Estimates of locus coeruleus function with functional magnetic resonance imaging are influenced by localization approaches and the use of multi-echo data

Abstract

Highlights• Manual tracing of locus coeruleus increased specificity of seed time series • Manual tracing of locus coeruleus increased specificity of intrinsic connectivity • Multi-echo fMRI increased temporal signal-to-noise ratio compared to single-echo fMRI • Connectivity maps across methodologies overlapped only moderately • Measurement of LC function benefits from multi-echo fMRI and tracing ROIs ESTIMATES OF LC FUNCTION 3 AbstractThe locus coeruleus (LC) plays a central role in regulating human cognition, arousal, and autonomic states. Efforts to characterize the LC's function in humans using functional magnetic resonance imaging have been hampered by its small size and location near a large source of noise, the fourth ventricle. We tested whether the ability to characterize LC function is improved by employing neuromelanin-T1 weighted images (nmT1) for LC localization and multi-echo functional magnetic resonance imaging (ME-fMRI) for estimating intrinsic functional connectivity (iFC). Analyses indicated that, relative to a probabilistic atlas, utilizing nmT1 images to individually localize the LC increases the specificity of seed time series and clusters in the iFC maps. When combined with independent components analysis (ME-ICA), ME-fMRI data provided significant gains in the temporal signal to noise ratio relative to denoised single-echo (1E) data. The effects of acquiring nmT1 images and ME-fMRI data did not appear to only reflect increases in power: iFC maps for each approach only moderately overlapped. This is consistent with findings that ME-fMRI offers substantial advantages over 1E data acquisition and denoising. It also suggests that individually identifying LC with nmT1 scans is likely to reduce the influence of other nearby brainstem regions on estimates of LC function.

Uncovering the locus coeruleus: comparison of localization methods for functional analysis

“…These masks are smaller than the expected size of LC, but capture the main concentration of noradrenaline-producing cells (Keren et al, 2015). Our results are in agreement with previously reported results from either segmentation method, for instance peak location, average volume, and supremacy of central part of LC (Betts et al, 2017;Turker et al, 2019;Ye et al, 2020). Semi-automatic segmentation has been used in the literature to identify intensity peaks across slices of NM-sensitive scans (Betts et al, 2017;Dahl et al, 2019).…”

“…The high similarity between the maps of AT mask and the others may indicate that it successfully captures LC signal from individual LC. However, the extent of the FC maps with LC AT as seed showed more widespread connectivity and the statistical values were larger than the other FC maps (Figure 11 and 12), which could be interpreted as the AT mask being not very specific and capturing signal from surrounding regions, as suggested also by Turker et al 2019. We consider that the pattern of similarity between maps from the LC consensus mask and the other maps ( Figure 10) strongly indicates that: 1) the consensus mask captured true LC signal that swept the differences inherent to each different approach, indicated by the low variability across similarity values between LC consensus and the other masks; 2) the masks from atlas-based approaches (LC AT and mean LC NM) are able to capture most of true LC signal, as indicated by the highest similarity between the maps of these masks with the map of LC consensus, but also 3) the consensus mask was able to capture sample-specific high-probability LC voxels, indicated by the similarly high correlation between the LC consensus maps and all the other sample specific masks (NM, mICA and pupil), which showed variable correlation with each other.…”

Real-world stress resilience is associated with the responsivity of the locus coeruleus

“…Optimal functional imaging of the brainstem, and in particular the LC, is notoriously difficult due to the small size of the nuclei involved, their proximity to the ventricles, and inherently low signal-to-noise ratio in the brainstem. In order to unequivocally identify LC-NE activity, nonstandard techniques would be ideal for both data acquisition and analysis [85][86][87] On the acquisition side this would, for instance, entail high-field imaging and partial-brain coverage, which allows particularly small (submillimeter) voxel resolution to avoid partial voluming and reduce pulsating artefacts from the adjacent 4th ventricle (please see the limitations section for a discussion of fundamental methodological steps to improve brainstem imaging). However, the use of such a specialized imaging protocol would preclude whole-brain imaging and therefore inferences about influences of the LC on other brain systems (e.g., the amygdala and neocortical areas involved in conflict processing).…”

“…First, optimal functional imaging of the brainstem, and in particular the LC, is difficult due to the small size of the nuclei, their proximity to the ventricles, and inherently low signal-to-noise ratio in the brainstem. Identification of LC-NE activity may thus benefit from more specialized data acquisition techniques than those applied here [85][86][87]136,137 . For instance, on the acquisition side, one may obtain high-field imaging for superior signal-to-noise ratio, high resolution T2-weighted anatomical imaging for accurate brainstem registration to standard space, neuromelanin-sensitive imaging for localization and dissociation of individual participants' LC from other brainstem nuclei.…”

Mental Effort When Playing, Listening, and Imagining Music in One Pianist’s Eyes and Brain

“…Specifically, we subtracted the activations related to the ''simple'' piano piece from the ''complex'' piano piece for all the conditions (listening, imagining playing, and playing; see Figure 10A). Thus, the T2 * -weighted scans were not optimized for the identification of functional activity within such a tiny structure as the LC (see Turker et al, 2019 for a review on issues with identifying LC with fMRI). Still, by applying the LC mask and a small volume correction analysis approach, we found one significant voxel in the left and two in the right superior LC The LC analysis was done as a small volume corrected analysis with peak activation at threshold of F = 0.001, K = 1.…”