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[[File:Face Selective ROIs Final (Localizer).jpg | Figure 1]]
[[File:Face Selective ROIs Final (Localizer).jpg | Figure 1]]
FIGURE 1
FIGURE 1            
 
                    ROIs are named as follows: Face Selective 1- Blue
                                                Face Selective 2- Red
                                                Face Selective 3- Cyan
                                                Face Selective 4- Yellow
Using the localizer data for limb selective regions from the same experiment I selected 3 limb selective ROIs (See figure 2). I chose these 3 ROIs in order to obtain data from one ROI in 3 differing anatomical regions.
Using the localizer data for limb selective regions from the same experiment I selected 3 limb selective ROIs (See figure 2). I chose these 3 ROIs in order to obtain data from one ROI in 3 differing anatomical regions.


Line 36: Line 39:
[[File:Limb Selective ROIs Final.png | FIGURE 2]]
[[File:Limb Selective ROIs Final.png | FIGURE 2]]
FIGURE 2
FIGURE 2
                    ROIs are named as follows: Limb Selective 1- Blue
                                                Limb Selective 2- Green
                                                Limb Selective 3- Magenta


These ROIs were then
These ROIs were then uploaded onto their respective adaptation maps obtained from the first experiment. Face Selective adaptation map(figure 3) and Limb selective adaptation map (figure 4).


[[LSAdaptaion.png | Figure 3]]
[[File:FSAdaptation.png | Figure 3]]
[[File:LSAdaptation.png | Figure 4]]
                      FIGURE 3                                              FIGURE 4


The time course for each ROI was then extracted from the adaptation data in order to compare the adaptation between categories, ROIs, and anatomical regions.


= Results - What you found =
= Results =


== Retinotopic models in native space ==
==Face Selective Time Courses==
Some text. Some analysis. Some figures.


== Retinotopic models in individual subjects transformed into MNI space ==
[[File:FS1 Timecourse2.png | Figure 5]]                Face Selective 1 (Blue) Time course
Some text. Some analysis. Some figures.


== Retinotopic models in group-averaged data on the MNI template brain ==
[[File:FS2Timecourse.png | Figure 6]]                  Face Selective 2 (Red) Time course
Some text. Some analysis. Some figures. Maybe some equations.


[[File:FS3Timecourse.png | Figure 7]]                  Face Selective 3 (Cyan) Time course


=== Equations===
[[File:FS4Timecourse.png | Figure 8]]                   Face Selective 4 (Yellow) Time course
If you want to use equations, you can use the same formats that are use on wikipedia. <br>
''See wikimedia help on  [http://meta.wikimedia.org/wiki/Help:Displaying_a_formula formulas] for help.'' <br>
This example of equation use is copied and pasted from [http://en.wikipedia.org/wiki/Discrete_Fourier_transform wikipedia's article on the DFT].


The [[sequence]] of ''N'' [[complex number]]s ''x''<sub>0</sub>, ..., ''x''<sub>''N''−1</sub> is transformed into the  sequence of ''N'' complex numbers ''X''<sub>0</sub>, ..., ''X''<sub>''N''−1</sub> by the DFT according to the formula:


:<math>X_k = \sum_{n=0}^{N-1} x_n e^{-\frac{2 \pi i}{N} k n} \quad \quad k = 0, \dots, N-1</math> 
As seen from these four time courses, all of ROIs based on faced selective localizer data remains category selective within the adaptation parameter map. Face selective ROI 1 does not show adaptation in response to faces, and furthermore shows a higher response to repeated limb stimuli than non repeated limb stimuli (both responses still less than those for faces). Face selective ROIs 1,2, and 3 show increasing levels of adaptation to all stimuli, excluding houses.  
           
where i is the imaginary unit and <math>e^{\frac{2 \pi i}{N}}</math>  is a primitive N'th [[root of unity]]. (This expression can also be written in terms of a [[DFT matrix]]; when scaled appropriately it becomes a [[unitary matrix]] and the ''X''<sub>''k''</sub> can thus be viewed as coefficients of ''x'' in an [[orthonormal basis]].)


