Microscopic functional specificity can be predicted from fMRI signals in ventral visual areas Daehun Kang, Uk-Su Choi, Yul-Wan Sung PII: DOI: Reference:
S0730-725X(14)00178-7 doi: 10.1016/j.mri.2014.05.006 MRI 8199
To appear in:
Magnetic Resonance Imaging
Received date: Revised date: Accepted date:
11 September 2013 20 March 2014 26 May 2014
Please cite this article as: Kang Daehun, Choi Uk-Su, Sung Yul-Wan, Microscopic functional specificity can be predicted from fMRI signals in ventral visual areas, Magnetic Resonance Imaging (2014), doi: 10.1016/j.mri.2014.05.006
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Title: Microscopic functional specificity can be predicted from fMRI signals in ventral visual areas
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(Short title: Microscopic functional specificity)
Daehun Kang1,3, Uk-Su Choi2, Yul-Wan Sung1 Fukushi Research Institute, Tohoku Fukushi University, Sendai, Japan 2 Neuroscience Research Institute, Gachon University of Medicine and Science, Incheon, Republic of Korea 3Graduate School of Information Sciences, Tohoku University, Sendai, Japan
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1Kansei
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Correspondence to: Yul-Wan Sung, PhD Kansei Fukushi Research Institute, Tohoku Fukushi University 6-149-1 Kunimigaoka, Aoba, Sendai, Miyagi 989-3201, Japan
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TEL : +81-22-728-7433 FAX : +81-22-728-7433 Email : [email protected]; [email protected]
Keywords: Category-selective, FFA, Transverse relaxation profile, Multiecho imaging, Neuronal population, PPA
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Abstract
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Functional areas specialized for recognition can be activated by a non-preferred stimulus as well as a preferred stimulus. The functional magnetic resonance imaging
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signals detected in response to different stimuli in an area may have the same or
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different amplitudes. However, it is uncertain whether the responses originate from the same neuronal populations or heterogeneous ones. To address this concern, we propose a novel method that uses multi-echo echo-planar imaging sequences to evaluate
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changes in the transverse relaxation profile caused by stimulation. According to this
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method, the areas related with visual recognition, i.e. fusiform face area and
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parahippocampal place area, have different transverse relaxation profiles to preferred and non-preferred stimuli, which can be considered as reflecting a difference in
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neuronal population processing stimuli in those areas. The proposed method can be useful for probing the microscopic functional specificity of brain areas.
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Introduction
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The functional units of the human brain comprise many neuronal populations
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(or circuits) that work together, although not always in the same manner. Functional
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magnetic resonance imaging (fMRI) observations of functional areas of the human brain
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show that these areas can be activated by multiple stimuli, but it is unclear whether each neuronal population (or circuit) processes the corresponding stimulus. Some brain areas in the ventral pathway are category-selective and the fMRI responses in these
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areas are different with their preferred and non-preferred stimuli (1-5). However, it is unclear whether all the neurons in an area are activated by both preferred stimulus and
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non-preferred stimulus, or whether part of the neuronal population is activated
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separately in response to each stimulus. Using conventional fMRI, it is difficult to
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discriminate the fMRI responses evoked in a specific neuronal population from that in another because of the limited spatial resolution of 27–125-mm3 voxels with 3-Tesla MRI, as well as the properties of the fMRI signal produced by transverse relaxation which is smoothed across the vessels and surrounding tissues in a voxel. However, some attempts have been made to probe neuronal information at the micro scale-using fMRI, such as the repetitive stimulation method where two stimuli are presented consecutively. The fMRI response to the second stimulus is less than that to the first when the contents of the two stimuli are the same. Therefore, information 3
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about the participating neuronal populations can be inferred by evaluating the decrease
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in the fMRI signal (6-9). Alternatively, multivariate analysis has been used to statistically evaluate fMRI data to determine whether distinct voxels can discriminate
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two different stimuli (10-13).
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In the present study, we propose a novel method for obtaining microscopic information related to a functional area. FMRI signals are generated by the changes in transverse relaxation caused by neuronal activation after stimulation. The change in
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the transverse relaxation is influenced by the neuronal populations and the distribution
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of blood vessels in the tissues. The vessels and tissues differ in their transverse
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relaxation profiles; therefore, they make different contributions to the fMRI signal (14, 15). Thus, the fMRI signal may reflect the microscopic functional specificity of neuronal
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populations, the vasculature, and their interactions. However, it is difficult to discriminate the specificity based only on typical fMRI signals, although different neuronal populations may have been activated by distinct stimuli. The profiles of the fMRI signals acquired at multiple acquisition times may help to solve this problem. There are fractional changes in the transverse relaxation profile of blood vessels and tissues; therefore, the distributions of blood vessels and tissues in a voxel may produce different signal profiles during signal evolution (15).
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In conventional fMRI, a functional image is measured at a single time point
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using single-echo gradient-echo echo-planar imaging (GE-EPI), which generates a response because of changes in the transverse relaxation and flow. In contrast,
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multi-echo EPI employs multiple echo trains to obtain images at different echo times
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(TEs) (16-19), which can yield information related to the transverse relaxation profiles. Neurophysiological and neuroimaging studies indicate that faces consistently engage a lateral portion of the posterior fusiform gyrus (fusiform face area, FFA) (1). In
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contrast, buildings activate the parahippocampal gyrus (parahippocampal place area,
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PPA) (2). These areas exhibit more significant signals with specific stimulus classes, i.e.,
for buildings).
