A new MRI subject position to explore simultaneous BOLD oscillations of the brain and the body
Introduction
MRI has been widely used in medicine since its development in the 1980s. The clinical applications are more on structural diagnosis in various fields, including neurology, cardiology, musculoskeletal, as well as detection of many cancers. Another extended application, namely, FMRI, is more commonly used for research purpose, predominantly in the central nervous system (CNS), and the ground theory of using FMRI as an investigational device is based on BOLD signal changes observed on FMRI images secondary to neuronal activities. BOLD reflects the blood ratio of oxyhemoglobin and deoxyhemoglobin (Ogawa et al., 1990) and is especially highly correlated with local field potentials (LFPs) (Logothetis et al., 2001).
Traditionally, FMRI studies focus on task-related, activation paradigm designed, and case-control comparisons between subjects (Bullmore, 2012). Recently, spontaneous fluctuations and temporal oscillations of BOLD signals during task or nontask status have been observed across the brain. These oscillations over distant but functionally related brain regions have certain patterns, defined as functional connectivity (FC) (Biswal et al., 1995), and may be classified as different “resting-state networks (RSNs)” (van den Heuvel et al., 2009). Many functional brain networks have been discovered, including executive control, frontoparietal, default mode, motor, medial visual and lateral visual, auditory, somatosensory, and cerebellum networks (Beckmann et al., 2005; Fox and Raichle, 2007; Rubinov and Sporns, 2010). These networks may be jeopardized under different pathological conditions (Fornito et al., 2015). Based on many previous works, there is now an attempt to classify neurologic symptoms and signs via the brain connectome (Fox, 2018).
FMRI is also used to explore the BOLD phenomenon in other organ systems, such as to detect ischemic areas in the heart (Manka et al., 2010) and to differentiate myometrium tissues in the uterus (Kido et al., 2007), because the interaction between blood flow and cellular activity is also considered present outside the brain. Due to high vascularity, muscle is also a suitable target to obtain BOLD imaging. BOLD contrast imaging was demonstrated to adequately reflect skeletal muscle perfusion changes after artificial ischemia and reactive hyperemia in the lower leg (Donahue et al., 1998), and the contrast is greater under high field strength (7 T) compared to low field strength (3 T) MRI, with improved signal-to-noise and contrast-to-noise ratio (Towse et al., 2016). Intravascular oxygen saturation and hemoglobin level seem to have a major contribution to the BOLD phenomenon (Meyer et al., 2004). BOLD can be used to explore the effect of skeletal muscle contraction (Towse et al., 2011) and intake of vasomodulative agents (Bulte et al., 2006); furthermore, BOLD is a potential useful tool to study peripheral pathologic conditions such as peripheral artery occlusion disease (PAOD). It was found that in PAOD patients, BOLD signal intensity was significantly lower compared to healthy controls after cuff deflation in lower limbs (Ledermann et al., 2006; Partovi et al., 2012). Moreover, the “task” FMRI concept is applied in other body areas (examples mentioned above), and the concept of RSNs oscillations is explored outside the brain. The spinal cord, an “extended” portion of the CNS outside the brain, has been a suitable research target. Early studies revealed that the BOLD signal responses in cervical spinal cord FMRI are detectable under different stimulation paradigms, such as finger tapping (Govers et al., 2006) and controlled hypercapnia (Cohen-Adad et al., 2010). With improved image quality of the spinal cord area, spatially distinct RSNs have been identified, mainly separated as dorsal and ventral networks mimicking spinal cord functional anatomy (Kong et al., 2014); additionally, bilateral FC communication of motor and sensory horns is noted (Barry et al., 2014).
Moreover, FC has a strong relationship, although not necessarily direct, with structural connectivity (SC) (Honey et al., 2009; van den Heuvel et al., 2009), and this relationship is supported by the striking loss of resting interhemispheric BOLD correlations after corpus callosotomy (Johnston et al., 2008). Similarly, in previous work on RSNs over the spinal cord (Barry et al., 2014; Kong et al., 2014; Wei et al., 2010), the observed spontaneous fluctuations over the spinal cord may be of neural origin, the same as those in the brain, since the spinal cord is part of the CNS and has constant ascending and descending neuronal activities via spinal tracts, which are also structurally related.