The transform is sometimes denoted by the symbol <math>\mathcal{F}</math>, as in <math>\mathbf{X} = \mathcal{F} \left \{ \mathbf{x} \right \} </math> or <math>\mathcal{F} \left ( \mathbf{x} \right )</math> or <math>\mathcal{F} \mathbf{x}</math>. 
== Limb Selective Time Courses ==


The '''inverse discrete Fourier transform (IDFT)''' is given by
[[File:LS1Timecourse.png | Figure 9]]                  Limb Selective 1 (Blue) Time course


:<math>x_n = \frac{1}{N} \sum_{k=0}^{N-1} X_k e^{\frac{2\pi i}{N} k n} \quad \quad n = 0,\dots,N-1.</math>
[[File:LS2Timecourse.png | Figure 10]]                  Limb Selective 2 (Green) Time course


== Retinotopic models in group-averaged data projected back into native space ==
[[File:LS3Timecourse.png | Figure 11]]                  Limb Selective 3 (Magenta) Time course
Some text. Some analysis. Some figures.




= Conclusions =
Similarly to face selective regions, all limb selective ROIs maintain a significant preference for images of limbs. However, unlike the anterior to posterior decrease in fMRI adaptation seen in face selective regions, the limb selective regions do not show this pattern. In fact, there is a slight increase in adaptation strength from the anterior to posterior limb selective ROIs.


Here is where you say what your results mean.


= References - Resources and related work =
= Conclusions =


References
My results show that fMRI adaptation is significant for both preferred and non-preferred categories, excepting face selective ROI 1. According to the original study from which this data was obtained, this result is expected across "the majority of subjects"[1]. However, the original study also concluded that the largest adaptation should occur within the preferred category which was a result only observed in face selective region 4.


Software
My results also show a decreasing fMRI adaptation ratio (Repeat/Non-repeat) from posterior to anterior regions within the face selective category, indicating a stronger adaptation response in the more anterior regions. This result is expected based on the original study, however, this was also expected but not observed for limb selective regions. This could be due to the fact that I only analyzed one hemisphere of one participant. Additionally, the selection of only three limb ROIs and my selection of ROI boundaries could have greatly influenced this result. As a next step I would go back and analyze additional ROIs within the limb selective regions in order to gain a better understanding of the fMRI adaptation patterns within that category's data.


= Appendix I - Code and Data =
= References =
1) Sayres & Grill-Spector, J. Neurophysiology, 2006


==Code==
2) Weiner K., Sayres R., Vinberg J., Grill-Spector K. fMRI Adaptation and Category Selectivity in Human Ventral Temporal Cortex: Regional Differences Across Time Scales. J. Neurophysiology, 2010
[[File:CodeFile.zip]]


==Data==
3) Weiner K., Grill-Spector K.. Sparsely distributed organization of face and limb activations in human ventral temporal cortex. Neuroimage, 2010
[[File:DataFile.zip | zip file with my data]]

Latest revision as of 16:12, 15 March 2012

The Relationship between fMRI adaptation and category selectivity

This project investigates the link between adaptation and areas of the cortex linked to face recognition and limb recognition using an event related design.

Background

fMRI Adaptation is a phenomena that takes place in higher order cortical areas in which the BOLD response signal decreases in response to repeated identical visual stimuli. Adaptation has been shown to occur in higher order visual areas but not early visual areas such as V1 [1]. This study investigates the relationship between object selectivity and fMRI adapation. More specifically, it will examine face selective areas as well as body part selective areas with regards to adaptation. Furthermore, it will look at the differences between adaptation within the ROIs of those two specific categories as well as the similarities in adaptation across various anatomical brain areas.

Methods

Pre-processed data was used from the lab of Kalanit Grill-Spector from the parts of an experiment outlined below [2].

Subjects

The data from the right hemisphere of one subject, a male in his late 20s, was analyzed.


MR acquisition

The subject participated in two experiments, an event related design followed by a block design localizer scan. The first, event related design, consisted of 8 runs of 156 trials lasting for 2 seconds each. Each trial consisted of the presentation of an image of either a face, limb, car, or house for 1000ms followed by a 1000ms blank. Within each run, only two of the images were repeated six times, the rest not repeated at all. None of the images were repeated across scans.

