Abstract
The sensitivity of the sensory systems to temporal changes of the environment constitutes one of the critical issues in perception. In the present study, we investigated the human early visual system's dependency on the temporal frequency of visual input using fMRI. Blood oxygen level-dependent (BOLD) responses of the lateral geniculate nucleus (LGN) and primary visual cortex (V1) were investigated in a wide frequency range (6-46 Hz) with fine frequency sampling (13 frequencies). Subject-specific functional-anatomic ROIs were derived from the combination of the anatomic template masks and the functional maps derived from multi-session fMRI analyses across all 13 stimulation conditions. Using functional-anatomic ROIs, average responses of LGN and V1 were calculated for each frequency. The V1 surface area was further parsed into 7 eccentricity sectors to detail central and peripheral responses.
Abstract
LGN's response revealed fluctuations on a background of non-significant decrease of the BOLD response with increasing stimulation frequency, while V1 response displayed similar fluctuations with a global maximum in the range of 8-12 Hz, but a rapid and significant decrease with increasing stimulation frequency especially above 14 Hz. This behavior of V1 response valid for both central and peripheral vision emphasizes that the profound low-pass effect of the visual system to visual input emerges in V1, presumably generated by the intra-cortical circuitry of V1 or projections from extra-striate areas. Besides, the high correlation between LGN and V1 BOLD responses across all visual stimulation frequencies supports the oscillatory tuning in thalamo-cortical interactions as previously claimed in electrophysiological studies.
a b s t r a c t
The sensitivity of the sensory systems to temporal changes of the environment constitutes one of the critical issues in perception. In the present study, we investigated the human early visual system's dependency on the temporal frequency of visual input using fMRI. Blood oxygen level-dependent (BOLD) responses of the lateral geniculate nucleus (LGN) and primary visual cortex (V1) were investigated in a wide frequency range (6-46 Hz) with fine frequency sampling (13 frequencies). Subject-specific functional-anatomic ROIs were derived from the combination of the anatomic template masks and the functional maps derived from multi-session fMRI analyses across all 13 stimulation conditions. Using functional-anatomic ROIs, average responses of LGN and V1 were calculated for each frequency. The V1 surface area was further parsed into 7 eccentricity sectors to detail central and peripheral responses.
a b s t r a c t
LGN's response revealed fluctuations on a background of non-significant decrease of the BOLD response with increasing stimulation frequency, while V1 response displayed similar fluctuations with a global maximum in the range of 8-12 Hz, but a rapid and significant decrease with increasing stimulation frequency especially above 14 Hz. This behavior of V1 response valid for both central and peripheral vision emphasizes that the profound low-pass effect of the visual system to visual input emerges in V1, presumably generated by the intra-cortical circuitry of V1 or projections from extra-striate areas. Besides, the high correlation between LGN and V1 BOLD responses across all visual stimulation frequencies supports the oscillatory tuning in thalamo-cortical interactions as previously claimed in electrophysiological studies.
a b s t r a c t
Ó 2016 Elsevier Ltd. All rights reserved.
Introduction
Visual stimuli at various temporal frequencies have been used as a convenient tool to investigate the frequency response of the visual system (Herrmann, 2001; Rager & Singer, 1998; Regan, 1989) . As thalamo-cortical loops are one major source of oscillatory neuronal activities in the brain, determination of the frequency characteristics of the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) is essential in order to evaluate the temporal frequency response of the visual system (Bas ßar, 1980) . Functional magnetic resonance imaging (fMRI) provides a non-invasive way of measuring the responses of both structures with a high spatial accuracy. Although the LGN has relatively small volume, its activities can still be measured using fMRI (Büchel, Turner, & Friston, 1997; Chen et al., 1998; Fujita et al., 2001) .
Introduction
The LGN is the main source of visual information for V1, and the main target nucleus of the optic tract, which receives almost 90% of retinal outputs (Gazzaniga, Ivry, & Mangun, 2002) . On the other hand, the proportion of retinal inputs with respect to total synapses in the LGN of primates is about 30-40% (Casagrande, Sáry, Royal, & Ruiz, 2005) , which implies that the majority of inputs are extra-retinal and have a modulatory effect on rather than being the driver of visual inputs (Sherman & Guillery, 1998) . V1 is the source of the largest number of synapses in the LGN, which carry feedback signals from the cortex to the thalamus (Casagrande et al., 2005) . Therefore, the LGN is an important site for the modulatory effects of V1 on visual input and can play an important role in the temporal frequency tuning of the visual system. A number of positron-emission tomography (PET) and fMRI studies reported activity changes in the visual cortex depending on the temporal frequency of the stimuli (Emir, Bayraktaroglu, Ozturk, Ademoglu, & Demiralp, 2008; Fox & Raichle, 1985; Kastner, Schneider, & Wunderlich, 2006; Kastner et al., 2004; Mullen, Thompson, & Hess, 2010; Muthukumaraswamy & Singh, 2008; Parkes, Fries, Kerskens, & Norris, 2004; Rosa, Kilner, Blankenburg, Josephs, & Penny, 2010; Singh, Kim, & Kim, 2003; Wan et al., 2006) . However, these results did not lead to a consensus for several reasons.
