MRI Archives - BIOPAC Blog

Imaging is Everything: Data Acquisition in the MRI Environment

The marriage of physiological data recording and magnetic resonance imaging (MRI) has allowed scientists to unravel intricate connections between the mind and body. However, this synergy comes with its unique set of challenges that researchers must be aware of to ensure the integrity of both data and the well-being of study participants.

One of the foremost challenges in recording physiological data within the MRI lab is the strong magnetic field. Traditional monitoring equipment can be rendered useless or, worse, pose extreme safety risks due to their ferromagnetic components. Another critical consideration is the impact of radiofrequency (RF) pulses used in the imaging process. RF pulses can introduce artifacts in physiological recordings, affecting the quality of data collected. In previous posts we have discussed the basics of integrating data collection in the functional MRI (fMRI) environment as well as techniques for avoiding artifacts and signal noise while recording data in the MRI.

Researchers must also contend with challenges related to the confined space and noise levels within an MRI environment. A 2021 study conducted by the Lab for Autonomic Neuroscience, Imaging and Cognition (LANIC) at Jena University Hospital in Jena, Germany, focused on how the functional MRI (fMRI) environment itself affected outcomes in autonomic research. Specifically, the study investigated the influence of a loud and cramped environment during MRI on resting heart rate variability (HRV) measures and whether a familiarization session with MRI equipment might lower anxiety levels among participants. The research team recorded an electrocardiogram (ECG) and a photoplethysmogram (PPG) over 15 minutes from participants using a BIOPAC data acquisition and analysis system connected to finger-attached PPG sensors during non-MRI recording. For recording in the fMRI, researchers used a separate BIOPAC data acquisition system equipped with an MR-Conditional PPG module. While the study did establish a link between participant anxiety and HRV levels in the lab and fMRI environment, it did not find that the familiarization sessions had a significant impact on HRV outcomes.

Despite the challenges, the recording of physiological signals in the MRI environment provides unique opportunities for gathering critical data. A study published in the journal Military Medicine in 2020 tested the effectiveness of F MRI (not to be confused with fMRI) scans in identifying cases of War Lung Injury (WLI) in Iraq and Afghanistan war veterans. WRI refers to reductions in lung function resulting from exposure to environmental factors such as sandstorms and burn pits. F MRI employs magnetic resonance imaging with perfluorinated gases as inhaled contrast agents to evaluate various pulmonary diseases. If successful, the approach would provide an alternative to surgical biopsy for diagnosing suspected WLI. The researchers measured respiratory waveforms throughout the imaging sessions, during both normal room air breathing and PFP-oxygen gas mixture breathing using a BIOPAC MR-Conditional pneumotachometer. The study found that “F MRI provides better insight regarding lung function abnormalities” than other standard methods and that “F MRI could provide a functional tool to assist in identifying subjects with the disorder and potentially could aid in the evaluation of novel therapeutic approaches.”

BIOPAC provides a wealth of tools and information resources for gathering data in the MRI environment. This includes multiple webinars on recording data in both MRI and fMRI settings. As the field continues to evolve, BIOPAC’s innovative solutions pave the way for groundbreaking discoveries at the intersection of physiological signal recording and medical imaging.

If you are interested in recording and analyzing physiological signals in your next MRI-based research study, BIOPAC sales representatives are standing by to help you choose the right tools and resources for the job.

T4 Conference Wrap-up

August is a month of barbecues, baseball, and last-minute vacations against the backdrop of a fading summer, and the eighth month of 2019 proved to be an equally exciting time at BIOPAC, with something of a pleasant vacation thrown in. On August 12-14, BIOPAC hosted its 2nd Biennial “Tools, Trends, Techniques, and Technology” Human Physiology Conference, known as “T4” to our friends and partners around the world. This three-day event, first held in 2017, featured lively, informative sessions hosted by BIOPAC staff, partners, noted academics, and industry experts, and covered a wide range of physiological techniques and disciplines. Over 150 attendees joined BIOPAC at the 2019 T4 Conference staged once again at the beautiful University of California at Santa Barbara campus. Participants and instructors winged in from China, Australia, Germany, Sweden, Italy, Spain, U.K., Ireland, and Egypt, as well as from wide regions of the U.S and Canada to soak up the idyllic surroundings of this ocean-side campus.

