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Cortical connective field estimates from resting state fMRI activity.

Gravel N, Harvey B, Nordhjem B, Haak KV, Dumoulin SO, Renken R, Curčić-Blake B, Cornelissen FW - Front Neurosci (2014)

Bottom Line: Just as population receptive field (pRF) mapping predicts the collective neural activity in a voxel as a function of response selectivity to stimulus position in visual space, CF modeling predicts the activity of voxels in one visual area as a function of the aggregate activity in voxels in another visual area.In combination with pRF mapping, CF locations on the cortical surface can be interpreted in visual space, thus enabling reconstruction of visuotopic maps from resting state data.Therefore, we conclude that-despite some variability in CF estimates between RS scans-neural properties such as CF maps and CF size can be derived from resting state data.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Experimental Ophthalmology, University Medical Center Groningen, University of Groningen Groningen, Netherlands ; Laboratorio de Circuitos Neuronales, Centro Interdisciplinario de Neurociencia, Pontificia Universidad Católica de Chile Santiago, Chile ; NeuroImaging Center, University Medical Center Groningen, University of Groningen Netherlands.

ABSTRACT
One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective field (CF) modeling to estimate the spatial profile of functional connectivity in the early visual cortex during resting state functional magnetic resonance imaging (RS-fMRI). This model-based analysis estimates the spatial integration between blood-oxygen level dependent (BOLD) signals in distinct cortical visual field maps using fMRI. Just as population receptive field (pRF) mapping predicts the collective neural activity in a voxel as a function of response selectivity to stimulus position in visual space, CF modeling predicts the activity of voxels in one visual area as a function of the aggregate activity in voxels in another visual area. In combination with pRF mapping, CF locations on the cortical surface can be interpreted in visual space, thus enabling reconstruction of visuotopic maps from resting state data. We demonstrate that V1 ➤ V2 and V1 ➤ V3 CF maps estimated from resting state fMRI data show visuotopic organization. Therefore, we conclude that-despite some variability in CF estimates between RS scans-neural properties such as CF maps and CF size can be derived from resting state data.

No MeSH data available.


Related in: MedlinePlus

Visualization of connective field maps for a single subject. From left to right: eccentricity, polar angle, and size. Top panel corresponds to visual field mapping (VFM)-based estimates. Lower panels show parameter estimates for each resting state (RS) scan. For V1 ➤ V2 CF models, the position displacement in CF cortical location (in mm) between VFM- and RS-based estimates for RS1 to RS5 is: median (MAD) = 10.0 (5.4); 8.5 (5); 5.8 (3.7); 3.8 (3.4); and 4.1 (3.0), respectively [total = 5.4 (3.9)]. Corresponding position displacement values between RS4 and RS5 (the RS scans with lowest displacement: 4.1 (3.1); between RS1 and RS2 (the RS scans with highest displacement): 8.5 (5.8); between RS1 and RS4: 10.5 (6.6); when grouping results for all RS scan pairs: 8.6 (5.9). For V1 ➤ V3 CF models, the corresponding values are: 13.6 (6.3); 14.4 (6.8); 7.9 (5.4); 6.7 (5.5); and 7.1 (4.2) [total = 8.7 (5.5)]. Eccentricity and polar angle are inferred from V1 pRF mapping (see Materials and Methods for details). Data are for V1 ➤ V2 and V1 ➤ V3 models estimated for subject 3 (data for other subjects included in Supplementary Materials). A threshold of 0.35 VE was applied. Median cortical displacements reflect the agreement between RS and VFM maps and between different RS maps.
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Figure 3: Visualization of connective field maps for a single subject. From left to right: eccentricity, polar angle, and size. Top panel corresponds to visual field mapping (VFM)-based estimates. Lower panels show parameter estimates for each resting state (RS) scan. For V1 ➤ V2 CF models, the position displacement in CF cortical location (in mm) between VFM- and RS-based estimates for RS1 to RS5 is: median (MAD) = 10.0 (5.4); 8.5 (5); 5.8 (3.7); 3.8 (3.4); and 4.1 (3.0), respectively [total = 5.4 (3.9)]. Corresponding position displacement values between RS4 and RS5 (the RS scans with lowest displacement: 4.1 (3.1); between RS1 and RS2 (the RS scans with highest displacement): 8.5 (5.8); between RS1 and RS4: 10.5 (6.6); when grouping results for all RS scan pairs: 8.6 (5.9). For V1 ➤ V3 CF models, the corresponding values are: 13.6 (6.3); 14.4 (6.8); 7.9 (5.4); 6.7 (5.5); and 7.1 (4.2) [total = 8.7 (5.5)]. Eccentricity and polar angle are inferred from V1 pRF mapping (see Materials and Methods for details). Data are for V1 ➤ V2 and V1 ➤ V3 models estimated for subject 3 (data for other subjects included in Supplementary Materials). A threshold of 0.35 VE was applied. Median cortical displacements reflect the agreement between RS and VFM maps and between different RS maps.

