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= Project Title - Multi Voxel Pattern Analysis in the Ventral Temporal Cortex =
= Multi Voxel Pattern Analysis in the Ventral Temporal Cortex =
Recent Studies have shown that there are clearly defined regions within the ventral temporal cortex that preferentially respond to faces (fusiform face area), places (Parahippocampal Place Area) and objects. In particular, the FFA has been shown to undergo development from childhood throughout adolescence. This study uses multi voxel pattern analysis to confirm both the location and the growth of the FFA in the VTC.
Recent Studies have shown that there are clearly defined regions within the ventral temporal cortex that preferentially respond to faces (fusiform face area), places (Parahippocampal Place Area) and objects. In particular, the FFA has been shown to undergo development from childhood throughout adolescence. This study uses multi voxel pattern analysis to confirm both the location and the growth of the FFA in the VTC.


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= Background =
= Background =
== Multi Voxel Pattern Analysis vs. ROI analysis  ==
== Multi Voxel Pattern Analysis vs. ROI analysis  ==
Most of the current research on the VTC and the FFA has focused on region of interest (ROI) analyses. This analysis focuses on the highest activations of the voxels selective for faces. Usually, activations are thresholded so that only voxels responding over a certain level are looked at. Supporters of this type of analysis say that the highest activations are the most interesting and provide the clearest evidence for the age related growth of the FFA (in volume and in more well-defined areas selective for faces vs. non-face stimuli). However, multi voxel pattern analysis (MVPA) supporters insist that there is much more information in all the activations in the VTC, not just the highest ones. They say that the activations are distributed throughout the entire VTC and that the aforementioned growth of the FFA is actually just growth of the entire VTC. This study aims to prove the MVPA supporters wrong by using MVPA to support the initial analyses provided by ROIs.
Most of the current research on the VTC and the FFA has focused on region of interest (ROI) analyses. This analysis focuses on the highest activations of the voxels selective for faces. Usually, activations are thresholded so that only voxels responding over a certain level are looked at. Supporters of this type of analysis say that the highest activations are the most interesting and provide the clearest evidence for the age related growth of the FFA (in volume and in more well-defined areas selective for faces vs. non-face stimuli). However, multi voxel pattern analysis (MVPA) supporters insist that there is much more information in all the activations in the VTC, not just the highest ones. They say that the activations are distributed throughout the entire VTC and that the aforementioned growth of the FFA is actually just growth of the entire VTC. This study aims to use MVPA to support the initial analyses provided by ROIs.
<br>
<br>
[[File:Example.jpg | Figure 1]]


Below is another example of a reinotopic map in a different subject.
= Methods =
<br>
 
[[File:Example2.jpg | Figure 2]]
== Subjects ==
Multi-Voxel Pattern Analysis was done on 14 adolescents (ages 12-16yrs) and 11 adults (ages 18-40yrs)


Once you upload the images, they look like this. Note that you can control many features of the images, like whether to show a thumbnail, and the display resolution.
== MR Acquisition ==
[[File:Example3.jpg |thumb|300px|center| Figure 3]]
Brain imaging was performed on a 3 tesla whole-body General Electric Signa MRI scanner.
<br>
During fMRI subjects viewed gray-scale images of the following types: faces of male children and adolescents (ages 6–16 years), faces of male adults (ages 18–40 years), abstract sculptures, cars, indoor scenes, outdoor scenes, and scrambled images (created by randomly scrambling pictures into 225, 8 × 8 pixel squares). Stimuli were presented in 12 s blocks followed by 12 s of a blank screen with a fixation at a rate of 1 Hz. Subjects participated in two 396-s runs with different images.


== MNI space ==
== MR Analysis ==


MNI is an abbreviation for [http://en.wikipedia.org/wiki/Montreal_Neurological_Institute Montreal Neurological Institute].


