Background

Magnetic resonance spectroscopy (MRS) can be used in vivo to measure the concentration of neurometabolites within the brain. The blood-oxygen-level-dependent signal measured using functional magnetic resonance imaging (fMRI) can provide evidence of neural activity. However, MRS can provide evidence that can be used to distinguish between different types of activity, e.g., excitatory and inhibitory. For the purpose of understanding neural mechanisms, identifying the involvement of neurotransmitter systems that support sensory/cognitive processes can be more informative than locating regions of neural representation.

The methodological approaches used to investigate sensory/cognitive processing with MRS can be separated into two categories: correlational and functional. The common correlational approach is to measure the concentration of metabolites in a group of participants, within a brain region of interest, and test whether individual variance of a particular metabolite is related to performance on a task supported by the sensory/cognitive process (Edden et al., 2009; Rideaux and Welchman, 2018).

A common functional approach used to identify metabolite involvement is to measure baseline metabolite concentration while a participant is “at rest” and compare this with another measurement taken while the participant performs a task. A difference in metabolite concentration observed between the two measurements may be taken as evidence of its involvement in the sensory/cognitive processes supporting the task (Kurcyus et al., 2018; Rideaux et al., 2019).

While MRS can be used to differentiate excitatory and inhibitory transmission within the brain, compared to fMRI, it is severely limited in terms of its temporal resolution, which is constrained by the signal-to-noise ratio of measurements acquired using the technique. Although the duration that restricts the temporal resolution of MRS (i.e., the relaxation time) can be similar to that of fMRI (i.e., ~2 s), in order to yield a reliable estimate of metabolite concentration from within the brain, multiple transients must be combined to reach a sufficient signal-to-noise ratio (Mikkelsen et al., 2018). For example, it is common to combine between 200-300 transients (~10 min) to produce a single measure of metabolite concentration (Puts and Edden, 2012).

The information provided using this approach is severely limited. In particular, by reducing the measure of neurometabolite concentration to a single estimate averaged across an experimental condition, dynamic changes in concentration during this period are obscured. By contrast, establishing the temporal dynamics in neurometabolite concentration reveals the timing and magnitude of change in different neurotransmitters and novel relationships between neurotransmitters. This information is essential for a comprehensive understanding of the role of neurotransmitters in sensory and cognitive processes, and may inform our understanding of the balance between excitation and inhibition, which is thought to be a key factor in multiple neurological and psychiatric illnesses (Bradford, 1995; Rubenstein and Merzenich, 2003; Kehrer et al., 2008).

Here we describe two analyses that can be used to estimate dynamic neurometabolite concentration from MRS spectra. The first analysis uses a sliding window approach to create a smoothed trace of neurometabolite concentration for each MRS scan. The second analysis combines transients across participants, rather than time, producing a single “group trace” with the highest possible temporal resolution achievable with the data.