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faces or buildings, and they are defined as category-selective (FFA for the face and PPA
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In the present study, we measured fMRI responses using single-shot multi-echo
EPI (triple-echo GE-EPI) to discriminate the neuronal populations in a functional area. Using multi-echo EPI, we acquired MRI signals at three different TEs and derived two transverse relaxivity maps (maps of R2* - the inverse of the apparent transverse relaxation time, T2*) from MRI images that corresponded to three TEs by pairing the two time points. Next, we estimated the changes in R2* to observe the functional specificity, which represented the existence of neuronal populations with different
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selectivity. We expected that the changes in R2* in a stimulated area would vary among
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stimuli if the neuronal populations in the area had different stimulus selectivity. Similar multi-echo EPIs have been used for other purposes such as noise reduction
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(16-19); however, to the best of our knowledge, the present study is the first application
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to the observation of functional specificity. To test the suitability of our method for measuring functional specificity, we focused on the category-selectivity FFA and PPA areas by examining whether these areas contained different neuronal populations for
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the preferred and non-preferred stimuli.
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Materials and Methods
Derivation of a transverse relaxivity map The MRI signal is evoluted after a single RF excitation, as follows (16, 20): S(TE) = S0(T1)· exp (−TE· R2*),
(1)
, where S0 is an initial intensity dependent on T1 of a voxel, which is responsible for the signal fluctuations in non-equilibrium fMRI, and TE is the effective TE. R2* is the transverse relaxivity of the corresponding voxel, which is the same as the inverse of
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the apparent transverse relaxation time T2*. As reported in a previous study (16), R2*
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is calculated directly using TE pairs, i.e., TE1 and TE2 or TE2 and TE3. The equation that yields R2* for the corresponding voxel is as follows:
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R2* = (log (S(TE2)) − log (S(TE1)))/(TE1 − TE2)
(2)
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Two types of R2* value, i.e., R2*_1 and R2*_2, are obtained from TE1 and TE2, and from TE2 and TE3. The signals from three different TEs originate sequentially from a single RF excitation of a spin system; thus, the effect of S0 depending on T1 is
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canceled out by the above equation.
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The change in the signal caused by activation is given as follows: (3)
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ΔS/S = ΔS0/S0 − ΔR2* TE
According to the equation above, S0 and R2* are independent of each other, and
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R2* only reflects the transverse relaxivity caused by activation. The signal changes in R2*_1 and R2*_2 were estimated using traditional generalized linear model analysis, as described in the data analysis section. The transverse relaxation profile after activation was estimated based on the ratio of %R2*_2 relative to %R2*_1.
Measurements
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All MRI experiments were performed using a Verio system (Siemens, Germany)
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with a standard, 12-channel head matrix coil operating at 3 Tesla. (Multi Echo EPI)
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Multi echo EPI sequence was modified from the default single-shot GE-EPI of
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Siemens to acquire images at three echo times of 13, 38 and 63 ms, which were determined as the shortest echo times as possible. For functional imaging, the multi echo GE-EPI sequence was used with a 2000 ms repetition time, 90 degree flip angle,
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220 mm field of view, 64 64 mm matrix size, and 3.4 mm slice thickness with 0.5 mm Before
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gaps. Twenty slices parallel to the AC-PC were acquired for each volume.
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functional imaging, T1-weighted anatomical images were obtained with an inverted recovery- and magnetization-prepared rapid acquisition using a gradient echo
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(MPRAGE) with a matrix size of 256 256 mm over 256 mm field of view and 1 mm slice thickness.
Stimulation procedure The stimulation consisted of eight blocks for totally 240 seconds. Prior to the initial block, a dummy time was given for 16 seconds. Each block had a control state period of 12 seconds and a post-stimulus period of 16 seconds. The block included 8
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pictures of either faces or buildings. On the control state period, the pictures were
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presented at the center of the visual field for 1 second with an inter-picture interval of 0.5 seconds. Then, a gray crosshair at the center of a black background was presented
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on a post-stimulus period. 4 face blocks and 4 building blocks were interspersed in a
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trial. The subjects were required to do a one-back task during the scan. The task was that they should press the button 1 when a picture was repeated or the button 2 to
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otherwise.
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Visual stimulation
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Visual stimuli were presented on a mirror mounted on a head coil through a projector (spatial resolution, 1024 768 pixels; refresh rate 60 Hz; Panasonic, Japan).
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The pictures occupied 5° 7° of the subject’s visual field around the fovea. All pictures were grayscale images. The pictures were novel stimuli developed by the investigators.
Subjects Eight healthy volunteers participated in this study. The health of the volunteers was ascertained based on physical examinations performed in the last 6 months of the experiment. All participants had normal or corrected vision to 20/20 acuity, no history of
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neurological disease, and no medical conditions that would prevent MRI screenings,
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such as pregnancy, presence of cardiac pacemaker, or claustrophobia, as determined through interview. After subjects were provided a complete description of the study,
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informed and written consent were obtained in accordance with the Declaration of
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Helsinki. This study was approved by the Institutional Review Board of the Tohoku Fukushi University.
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Image data analysis
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The image data obtained from fMRI were processed using Brain Voyager QX
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(Brain Innovation B.V., Postbus, The Netherlands) software. All image data from the functional session for each subject were preprocessed with Brain Voyager QX and
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motion correction, scan time correction, and high-pass filtering with a cut-off frequency of 0.005 Hz. 2D-data from the functional session was converted into 3D-data via trilinear interpolation and transformed into Talairach space using BrainVoyager QX. Statistical analysis was performed by a procedure based on general linear modeling using BrainVoyager QX. Each experimental condition, except for the control, was defined as a separate predictor. The reference time-course used as the predictor was the two-gamma
hemodynamic
response
function.
