Transient attention activates magnocellular subdivision of the human LGN
Poster No:
2007
Submission Type:
Abstract Submission
Authors:
Shuoyi Liu1, Yanzhu Qian2, Peng Zhang1
Institutions:
1Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, 2Institute of Biophysics， Chinese Academy of Sciences, Beijing, Beijing
First Author:
Co-Author(s):
Introduction:
Our previous study showed that functional localization of magnocellular (M) layers of the human LGN is challenging at the individual level using block-designed stimulus in an unattended condition (Qian et al., 2020). Also, it remains unclear whether top-down attention can modulate layer-specific response in the LGN. To address these questions, we investigated laminar response patterns in the human LGN using magnocellular, parvocellular (P) and koniocellular (K) stimuli in an event-related design in attended and unattended conditions.
Methods:
M-, P-, and K-biased stimuli were 1 c.p.d. sine wave checkerboards with different temporal frequencies (10/15 Hz for M, and 4 Hz for P and K stimuli) and achromatic contrast (M) or isoluminant cone contrast (P and K) (Figure 1) (Derrington et al., 1984; Kuriki, 2006; MacLeod & Boynton, 1979). Each stimulus presentation lasts 1 second, with 5–9 seconds of inter-stimulus interval. Participants either paid attention to the stimuli (attended condition), or to detect occasional lumiance change of fixation (unattended condition). In the attended condition, for the M stimulus, participants discriminate temporal frequency difference in the first and second 500 ms of the stimuli (increase from 10 Hz to 15 Hz, or vice versa). For P and K stimuli, they discriminate contrast difference of the first and second periods of the stimuli (contrast increment or decrement). A total of 4 runs of data were collected for each stimulus type (2 runs each for attended and unattended conditions) in separate sessions.
Two young healthy volunteers participanted the experiment. MRI data were acquired using a 7T Siemens Magnetom scanner with a 32-channel receive and 8-channel transmit head coil in CP mode. T1-weighted anatomical volumes were acquired using a MP2RAGE sequence (0.7 mm isotropic resolution). Functional images were acquired using a gradient echo 2D-EPI sequence (1.2 mm isotropic voxels, TR = 2000 ms, FOV = 180*180 mm). Optimal echo time (TE) and flip angle (FA) of fMRI in the LGN were calculated using data from the same EPI sequence collected at 5 TEs (14, 24, 34, 44 and 54ms, FA = 68 deg) and 5 FAs (30, 50, 70, 90, 110 degrees, TE = 24 ms). The optimized parameters were TE = 22.6 ms and FA = 72 deg for S01, and TE = 22 ms and FA = 66 deg for S02.
MRI data were analyzed using AFNI and Matlab. Quantitative T1 relaxation times (qT1) were fitted by the Ernst equation, the M and P subdivisions of the LGN were segmented using a two-gaussian mixture model (Müller-Axt et al., 2021). T2* values were calculated with multi-echo EPI data to locate vessel voxels (T2* less than 15 ms).
Two young healthy volunteers participanted the experiment. MRI data were acquired using a 7T Siemens Magnetom scanner with a 32-channel receive and 8-channel transmit head coil in CP mode. T1-weighted anatomical volumes were acquired using a MP2RAGE sequence (0.7 mm isotropic resolution). Functional images were acquired using a gradient echo 2D-EPI sequence (1.2 mm isotropic voxels, TR = 2000 ms, FOV = 180*180 mm). Optimal echo time (TE) and flip angle (FA) of fMRI in the LGN were calculated using data from the same EPI sequence collected at 5 TEs (14, 24, 34, 44 and 54ms, FA = 68 deg) and 5 FAs (30, 50, 70, 90, 110 degrees, TE = 24 ms). The optimized parameters were TE = 22.6 ms and FA = 72 deg for S01, and TE = 22 ms and FA = 66 deg for S02.
MRI data were analyzed using AFNI and Matlab. Quantitative T1 relaxation times (qT1) were fitted by the Ernst equation, the M and P subdivisions of the LGN were segmented using a two-gaussian mixture model (Müller-Axt et al., 2021). T2* values were calculated with multi-echo EPI data to locate vessel voxels (T2* less than 15 ms).
Results:
Segmentation results from qT1 revealed a laminar pattern highly consistent with the functional organization of the LGN. When subjects were paying attention to the transient stimuli, the ventral LGN was strongly activated in all stimulus conditions (Figure 2A). The activation patterns were qualitatively similar to the qT1 results, but with clear differences. Functional activations and M voxels in qT1 patterns are not consistent with the vessel voxels with low T2* values. Deconvolved event-related response timecourses in M subdivision of the LGN were shown in Figure 2B.
Conclusions:
Transient attention to different types of stimuli strongly activated the ventral LGN, consistent with the anatomical location of the M layers. Remarkably, these activations were robust at the level of individual subject, reinforcing the reliability of the observed effects. These findings provide compelling evidence for a critical role of the M pathway in transient attention, highlighting its contribution to the dynamic processing of visual information.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Novel Imaging Acquisition Methods:
Anatomical MRI
BOLD fMRI
Perception, Attention and Motor Behavior:
Attention: Visual 1
Keywords:
FUNCTIONAL MRI
MRI
STRUCTURAL MRI
Sub-Cortical
Thalamus
Vision
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Kuriki, I. (2006). The loci of achromatic points in a real environment under various illuminant chromaticities. Vision Research, 46(19), 3055–3066.
MacLeod, D. I. A., & Boynton, R. M. (1979). Chromaticity diagram showing cone excitation by stimuli of equal luminance. Journal of the Optical Society of America, 69(8), 1183.
Müller-Axt, C., Eichner, C., Rusch, H., Kauffmann, L., Bazin, P.-L., Anwander, A., Morawski, M., & Von Kriegstein, K. (2021). Mapping the human lateral geniculate nucleus and its cytoarchitectonic subdivisions using quantitative MRI. NeuroImage, 244, 118559.
Qian, Y., Zou, J., Zhang, Z., An, J., Zuo, Z., Zhuo, Y., Wang, D. J. J., & Zhang, P. (2020). Robust functional mapping of layer-selective responses in human lateral geniculate nucleus with high-resolution 7T fMRI. Proceedings of the Royal Society B: Biological Sciences, 287(1925), 20200245.