The focus of BIOPAC’s T4 Conference was to provide a fun, relaxed environment while hosting nearly 40 hands-on, human physiology training workshops chaired by over 20 distinguished instructors from around the globe. One of the many conference highlights was the “Mindfulness, Meditation, and Measurement: Mindfulness Exercises in Physiological Monitoring” session, hosted by Dr. Allan Weiss of the University of Southern California and BIOPAC Co-founder and Director of R & D Alan Macy. These sessions, conducted in two parts over a two day period were booked beyond capacity, even though the early morning fog hadn’t yet lifted from the campus skyline.

The varied additional sessions covered a range from the basics, “Setting Up Your Lab, Getting Great Data and Troubleshooting,” (hosted by Dr. Terry Blumenthal of Wake Forest University, and BIOPAC’s Alan Macy and Tim Cook), and “Creating Your Own Lessons Using BSL PRO,” (hosted by Dr. Taffeta Elliot of the New Mexico Institute of Mining and Technology), to exciting new technologies such as “Integrating Screen-based Eye Trackers with AcqKnowledge,” with Keith Jackson of BIOPAC partner company EyeTech, BIOPAC CEO Frazer Findlay, and BIOPAC Engineering Manager Ed Peterlin. Additional wide-ranging physiological disciplines were also covered, including MRI techniques, stimulation, and exercise physiology. All sessions were conducted using BIOPAC hardware and software systems, as well as platforms provided by BIOPAC partners such as Cedrus, fNIR, WorldViz, Psychology Software Tools, and CNSystems. At the end of each day, it was time to relax and unwind with a beverage of choice, a a walk along the beach, and some good conversation. Each evening featured excellent meals and camaraderie in the relaxed yet elegant environment of some of Santa Barbara and Goleta’s most stunning surroundings. After three great days of immersive sessions, it came time for everyone to part ways, having forged new collaborative partnerships and an enhanced knowledge about the full potential of BIOPAC and BIOPAC partner products.

You don’t have to wait for T4 2021 to learn more about life science data—watch Fundamentals of Physiological Data Recording now!

For a closer look at T4, and a preview of what is sure to be an exciting 2021 event, we invite you to visit our T4 Human Physiology Conference page.

Looking for additional resources, tips, tricks, and expert advice on BIOPAC and BIOPAC Partner products? Please also visit our Webinars and Video Tutorial pages.

BIOPAC offers a wide array of wired and wireless equipment that can be used in your research. To find more information on solutions for recording and analyzing signals such as ECG, heart rate, respiration and more using any platforms mentioned in this blog post, you can visit the individual application pages on the BIOPAC website.

The Physiology of Election Decision-Making

The psychophysiological factors of stress, the behaviors of others, and of introception can play major roles in decision-making, and BIOPAC systems provide measurements of how physiological factors such as these influence decisions.

People experienced varying stress levels because of the 2016 US presidential election. There are a variety of factors that can impact voting behaviors and decision-making. Several recent studies using BIOPAC systems investigated the psychophysiological effects of stress, the behaviors of others, and introception on decision-making.

Stress has been shown to be very influential in decision-making. In their study, Yamakawa, Ohira, and Mastunga Isowa (2016) investigated effects of acute stress and risk taking in decision-making. Participants were asked to perform a decision-making choice where they either kept or bet the money they were given. Cardiodynamic activity was measured with ECG using a BIOPAC MP system. Results suggest that with an increase in cortisol, adrenaline, heart rate, and subjective stress, participants were likely to make fewer risky choices. So, high levels of stress may be key in helping make better decisions when selecting a future president.