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This
analysis
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performed
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independently for the time-course of each individual voxel for each subject. To complete
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this analysis, the time series of the images obtained from each subject were z-normalized. MRI data acquired at TE2 (TE = 38 ms) was used to localize regions of
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interest (ROIs) for extracting signal responses. The primary visual cortex (V1) of each
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subject was defined as a cubic area of 5 × 5 × 5 mm3 around the posterior tip of the calcarine sulcus of activation maps (p < 0.0001, corrected) that was obtained by contrasting the stimulation condition to the control. A face-selective area and a
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building-selective area were identified for each subject by a conjunction analyses of (face
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> building and face > rest) and (building > face and building > rest) with the statistical
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threshold of p = 0.005 (uncorrected), respectively. The ROIs of the FFA and PPA were defined as a cubic area of 5 × 5 × 5 mm3 around the maximum statistical values in the
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identified areas. ROIs were identified at the right hemisphere because face processing is known dominant at the right hemisphere. Percent signal changes for R2*_1 and R2*_2, i.e. %R2*_1 and %R2*_2, were calculated at the ROIs by the event-related-averaging option implemented in Brain Voyager. Ratios of %R2*_2 to %R2*_1 were derived at each condition and each area. The ratio differences across the areas of V1, OFA, and FFA were examined through a two-way analysis of variance for the “region × stimulus condition (category)” using the SPSS statistical package (SPSS Inc; Illinois, USA). The
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region factor had three levels of V1, OFA, and FFA, and the stimulus condition factor
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included face and building conditions. The dependent variable was the ratio for each
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stimulus condition at each ROI.
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Results
The three sets of original images are shown in Figure 1, which were acquired by multiecho sequences with multiple TEs of 13, 38, and 63 ms, respectively. The image
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contrast was stronger with a longer TE. The maps of R2*_1 and R2*_2 are shown in
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Figure 2. The geometrical distortions of the R2*_1 and R2*_2 maps are similar to those
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of TE2 and TE3, respectively, although the image artifacts in the boundary areas between the cortex and air regions are highlighted slightly more in the R2* maps than
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in the original images. Figure 3 shows the functional areas of V1, FFA, and PPA that were identified based on the MRI data at TE2, which corresponded to the conventional fMRI data. The activation maps were estimated based on group analysis of eight participants for display purposes, although the response signals were extracted from each area for each participant to perform subsequent variance analysis. The percentage signal changes were acquired for R2*_1 and R2*_2 in each area for each participant (Fig. 4) and used to calculate the ratio r (%R2*_2/%R2*_1), i.e., r_f
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for the face images and r_b for the building images, to evaluate the transverse
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relaxation profiles. The percentage signal change in R2* was negative because the transverse relaxation time was increased at activated sites and R2* decreased as a
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consequence. Two-way ANOVA of the ROI factors for V1, FFA, and PPA using the
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category factors of face and building images detected a significant interaction between ROI and category [F (48, 2) = 6.80, p = 0.002; Fig. 5). The ratios were analyzed for each area. The V1 ratios did not differ significantly after stimulation with the face and
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building images [t (7) = −1.79, p = 0.09 (two-tailed); Student’s t-test, paired]. There were
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significant differences in the ratios for FFA [t (7) = −3.16, p = 0.007 (two-tailed);
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Student’s t-test, paired] and PPA [t (7) = 2.54, p = 0.023 (two-tailed); Student’s t-test, paired). The ratio with face image stimulation was smaller than that with building
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image stimulation in the FFA, whereas the ratio with building image stimulation was smaller than that with face image stimulation in the PPA. These ratios indicate that the FFA and PPA have different transverse relaxation profiles, depending on whether the stimuli were face or building images, thereby suggesting the presence of diverse neuronal populations and vasculature regions related to the face and building images in these areas.
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Discussion
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The aim of the present study was to determine whether the transverse relaxation changes induced by neuronal activation across TEs differed in each
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category-selective functional area when two stimuli categories were presented, i.e.,
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whether a neuronal population activated by one category stimulus was different from that activated by another category stimulus.
The three shortest possible TEs were determined given the machine
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constraints and the imaging parameters to obtain valid images with a high
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signal-to-noise ratio. Thus, TEs of 13, 38, and 63 ms were employed. These TEs
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provided some benefits when study the T2* of gray matter at 3T. According to a previous study (21), the factor δ/T2*_target has a key effect on the variation in CNR, where δ is
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the initial TE (TE1) of 13.0 ms and the subsequent TEs are given by TEj = ( 2j − 1 )·δ. The T2*_target was set to 66.0 ms as T2* for typical gray matter (22). The value of this factor was approximately 0.2, which is almost the theoretical maximum variation in CNR. If the modeling of equation 1 is valid for all stimulus types in ROI areas, the map of R2*_1 should be the same or similar to the map of R2*_2, even when considering noise disturbance, except for the geometric distortion that depends on the TEs. Thus, if
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a functional area recruits the same neuronal population to process the face and building
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image stimuli, the ratios of %R2*_2 relative to %R2*_1 should be the same for both stimuli, i.e., r_f should be the same as r_b. The data obtained for the V1 area agreed
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with this prediction. However, the data obtained for the FFA and PPA areas showed that
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the ratios of r_f and r_b were significantly different. This suggests that the neuronal population activated by the face images differed from that activated by the building images in the FFA and PPA. It is known that the actual signal decay curve for the
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transverse relaxation of a voxel is not simple because the distribution of the field offsets
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has a rather irregular shape, although the curve can be approximated as a simple decay
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curve in appropriate conditions (23, 24). Therefore, the results showed that the signal decay curves could have a non-simple exponential decay shape, which depended on the
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neuronal population activated, and the difference affected the ratios of r_f and r_b. Based on the changes in R2*, the differences in the ratios r_f and r_b
demonstrated that there were differences between the neuronal populations activated by the face and building images in the same voxel. If the face and building stimuli activates the same neuronal population, there should be no difference in the ratio, regardless of the differences in R2*. Overall, the results showed that the neuronal population in the FFA area activated by face image processing differed from that
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activated by the other stimulus, which is consistent with the predictions of previous
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conventional fMRI studies based on repetition suppression and multivariate analysis methods (7, 8, 25).