Introduction
First of all, a common problem with the studies that used retinotopic mapping for identifying the V1 region (Kastner et al., 2004; Mullen et al., 2010; Singh, Smith, & Greenlee, 2000) is the variability of responses due to different spatial properties of the visual stimuli. As Mirzajani, Riyahi-Alam, Oghabian, Saberi, and Firouznia (2007) demonstrated, interaction among temporal and spatial frequency effects need to be taken into consideration when investigating the response characteristics of the visual system.
Introduction
With the exception of the studies by Emir et al. (2008) and Singh et al. (2003) another disadvantage of these studies is the logarithmic sampling of temporal frequencies because of the raster screens used, which allow to vary the temporal frequency only by integer multiples of the refresh rate. As a result, these studies described the temporal frequency-response of V1 by investigating BOLD responses at only three or four frequencies, which resulted in limited correspondence among the studies.
Introduction
Finally, ambiguity in results might also stem from different ways of defining the region of interest (ROI) in fMRI analyses. Studies that defined the ROI as a limited number of the most active voxels in V1 (Emir et al., 2008; Muthukumaraswamy & Singh, 2008; Parkes et al., 2004; Wan et al., 2006) reported increased BOLD responses with higher temporal frequency, which peaked and plateaued at around 6-10 Hz. Contrarily, studies that defined the ROI by thresholding the BOLD responses more liberally lead to a broader area covering V1 and extra-striatal areas and reported a band-pass response with a peak at approximately 8 Hz (Rosa et al., 2010; Singh et al., 2003) .
Introduction
In the present study, we aimed to study the fMRI frequencyresponse characteristics of the LGN and V1 in a robust and unequivocal manner. For this, we used diffuse light stimuli to avoid confounding effects of spatial visual patterns on the BOLD response. In addition, light-emitting diodes (LED) were used as light sources, which allowed us to sample the temporal frequencies with a finer resolution compared with raster screens. Finally, we used the intersection area of the functional and anatomic maps for ROI definition. Consequently, the frequency characteristics of the LGN and V1 BOLD responses revealed a clear low-pass effect in V1 but not in LGN. Despite this overall difference between the general frequency responses of both structures, their highly correlated BOLD fluctuations across visual stimulation frequencies supports the existence of oscillatory tuning in thalamo-cortical networks.
Materials and methods

Subjects
Thirty-five healthy volunteers (20 women, 15 men, mean age: 25.60 ± 3.87 years) were recruited for this study after receiving their informed consent. All subjects had normal or corrected-tonormal vision acuity and had no history of sensory systemrelated pathology or neuro-psychiatric disorder. Ethical approval was obtained from the local ethics committee of Istanbul University, Istanbul Faculty of Medicine, in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) prior to commencing the study.
Visual stimuli and experimental protocol
The stimuli were reflected on a white surface by a flickering light at temporal frequencies of 6, 8, 10, 12, 14, 18, 22, 26, 30, 34, 38, 42, and 46 Hz. The square-wave stimuli were ''on" for the half of a period, thus the energy was constant across all frequencies. Although the edges of the square waves may produce high frequency harmonics, their effect on mean intensity is the same for all stimulation frequencies and far beyond the time constant of the BOLD response.
Visual stimuli and experimental protocol
The mean luminance of the visual stimuli was 100 cd/m 2 . The light source was a shielded set of LEDs driven by a digital I/O card (NI DAQCard-6062E) located one meter away from the rear side of the magnet. The screen was a rear-facing 45°-inclined diffuser surface (field of view 54.8°) attached to the top of the head coil of the MRI system and used for diffuse reflection of the exposed light. The subjects' task was to maintain focus on and passively view the fixation cross drawn on the center of the reflection surface.