Based on results of the previous studies, we hypothesize that since both afferent and efferent fibers are present outside of the brain, the complex BOLD oscillations that have been observed in the brain should exist outside the brain and can be detected, e.g., in the trunk or in the limbs where nerves reside (Byrge et al., 2014; Tang et al., 2011). Like the RSNs in the brain, the spontaneous FMRI oscillations in and out of the brain are speculated to spread throughout the body possibly through nerve fibers, synapses, and later on e.g. neuromuscular junctions. These oscillations might also exist when the whole body is in a resting state, and would be a reflection of the baseline homeostatic status of the body. This also might be different from the well-known corticomuscular coherence that exists during active or pathological muscle contraction (Mima and Hallett, 1999; Timmermann et al., 2002). This speculation might be supported by certain studies (Bashan et al., 2012; Fultz et al., 2019; Kerkman et al., 2018; Klimesch, 2018; Tang et al., 2019). Bashan et al. introduced network physiology by recording electroencephalogram (EEG), electrocardiogram (ECG), respiration, electrooculogram (EOG), and electromyogram (EMG) of the chin and leg (Bashan et al., 2012). Another popular example is the interaction between the brain and gut. There are communications through neuronal, endocrine, and immune-related signals, and are noted to be disturbed in certain diseases (Mayer, 2011).
Nonetheless, differentiating neuronal and non-neuronal activities in MR BOLD studies is challenging and even more serious in FC and network analyses. Brain BOLD signals are coupled with various body signals—although mostly theoretically with the very origin the brain—including heartbeats, respirations, and head motion. Many strategies have been developed to counteract this problem. In seed-based or ROI-based approaches, voxels are chosen based on previous background knowledge. Then, FC and functional networks are calculated and built-up after a series of preprocessing steps, including frequency band filtering and motion correction (Fox and Raichle, 2007; Fox, 2010). Another approach, independent component analysis (ICA), a method of blind source signal separation, is also widely used, taking advantage of its great ability to decompose functional time series into various temporo-spatial components, usually with no need for meticulous preprocessing steps or prior assumptions regarding the selection of prespecified brain regions (Calhoun et al., 2001; Wang and Peterson, 2008). Therefore, ICA is also a powerful tool to explore the possible network organization under the new platform we are proposing.
In summary, it is plausible to speculate that during either a task or nontask status of the brain there are also interactions between the brain and other related body systems, including but not limited to the peripheral nervous system (PNS), and MRI BOLD signals could be used to detect them (please see Fig. 1 diagram). A new platform focusing on both the brain and another body part should be demonstrated to be feasible and stable for different subjects. Optimal parameters are required to obtain images with satisfactory quality. Furthermore at this preliminary stage certain temporal oscillations of functional time series between the brain and another body part are expected. We aimed to establish a platform by developing a new MR subject position and explore simultaneous BOLD signals of the brain and upper limb. We argue that BOLD signal oscillations can be detected in the body and exist in a form correlated to certain existing brain networks. More importantly, we establish a new platform that can be used for further research.