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With regard to R2* changes, inflow effects should be considered. Some previous
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studies reported inflow-induced signal changes at acquisitions with large flip angles and short TRs (26, 27). However, some other studies did not find significant inflow contribution to fMRI signals in multi-slices EPI-based BOLD fMRI experiments (27, 28).
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It is widely accepted that inflow effects are negligible in the EPI-based fMRI
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experiments in which typically 20 – 40 slices are acquired with a TR ranges from 2 to 4 s
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(27, 28). In our present experiment, with imaging parameters of 2s TR and 20 slices, the general inflow effect is insufficient to explain the variation of the transverse relaxation
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profiles to different types of stimuli at the recognition-related areas. Therefore, inflow effects do not have a significant impact on our results even if some contribution of the effects to R2* changes might exist with our imaging parameters. The repetition suppression method can encode neuronal information based on parameters obtained from a stimulation scheme, such as the interval between stimuli (7, 25). However, the problem with repetition suppression is that the fMRI responses are vulnerable to the effects of disturbances such as signal fluctuations. Multivariate
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analysis is a postprocessing method, which can only estimate the voxels involved with
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discriminatory fMRI responses, and it cannot determine whether the difference originates from diverse neuronal populations or simple variation in sensitivity within
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the same population (11, 12). Compared with these approaches, the new method
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proposed in the present study can generate neuronal information without considering the effects of disturbances, and it can also determine whether different responses are related to variable sensitivity within the same neuronal population or the presence of
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different neuronal populations.
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We expect that the proposed method could be used to measure microscopic
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information in response to cognitively categorized stimuli in functional areas, but further studies are required, particularly to improve the sensitivity. This method is
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based on the hypothesis that components with different transverse relaxations are distributed within a voxel or a region and that the response of the mixture to given stimuli can lead to variations in the ratio of %R2*_2 relative to %R2*_1. Therefore, the applicability of this method may be restricted to determining differences in neuronal populations with variable transverse relaxation profiles in blood vessels and their surrounding tissues, which may be a sensitivity limitation of the proposed method. Using a long TE may be advantageous for detecting subtle changes in transverse
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relaxation profile, but this would create further problems related to the signal-to-noise
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ratio. The image signal intensity decreases as the TE increases, thereby making it vulnerable to physical noise. Therefore, there may be a trade-off between the
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signal-to-noise ratio and the use of a long TE to increase the difference in the ratio
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between neuronal populations. We considered vessels and tissues together as sources of transverse relaxation profile, but it may be possible to measure changes in transverse relaxivity that are attributable to the characteristics of tissues per se, such as the cell
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membrane potential and the intracellular water changes that accompany the activation
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of neurons, provided that imaging methods with high sensitivity to tissues can be used
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(14, 29).
MRI techniques have advanced recently and it is now possible to produce
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images with a high spatial resolution of 1 mm3 (5). However, even these small voxels contain many neurons and it is still necessary to determine the microscopic structure/functional specificity of a voxel or region. The combined use of our method and high-spatial resolution images may be beneficial for probing functional structures in brain areas.
Conclusions
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The present study suggests that two category-selective areas, i.e., FFA and PPA,
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contain different neuronal populations, which are activated by a preferred stimulus and a non-preferred stimulus and that the microscopic functional characteristics of a
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functional site can be examined using the proposed method.
Acknowledgments
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This work was supported by JSPS KAKEN Grant Number 25330173.
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Figure legends
Figure 1. MRI images acquired at different echo times (TEs).
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(a) Images at TE1 = 13 ms, (b) TE2 = 38 ms, and (c) TE3 = 64 ms.
occurs around 25–35 ms with 3T.
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The image at TE2 corresponds to that obtained at the conventional TE, which usually
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Figure 2. Maps of R2*_1 and R2*_2.
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Figure 3. Maps of regions of interest identified from TE2 images. (a) V1 (x = 15, y = −92, z = −4), (b) the fusiform face area (x = 40, y = −46, z = −22), and (c) the parahippocampal place area (x = 30, y = −40, z = −12), using Talairach coordinates. A, anterior; P, posterior; R, right; L, left.
p < 0.005 (uncorrected)
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Figure 4. Changes in the response after stimulation based on the R2* values.
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The R2* signals of eight participants were averaged for each area. (a) V1, (b) fusiform face area, and (c) parahippocampal place area.
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The error bars represent the S.E.M.
Figure 5. Ratios of %R2*_2 relative to %R2*_1 for the V1, fusiform face area, and parahippocampal place area.