Visual stimuli and experimental protocol
Each experimental run, during which the fMRI response to one of the 13 flicker frequencies was measured, started with a rest period of 10 dynamic scans, which was followed by 3 alternating blocks of stimulation and rest periods (Fig. 1) . Each stimulation and rest block lasted for 15 dynamic scans yielding a total of 100 dynamic scans for each run. Flickers were synchronized to the fMRI volumes by using the synchronization output of the MR scanner. The order of the runs was randomized among the subjects to avoid any systematic effect of time on BOLD responses.
MRI data acquisition and analysis
Magnetic resonance imaging was performed using a 1.5T Philips Achieva MRI system equipped with SENSE-Head-8 coil at NPIS-TANBUL Neuropsychiatry Hospital, Istanbul. At the beginning of each experiment, routine cranial MRI examination was performed to detect possible abnormalities in healthy subjects. Additionally, whole-brain high-resolution structural scans were acquired using a T1-weighted MPRAGE sequence with voxel size of 1.25 Â 1.25 Â 1.2 mm (130 sagital slices, TR/TE = 8.6/4.0 ms, acquisition matrix 192 Â 192, duration 369.8 s). Thirteen experimental runs were acquired using T2 ⁄ -weighted gradient echo (GE), echo planar imaging (EPI) with identical scan parameters (100 dynamic scans, 32 axial slices, slice thickness = 4 mm (without gap), Fig. 1 . Experimental protocol and average pre-processed BOLD response of one subject. Each experimental run for one of the 13 flicker frequencies started with a rest period of 10 dynamic scans, which was followed by 3 alternating blocks of stimulation and rest periods of 15 dynamic scans each.
MRI data acquisition and analysis
in-plane resolution = 3.59 Â 3.59 mm, FOV = 230 Â 230 mm 2 , TE = 50 ms, TR = 2981 ms). Two dynamic scans were acquired prior to each fMRI run in order to obtain tissue magnetization. Subjects were allowed to rest between fMRI runs to relax their eyes without changing their positions in the scanner.
MRI data acquisition and analysis
Pre-processing steps of fMRI data were performed using the SPM5 software package (Ashburner, 2012) for Matlab (The Mathworks, Inc.). Motion correction and spatial smoothing with a Gaussian kernel of FWHM 8 mm were applied. All functional data sets were spatially normalized into a standard space (MNI152, 2 mm) prior to performing general linear model (GLM) analysis. For this purpose, a design matrix was created using a boxcar model of visual stimulation, which was convolved with a synthetic model of the hemodynamic response function (HRF), its time and dispersion derivatives, and the motion correction parameters. A highpass temporal filtering with a cut-off of 128 s was applied.
MRI data acquisition and analysis
In the first stage, a single-subject multi-session fixed-effects GLM analysis was performed in order to investigate the single and combined effects of different stimulation frequencies. Parameter estimates ('beta' images) and statistical parametric maps of t-statistics (SPM{t} maps) were generated for each visual stimulation frequency of each subject.
Defining visual areas
In this study, ROIs were defined as binary masks that resulted from the intersection area of the functional and the anatomic regions. Subject-specific functional ROIs were derived from the mildly-thresholded (p < 0.05, uncorrected) results of the fixedeffects multi-session GLM analysis, which included all 13 stimulation frequencies.
Defining visual areas
Anatomic ROIs were defined by registering standard template ROIs extracted from two separate probabilistic atlases for LGN and V1. In order to define the structural LGN ROI in the MNI space, the cytoarchitectonic probabilistic map for lateral geniculate body (Bürgel et al., 2006) from the Jülich histologic atlas implemented in the FMRIB Software Library (Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012) was thresholded at p = 0.01 (Fig. 2a) . The structural V1 ROI was defined by using the Freesurfer V1 label that is the spatial probability map of V1 derived from the cortical folding patterns (Fischl et al., 2008) . Since the primary visual cortex exhibits a folding pattern, surface-based registration, that decreases prediction error of the probabilistic atlases in volumetric coordinates, was employed using the Freesurfer software .
Defining visual areas
Subject-specific functional-anatomic LGN and V1 ROIs (Fig. 3 ) were used to extract average 'beta'-values (parameter estimates) for each visual stimulation frequency. As the output of the GLM analysis, the individual parameter estimate images for 13 different stimulation frequencies were multiplied by each subject's own binary LGN and V1 masks (Fig. 3) . Averaged beta-values were calculated separately for the LGN and V1 for each stimulation frequency. In order to investigate the differences in the frequency characteristics among various portions of V1 we additionally computed mean beta-values of V1 sub-areas by applying the visuotopic parcellation performed by Griffis, Elkhetali, Burge, Chen, and Visscher (2015) . This parcellation is based on 7 consecutive eccentricity ROIs of $10 mm width, which correspond to visual angles of 1.3, 2.2, 4, 7.3, 14.1, 25.5 and 40° (Fig. 7) .
