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<h1>Magnetic Resonance Spectroscopy</h1>
<span class="image main"><img src="images/pic13.jpg" alt="" /></span>
<h2>What is Magnetic Resonance Spectroscopy?</h2>
<p>Magnetic Resonance Spectroscopy (MRS) is a non-invasive imaging procedure used to investigate the metabolic pathways in the brain that can be acquired during <a href="mri_info.html">Magnetic Resonance Imaging (MRI)</a>. With MRS, we can obtain information about the biochemistry of the human brain by measuring the resonant frequencies of brain metabolites.</p>
<h2>What are brain metabolites?</h2>
<p>Brain metabolites, also known as neurometabolites, neurochemicals, or neurotransmitters, are small molecules involved in communication between neurons. There are several commonly measured neurometabolites in the brain:</p>
<ul>
<li>Dopamine (DA): most extensively studied; a monoamine neurotransmitter found in the brain and essential for the normal functioning; implicated in motivation, decision-making, movement, reward processing, attention, working memory, and learning; often referred to as the “pleasure chemical” because it is released in response to rewarding experiences (e.g., eating, partaking in drugs, having sex). Dopamine has also been implicated in several neuropsychiatric disorders, including schizophrenia, Parkinson’s disease, addiction, and more.</li>
<li>Serotonin (5HT): sometimes referred to as the “calming chemical”; implicated in mood modulation, appetite management, sleep, memory, and decision-making behaviors. Serotonin is also implicaited in several psychiatric disorders, most commonly depression. </li>
<li>Glutamate (Glu): the most abundant free amino acid and the main excitatory neurotransmitter in the brain; implicated in learning, memory, long term potentiation (LTP). However, too much glutamate can cause excitotoxicity, which can cause nerve cells to become overexcited and potentially die. Glutamate has been implicated in several neuropsychiatric disorders including schizophrenia, autism.</li>
<li>Glutamine (Gln): a neurometabolite synthesized from glutamate that is difficult to measure.</li>
<li>Gamma-Aminobutyric Acid (GABA): also known as gamma (γ)-amino-butrate; the main inhibitory neurotransmitter; responsible for inhibiting glutamatergic (Glu) neural signaling; important for brain development; sometimes referred to as the “learning chemical”, as research has found links between GABA levels and whether or not learning is successful. Too much inhibition from GABA can result in seizures and other problems.</li>
<li>N-acetylaspartate (NAA): second highest concentrated free amino acid in the brain (after Glutamate), used as a measure of neuronal integrity.</li>
<li>N-Acetylaspartylglutamic acid (N-acetylaspartylglutamate; NAAG): third most-prevalent neurotransmitter; modulates glutamatergic transmission. </li>
<li>Norepinephrine (NE): a neurotransmitter and a hormone, sometimes referred to as noradrenalin; linked to mood, arousal, vigilance, memory, and stress. NE has also been implicated in posttraumatic stress disorder (PTSD) and Parkinson’s disease.</li>
<li>Acetylcholine (Ach): facilitates neuroplasticity across the cortex, helps direct attention.</li>
<li>Glutathione (GSH): primary antioxidant, difficult to measure due to significant resonance overlap (even at high fields).</li>
<li>Other neurotransmitters include: Myo-inositol (myoI or mI), Creatine (Cre or Cr), Choline (Cho), Oxytocin, Vasopressin, lactate, corticotropin-releasing factor (CRF), galanin, enkephalin, dynorphin, and neuropeptide Y. Estrogen & testosterone can also work as neurotransmitters.</li>
</ul>
<p><strong>Figure 1.</strong> Example 1H magnetic resonance spectra using the PRESS sequence at 3T with a range of echo times from <a href="https://link.springer.com/article/10.1007/s11064-013-1199-5">A Guide to the Metabolic Pathways and Function of Metabolites Observed in Human Brain 1H Magnetic Resonance Spectra.</a></p>
<img src="https://media.springernature.com/full/springer-static/image/art%3A10.1007%2Fs11064-013-1199-5/MediaObjects/11064_2013_1199_Fig1_HTML.gif?as=webp" alt="Example Spectra">
<p><h2>MRS Data Acquisition</h2></p>
<p>To start, anatomical/structural MRI scans are used to select tissue volumes and place voxels. Researchers and clinicians can choose to use either a single voxel, known as single voxel spectroscopy (SVS), or multiple voxels, which is referred to as magnetic resonance spectroscopic imaging (MRSI; previously named chemical shift imaging, CSI). To acquire spectroscopy, the signal from water in the brian must be suppressed in order to measure the spectra of the neurometabolites.</p>
<p>There are several SVS techniques to acquire spectra during imaging. STEAM (Stimulated Echo Acquisition Mode) and PRESS (Point-Resolved Spectroscopy) are both pulse sequence techniques used in magnetic resonance spectroscopy (MRS) to acquire signals from different molecules to generate a spectrum. PRESS and STEAM sequences use different methods for the acquisition of a stimulated echo. Both STEAM and PRESS have advantages and limitations, which can vary based on the study design, but it seems that PRESS is currently the most utilized SVS sequences for 1H MRS. For MRSI, spectra are acquired simultaneously from MRSI voxels inside the volume of interest. While single-voxel MRS (i.e., SVS) can provide more accurate quantification of the neurometabolite spectra from a specific location, multi-voxel MRS (i.e., MRSI) can obtain spatial distributions of metabolites within a single experiment. See In vivo magnetic resonance spectroscopy: basic methodology and clinical applications for an in-depth review of MRS data acquisition, including physics principles, pulse sequences, and spectra analysis</p>
<p>The specificity of the information gathered is dependent on the MRI field strength (link to MRI page). For example, low field MRI strengths can result in “resonance frequency overcrowding”, such that some neurometabolites cannot be distinguished from those of similar resonant frequencies. For example, at 1.5T and 3T strengths, glutamate (Glu) and glutamine (Gln) resonant frequencies can be too close to separate, so some researchers may instead use a composite measure called Glx (Glu + Gln; link here). Another example is N-acetyl resonance (NA), which is derived from N-acetylaspartate (NAA) and N-acetylaspartylglutamate (NAAG) at lower field strengths. On the other hand, this is minimal metabolite spectra overlap when MRS is acquired on a higher field strength MRI.