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The ratios of eight participants were averaged for each area. r_f is the ratio for face
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S0730-725X(14)00178-7 doi: 10.1016/j.mri.2014.05.006 MRI 8199
To appear in:
Magnetic Resonance Imaging
Received date: Revised date: Accepted date:
11 September 2013 20 March 2014 26 May 2014
Please cite this article as: Kang Daehun, Choi Uk-Su, Sung Yul-Wan, Microscopic functional specificity can be predicted from fMRI signals in ventral visual areas, Magnetic Resonance Imaging (2014), doi: 10.1016/j.mri.2014.05.006
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Title: Microscopic functional specificity can be predicted from fMRI signals in ventral visual areas
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(Short title: Microscopic functional specificity)
Daehun Kang1,3, Uk-Su Choi2, Yul-Wan Sung1 Fukushi Research Institute, Tohoku Fukushi University, Sendai, Japan 2 Neuroscience Research Institute, Gachon University of Medicine and Science, Incheon, Republic of Korea 3Graduate School of Information Sciences, Tohoku University, Sendai, Japan
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1Kansei
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Correspondence to: Yul-Wan Sung, PhD Kansei Fukushi Research Institute, Tohoku Fukushi University 6-149-1 Kunimigaoka, Aoba, Sendai, Miyagi 989-3201, Japan
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TEL : +81-22-728-7433 FAX : +81-22-728-7433 Email : [email protected]; [email protected]
Keywords: Category-selective, FFA, Transverse relaxation profile, Multiecho imaging, Neuronal population, PPA
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Abstract
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Functional areas specialized for recognition can be activated by a non-preferred stimulus as well as a preferred stimulus. The functional magnetic resonance imaging
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signals detected in response to different stimuli in an area may have the same or
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different amplitudes. However, it is uncertain whether the responses originate from the same neuronal populations or heterogeneous ones. To address this concern, we propose a novel method that uses multi-echo echo-planar imaging sequences to evaluate
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changes in the transverse relaxation profile caused by stimulation. According to this
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method, the areas related with visual recognition, i.e. fusiform face area and
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parahippocampal place area, have different transverse relaxation profiles to preferred and non-preferred stimuli, which can be considered as reflecting a difference in
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neuronal population processing stimuli in those areas. The proposed method can be useful for probing the microscopic functional specificity of brain areas.
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Introduction
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The functional units of the human brain comprise many neuronal populations
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(or circuits) that work together, although not always in the same manner. Functional
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magnetic resonance imaging (fMRI) observations of functional areas of the human brain
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show that these areas can be activated by multiple stimuli, but it is unclear whether each neuronal population (or circuit) processes the corresponding stimulus. Some brain areas in the ventral pathway are category-selective and the fMRI responses in these
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areas are different with their preferred and non-preferred stimuli (1-5). However, it is unclear whether all the neurons in an area are activated by both preferred stimulus and
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non-preferred stimulus, or whether part of the neuronal population is activated
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separately in response to each stimulus. Using conventional fMRI, it is difficult to
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discriminate the fMRI responses evoked in a specific neuronal population from that in another because of the limited spatial resolution of 27–125-mm3 voxels with 3-Tesla MRI, as well as the properties of the fMRI signal produced by transverse relaxation which is smoothed across the vessels and surrounding tissues in a voxel. However, some attempts have been made to probe neuronal information at the micro scale-using fMRI, such as the repetitive stimulation method where two stimuli are presented consecutively. The fMRI response to the second stimulus is less than that to the first when the contents of the two stimuli are the same. Therefore, information 3
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about the participating neuronal populations can be inferred by evaluating the decrease
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in the fMRI signal (6-9). Alternatively, multivariate analysis has been used to statistically evaluate fMRI data to determine whether distinct voxels can discriminate
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two different stimuli (10-13).
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In the present study, we propose a novel method for obtaining microscopic information related to a functional area. FMRI signals are generated by the changes in transverse relaxation caused by neuronal activation after stimulation. The change in
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the transverse relaxation is influenced by the neuronal populations and the distribution
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of blood vessels in the tissues. The vessels and tissues differ in their transverse
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relaxation profiles; therefore, they make different contributions to the fMRI signal (14, 15). Thus, the fMRI signal may reflect the microscopic functional specificity of neuronal
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populations, the vasculature, and their interactions. However, it is difficult to discriminate the specificity based only on typical fMRI signals, although different neuronal populations may have been activated by distinct stimuli. The profiles of the fMRI signals acquired at multiple acquisition times may help to solve this problem. There are fractional changes in the transverse relaxation profile of blood vessels and tissues; therefore, the distributions of blood vessels and tissues in a voxel may produce different signal profiles during signal evolution (15).
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In conventional fMRI, a functional image is measured at a single time point
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using single-echo gradient-echo echo-planar imaging (GE-EPI), which generates a response because of changes in the transverse relaxation and flow. In contrast,
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multi-echo EPI employs multiple echo trains to obtain images at different echo times
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(TEs) (16-19), which can yield information related to the transverse relaxation profiles. Neurophysiological and neuroimaging studies indicate that faces consistently engage a lateral portion of the posterior fusiform gyrus (fusiform face area, FFA) (1). In
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contrast, buildings activate the parahippocampal gyrus (parahippocampal place area,
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PPA) (2). These areas exhibit more significant signals with specific stimulus classes, i.e.,
for buildings).