Mentions: The next question we address is whether the topographical maps based on RS data have similar characteristics as the one based on VFM data (our current reference). Also, how variable are the results between RS scans? To provide an impression of this variability, Figure 3 shows both VFM and RS derived CF maps for a single participant (maps for other participants are shown in Supplementary Materials). V2 and V3 CF parameter maps (V1-referred) are plotted on a smoothed 3D mesh representing gray matter along the cortical surface. Eccentricity, polar angle and size (σ) are plotted in three columns. In top row of panels, CF parameters estimated based on VFM data are shown. These maps serve as our reference. In the lower rows of panels, these same parameters are plotted for all RS scans. As shown previously (Haak et al., 2013), the VFM derived maps show a clear retinotopic organization (note that in the context of CF modeling, eccentricity and polar angle maps are inferred from a pRF mapping and associated to each recording site in the source region, in this case V1). In some RS scans eccentricity and polar angles maps resembles the VFM-based reference, although some variability can be observed (Figure 3, RS4, RS5). To quantify the variability of the individual maps, the median position displacement in CF cortical location (relative to the VFM reference and between all RS scan pairs; in mm) and the MAD were calculated for RS1 to RS5 (values are reported in the legend of Figure 3). These values confirm the impression that RS4 and RS5 most clearly resemble the visuotopic organization observed in the VFM-based maps (results are shown for participant 3, those for the other participants are shown in the Supplementary Material).


Cortical connective field estimates from resting state fMRI activity.

Gravel N, Harvey B, Nordhjem B, Haak KV, Dumoulin SO, Renken R, Curčić-Blake B, Cornelissen FW - Front Neurosci (2014)

Visualization of connective field maps for a single subject. From left to right: eccentricity, polar angle, and size. Top panel corresponds to visual field mapping (VFM)-based estimates. Lower panels show parameter estimates for each resting state (RS) scan. For V1 ➤ V2 CF models, the position displacement in CF cortical location (in mm) between VFM- and RS-based estimates for RS1 to RS5 is: median (MAD) = 10.0 (5.4); 8.5 (5); 5.8 (3.7); 3.8 (3.4); and 4.1 (3.0), respectively [total = 5.4 (3.9)]. Corresponding position displacement values between RS4 and RS5 (the RS scans with lowest displacement: 4.1 (3.1); between RS1 and RS2 (the RS scans with highest displacement): 8.5 (5.8); between RS1 and RS4: 10.5 (6.6); when grouping results for all RS scan pairs: 8.6 (5.9). For V1 ➤ V3 CF models, the corresponding values are: 13.6 (6.3); 14.4 (6.8); 7.9 (5.4); 6.7 (5.5); and 7.1 (4.2) [total = 8.7 (5.5)]. Eccentricity and polar angle are inferred from V1 pRF mapping (see Materials and Methods for details). Data are for V1 ➤ V2 and V1 ➤ V3 models estimated for subject 3 (data for other subjects included in Supplementary Materials). A threshold of 0.35 VE was applied. Median cortical displacements reflect the agreement between RS and VFM maps and between different RS maps.
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Related In: Results  -  Collection