<p>See <a href="https://link.springer.com/article/10.1007/s11064-013-1199-5">A Guide to the Metabolic Pathways and Function of Metabolites Observed in Human Brain 1H Magnetic Resonance Spectra</a> for an in-depth review of neurometabolites.</p>
<h2>MRS in Neuroscience: Applications</h2>
<p>
MRS can be used to study diverse research questions in various healthy and clinical populations. In the domain of (cognitive) neuroscience, the neurotransmitter levels of interest are often GABA and glutamate (+ its precursor glutamine). The following examples are mostly in the domain of visual learning; however, given the evidence linking the two neurotransmitters with several psychiatric disorders and cognitive aging, impaired plasticity in glutamatergic and GABAergic systems can be studied with MRS to shed more light on these domains as well (Stanley & Raz, 2018).
</p>
<p>
GABA and glutamate are usually analyzed in relation to each other: the “balanced” excitatory and inhibitory (E/I) synaptic drive serves as the functional basis of networks in the brain (Stanley & Raz, 2018). Both GABA and glutamate have been shown to correlate with plasticity measures (Kim et al., 2014; Nikolova et al., 2017; Stagg et al., 2011). Moreover, task-specific alterations in GABA levels in association with learning paradigms have been demonstrated (Frangou et al., 2018, 2019).
</p>
<h2>Examples for MRS:</h2>
<p>Shibata et al. (2017) and Bang et al. (2018) associated consolidation processes with the excitation/inhibition (E/I) ratio in the human early visual cortex. The authors refer to the E/I ratio as a measure of plasticity (Bang et al., 2018). The change in E/I ratio from baseline after performing a task is calculated as:</p>
<code>
E/I change (t) = ((Glu(t)/GABA(t)) / (Glu(t=0)/GABA(t=0)) - 1) * 100
</code>
<p>
with <code>t</code> corresponding to the time point of the respective MRS measurement (<code>t=0</code>: MRS baseline measurement before performing the task and <code>t=1</code>: MRS measurement immediately after performing the task).
</p>
<p>
For more detailed information and interpretation of how E/I ratio changes are linked to learning processes, see Bang et al. (2018).
</p>
<p>Further information about MRS studies analyzing GABA and glutamate: Duncan et al. (2014) (sadly not open access)</p>
<h2>Examples for functional MRS (fMRS):</h2>
<p>
Usually, MRS is acquired during rest without a particular task (except maybe a fixation task to keep attention constant over several sessions). fMRS, however, tracks the neurotransmitter concentrations during performance on a task and allows for more dynamic tracking of neurotransmitter concentrations. Further information and studies utilizing fMRS can be found in these reviews:
</p>
<ul>
<li>Stanley & Raz (2018)</li>
<li>Kiemes et al. (2021)</li>
<li>Pasanta et al. (2023)</li>
</ul>
<h2>FAQ and Practical Tips:</h2>
<ul>
<li>Practical tips for new users of MRS</li>
<li>In case of repeated-measures design: save your individual voxel placement for the next session of each participant</li>
<li>During MRS without a task: think about whether a simple fixation task would help your design to keep attention stable</li>
</ul>
<h2>Resources for Further Learning</h2>
<p>Recommended papers:</p>
<ul>
<li>Frank, S. M., Becker, M., Malloni, W. M., Sasaki, Y., Greenlee, M. W., & Watanabe, T. (2023). Protocol to conduct functional magnetic resonance spectroscopy in different age groups of human participants. <i>STAR protocols, 4</i>(3), 102493.</li>
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<h2>Funding</h2>
<p> We would like to express our heartfelt gratitude to <strong>Neurohackademy</strong> at the <strong>University of Washington eScience Institute</strong> for providing invaluable training and support. This experience has significantly enriched our understanding of neuroimaging and data science. We also acknowledge the support of the National Institute of Mental Health (NIMH) grant number <strong>5R25MH112480-08</strong>, which made this opportunity possible.</p>
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