Defining visual areas
Because beta-values might display high inter-subject variability, averaged 'beta'-values of each subject were normalized over the stimulation frequencies and the ROIs by dividing them by their maxima.
Defining visual areas
For statistical analysis, repeated measures ANOVA (SPSS 21.0) tests were performed to compare mean beta-values among various stimulation frequencies and ROIs. Greenhouse-Geisser-corrected significance values were reported for factors with more than 2 levels. Additionally, the correlation between the V1 and the LGN frequency characteristics were investigated by computing the correlation coefficients between the beta-values of both ROIs across stimulation frequencies for each subject. The resulting correlation coefficients of all subjects were exposed to one-sample t-test to evaluate whether they significantly deviate from 0.
Results
Before reporting the final results of the study, the raw temporal and spatial characteristics of the BOLD signals obtained during various stages of processing will be presented to explain the rationale behind the applied method. Fig. 4 shows the time courses of BOLD percent changes of a sample subject during the visual stimulations at 13 frequencies. A subject-specific V1 ROI (Fig. 3) was created by the intersection area of the structural V1 mask and the functional map obtained by the mildly thresholded (p < 0.05) BOLD response of the subject to all 13 stimulation frequencies. Extracted BOLD time series of the ROI for each stimulation frequency were averaged over the three consecutive stimulation blocks. Signal intensity was then presented as BOLD percent change relative to the first rest period of each run corresponding to first 10 TRs. It is clear that the temporal characteristics of the BOLD response depend on the stimulation frequency (Fig. 4) . Average BOLD percent change decreases with increased stimulation frequency, and a separation of transient and sustained BOLD components are observed with a steeper decrease in the sustained activity (plateau) compared with that in the transient on-and off-responses. Although attempts have been made to model the transient components of the BOLD response, there are no clear definitions for these waveforms yet (Uludag, 2008) . Therefore, in the present study we preferred the canonical hemodynamic response function to quantify the overall activity at various stimulation frequencies instead of defining arbitrary time windows for each transient component of the BOLD response.
Results
After presenting the temporal characteristics of the BOLD responses, their spatial distributions are displayed in Fig. 5 , showing the SPM{t} maps of a sample subject for each stimulation frequency without applying any mask. The activation maps are derived from the GLM analysis with the same threshold value (|t| > 3.00), which is applied to all stimulation frequencies in order to display the variability of the spatial distribution of the visual cortex activity at various visual stimulation frequencies. It is evident that both the level and spatial extent of the activity depend on stimulation frequency. This creates an important problem in comparing the activations among different stimulation frequencies, because a fixed threshold on functional data would lead to a variable extent of the investigated neural circuit for each stimulation frequency. On the other hand, using a fixed number of the most active voxels at each stimulation frequency would lead to less indicative results, which could spatially correspond to different neural circuits. This was the reason for using the intersection area of the subject-specific anatomic LGN and V1 masks with the functional mask created by mildly thresholding the multi-session fMRI data to define the subject-specific LGN and V1 ROIs. 4 . The average time courses of BOLD percent changes in V1 of a sample subject during stimulations with 13 different frequencies. Time series were averaged for the subject-specific V1 ROI (Fig. 3) and averaged over the three consecutive stimulus and rest cycles. BOLD percent change was calculated relative to the first rest period of each run that corresponded to the first 10 TRs.