Perhaps it’s not stress that will influence the choice of the next POTUS (President of the United States), but rather the behaviors of others. Work by Bault, Joffily, Rustichini, and Coricelli (2011) sought to compare private and social decision-making to discover the effect of social comparison on risky choices. The objective of the study was to understand more about how individuals evaluated the outcomes of their decisions in private as opposed to in a social context. Combining data from functional MRI (FMRI) and skin conductance recordings, subjects’ reactions were measured using a BIOPAC MP150 System. Researchers found that participants took significantly longer to make a decision in a social setting than in a private one.

In the end, many feel that it is important to listen to your heart when deciding who you find qualified to be the next president. A study by Dunn et al. (2010) examined whether introception moderates the coupling of bodily responses to emotional experience and intuitive decision-making. Bodily response and heart rate were recorded using a BIOPAC Research System and electrodes to measure introception through changes in heart rate. Researchers found that introception was not related to arousal, valence ratings, or heart rate response to images.

For more information on BIOPAC’s wide range of tools for recording, displaying, and analyzing physiological measurements from human and animal subjects including respiration, heart rate variability, ECG and EDA, visit BIOPAC’s psychophysiology page and view BIOPAC’s full line of electrodes, amplifiers, and wearable, wireless transmitters and loggers.

BIOPAC Systems, Inc. provides life science researchers and educators with data acquisition and analysis systems that inspire people and enable greater discovery about life. Visit us at www.biopac.com.

Dark Current Measurement as a Noise Management Tool

Noise in functional near infrared (FNIR) imaging data utilized for neurophysiological brain functioning research is a challenge to researchers, The use of dark current measurement in functional near infrared (fNIR) brain imaging can provide a valuable tool in identifying and managing certain type of noise in recordings.

By Davood Tashayyod, FNIR Devices

The use of dark current measurement in functional near infrared (fNIR) brain imaging (measurements taken not only when LED type light sources shine light at wavelengths 730 and 850 nm but also when they do not shine light (dark current condition)) can provide a valuable tool in identifying and managing certain types of noise in recordings.

Noise in functional near infrared (fNIR) imaging data utilized for neurophysiological brain functioning research is a challenge to researchers, in that excessive or unmanaged noise may pose a risk to the quality of gathered data and hence in the interpretation of the results. This noise arises from two general categories of sources, biological sources and non-biological sources. It should be noted that sources of noise are routinely present, so that noise cannot be eliminated but rather must be managed. Biological sources of noise include the respiratory and cardiac cycles and slow vasomotor oscillations. Non-biological sources of noise may include contamination from other external devices and noise induced by improper sensor application (light leakage, in the case of fNIR sensors), cable movements, or instruments or equipment used in the course of the experiment. These sources, independently or in combination, can mask the signals of interest.

Close attention to good laboratory practices, to include proper sensor placement, trial runs to establish baselines, and other techniques can help to reduce noise. Close attention to experiment protocols during the experiment can also be of assistance. However, it is in post-experiment data processing and analysis phases that the researcher has the greatest number of tools to manage noise. These tools include intuitive or visual analysis, filtering, and specialized analysis. Each method has strengths and weaknesses, but used in combination they offer a reliable method of managing noise and improving data quality.

Visual or intuitive analysis depends upon researcher experience and qualification, and some artifacts may be too small to be readily apparent. Filtering is useful, but in some range of measurements noisy data segments may be so great or may not contain any signal originated from the brain such as in the case when the sensor loses contact with the skin and captures data only from the ambient that it may not make sense to remove noise, rather such data segments should be identified and discarded.

Specialized analysis, such as dark current measurement, provide a very useful method for both identifying the noise not readily apparent to the naked eye and identifying data segments where filtering is likely to not be useful. A discussion of the use of dark current measurement can be found here.

