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Multiple behavioural values for one condition using Behavioural PLS
deb.tang
Posted on 04/11/13 10:38:07
Number of posts: 5
deb.tang posts:

When doing a behavioural PLS, is it possible to have multiple behaviours in a single condition per subject?

In my experiment, subjects placed bids on each food item they saw (ie. 35 bids per run per subject). I'd like to include all of these bids in my behavioural PLS analysis, and tie them to the onset of each food item. Is that possible, and if so, how can I do it? Based on what I can take away from the tutorial, it looks like you enter you can enter only one bid per subject. I am not sure if my understanding of this is correct. The section in the PLS manual that specifies how to enter the behaviour is copied and pasted below.

Thanks!

 

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For example, let's say you have group1 and group2 with condition1, and condition2. In group1, you have 2 subjects, and in group2 you have 3 subjects. Here is the order of the behavior file:

 

group1      condition1      subject1

group1      condition1      subject2

group1      condition2      subject1

group1      condition2      subject2

group2      condition1      subject1

group2      condition1      subject2

group2      condition1      subject3

group2      condition2      subject1

group2      condition2      subject2

group2      condition2      subject3
 

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jshen
Posted on 04/11/13 11:32:24
Number of posts: 291
jshen replies:

If I understood correctly, you would like to enter multiple behaviors for each condition per subject like the one listed below, is it correct?

=====================================================================

                   Label                                                                           Behaviors

==========================                              ============================

group1      condition1      subject1                              bid1      bid2      bid3       ...     bid35

group1      condition1      subject2                              bid1      bid2      bid3       ...     bid35

group1      condition2      subject1                              bid1      bid2      bid3       ...     bid35

group1      condition2      subject2                              bid1      bid2      bid3       ...     bid35

group2      condition1      subject1                              bid1      bid2      bid3       ...     bid35

group2      condition1      subject2                              bid1      bid2      bid3       ...     bid35

group2      condition1      subject3                              bid1      bid2      bid3       ...     bid35

group2      condition2      subject1                              bid1      bid2      bid3       ...     bid35

group2      condition2      subject2                              bid1      bid2      bid3       ...     bid35

group2      condition2      subject3                              bid1      bid2      bid3       ...     bid35

 



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deb.tang
Posted on 04/11/13 13:55:48
Number of posts: 5
deb.tang replies:

Hi Jimmy,

 

Yes, that's correct. So if I upload one large file with all of the bids (for all subjects) in it, that's all I need to do then?

 

Thanks!

Deb



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nlobaugh
Posted on 04/11/13 14:02:33
Number of posts: 229
nlobaugh replies:

35 bids per run per subject). I'd like to include all of these bids in my behavioural PLS analysis, and tie them to the onset of each food item

Deb..

I think that the design Jimmy shows would allow you to determine which of the 35 bids in each condition is most strongly correlated with the AVERAGE response in the condition -

If you want to look at Condition1_Bid1_Food1 separately from Condition1_Bid35_Food35, you would need to turn off 'average across runs/trials' when you make the datamat - and be concerned that you have sufficient numbers of subjects to look at the data at this single trial correlation level..

Let us know if that is what you meant..

cheers

Nancy

 

 



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deb.tang
Posted on 04/11/13 14:11:24
Number of posts: 5
deb.tang replies:

quote:

35 bids per run per subject). I'd like to include all of these bids in my behavioural PLS analysis, and tie them to the onset of each food item

Deb..

I think that the design Jimmy shows would allow you to determine which of the 35 bids in each condition is most strongly correlated with the AVERAGE response in the condition -

If you want to look at Condition1_Bid1_Food1 separately from Condition1_Bid35_Food35, you would need to turn off 'average across runs/trials' when you make the datamat - and be concerned that you have sufficient numbers of subjects to look at the data at this single trial correlation level..

Let us know if that is what you meant..

cheers

Nancy

 

 

Hi Nancy,

The goal of my analysis is to look at how changing bid effects brain response, so it would be important to look at the differences between bid1_food1 and bid35_food35, as you suggested in your last message.

In terms of data, I have:

29 subjects * 35 bids per run per subject * 6 runs per subject = 6090 data points

Hopefully that will be enough data.

So in addition to uploading the file with all my datapoints, I'll need to turn off average across runs/trials. Is there anything else I need to do?