Results
After the presentation of the raw data, we will report the final statistical analysis of the mean activations for the LGN and V1, which were obtained by masking the beta images for each stimulation frequency with the subject-specific LGN and V1 ROIs. In order to evaluate the frequency dependencies of both structures, we first performed repeated measures ANOVA including both the ''ROI" (2 areas: LGNs and V1) and ''stimulation frequency" (13 levels: 6, 8, 10, 12, 14, 18, 22, 26, 30, 34, 38, 42 and 46 Hz) as within-subject factors. The main effect of ROI was nonsignificant, which implied that the mean activation levels were comparable for active voxels of the LGN and V1. On the other hand, the overall effect of the ''stimulation frequency" (F(12, 408) = 13.56; p < 0.001) and the ''ROI Â stimulation frequency" interaction (F(12, 408) = 17.11, p < 0.001) were significant. These findings imply that the decrease rates of percent BOLD changes were significantly different between the LGN and V1. This effect can be observed in Fig. 6 as a steep decrease in the strength of V1 activations compared with the relatively smaller change of intensity in the LGN response. To validate this result, the frequency dependencies of the LGN and V1 were analyzed for the single within-subject factor ''stimulation frequency" in separate ANOVA tests. In this case, the main effect of frequency was significant for V1 (F(12, 408) = 32.60, p < 0.001), whereas it was non-significant for the LGN activations (F(12, 408) = 1.30, p = 0.21). These posthoc analyses revealed that the frequency dependent difference between both structures (significant ROI Â stimulation frequency interaction) could be attributed to a strong decrease in V1 activity with increasing stimulation frequency, which is not the case for the LGN.
Results
In the next step, post-hoc paired t-tests between the LGN and V1 activations were computed at each stimulation frequency. Significant differences were found for the regions lower than 18 Hz and higher than 22 Hz. This result implies that V1 activations are significantly higher than the LGN activations in the 6-14 Hz frequency band, and significantly lower in the 26-46 Hz frequency band (Table 1) .
Results
In order to evaluate the responses of V1 to foveal and peripheral stimulation separately, we further investigated the frequency tuning characteristics of sub-areas of V1 by applying the anatomic parcellation described by Griffis et al. (2015) , which yielded 7 consecutive eccentricity sectors of $10 mm width that correspond to visual angles of 1.3, 2.2, 4, 7.3, 14.1, 25.5, and 40°. The results of an ANOVA test on the mean beta values of 7 sectors and 13 stimulation frequencies as within subject factors yielded significant sector (F(6,204) = 4.245; p = 0.022) and stimulation frequency effects (F(12,408) = 27.88; p < 0.001), which points to the existence of a frequency effect over all sectors of V1 and to a significant lower activation of foveal area compared with the peripheral vision. Furthermore, the sector x stimulation frequency interaction effect on the beta values was also highly significant (F(72, 2448) = 6.708; p < 0.001). As seen in Fig. 7 , this significant difference among the sectors stemmed from a less pronounced change in central visual BOLD responses through the stimulation frequencies compared with those of the peripheral retina, although a general low-pass effect was present for all sectors. For testing the significance of the decrease in BOLD responses with increased stimulation frequencies in each sector of V1, post-hoc ANOVA tests were performed for each sector separately, which yielded highly significant results for all sectors (p < 0.001 for all 7 sectors).
Results
Correlation coefficients between the LGN and V1 activations were computed for each subject in order to test the existence of any correlation among the frequency response characteristics of both ROIs. The mean value and standard deviation of correlation coefficients among subjects were 0.4389 ± 0.315, which corresponds to a significant correlation at group level (p < 0.001). Despite the proven difference in the decay of V1 response with increasing frequency in contrast to the rather flat LGN response, the significant correlation between the LGN and V1 responses reflects the mutual fluctuations around certain stimulation frequencies (Fig. 6 ).
Discussion
By using BOLD fMRI we investigated the dependency of the human early visual system on the temporal frequency of the visual input. BOLD responses of the LGN and V1 were investigated in a wide frequency range (6-46 Hz) with fine frequency sampling.
BOLD response characteristics of LGN and V1
Only a few earlier fMRI studies have reported temporal frequency response characteristics of the LGN (D'Souza, Auer, Strasburger, Frahm, & Lee, 2011; Kastner et al., 2004; Mullen et al., 2010) , while there is a high number of studies on the visual cortex (Emir et al., 2008; Hagenbeek, Rombouts, van Dijk, & Barkhof, 2002; Muthukumaraswamy & Singh, 2008; Ozus et al., 2001; Parkes et al., 2004; Rosa et al., 2010; Singh et al., 2003; Thomas & Menon, 1998; Wan et al., 2006) . Kastner et al. (2004) investigated the LGN responses at temporal frequencies of 0.25, 3.75, and 10 Hz (corresponding to reversal rates of 0.5, 7.5, and 20 Hz) by using checkerboard stimuli with high contrast (100%) and found similar kelly90@example.com and 10 Hz and significantly smaller kelly90@example.com Hz, which indicated a high-pass characteristic. On the other hand, in a study based on contrast reversal of achromatic radial sine-wave gratings at 2, 8, and 16 Hz, Mullen et al. (2010) showed that the LGN response was significantly smaller at 16 Hz with respect to 8 Hz for high-contrast stimuli, and significantly smaller at 16 Hz with respect to 2 Hz for low contrast stimuli, which pointed toward a low-pass behavior of the system. The discrepancy between the results depends mainly on the limited number and overlap of stimulation frequencies.