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faces or buildings, and they are defined as category-selective (FFA for the face and PPA
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In the present study, we measured fMRI responses using single-shot multi-echo
EPI (triple-echo GE-EPI) to discriminate the neuronal populations in a functional area. Using multi-echo EPI, we acquired MRI signals at three different TEs and derived two transverse relaxivity maps (maps of R2* - the inverse of the apparent transverse relaxation time, T2*) from MRI images that corresponded to three TEs by pairing the two time points. Next, we estimated the changes in R2* to observe the functional specificity, which represented the existence of neuronal populations with different
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selectivity. We expected that the changes in R2* in a stimulated area would vary among
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stimuli if the neuronal populations in the area had different stimulus selectivity. Similar multi-echo EPIs have been used for other purposes such as noise reduction
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(16-19); however, to the best of our knowledge, the present study is the first application
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to the observation of functional specificity. To test the suitability of our method for measuring functional specificity, we focused on the category-selectivity FFA and PPA areas by examining whether these areas contained different neuronal populations for
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the preferred and non-preferred stimuli.
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Materials and Methods
Derivation of a transverse relaxivity map The MRI signal is evoluted after a single RF excitation, as follows (16, 20): S(TE) = S0(T1)· exp (−TE· R2*),
(1)
, where S0 is an initial intensity dependent on T1 of a voxel, which is responsible for the signal fluctuations in non-equilibrium fMRI, and TE is the effective TE. R2* is the transverse relaxivity of the corresponding voxel, which is the same as the inverse of
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the apparent transverse relaxation time T2*. As reported in a previous study (16), R2*
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is calculated directly using TE pairs, i.e., TE1 and TE2 or TE2 and TE3. The equation that yields R2* for the corresponding voxel is as follows:
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R2* = (log (S(TE2)) − log (S(TE1)))/(TE1 − TE2)
(2)
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Two types of R2* value, i.e., R2*_1 and R2*_2, are obtained from TE1 and TE2, and from TE2 and TE3. The signals from three different TEs originate sequentially from a single RF excitation of a spin system; thus, the effect of S0 depending on T1 is
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canceled out by the above equation.
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The change in the signal caused by activation is given as follows: (3)
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ΔS/S = ΔS0/S0 − ΔR2* TE
According to the equation above, S0 and R2* are independent of each other, and
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R2* only reflects the transverse relaxivity caused by activation. The signal changes in R2*_1 and R2*_2 were estimated using traditional generalized linear model analysis, as described in the data analysis section. The transverse relaxation profile after activation was estimated based on the ratio of %R2*_2 relative to %R2*_1.
Measurements
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All MRI experiments were performed using a Verio system (Siemens, Germany)
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with a standard, 12-channel head matrix coil operating at 3 Tesla. (Multi Echo EPI)
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Multi echo EPI sequence was modified from the default single-shot GE-EPI of
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Siemens to acquire images at three echo times of 13, 38 and 63 ms, which were determined as the shortest echo times as possible. For functional imaging, the multi echo GE-EPI sequence was used with a 2000 ms repetition time, 90 degree flip angle,
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220 mm field of view, 64 64 mm matrix size, and 3.4 mm slice thickness with 0.5 mm Before
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gaps. Twenty slices parallel to the AC-PC were acquired for each volume.
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functional imaging, T1-weighted anatomical images were obtained with an inverted recovery- and magnetization-prepared rapid acquisition using a gradient echo
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(MPRAGE) with a matrix size of 256 256 mm over 256 mm field of view and 1 mm slice thickness.
Stimulation procedure The stimulation consisted of eight blocks for totally 240 seconds. Prior to the initial block, a dummy time was given for 16 seconds. Each block had a control state period of 12 seconds and a post-stimulus period of 16 seconds. The block included 8
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pictures of either faces or buildings. On the control state period, the pictures were
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presented at the center of the visual field for 1 second with an inter-picture interval of 0.5 seconds. Then, a gray crosshair at the center of a black background was presented
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on a post-stimulus period. 4 face blocks and 4 building blocks were interspersed in a
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trial. The subjects were required to do a one-back task during the scan. The task was that they should press the button 1 when a picture was repeated or the button 2 to
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otherwise.
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Visual stimulation
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Visual stimuli were presented on a mirror mounted on a head coil through a projector (spatial resolution, 1024 768 pixels; refresh rate 60 Hz; Panasonic, Japan).
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The pictures occupied 5° 7° of the subject’s visual field around the fovea. All pictures were grayscale images. The pictures were novel stimuli developed by the investigators.
Subjects Eight healthy volunteers participated in this study. The health of the volunteers was ascertained based on physical examinations performed in the last 6 months of the experiment. All participants had normal or corrected vision to 20/20 acuity, no history of
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neurological disease, and no medical conditions that would prevent MRI screenings,
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such as pregnancy, presence of cardiac pacemaker, or claustrophobia, as determined through interview. After subjects were provided a complete description of the study,
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informed and written consent were obtained in accordance with the Declaration of
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Helsinki. This study was approved by the Institutional Review Board of the Tohoku Fukushi University.
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Image data analysis
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The image data obtained from fMRI were processed using Brain Voyager QX
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(Brain Innovation B.V., Postbus, The Netherlands) software. All image data from the functional session for each subject were preprocessed with Brain Voyager QX and
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motion correction, scan time correction, and high-pass filtering with a cut-off frequency of 0.005 Hz. 2D-data from the functional session was converted into 3D-data via trilinear interpolation and transformed into Talairach space using BrainVoyager QX. Statistical analysis was performed by a procedure based on general linear modeling using BrainVoyager QX. Each experimental condition, except for the control, was defined as a separate predictor. The reference time-course used as the predictor was the two-gamma
hemodynamic
response
function.