= Methods =
Below is an example of an initial MVPA analysis on the VTC. The MVP reflects differences in category selectivity in each voxel, rather than amplitude differences across voxels. For each stimulus type, MVPs were generated across the anatomical ROI of VTC, separately for data from run 1 and run 2. Corresponding graphs reflect the within-category reproducibility and across-category distinctness of MVPs to determine if there are between age group differences that extend beyond functionally defined ROIs.
== Measuring retinotopic maps ==
Retinotopic maps were obtained in 5 subjects using Population Receptive Field mapping methods [http://white.stanford.edu/~brian/papers/mri/2007-Dumoulin-NI.pdf Dumoulin and Wandell (2008)]. These data were collected for another [http://www.journalofvision.org/9/8/768/ research project] in the Wandell lab. We re-analyzed the data for this project, as described below.  


=== Subjects ===
[[File:Initial_MVPA.jpg |thumb|300px|center| Figure 1]]
Subjects were 5 healthy volunteers.


=== MR acquisition ===
Data were obtained on a GE scanner. Et cetera.


=== MR Analysis ===
From these initial MVP results we decided to create new ROIs by dividing the VTC in two. We created lateral and medial VTC ROIs for each subject using MrVista. We used the middle fusiform sulcus as the anatomical landmark to divide our ROIs.
The MR data was analyzed using [http://white.stanford.edu/newlm/index.php/MrVista mrVista] software tools.  


==== Pre-processing ====
==== Pre-processing ====
All data were slice-time corrected, motion corrected, and repeated scans were averaged together to create a single average scan for each subject. Et cetera.
No thresholding or spatial smoothing was applied to the MVPA data. Using Z-scores to calculate correlations minimizes between-voxel effects


==== PRF model fits ====
PRF models were fit with a 2-gaussian model.


==== MNI space ====
= Results =
After a pRF model was solved for each subject, the model was trasnformed into MNI template space. This was done by first aligning the high resolution t1-weighted anatomical scan from each subject to an MNI template. Since the pRF model was coregistered to the t1-anatomical scan, the same alignment matrix could then be applied to the pRF model. <br>
Once each pRF model was aligned to MNI space, 4 model parameters - x, y, sigma, and r^2 - were averaged across each of the 6 subjects  in each voxel.


Et cetera.
== Lateral VTC MVPs ==
The Lateral VTC MVPs show good reproducibility of patterns within category and show activation in the lateral part of the lateral VTC in response to faces where previous ROI analysis has shown the FFA is located. These MVPs are of a 14 yr old boy.


[[File:Lateral VTC scans.jpg|thumb|300px|center| Figure 1]]


= Results - What you found =
== Lateral VTC correlation graphs ==
These graphs confirm the good reproducibility within category for all subjects both adolescents and adults. Reproducibility is better for faces and scenes than objects. Between category correlations suggest that there are no age related differences in age of face effects. Adults have better ability to distinguish between faces and cars, faces and absobj and faces and scenes. This also means that the areas of activation are more distinct in adults vs adolescents.


== Retinotopic models in native space ==
[[File:Lateral vtc graphs.jpg|thumb|300px|center| Figure 1]]
Some text. Some analysis. Some figures.


== Retinotopic models in individual subjects transformed into MNI space ==
Some text. Some analysis. Some figures.


== Retinotopic models in group-averaged data on the MNI template brain ==
Some text. Some analysis. Some figures. Maybe some equations.


== Medial VTC MVPs ==
Medial VTC scans show good reproducibility but no above average activation for faces. Highest activation in medial MVPs is for places and absobj> faces. MVPs provide further evidence that FFA is not in the medial side of the VTC.


=== Equations===
[[File: medial vtc scans.jpg|thumb|300px|center| Figure 1]]
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:
== Medial VTC correlation graphs ==
Medial VTC graphs again show good within category reproducibility better for faces and places than objects. There are no age related differences in age of face stimuli effects. Adult MVPs still show greater differentiation between faces and cars and faces and abstract objects although this differentiation is not as significant as in the lateral side of the VTC. Adolescents show less differentiation in the medial side as well for faces vs cars, faces vs abstract objects.