BOLD response characteristics of LGN and V1
In the present study, we limited our analyses to the achromatic domain by relying on the sensitivity of the visual system to higher frequencies for the achromatic stimuli. By doing this, and using diffuse light instead of spatially-patterned stimuli, we had the opportunity to sample a wider range of temporal frequencies with finer frequency sampling compared with raster screens. In this way, more detailed information about the temporal frequency characteristics of the LGN could be obtained at 13 different stimulation frequencies within the 6-46 Hz range. BOLD responses of the LGN revealed fluctuations on a background of a non-significant decrease of the responses with increasing stimulation frequencies. In contrast, V1-BOLD responses displayed a global maximum in the range of 8-12 Hz with a rapid and significant decrease of the responses at higher stimulation frequencies, while fluctuations existed around similar frequencies as in LGN. This low-pass behavior was evident for both central and peripheral vision, although it was less pronounced for the foveal area.
Low-pass character of V1 activity
The strong decay of BOLD activity in V1 with increasing stimulation frequency, which is absent in the LGN, is a clear indication that the selectivity of the early visual circuitry for lower frequencies can be initiated earliest at the stage of LGN-V1 interaction. BOLD signal is mainly driven by synaptic input rather than the spiking activity of a brain volume (Logothetis, Pauls, Augath, Trinath, & Oeltermann, 2001) . Hence, the low-pass effect in the BOLD activity of V1 may depend on decreased spiking output of the LGN to V1 or shaped by the reduced internal synaptic activity of V1 at higher stimulation rates, where the modulatory feedback from extra-striate areas to V1 might also play a role. A conclusive statement in favor of one of these possibilities cannot be made on the basis of our findings. However, considering the results of earlier electrophysiological studies (Hawken, Shapley, & Grosof, 1996; Viswanathan & Freeman, 2007) , that reported high LGN spiking output in contrast to the strongly decayed spiking activity of V1 neurons (68%) with increasing stimulation frequency (Foster, Gaska, Nagler, & Pollen, 1985) , we claim that the BOLD low-pass effect in V1 reflects the role of the internal circuitry of V1, which might be modulated by the feedback projections from extrastriate areas. Either way, our results reveal that the selectively weaker neural activity for higher temporal frequencies primarily emerges in V1.
Low-pass character of V1 activity
Although increasing with eccentricity, the low-pass effect observed for the whole V1 area was valid for all sub-sectors. However, the less strong overall activation in foveal area compared with the peripheral vision in line with a less strong foveal lowpass effect can be explained by the lower light sensitivity of the cone pathway in comparison to the rods.
Correlated LGN & V1 response
In the present study, the fluctuations of the V1 frequency response characteristics were at similar stimulation frequencies as those of the LGN responses, which lead to a significant correlation among the frequency characteristics of both structures. This finding is in line with the results of both Kastner et al. (2004) and Mullen et al. (2010) , even though their reports on the correlation of the LGN and V1 activities rely on only 3 stimulation frequencies. The high correlation we observed within a wide range of stimulation frequencies from 6 to 46 Hz may suggest preferred signal transmission frequencies in the thalamo-cortical circuit of the visual system at these temporal frequencies. Although present results are not sufficient to explain whether these selectivities are generated by one of the structures or by their interactions, we can tentatively state that specific tuning frequencies play a role in thalamo-cortical communication in the visual system. The presence of maxima at similar frequencies in surface EEG responses and intracranially-recorded local field potentials of the visual system (Herrmann, 2001; Rager & Singer, 1998; Regan, 1989 ) support this interpretation.
Selection of the stimulus parameters
Major dissociations among similar studies in the literature are related to the various spatial patterns such as checkerboard, ring, horizontal or radial gratings, as well as to the temporal pattern of the stimuli. A PET study conducted by Fox and Raichle (1985) showed that the response curves of regional cerebral blood flow (rCBF) percent change as a function of temporal frequency were nearly identical for flashing light and reversing checkerboard stimuli. There are also fMRI studies that used light flickers, which agreed closely with PET results (Hagenbeek et al., 2002; Ozus et al., 2001; Thomas & Menon, 1998) . However, Mirzajani et al. (2007) showed that the spatial frequency of patterned stimuli introduced modulatory effects on the temporal frequency tuning in an fMRI study. Therefore, no spatial pattern or contrast was used in the present study.