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analysis
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performed
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independently for the time-course of each individual voxel for each subject. To complete
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this analysis, the time series of the images obtained from each subject were z-normalized. MRI data acquired at TE2 (TE = 38 ms) was used to localize regions of
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interest (ROIs) for extracting signal responses. The primary visual cortex (V1) of each
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subject was defined as a cubic area of 5 × 5 × 5 mm3 around the posterior tip of the calcarine sulcus of activation maps (p < 0.0001, corrected) that was obtained by contrasting the stimulation condition to the control. A face-selective area and a
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building-selective area were identified for each subject by a conjunction analyses of (face
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> building and face > rest) and (building > face and building > rest) with the statistical
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threshold of p = 0.005 (uncorrected), respectively. The ROIs of the FFA and PPA were defined as a cubic area of 5 × 5 × 5 mm3 around the maximum statistical values in the
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identified areas. ROIs were identified at the right hemisphere because face processing is known dominant at the right hemisphere. Percent signal changes for R2*_1 and R2*_2, i.e. %R2*_1 and %R2*_2, were calculated at the ROIs by the event-related-averaging option implemented in Brain Voyager. Ratios of %R2*_2 to %R2*_1 were derived at each condition and each area. The ratio differences across the areas of V1, OFA, and FFA were examined through a two-way analysis of variance for the “region × stimulus condition (category)” using the SPSS statistical package (SPSS Inc; Illinois, USA). The
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region factor had three levels of V1, OFA, and FFA, and the stimulus condition factor
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included face and building conditions. The dependent variable was the ratio for each
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stimulus condition at each ROI.
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Results
The three sets of original images are shown in Figure 1, which were acquired by multiecho sequences with multiple TEs of 13, 38, and 63 ms, respectively. The image
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contrast was stronger with a longer TE. The maps of R2*_1 and R2*_2 are shown in
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Figure 2. The geometrical distortions of the R2*_1 and R2*_2 maps are similar to those
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of TE2 and TE3, respectively, although the image artifacts in the boundary areas between the cortex and air regions are highlighted slightly more in the R2* maps than
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in the original images. Figure 3 shows the functional areas of V1, FFA, and PPA that were identified based on the MRI data at TE2, which corresponded to the conventional fMRI data. The activation maps were estimated based on group analysis of eight participants for display purposes, although the response signals were extracted from each area for each participant to perform subsequent variance analysis. The percentage signal changes were acquired for R2*_1 and R2*_2 in each area for each participant (Fig. 4) and used to calculate the ratio r (%R2*_2/%R2*_1), i.e., r_f
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for the face images and r_b for the building images, to evaluate the transverse
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relaxation profiles. The percentage signal change in R2* was negative because the transverse relaxation time was increased at activated sites and R2* decreased as a
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consequence. Two-way ANOVA of the ROI factors for V1, FFA, and PPA using the
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category factors of face and building images detected a significant interaction between ROI and category [F (48, 2) = 6.80, p = 0.002; Fig. 5). The ratios were analyzed for each area. The V1 ratios did not differ significantly after stimulation with the face and
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building images [t (7) = −1.79, p = 0.09 (two-tailed); Student’s t-test, paired]. There were
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significant differences in the ratios for FFA [t (7) = −3.16, p = 0.007 (two-tailed);
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Student’s t-test, paired] and PPA [t (7) = 2.54, p = 0.023 (two-tailed); Student’s t-test, paired). The ratio with face image stimulation was smaller than that with building
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image stimulation in the FFA, whereas the ratio with building image stimulation was smaller than that with face image stimulation in the PPA. These ratios indicate that the FFA and PPA have different transverse relaxation profiles, depending on whether the stimuli were face or building images, thereby suggesting the presence of diverse neuronal populations and vasculature regions related to the face and building images in these areas.
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Discussion
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The aim of the present study was to determine whether the transverse relaxation changes induced by neuronal activation across TEs differed in each
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category-selective functional area when two stimuli categories were presented, i.e.,
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whether a neuronal population activated by one category stimulus was different from that activated by another category stimulus.
The three shortest possible TEs were determined given the machine
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constraints and the imaging parameters to obtain valid images with a high
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signal-to-noise ratio. Thus, TEs of 13, 38, and 63 ms were employed. These TEs
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provided some benefits when study the T2* of gray matter at 3T. According to a previous study (21), the factor δ/T2*_target has a key effect on the variation in CNR, where δ is
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the initial TE (TE1) of 13.0 ms and the subsequent TEs are given by TEj = ( 2j − 1 )·δ. The T2*_target was set to 66.0 ms as T2* for typical gray matter (22). The value of this factor was approximately 0.2, which is almost the theoretical maximum variation in CNR. If the modeling of equation 1 is valid for all stimulus types in ROI areas, the map of R2*_1 should be the same or similar to the map of R2*_2, even when considering noise disturbance, except for the geometric distortion that depends on the TEs. Thus, if
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a functional area recruits the same neuronal population to process the face and building
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image stimuli, the ratios of %R2*_2 relative to %R2*_1 should be the same for both stimuli, i.e., r_f should be the same as r_b. The data obtained for the V1 area agreed
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with this prediction. However, the data obtained for the FFA and PPA areas showed that
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the ratios of r_f and r_b were significantly different. This suggests that the neuronal population activated by the face images differed from that activated by the building images in the FFA and PPA. It is known that the actual signal decay curve for the
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transverse relaxation of a voxel is not simple because the distribution of the field offsets
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has a rather irregular shape, although the curve can be approximated as a simple decay
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curve in appropriate conditions (23, 24). Therefore, the results showed that the signal decay curves could have a non-simple exponential decay shape, which depended on the
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neuronal population activated, and the difference affected the ratios of r_f and r_b. Based on the changes in R2*, the differences in the ratios r_f and r_b
demonstrated that there were differences between the neuronal populations activated by the face and building images in the same voxel. If the face and building stimuli activates the same neuronal population, there should be no difference in the ratio, regardless of the differences in R2*. Overall, the results showed that the neuronal population in the FFA area activated by face image processing differed from that
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activated by the other stimulus, which is consistent with the predictions of previous
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conventional fMRI studies based on repetition suppression and multivariate analysis methods (7, 8, 25).