The second experiment was a block design used to identify category selective areas within the ventral stream. Blocks were 12 seconds long with a 750ms stimulus presentation period followed by a 250 ms blank period. Each run consisted of 32 blocks, 4 blocks for each category (faces, limbs, flowers, cars, guitars, houses, and scrambled) as well as four blank blocks.

12 slices were acquired at a resolution of 1.5 x 1.5 x 3mm per voxel and a TR of 1000 ms.

MR Analysis

As mentioned above, the data I analyzed was pre-processed. That high resolution MR data was was then analyzed using mrVista software tools.

ROIs

Using the localizer data from the second experiment, I created ROIs specific for areas selective for faces and areas selective for limbs. I chose the 4 different face selective areas seen in figure 1.


Figure 1 FIGURE 1 

                    ROIs are named as follows: Face Selective 1- Blue
                                               Face Selective 2- Red
                                               Face Selective 3- Cyan
                                               Face Selective 4- Yellow

Using the localizer data for limb selective regions from the same experiment I selected 3 limb selective ROIs (See figure 2). I chose these 3 ROIs in order to obtain data from one ROI in 3 differing anatomical regions.


FIGURE 2 FIGURE 2 

                    ROIs are named as follows: Limb Selective 1- Blue
                                               Limb Selective 2- Green
                                               Limb Selective 3- Magenta

These ROIs were then uploaded onto their respective adaptation maps obtained from the first experiment. Face Selective adaptation map(figure 3) and Limb selective adaptation map (figure 4).

Figure 3 Figure 4 

                     FIGURE 3                                               FIGURE 4

The time course for each ROI was then extracted from the adaptation data in order to compare the adaptation between categories, ROIs, and anatomical regions.

Results

Face Selective Time Courses

Figure 5 Face Selective 1 (Blue) Time course

Figure 6 Face Selective 2 (Red) Time course

Figure 7 Face Selective 3 (Cyan) Time course

Figure 8 Face Selective 4 (Yellow) Time course


As seen from these four time courses, all of ROIs based on faced selective localizer data remains category selective within the adaptation parameter map. Face selective ROI 1 does not show adaptation in response to faces, and furthermore shows a higher response to repeated limb stimuli than non repeated limb stimuli (both responses still less than those for faces). Face selective ROIs 1,2, and 3 show increasing levels of adaptation to all stimuli, excluding houses.

Limb Selective Time Courses

Figure 9 Limb Selective 1 (Blue) Time course

Figure 10 Limb Selective 2 (Green) Time course

Figure 11 Limb Selective 3 (Magenta) Time course


Similarly to face selective regions, all limb selective ROIs maintain a significant preference for images of limbs. However, unlike the anterior to posterior decrease in fMRI adaptation seen in face selective regions, the limb selective regions do not show this pattern. In fact, there is a slight increase in adaptation strength from the anterior to posterior limb selective ROIs.


Conclusions

My results show that fMRI adaptation is significant for both preferred and non-preferred categories, excepting face selective ROI 1. According to the original study from which this data was obtained, this result is expected across "the majority of subjects"[1]. However, the original study also concluded that the largest adaptation should occur within the preferred category which was a result only observed in face selective region 4.

My results also show a decreasing fMRI adaptation ratio (Repeat/Non-repeat) from posterior to anterior regions within the face selective category, indicating a stronger adaptation response in the more anterior regions. This result is expected based on the original study, however, this was also expected but not observed for limb selective regions. This could be due to the fact that I only analyzed one hemisphere of one participant. Additionally, the selection of only three limb ROIs and my selection of ROI boundaries could have greatly influenced this result. As a next step I would go back and analyze additional ROIs within the limb selective regions in order to gain a better understanding of the fMRI adaptation patterns within that category's data.

References

1) Sayres & Grill-Spector, J. Neurophysiology, 2006

2) Weiner K., Sayres R., Vinberg J., Grill-Spector K. fMRI Adaptation and Category Selectivity in Human Ventral Temporal Cortex: Regional Differences Across Time Scales. J. Neurophysiology, 2010

3) Weiner K., Grill-Spector K.. Sparsely distributed organization of face and limb activations in human ventral temporal cortex. Neuroimage, 2010

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