Yuhui Chai1, Daniel Handwerker1, Sean Marrett1, Andrew Hall1, Javier Gonzalez-Castillo1, Peter Molfese1, and Peter Bandettini1
1National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
Synopsis
The functional architecture
of temporal frequency preference of human visual system is not well
characterized. We collected fMRI data with visual stimuli varying flicker
frequency from 1 to 40 Hz. Using model fit and K-means clustering on frequency
tuning curve we were able to show evidence for a temporal frequency specific
architecture of visual system.
Introduction
Temporal
frequency is a fundamental attribute of visual stimuli. Frequency sensitivity
has been studied in multiple areas of the visual system, including lateral
geniculate nucleus, superior colliculus, primary visual cortex and high-level
visual areas1–4. But these studies typically examine
frequencies under 20 Hz and have not attempted to map
preferred temporal frequency across many visual areas. In this study, we aimed to generate a temporal
frequency preference map for the human visual system, and ask if this temporal
frequency selectivity is organized into a functional architecture.
Methods
Twenty right-handed
subjects (ten males/ten females; aged 18-50, mean age 26.5) were
scanned on a 3T system (Siemens Prisma). A block design was utilized (10 s ON/20.6 s fixation). During
the ON segment, visual full-field stimulation at one of
five temporal frequencies (1 Hz, 5Hz, 10 Hz, 20 Hz and 40 Hz) was applied,
alternating the whole screen (BOLDscreen 32 LCD) from black to white. Frequency
order was randomized. Simultaneous multi-slice (SMS) EPI was used with TR/TE =
1700/28 ms, matrix 90×90, 2.5mm isotropic voxels, 52 slices, SMS factor = 2, no
in-plane acceleration. Statistical analyses were conducted with AFNI using an onset + sustained + offset BOLD response
model5,6. For active voxels (cluster-based thresholding p <
0.01), the sustained BOLD signal changes across temporal frequencies were
fitted with a difference of exponentials function7,8. The peak frequency was used to display tuning maps
only if the correlation coefficient of the fitting was larger than 0.32. BOLD signal changes at each frequency were also
inputted to the K-Means clustering algorithm to group areas according to the
similarity of their response across frequencies.
Results and discussion
Group-level activation maps
for the sustained, onset and offset BOLD response are shown in Fig. 1. Frequency-dependent
spatial pattern changes are most obvious from the sustained responses,
especially when comparing activation maps between 01 Hz and 40 Hz stimuli.
Based on the activation map of the sustained responses, we defined three regions-of-interest
(ROI) as in Fig. 2A: (1) Low frequency ROI, regions activated by 1 Hz stimuli
and restricted to the calcarine sulcus; (2) High frequency ROI, occipital regions
activated by 40 Hz stimuli outside the calcarine region; (3) Activated thalamic
regions. The mean signal changes from these three ROIs were extracted and
averaged across subjects, and plotted as a function of stimulation frequency (Fig.
2B). The signal changes vs. temporal frequencies were fitted with a difference
of exponentials function and the peak frequency of the fitting curve was
extracted. Then we applied this model fit to every voxel inside the visual
activated areas and displayed the voxel-wise peak frequency as Fig. 3. In this
tuning map, we can observe how calcarine sulcus is tuned to the lowest
frequencies. When moving from the anterior to the posterior calcarine, the
preferred frequency increases. Lateral occipital areas show a preference for
the highest flickering frequency. In addition to model fit, we also applied K-means
to decompose visual areas according to their temporal frequency response (Fig.
4). For k=2, visual areas were segmented into low and high frequency clusters.
The cluster distributions of k=4 suggests a clear peak frequency increasing
trend from anterior to posterior calcarine and then to lateral occipital areas.
Frequency tuning curves for V1, V2 and V3 (based on a visual template9 from Benson et al. (2014)) are shown in Fig. 5B; while
voxel counts tuned to the different frequencies in these three regions are
shown in Fig. 5C. The mean BOLD response of V1, V2 and V3 all peaked at about
8-11 Hz, which is consistent with previous visual functional imaging researches3,10. However, most
voxels in these visual areas have peak frequency of either 4 Hz or 12-14 Hz.
Conclusions
The
human visual areas could be arranged into several clusters, each comprising a
group of areas that share a common frequency tuning response. There is a clear increasing trend of preferred
temporal frequency from anterior to posterior calcarine and then to lateral
occipital cortex.Acknowledgements
No acknowledgement found.References
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