Thanks!
Deb



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nlobaugh
Posted on 04/11/13 14:23:22
Number of posts: 229
nlobaugh replies:

are all of the runs the same? - i.e., you have n=6 observations of condition1_bid1?

 



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deb.tang
Posted on 04/11/13 14:28:38
Number of posts: 5
deb.tang replies:

Hmm... I'm not sure what you mean by are all the runs the same?

It is an event related study and food pictures are presented in pseudo random order. The runs are the same in that subjects will always see 35 food items and make 35 bids, but they are also different in that the order of the stimuli they will see is different. Timing will also be different because they see the next food item only after they enter their bid for the previous stimuli.

Hope that clears things up!

 



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jshen
Posted on 04/11/13 14:56:52
Number of posts: 291
jshen replies:

I need to continue my question of "If I understood correctly, you would like to enter multiple behaviors for each condition per subject like the one listed below, is it correct?" here.

Since the answer is "yes", theoretically, the large file with all the bids under this "row order" can be loaded after you click "Load Behavior Data" button. However, in reality, you can't do this!

Assuming you only have 2 groups and 2 conditions (from the context above), you will end up with 140 rows. Depending on the brain size and temporal window size, the dataset could easily beyond the capability of your computer. Let's assuming that you have temporal window size of 8, and your volume size is [91 109 91], and your brain size could be 91*109*91/2 = 451314. Then, 140*8*451314 = 505471680. Assuming you are using single datatype (4 bytes), The total size of your dataset could be end up to 140*8*451314*4 = 2021886720, i.e. 2GB. Plus, the accessory data etc, I am sure that it will go beyond your computer (or even MATLAB) capability.

I think that the above design need to be reconsidered. One suggestion is that you enter 2~3 bids each time. i.e.:

============================================================

                   Label                                                                   Behaviors

==========================                              ===================

group1      condition1      subject1                              bid1      bid10       bid35

group1      condition1      subject2                              bid1      bid10       bid35

......

 

Frankly speaking, I am an entrylevel application developer here, and have only very limited knowledge about the PLS design. If you encounter problem like "application crash", "how to use", etc. Please feel free to ask me. Other theoretical questions, or any question related to PLS design, I don't have much answer, and people with more PLS experience will provide you appropriate answer here.

 



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nlobaugh
Posted on 04/11/13 15:56:31
Number of posts: 229
nlobaugh replies:

I'm going to echo Jimmy on the design issue: if you have 35 bid/food pairings *6 runs, with no way to distinguish either bids or food (perhaps you do and weren't being clear), then you have a single vector of bid values and  a matrix reflecting  food onsets *#TRs, with no way to distinguish among them -- it's not clear at this point what your hypothesis/question is.

At a minimum - you should collapse the brain data across bid type (e.g., hi/med/low; ignoring the specific food presented).  You could still use each subject's individual value for the three categories.

Or you could generate "mean bid value" for specific food types (e.g., fruits, veggies)

hope this helps

cheers

nancy

 



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deb.tang
Posted on 04/17/13 10:29:54
Number of posts: 5
deb.tang replies:

 

Thank you Jimmy and Nancy for your help! My apologies for the delay in responding -- I was waiting to hear back from my supervisor before writing more.

At any rate, colleagues of ours carried out the same analysis, so I'm sure it's possible, but perhaps I am not explaining it properly. For each subject, I want to input the following information:

 

subject1                              bid1      bid2      bid3       ...     bid35

subject2                              bid1      bid2      bid3       ...     bid35

...

 
So if anyone has any other ideas on how to enter this information in PLS without running out of memory, please let me know. In the meantime, I will try to contact my colleagues and see if they have any additional information to share.
 
Many thanks for everyone's help!


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jshen
Posted on 04/17/13 12:01:59
Number of posts: 291
jshen replies:

Based on my previous assumption, if you have 6 runs and do "Merge Data Within Each Run" (i.e. turn off 'average across runs/trials'), the size of your dataset will exceed 6*2GB (12GB). Unless your colleagues who carried out the same analysis was using a very small raw data or they have huge amount of memory in their computer, running a dataset of 12GB is very challenging for most people. In order to enter all 35 bids in PLS without running out of memory, one possible solution could be resampling your raw data and making it very small. Tools "reslice_nii.m" & "clip_nii.m" can be used for this solution. The usage can be found under: http://research.baycrest.org/~jimmy/NIfTI

 




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