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Figure 3: Visualization of connective field maps for a single subject. From left to right: eccentricity, polar angle, and size. Top panel corresponds to visual field mapping (VFM)-based estimates. Lower panels show parameter estimates for each resting state (RS) scan. For V1 ➤ V2 CF models, the position displacement in CF cortical location (in mm) between VFM- and RS-based estimates for RS1 to RS5 is: median (MAD) = 10.0 (5.4); 8.5 (5); 5.8 (3.7); 3.8 (3.4); and 4.1 (3.0), respectively [total = 5.4 (3.9)]. Corresponding position displacement values between RS4 and RS5 (the RS scans with lowest displacement: 4.1 (3.1); between RS1 and RS2 (the RS scans with highest displacement): 8.5 (5.8); between RS1 and RS4: 10.5 (6.6); when grouping results for all RS scan pairs: 8.6 (5.9). For V1 ➤ V3 CF models, the corresponding values are: 13.6 (6.3); 14.4 (6.8); 7.9 (5.4); 6.7 (5.5); and 7.1 (4.2) [total = 8.7 (5.5)]. Eccentricity and polar angle are inferred from V1 pRF mapping (see Materials and Methods for details). Data are for V1 ➤ V2 and V1 ➤ V3 models estimated for subject 3 (data for other subjects included in Supplementary Materials). A threshold of 0.35 VE was applied. Median cortical displacements reflect the agreement between RS and VFM maps and between different RS maps.
Mentions: The next question we address is whether the topographical maps based on RS data have similar characteristics as the one based on VFM data (our current reference). Also, how variable are the results between RS scans? To provide an impression of this variability, Figure 3 shows both VFM and RS derived CF maps for a single participant (maps for other participants are shown in Supplementary Materials). V2 and V3 CF parameter maps (V1-referred) are plotted on a smoothed 3D mesh representing gray matter along the cortical surface. Eccentricity, polar angle and size (σ) are plotted in three columns. In top row of panels, CF parameters estimated based on VFM data are shown. These maps serve as our reference. In the lower rows of panels, these same parameters are plotted for all RS scans. As shown previously (Haak et al., 2013), the VFM derived maps show a clear retinotopic organization (note that in the context of CF modeling, eccentricity and polar angle maps are inferred from a pRF mapping and associated to each recording site in the source region, in this case V1). In some RS scans eccentricity and polar angles maps resembles the VFM-based reference, although some variability can be observed (Figure 3, RS4, RS5). To quantify the variability of the individual maps, the median position displacement in CF cortical location (relative to the VFM reference and between all RS scan pairs; in mm) and the MAD were calculated for RS1 to RS5 (values are reported in the legend of Figure 3). These values confirm the impression that RS4 and RS5 most clearly resemble the visuotopic organization observed in the VFM-based maps (results are shown for participant 3, those for the other participants are shown in the Supplementary Material).

Bottom Line: Just as population receptive field (pRF) mapping predicts the collective neural activity in a voxel as a function of response selectivity to stimulus position in visual space, CF modeling predicts the activity of voxels in one visual area as a function of the aggregate activity in voxels in another visual area.In combination with pRF mapping, CF locations on the cortical surface can be interpreted in visual space, thus enabling reconstruction of visuotopic maps from resting state data.Therefore, we conclude that-despite some variability in CF estimates between RS scans-neural properties such as CF maps and CF size can be derived from resting state data.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Experimental Ophthalmology, University Medical Center Groningen, University of Groningen Groningen, Netherlands ; Laboratorio de Circuitos Neuronales, Centro Interdisciplinario de Neurociencia, Pontificia Universidad Católica de Chile Santiago, Chile ; NeuroImaging Center, University Medical Center Groningen, University of Groningen Netherlands.

ABSTRACT
One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective field (CF) modeling to estimate the spatial profile of functional connectivity in the early visual cortex during resting state functional magnetic resonance imaging (RS-fMRI). This model-based analysis estimates the spatial integration between blood-oxygen level dependent (BOLD) signals in distinct cortical visual field maps using fMRI. Just as population receptive field (pRF) mapping predicts the collective neural activity in a voxel as a function of response selectivity to stimulus position in visual space, CF modeling predicts the activity of voxels in one visual area as a function of the aggregate activity in voxels in another visual area. In combination with pRF mapping, CF locations on the cortical surface can be interpreted in visual space, thus enabling reconstruction of visuotopic maps from resting state data. We demonstrate that V1 ➤ V2 and V1 ➤ V3 CF maps estimated from resting state fMRI data show visuotopic organization. Therefore, we conclude that-despite some variability in CF estimates between RS scans-neural properties such as CF maps and CF size can be derived from resting state data.

No MeSH data available.


Related in: MedlinePlus