Selection of the stimulus parameters
There are also critical considerations in the studies with diffuse light stimuli, which complicate drawing conclusions about frequency characteristics of the visual system. For instance, in the studies mentioned above, the stimuli are monochromatic but not achromatic (diffuse red light), thus activating rather the parvocellular pathway (L-cone input) while suppressing the magnocellular pathway, for which temporal dynamics of the visual input is of higher importance (Chase, Ashourzadeh, Kelly, Monfette, & Kinsey, 2003) . There are clues in the recent literature displaying different BOLD responses for the chromatic vs. achromatic stimuli as a function of stimulation frequency (D'Souza et al., 2011; Mullen, Dumoulin, & Hess, 2008; Mullen et al., 2010) . Therefore, we preferred to use achromatic stimuli for a general characterization of the visual system's capacity to process inputs at various rates.
Selection of the stimulus parameters
Another important problem with the flashing light experiments used in many studies is the constant duration of each stimulus, which yields a confounding linear increase in mean luminance with the increasing stimulation rate. In our study, flickers at different frequencies were compatible with each other regarding the mean luminance and total time of visual field illumination, since the duty cycle of flashing light stimuli was 50%. Thus, light exposure was identical for each temporal frequency as in the experiments with pattern reversal. Therefore, we consider this setup as an optimal design for investigating frequency response characteristics of the visual system.
Definition of the V1 -ROI
Another factor that directly influences the results of similar studies is the approach for ROI definition, which is an essential parameter of the data analysis. In the literature, various ROI definition approaches have been applied, which may introduce serious ambiguities among the results. These techniques can generally be classified into three groups: those using a limited number of the most active voxels, those using a broader active area based on a significance level, and those using retinotopic mapping scans to define the borders of V1.
Definition of the V1 -ROI
Examples for the first group of studies are those by Emir et al. (2008) , Muthukumaraswamy and Singh (2008) and Wan et al. (2006) . In the first study, the average hemodynamic response of a very small volume (6-mm spherical, about 4 voxels) centered on maximally active voxels across all investigated stimulation frequencies (conjunction analysis) was computed for each subject, while only the one most active voxel was used in the second study. Wan et al. (2006) reported that the BOLD response increased up to 8 Hz when using radial checkerboard at stimulation frequencies of 0.5, 1.0, 4.0, 8.0, and 16.0 Hz, while Muthukumaraswamy and Singh (2008) by using square-wave vertical gratings at 0, 1, 6, 10, 15 Hz frequencies reported a BOLD response increase up to 6 Hz and a plateau between 6 and 15 Hz. In a previous study our group used a similar visual stimulation setup as in the present study and applied diffuse LED light at 24 different frequencies between 1 and 44 Hz (Emir et al., 2008) . By analyzing the most active voxels in the visual cortex, that study reported a global peak at 8 Hz which entailed a plateau superimposed by secondary peaks at 16 and 24 Hz. As such, there is a correspondence between the results of the studies that used a ROI based on a small number of the most active voxels in V1 in terms of a BOLD peak around 6-8 Hz followed by a plateau.
Definition of the V1 -ROI
Examples of the second group of studies that defined the ROI on the basis of a significance threshold applied on the functional data are those by Rosa et al. (2010) and Singh et al. (2003) . Singh et al. (2003) used checkerboard illumination at 2, 4, 6, 8, 10 and 12 Hz, and found a band pass response that peaked at approximately 8 Hz by thresholding the responses to each frequency and subject separately at the same significance level. By using checkerboard reversals at 2, 3.75, 5, 6, 7.5, 10, 15 and 30 Hz and by defining the ROI through thresholding the average response of each subject to all stimulation frequencies, Rosa et al. (2010) found an inverted U-shaped frequency response with a kelly90@example.com Hz and sharp drop above 15 Hz.