For more information on optical braining imaging systems, visit BIOPAC’s fNIR page. BIOPAC also provides a wide range of tools for recording, displaying, classifying, and analyzing psychophysiological measurements—including EEG, ECG, EDA, PPG, and emotional states—visit BIOPAC’sapplications pages and view BIOPAC’s full line of physiology data acquisition and analysis systems, electrodes,amplifiers, and wearable, wireless transmitters and loggers.

Practical Tips for Recording Physiological Data in MRI

The MRI system incorporates a VERY strong magnetic field and both emits and records radio frequency (RF) energy. This has impacts on subject and experimenter safety and on data accuracy.

By Alan Macy, BIOPAC Systems, Inc.

When recording physiological signals from subjects in MRI systems, two issues are very important:

The MRI system incorporates a VERY strong magnetic field. It is extremely important that no significant magnetically susceptible objects are taken into the room that houses the MRI System. All materials have some degree of magnetic susceptibility. However, materials with mass magnetic susceptibility values, similar to or have magnitude less than that of water (-0.91E-8 m^3/kg), are typically permitted to be used in the vicinity of MRI Systems. No electrodes, electrode leads, transducers or cabling, that contain significant magnetically susceptible components, should be used when recording data from a subject in the MRI.
The MRI System both emits and records radio frequency (RF) energy. This energy is typically in the range of 5 to 500 MHz. For a 3T MRI, the characteristic proton precession frequency will be 3T * 42.7 MHz or 128.1 MHz. MRI generated RF energy will, at least, partially reflect from encountered conductors and induce current flow in those conductors. Signal reflections and induced currents can disrupt sensitive imaging operations by creating signal artifacts. Also, RF induced currents, if not properly controlled, can cause local heating.

Care must be taken to limit the flow of RF energy both in and out of the room that houses the MRI. RF energy from the MRI superimposes with the very small biopotentials and transducer signals in the vicinity of the subject. Alternatively, RF energy from recording or other equipment in the MRI Control Room (e.g. digital clocks and power supply switching noise) can leak into the MRI chamber, via subject cabling, and cause degradation of the image created by the MRI. It can be complicated to record biopotentials, from a subject’s body, during an MRI scan. These signals are very tiny and the RF energy, and associated magnetic field shifting, generated by the MRI corrupts the recording. Accordingly, successful biopotential recording in the MRI involves the use of specialized amplifiers, optimized cabling, patch-panel filtering, advanced signal processing and/or synchronization with MRI scanning processes to perform clean measurements.

When RF energy is pulsed into the subject, this energy spreads throughout the subject because the subject is a volume conductor. As the MRI magnetic gradient shifts in synchrony with RF energy pulsing, the gradient shift induces a current flow in conductors that intersect with the field. From a practical standpoint, RF energy pulsing and gradient-induced current flows often manifest themselves as repetitive artifact signals directly in the band of the physiological generated biopotentials. This is because RF energy pulsing and magnetic field gradient switching are performed synchronously for imaging and the repetition rates for image slice generation usually fall within the range of 1-100 Hz. Depending upon the biopotential frequency range (from lowest to highest: EGG, EOG, ECG, EEG and EMG), different amplification and signal processing techniques can be used to isolate the signal of interest. Furthermore, it’s often helpful to control imaging processes so that the MRI artifact signal spectrum does not unduly impact the measured physiological signal spectrum.

For more information on BIOPAC’s MR Safe and MR Conditional solutions, visit BIOPAC’s MRI page or view BIOPAC’s full line of electrodes, amplifiers, and wearable, wireless transmitters and loggers.

Download a related and important application note that describes the relationship between MRI imaging sequences and associated physiological measurements, when subjects are monitored in the MRI. In particular, this note addresses newfound insights on the impact that imaging sequence types have upon simultaneously recorded electrocardiography (ECG) and impedance cardiography (ICG) subject measurements.