:<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> 
[[File:Medial vtc graphs.jpg|thumb|300px|center| Figure 1]]
           
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>.
= Conclusion =
MVPs on both medial and lateral ROIs provide further evidence that previous ROI analyses has been correct in assuming that the FFA is located in the lateral part of the VTC. Adult MVPs show better distinctness for faces vs non-face stimuli than adolescents in both sides of the VTC although this distinctness is more pronounced and statistically significant in the lateral VTC.
It appears as though the ROI hypothesis is holding true but we cannot rule out the pattern analysis hypothesis quite yet. More importantly, using pattern analysis in tandem with ROI analysis provides a more thorough investigation of the areas in question and neither technique should be ruled out when trying to localize novel brain regions.


The '''inverse discrete Fourier transform (IDFT)''' is given by  
ROIs may need to be redrawn due to the activation of the PPA in the medial part of the lateral VTC which we should not see. This could be accomplished by using the MVPs to redraw the division between the lateral and medial VTC in all the ROIs or the PPA could be subtracted out of the lateral VTC ROIs possibly saving time.


:<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>
= Implications =


== Retinotopic models in group-averaged data projected back into native space ==
Using the evidence obtained in this analyses, further analyses is needed of the lateral part of VTC to better define location and growth of the FFA. One idea would be to create an ROI with further divisions in the lateral VTC and subtract out the highest activations in the lateral VTC (what we think is the FFA area) to look at activations in rest of the lateral VTC without them. Running a multi voxel pattern analyses on the lateral VTC without the FFA could reveal new findings.
Some text. Some analysis. Some figures.
At some point we want to correlate findings to behavior outside the scanner (increased memory for faces, perceptual discrimination for faces)
 
= Conclusions =
 
Here is where you say what your results mean.


= References - Resources and related work =
= References - Resources and related work =
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References
References


Software
Golarai G, Liberman A, Yoon JM and Grill-Spector K (2010) Differential development of the ventral visual cortex extends through adolescence. Front. Hum. Neurosci. 3:80. doi:10.3389/neuro.09.080.2009
 
= Appendix I - Code and Data =
 
==Code==
[[File:CodeFile.zip]]
 
==Data==
[[File:DataFile.zip | zip file with my data]]
 
= Appendix II - Work partition (if a group project) =
Brian and Bob gave the lectures. Jon mucked around on the wiki.

Latest revision as of 04:56, 20 March 2010

Back to Psych 204 Projects 2009

Multi Voxel Pattern Analysis in the Ventral Temporal Cortex

Recent Studies have shown that there are clearly defined regions within the ventral temporal cortex that preferentially respond to faces (fusiform face area), places (Parahippocampal Place Area) and objects. In particular, the FFA has been shown to undergo development from childhood throughout adolescence. This study uses multi voxel pattern analysis to confirm both the location and the growth of the FFA in the VTC.


Background

Multi Voxel Pattern Analysis vs. ROI analysis

Most of the current research on the VTC and the FFA has focused on region of interest (ROI) analyses. This analysis focuses on the highest activations of the voxels selective for faces. Usually, activations are thresholded so that only voxels responding over a certain level are looked at. Supporters of this type of analysis say that the highest activations are the most interesting and provide the clearest evidence for the age related growth of the FFA (in volume and in more well-defined areas selective for faces vs. non-face stimuli). However, multi voxel pattern analysis (MVPA) supporters insist that there is much more information in all the activations in the VTC, not just the highest ones. They say that the activations are distributed throughout the entire VTC and that the aforementioned growth of the FFA is actually just growth of the entire VTC. This study aims to use MVPA to support the initial analyses provided by ROIs.

Methods

Subjects

Multi-Voxel Pattern Analysis was done on 14 adolescents (ages 12-16yrs) and 11 adults (ages 18-40yrs)

MR Acquisition

Brain imaging was performed on a 3 tesla whole-body General Electric Signa MRI scanner. During fMRI subjects viewed gray-scale images of the following types: faces of male children and adolescents (ages 6–16 years), faces of male adults (ages 18–40 years), abstract sculptures, cars, indoor scenes, outdoor scenes, and scrambled images (created by randomly scrambling pictures into 225, 8 × 8 pixel squares). Stimuli were presented in 12 s blocks followed by 12 s of a blank screen with a fixation at a rate of 1 Hz. Subjects participated in two 396-s runs with different images.