Section snippets
Subjects
A total of 11 right-handed healthy subjects were recruited (5 males/6 females; mean age 26.8 ± 4.8 years old) on the campus. All subjects were screened to exclude past history of neurologic disorder, psychiatric illness, and concomitant drug use. Informed consent was obtained from all subjects in line with the protocol approved by the National Taiwan University Institutional Review Board. Of note, each subject might participate in the experiment more than once (e.g., in the stage 1 and stage 2
Results
We planned to establish a new MR platform to study possible simultaneous BOLD oscillations between the brain and periphery. First, in experiment stage 1, the main objective was to test the tolerability of the new AOH MR position and MR image quality. Then in stage 2, the objective was to further test whether BOLD signals under certain tasks can be detected via this platform. All subjects completed the experiments and thus proved the tolerability of this new MR position. The results of the new
Discussion
To the best of our knowledge, we are the first to design the new AOH MR position, verify the image quality, and use this new simultaneous brain-periphery platform to illustrate possible functional connections between the brain and periphery in an MRI scanner (Tang et al., 2019). Our results support the following: (1) the new AOH MR position is feasible and tolerable for subjects in regular MR experiments; (2) MRI can be used to study the brain and periphery in the same plane with adequate SNR
Conclusion
In conclusion, mapping symptoms to brain networks with the human connectome has been used in various neurologic and psychiatric diseases (Fox, 2018), but pathophysiologically, the findings are not expected to be restricted to the brain. Considering the to and fro interactions between the central and periphery, it is reasonable to speculate that studying the brain alone is insufficient. Our new platform for simultaneous brain-body MRI may contribute to new knowledge of interactions between human
CRediT authorship contribution statement
I-Ning Tang: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft, Writing - review & editing. Tun Jao: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft, Writing - review & editing, Visualization, Funding acquisition. Yun-An Huang: Methodology, Software, Validation, Formal analysis, Investigation. Chia-Wei Li: Methodology, Validation, Investigation. Ya-Chih Yu: Methodology, Validation, Investigation. Jyh-Horng
Acknowledgments
This research was funded by the Ministry of Science and Technology (NSC 99-2321-B-002-007 and MOST 103-2321-B-002-097). T.J. is supported by the Ministry of Science and Technology (MOST 107-2314-B-002-070) and National Taiwan University Hospital (NTUH 107-N4022). We would also like to thank Dr. Che-Wei Chang of National Taiwan University for providing the illustration of the AOH position in Fig. 2A.
References (63)
- et al.
The effects of shoulder laxity on upper extremity blood flow in professional baseball pitchers
J. Shoulder Elbow Surg.
(2011) - et al.
Developmental process emerges from extended brain–body–behavior networks
Trends Cogn. Sci.
(2014) - et al.
BOLD signal responses to controlled hypercapnia in human spinal cord
Neuro. Image
(2010) The neurophysiological basis of motor imagery
Behav. Brain Res.
(1996)- et al.
Positional compression of the axillary artery causing upper extremity thrombosis and embolism in the elite overhead throwing athlete
J. Vasc. Surg.
(2011) - et al.
Volitional eyes opening perturbs brain dynamics and functional connectivity regardless of light input
Neuro. Image
(2013) - et al.
BOLD cardiovascular magnetic resonance at 3.0 tesla in myocardial ischemia
J. Cardiovasc. Magn. Reson.
(2010) - et al.
Electroencephalographic analysis of cortico-muscular coherence: reference effect, volume conduction and generator mechanism
Clin. Neurophysiol.
(1999) - et al.
The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?
NeuroImage
(2009) - et al.
No graded responses of finger muscles to TMS during motor imagery of isometric finger forces
Neurosci. Lett.
(2011)
Axillary artery compression and thrombosis in throwing athletes
J. Vasc. Surg.
Complex network measures of brain connectivity: uses and interpretations
Neuro. Image
Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies
Neuro. Image
A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs
J. Neurosci.
Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia
Front. Syst. Neurosci.
SPM12 manual. Functional imaging laboratory
Wellcome Trust Centre for Neuroimaging
Resting state functional connectivity in the human spinal cord
Elife
Network physiology reveals relations between network topology and physiological function
Nat. Commun.
Small-world brain networks
Neuroscientist
Investigations into resting-state connectivity using independent component analysis
Philos. Trans. Biol. Sci.
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI
Magn. Reson. Med.
The future of functional MRI in clinical medicine
Neuro. Image
Vasomodulation of skeletal muscle BOLD signal
J. Magn. Reson. Imaging
A method for making group inferences from functional MRI data using independent component analysis
Hum. Brain Mapp.
ICA of functional MRI data: an overview
Consistent resting-state networks across healthy subjects
Proc. Natl. Acad. Sci.
Simultaneous gradient‐echo/spin‐echo EPI of graded ischemia in human skeletal muscle
J. Magn. Reson. Imaging
The connectomics of brain disorders
Nat. Rev. Neurosci.
Clinical applications of resting state functional connectivity
Front. Syst. Neurosci.
Mapping symptoms to brain networks with the human connectome
N. Engl. J. Med.
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Nat. Rev. Neurosci.
Cited by (0)
- 1
Co-first authors.