Depth relationships and measures of tissue thickness in dorsal midbrain

Abstract

Dorsal human midbrain contains two nuclei with clear laminar organization, the superior and inferior colliculi. These nuclei extend in depth between the superficial dorsal surface of midbrain and a deep midbrain nucleus, the periaqueductal gray matter (PAG). The PAG, in turn, surrounds the cerebral aqueduct (CA). This study examined the use of two depth metrics to characterize depth and thickness relationships within dorsal midbrain using the superficial surface of midbrain and CA as references. The first utilized nearest-neighbor Euclidean distance from one reference surface, while the second used a level-set approach that combines signed distances from both reference surfaces. Both depth methods provided similar functional depth profiles generated by saccadic eye movements in a functional MRI task, confirming their efficacy for delineating depth for superficial functional activity. Next, the boundaries of the PAG were estimated using Euclidean distance together with elliptical fitting, indicating that the PAG can be readily characterized by a smooth surface surrounding PAG. Finally, we used the level-set approach to measure tissue depth between the superficial surface and the PAG, thus characterizing the variable thickness of the colliculi. Overall, this study demonstrates depth-mapping schemes for human midbrain that enables accurate segmentation of the PAG and consistent depth and thickness estimates of the superior and inferior colliculi.

Characterization of the blood oxygen level dependent hemodynamic response function in human subcortical regions with high spatiotemporal resolution

“…For the comparison between spiral and PSF-EPI sequences, we observed very similar time series (R 2 ≥ 0.89) without any significant differences of their temporal parameters between the two sequences, which confirms that the HRF measurements obtained by the two sequences are comparable (Figure 8A; Kim et al, 2013;Kim and Ress, 2016;Taylor et al, 2018;Truong et al, 2020).…”

“…This is in contrast to cortex, where it has been noted that late CMRO 2 demand could contribute to the BOLD undershoot in cortex (Masamoto and Tanishita, 2009;Vazquez et al, 2010a,b;Kim and Ress, 2016). Further experiments with simultaneous BOLD and arterial spin labeling (ASL) measurements (Huppert et al, 2006;Truong et al, 2020), combined with modeling to estimate CMRO 2 dynamics, will be necessary to explain the temporal differences of the HRF between subcortical nuclei and visual cortex.…”

“…However, all subjects do not produce ringing. Although ringing is observed in various imaging modalities such as optical imaging spectroscopy (Martin et al, 2006), two-photon imaging (Drew et al, 2011), laser speckle (Khan et al, 2011), fMRI BOLD (Kim and Ress, 2016), and ASL (Truong et al, 2020), its existence is still controversial. In our experiment, the higher CNR with simple experimental paradigm enable characterization of early-time behavior of the HRF.…”

“…Functional acquisition comprised 16 quasi-axial slices (1-mm thick) (Figure 1B, yellow box). At 3T, based on the success of our previous work in the colliculi (Katyal et al, 2009;Katyal and Ress, 2014;Savjani et al, 2018;Truong et al, 2020), we used a two-shot outward-spiral acquisition (Glover, 1999;Singh et al, 2018) to obtain an inplane pixel size of 1.5 mm, 160 mm FOV with ten 1.5-mm-quasiaxial slices (Figure 1B, red box), resulting in an acquisition time of 25 ms for each shot. Considering a linear increase in signal-to-noise-ratio (SNR) with magnetic field strength, this voxel size gives similar SNR as 1 mm 3 voxels at 9.4T.…”

“…Then, we compensated for head movements using motion correction with a robust intensity-based expectation-maximization algorithm (Nestares and Heeger, 2000). Next, we corrected slow baseline-intensity drift using a form of high-pass filter (Friston et al, 2000;Ress et al, 2000;Smith et al, 2004;Shmuel et al, 2007;Taylor et al, 2018Taylor et al, , 2022Truong et al, 2020). Specifically, a baseline was estimated from the time series by smoothing it twice using a RECT-function kernel with the same duration as HRF stimulation period, then this baseline was subtracted from the time series.…”