI scanner

The stomach continuously produces a 0.05 Hz electrical oscillation (i.e. 1 cycle every 20 seconds), which serves to pace the stomach contractions necessary for digestion. This so-called gastric rhythm can be measured non-invasively by placing electrodes over the abdomen, a technique known as the electrogastrogram. This electrophysiological technique can be safely incorporated during fMRI recordings by using MRI-compatible electrodes and amplifiers of the same type used to measure electrocardiography or electromyography. While common in the field of gastroenterology, electrogastrography has yet to gain wider adoption in the field of cognitive neuroscience. The objective of this educational talk is thus to introduce the electrogastrography technique, as well as its potential applications for cognitive neuroscience. During the talk, I will review the physiological basis of the gastric rhythm, and provide an overview of brain-stomach anatomical pathways. I will then present the standard practices to acquire, analyze and interpret the electrogastrogram inside the fMRI scanner.


Ignacio Rebollo, German Institute of Human Nutrition Potsdam-Rebrücke, Nuthetal 

5. Imaging and interpreting cerebrospinal fluid dynamics in the human brain

The movement of blood and cerebrospinal fluid (CSF) is essential for brain health. These dynamics are closely linked to neuronal activity and cognitive and vigilance states, and are an important consideration for fMRI analyses. In addition, due to the critical role of CSF in waste clearance and brain function, understanding its properties is important for cognitive and clinical neuroscience. This lecture will provide an overview of how fMRI can measure CSF flow and its coupling to hemodynamics. It will then discuss mechanistic theories for how CSF and waste clearance are carried out in the brain. It will also discuss how these CSF signals can influence standard fMRI studies through unintended effects, and how they can be analyzed in fMRI data more broadly. 


Laura Lewis, Massachusetts Institute of Technology Cambridge, MA 
United States

6. Contributions of the Autonomic Nervous System to Brain Dynamics During Sleep and Wake

It is well known that part of the fMRI signals variance is associated with fluctuations in systemic physiology, encompassing cardiac rate, respiration, and peripheral physiology. Recent research has revealed significant correlations between peripheral vascular tone, serving as a proxy for sympathetic activity, global fMRI signals, and autonomic arousals during light sleep. Additionally, investigations into the pulsatile movement and dynamics of cerebrospinal fluid (CSF) during sleep have unveiled its role in metabolic waste removal. These CSF pulsations may be induced by direct pressure effects from cardiac and respiratory cycles. For instance, deep inspirations altering intravascular CO2 can result in cerebral blood volume changes, inducing CSF pulsations through an autonomic pathway accompanied by variations in vascular tone during alert conditions. This pathway introduces a new dimension, illustrating the variability of sympathetic activity triggered by diverse stimuli, such as emotional and mental stress.
This educational talk will review recent findings emphasizing the potential to isolate respiratory-related fluctuations, such as CO2, from contributions of sympathetic activity to the fMRI signal. Furthermore, we will see how metrics like PPG amplitude and pupil diameter can serve as “autonomic regressors”, enhancing the interpretation of fMRI-based studies.


Pinar S Ozbay, Bogazici University Istanbul