MR Analysis

Below is an example of an initial MVPA analysis on the VTC. The MVP reflects differences in category selectivity in each voxel, rather than amplitude differences across voxels. For each stimulus type, MVPs were generated across the anatomical ROI of VTC, separately for data from run 1 and run 2. Corresponding graphs reflect the within-category reproducibility and across-category distinctness of MVPs to determine if there are between age group differences that extend beyond functionally defined ROIs.

Figure 1


From these initial MVP results we decided to create new ROIs by dividing the VTC in two. We created lateral and medial VTC ROIs for each subject using MrVista. We used the middle fusiform sulcus as the anatomical landmark to divide our ROIs.

Pre-processing

No thresholding or spatial smoothing was applied to the MVPA data. Using Z-scores to calculate correlations minimizes between-voxel effects


Results

Lateral VTC MVPs

The Lateral VTC MVPs show good reproducibility of patterns within category and show activation in the lateral part of the lateral VTC in response to faces where previous ROI analysis has shown the FFA is located. These MVPs are of a 14 yr old boy.

Figure 1

Lateral VTC correlation graphs

These graphs confirm the good reproducibility within category for all subjects both adolescents and adults. Reproducibility is better for faces and scenes than objects. Between category correlations suggest that there are no age related differences in age of face effects. Adults have better ability to distinguish between faces and cars, faces and absobj and faces and scenes. This also means that the areas of activation are more distinct in adults vs adolescents.

Figure 1


Medial VTC MVPs

Medial VTC scans show good reproducibility but no above average activation for faces. Highest activation in medial MVPs is for places and absobj> faces. MVPs provide further evidence that FFA is not in the medial side of the VTC.

Figure 1

Medial VTC correlation graphs

Medial VTC graphs again show good within category reproducibility better for faces and places than objects. There are no age related differences in age of face stimuli effects. Adult MVPs still show greater differentiation between faces and cars and faces and abstract objects although this differentiation is not as significant as in the lateral side of the VTC. Adolescents show less differentiation in the medial side as well for faces vs cars, faces vs abstract objects.

Figure 1

Conclusion

MVPs on both medial and lateral ROIs provide further evidence that previous ROI analyses has been correct in assuming that the FFA is located in the lateral part of the VTC. Adult MVPs show better distinctness for faces vs non-face stimuli than adolescents in both sides of the VTC although this distinctness is more pronounced and statistically significant in the lateral VTC. It appears as though the ROI hypothesis is holding true but we cannot rule out the pattern analysis hypothesis quite yet. More importantly, using pattern analysis in tandem with ROI analysis provides a more thorough investigation of the areas in question and neither technique should be ruled out when trying to localize novel brain regions.

ROIs may need to be redrawn due to the activation of the PPA in the medial part of the lateral VTC which we should not see. This could be accomplished by using the MVPs to redraw the division between the lateral and medial VTC in all the ROIs or the PPA could be subtracted out of the lateral VTC ROIs possibly saving time.

Implications

Using the evidence obtained in this analyses, further analyses is needed of the lateral part of VTC to better define location and growth of the FFA. One idea would be to create an ROI with further divisions in the lateral VTC and subtract out the highest activations in the lateral VTC (what we think is the FFA area) to look at activations in rest of the lateral VTC without them. Running a multi voxel pattern analyses on the lateral VTC without the FFA could reveal new findings. At some point we want to correlate findings to behavior outside the scanner (increased memory for faces, perceptual discrimination for faces)

References - Resources and related work

References

Golarai G, Liberman A, Yoon JM and Grill-Spector K (2010) Differential development of the ventral visual cortex extends through adolescence. Front. Hum. Neurosci. 3:80. doi:10.3389/neuro.09.080.2009

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