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With regard to R2* changes, inflow effects should be considered. Some previous
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studies reported inflow-induced signal changes at acquisitions with large flip angles and short TRs (26, 27). However, some other studies did not find significant inflow contribution to fMRI signals in multi-slices EPI-based BOLD fMRI experiments (27, 28).
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It is widely accepted that inflow effects are negligible in the EPI-based fMRI
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experiments in which typically 20 – 40 slices are acquired with a TR ranges from 2 to 4 s
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(27, 28). In our present experiment, with imaging parameters of 2s TR and 20 slices, the general inflow effect is insufficient to explain the variation of the transverse relaxation
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profiles to different types of stimuli at the recognition-related areas. Therefore, inflow effects do not have a significant impact on our results even if some contribution of the effects to R2* changes might exist with our imaging parameters. The repetition suppression method can encode neuronal information based on parameters obtained from a stimulation scheme, such as the interval between stimuli (7, 25). However, the problem with repetition suppression is that the fMRI responses are vulnerable to the effects of disturbances such as signal fluctuations. Multivariate
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analysis is a postprocessing method, which can only estimate the voxels involved with
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discriminatory fMRI responses, and it cannot determine whether the difference originates from diverse neuronal populations or simple variation in sensitivity within
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the same population (11, 12). Compared with these approaches, the new method
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proposed in the present study can generate neuronal information without considering the effects of disturbances, and it can also determine whether different responses are related to variable sensitivity within the same neuronal population or the presence of
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different neuronal populations.
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We expect that the proposed method could be used to measure microscopic
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information in response to cognitively categorized stimuli in functional areas, but further studies are required, particularly to improve the sensitivity. This method is
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based on the hypothesis that components with different transverse relaxations are distributed within a voxel or a region and that the response of the mixture to given stimuli can lead to variations in the ratio of %R2*_2 relative to %R2*_1. Therefore, the applicability of this method may be restricted to determining differences in neuronal populations with variable transverse relaxation profiles in blood vessels and their surrounding tissues, which may be a sensitivity limitation of the proposed method. Using a long TE may be advantageous for detecting subtle changes in transverse
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relaxation profile, but this would create further problems related to the signal-to-noise
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ratio. The image signal intensity decreases as the TE increases, thereby making it vulnerable to physical noise. Therefore, there may be a trade-off between the
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signal-to-noise ratio and the use of a long TE to increase the difference in the ratio
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between neuronal populations. We considered vessels and tissues together as sources of transverse relaxation profile, but it may be possible to measure changes in transverse relaxivity that are attributable to the characteristics of tissues per se, such as the cell
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membrane potential and the intracellular water changes that accompany the activation
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of neurons, provided that imaging methods with high sensitivity to tissues can be used
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(14, 29).
MRI techniques have advanced recently and it is now possible to produce
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images with a high spatial resolution of 1 mm3 (5). However, even these small voxels contain many neurons and it is still necessary to determine the microscopic structure/functional specificity of a voxel or region. The combined use of our method and high-spatial resolution images may be beneficial for probing functional structures in brain areas.
Conclusions
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The present study suggests that two category-selective areas, i.e., FFA and PPA,
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contain different neuronal populations, which are activated by a preferred stimulus and a non-preferred stimulus and that the microscopic functional characteristics of a
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functional site can be examined using the proposed method.
Acknowledgments
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This work was supported by JSPS KAKEN Grant Number 25330173.
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Figure legends
Figure 1. MRI images acquired at different echo times (TEs).
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(a) Images at TE1 = 13 ms, (b) TE2 = 38 ms, and (c) TE3 = 64 ms.
occurs around 25–35 ms with 3T.
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Figure 2. Maps of R2*_1 and R2*_2.
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R2*_1 was estimated from TE1 and TE2, whereas R2*_2 was estimated from TE2 and
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Figure 3. Maps of regions of interest identified from TE2 images. (a) V1 (x = 15, y = −92, z = −4), (b) the fusiform face area (x = 40, y = −46, z = −22), and (c) the parahippocampal place area (x = 30, y = −40, z = −12), using Talairach coordinates. A, anterior; P, posterior; R, right; L, left.
p < 0.005 (uncorrected)
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Figure 4. Changes in the response after stimulation based on the R2* values.
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The R2* signals of eight participants were averaged for each area. (a) V1, (b) fusiform face area, and (c) parahippocampal place area.
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The error bars represent the S.E.M.
Figure 5. Ratios of %R2*_2 relative to %R2*_1 for the V1, fusiform face area, and parahippocampal place area.
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images and r_b is the ratio for building images.
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