Thalamo-visual connections play an important role in the visual system. Little is known about the temporal frequency tuning properties of the thalamo-visual correlation in humans. Here we demonstrated that thalamo-visual correlation is significantly modulated by the temporal frequency of a stimulus. Using correlation with thalamus as an index, human visual cortex is organized along a temporal dimension, with the anterior calcarine preferring low temporal frequencies and posterior calcarine preferring higher temporal frequencies.
Twenty right-handed subjects (ten males/ten females; aged 18-50, mean age 26.5) were scanned on the Siemens 3T Prisma scanner. Visual stimulation at multiple different frequencies (1Hz, 5Hz, 10Hz, 20Hz and 40Hz) were applied through full-field (black/white) reversal of an LCD screen (BOLDscreen 32). Temporal accuracy of the LCD screen was verified independently using a photodiode. Separate runs with (i) continuous stimulation for 386 s (and a control run with no stimulation) and (ii) with a block-designed experiment (10 s ON, 20.6 s OFF). Simultaneous multi-slice (SMS) EPI was used with TR/TE = 1700/28 ms, matrix 90×90, 2.5 mm isotropic voxels, 52 slices, SMS factor = 2.
Block-design data was used to define ROIs for subsequent connectivity analyses. In particular, we used linear regression with an onset + sustained + offset BOLD response model3,4 to define two regions: (1) a calcarine region activated by 1 Hz stimuli (low-frequency ROI); (2) an occipital non-calcarine region activated by 40 Hz stimuli (high-frequency ROI).
The steady-state data was used for the connectivity analyses. For this, we take low-frequency and high-frequency ROIs as the seed regions and computed their connectivity to the whole brain. In every voxel of gray matter, we calculated its mean correlation with all voxels inside each of these two ROIs for each subject and applied group-level statistical comparison between different stimulation conditions. Then the thalamo-visual correlation vs. flickering frequencies were fitted using a Gaussian function on an ROI and voxel-wise basis. In each visual voxel, the peak frequency was extracted from the fitting curve to display if the correlation coefficient of the fitting was larger than 0.35. The frequency-dependent thalamo-visual correlation changes were also inputted to the K-means clustering algorithm to group areas according to the similarity in their frequency response properties.
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