Definition of the V1 -ROI
The discrepancy between the results of both groups of studies clearly shows that identification of the ROI critically determines the results concerning the frequency characteristics of the visual cortex. No significant BOLD decrease with increasing stimulation frequency is obtained by relying on a limited numbers of most active voxels, whereas a band bass response is obtained with broader ROI based on significance thresholding. It is clear that the average activity of a limited number of most active voxels as used in the first group of studies does not represent the response of a structure as a whole. Besides, relying on different sub-areas of the visual cortex for each stimulation frequency as is the case with Singh et al. (2003) may be misleading in describing the frequency tuning properties of the visual cortex as a whole. The study by Rosa et al. (2010) overcame these weaknesses by defining a constant and broad ROI for all stimulation frequencies that relies on thresholding the average response of each subject to all stimulation frequencies. However, it is well known that (Mullen et al., 2010 ) the use of a broader term such as occipital cortex, instead of a well-described functional-anatomic area such as V1, might lead to confounders arising from different frequency behaviors of various visual areas. In this sense, the ROI definition must rely on a clear and reliable identification of V1, which might either be obtained by retinotopic mapping or by a surface-based method that relies on a template cortical folding pattern. It has been shown that there is a good correspondence between the borders of V1 as defined by a retinotopic functional scan and a high resolution anatomic scan that visualizes cortical striation consistent with the stria of Genari (Bridge et al., 2005) . Therefore, we defined the subject-specific ROIs as the intersection areas of significant fMRI activations to all stimulation frequencies with the anatomic template of V1.
Transient and sustained BOLD responses
Another interesting aspect of the data is the temporal characteristics of the BOLD responses presented in Fig. 4 . The transient onand off-responses displayed different decay rates with increasing stimulation frequency compared with a sustained plateau component in-between. Uludag (2008) used an 8-Hz flickering black-andwhite radial checkerboard in order to stimulate only one hemifield over 20 s and demonstrated a separate activity of the phasic (transient) network covering both hemispheres in addition to a sustained response in the contralateral hemisphere. This report suggests separate transient and sustained networks in the visual cortex.
Transient and sustained BOLD responses
A previous study of our group (Emir et al., 2008) based on similar experiments with flickers at various frequencies focused on two components of the BOLD signal, that are the positive BOLD (pBOLD) and post-stimulus undershoot (PSU). In that study, we showed that the pBOLD and PSU intensities were correlated between 1 and 13 Hz, whereas they became uncorrelated at higher stimulation frequencies. The neurovascular model proposed by Sotero and Trujillo-Barreto (2007) imposes the amplification of both the pBOLD and the PSU with excitatory activity, whereas increased inhibitory activity evokes a decrease in the pBOLD signal without altering the PSU. Basing on this model the correlated pBOLD/PSU pattern in the low stimulation frequency range was interpreted as a result of dominating excitatory input to visual cortex, while uncorrelated pBOLD and PSU pattern across the stimulation frequencies between 13 and 44 Hz seem to depend on the differing contribution of inhibitory elements in responses to stimulations in the high frequency range. Interestingly, in the present study the strong decay of the BOLD plateau corresponding to pBOLD in the earlier work also starts at 14 Hz and leads to the appearance of clear on-and off-transients at higher stimulation frequencies (Fig. 4) . Within the framework of the reports by Emir et al. (2008) and Uludag (2008) present observation on the differing dependency of transient and sustained BOLD components on stimulation frequency might reflect the contribution of different V1 circuitry in the generation of BOLD responses to low and high frequency visual input.
Transient and sustained BOLD responses
Despite the preliminary description of these distinct BOLD components, there is still no well-established model for the decomposition of the BOLD time-course into physiologically meaningful temporal components. Therefore, we preferred to use the hemodynamic response function and its first and second derivatives as implemented in SPM5 in the present study. Future work, however, will focus on frequency dependencies of the BOLD transients in search for a physiologically plausible model for these components.
Conclusions
In conclusion, we observed steep low-pass frequency characteristics of the BOLD response of V1 as opposed to smooth high-, lowor band-pass characteristics reported in earlier studies. The LGN, on the other hand, displayed rather a flat frequency response. These results emphasize that the profound low-pass effect of the visual system to visual input with a maximum around 10 Hz emerges in V1, which suggests the possible role of the intracortical circuitry of V1 or projections from extra-striate areas on V1. Besides this overall contrast, there is a high correlation between LGN and V1 BOLD responses across visual stimulation frequencies, as accounted for by the fluctuations around similar stimulation frequencies in both structures. This finding supports oscillatory tuning in thalamo-cortical interactions, which was previously proposed in several electrophysiological studies (Herrmann, 2001; Rager & Singer, 1998) .

{'paper_id': '4487871', 'title': 'Achromatic temporal-frequency responses of human lateral geniculate nucleus and primary visual cortex', 's2_url': 'https://api.semanticscholar.org/CorpusID:4487871', 'source': 'S2ORC'}