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SPM  November 2009

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Subject:

A newbie's question (or problem?)

From:

Panagiotis Tsiatsis <[log in to unmask]>

Reply-To:

Panagiotis Tsiatsis <[log in to unmask]>

Date:

Thu, 19 Nov 2009 17:03:29 +0100

Content-Type:

multipart/mixed

Parts/Attachments:

Parts/Attachments

text/plain (98 lines) , Review_of_Trials_AUX.txt (1 lines)


Hello dear all,

I am facing the following problem:

I am using the convert command from the SPM interface to read a continuus CTF meg data file and to isolate its parts of interest. Therefore, I am selecting "Define Settings:" -> Yes, "How to read?" -> Trials, "Where to look for trials?" -> define, "Start.." -> -200, "End.." -> 500, "How many conditions?" 6, and then I have to select my triggers from a list, which is:

Type: Tr1 ; Value: ; 47 instances
Type: Tr2 ; Value: ; 50 instances
Type: Tr3 ; Value: ; 42 instances
Type: Tr4 ; Value: ; 51 instances
Type: UDIO001_down ; Value: 5 ; 7 instances
Type: UDIO001_down ; Value: 6 ; 7 instances
Type: UDIO001_down ; Value: 7 ; 28 instances
Type: UDIO001_down ; Value: 8 ; 24 instances
Type: UDIO001_down ; Value: 9 ; 12 instances
Type: UDIO001_down ; Value: 10 ; 14 instances
Type: UDIO001_up ; Value: 5 ; 7 instances
Type: UDIO001_up ; Value: 6 ; 7 instances
Type: UDIO001_up ; Value: 7 ; 28 instances
Type: UDIO001_up ; Value: 8 ; 24 instances
Type: UDIO001_up ; Value: 9 ; 12 instances
Type: UDIO001_up ; Value: 10 ; 14 instances
Type: trial ; Value: ; 257 instances 

From there I select my (known) triggers to be 

Type: UDIO001_up ; Value: 5 ; 7 instances
Type: UDIO001_up ; Value: 6 ; 7 instances
Type: UDIO001_up ; Value: 7 ; 28 instances
Type: UDIO001_up ; Value: 8 ; 24 instances
Type: UDIO001_up ; Value: 9 ; 12 instances
Type: UDIO001_up ; Value: 10 ; 14 instances

as these correspond to my rising edge of my event triggers.

When I review the individual trials, everything looks fine, that is the event order and the timings match the data that I have from the program producing the stimuli. 

Now, the crucial part is the question "Read across trial borders?" From what I understand, and since I could not find any detailed explanations in the SPM manual, the trial borders should be defined as the "start" and "end" time of the trials that are characterized by the triggers of the 6 events that I have explicitly defined. Note that my trials are always at least 4 seconds apart (you can also verify that by the SPM trial file that I have attached). 

Now when I select "No" in the option "Read across trials" to assure that I am not including overlapping trials and then select "all" for the channels to be read, I am getting the following error message:


SPM8: spm_eeg_convert_ui (v3059)                   16:22:02 - 19/11/2009
 =======================================================================

	SPM8: spm_eeg_definetrial (v2902)          16:26:35 - 19/11/2009
	----------------------------------------------------------------

SPM8: spm_eeg_channelselection (v2866)             16:43:34 - 19/11/2009
 =======================================================================
??? Error using ==> read_data at 530
requested data segment extends over a discontinuous trial boundary

Error in ==> fileio_read_data at 11
[varargout{1:nargout}] = funhandle(varargin{:});

Error in ==> spm_eeg_convert at 360
        dat = fileio_read_data(S.dataset,'header',  hdr, 'begsample', trl(i, 1), 'endsample', trl(i, 2),...

Error in ==> spm_eeg_convert_ui at 89
D = spm_eeg_convert(S);
 
??? Error while evaluating uicontrol Callback




So I cannot understand how it is possible that "requested data segment extends over a discontinuous trial boundary" when I am reading a continuus file where the trials are certainly sufficiently further apart. Note that the error remains even if a set really smallo numbers for the start and end of trial, i.e. Start->0, End - >10, it just appears later on in the reading process of the file.

Funilly enough, when I try to repeat the process in the already segmented to trials file produced by the CTF software (**.ds, without the AUX suffix) with exactly the same settings and I select "No" to read across trials, everything seems to work just fine!! The difference between the two cases, is that for the already segmented .ds file, I am getting the following conditions list:

Type: Tr1 ; Value: ; 47 instances
Type: Tr2 ; Value: ; 49 instances
Type: Tr3 ; Value: ; 42 instances
Type: Tr4 ; Value: ; 50 instances
Type: UDIO001_down ; Value: 5 ; 7 instances
Type: UDIO001_down ; Value: 6 ; 7 instances
Type: UDIO001_down ; Value: 7 ; 28 instances
Type: UDIO001_down ; Value: 8 ; 24 instances
Type: UDIO001_down ; Value: 9 ; 12 instances
Type: UDIO001_down ; Value: 10 ; 14 instances
Type: UDIO001_up ; Value: 5 ; 7 instances
Type: UDIO001_up ; Value: 6 ; 7 instances
Type: UDIO001_up ; Value: 7 ; 28 instances
Type: UDIO001_up ; Value: 8 ; 24 instances
Type: UDIO001_up ; Value: 9 ; 12 instances
Type: UDIO001_up ; Value: 10 ; 14 instances
Type: trial ; Value: ; 92 instances

As you might notice, although the numbers of instances for the UDIO001_up triggers agree, there is a significant difference in the number of "trial" events: this is now 92 (which is exactly the number of trials that I have, 7+7+28+24+12+14 = 92", whereas before, for the continuus dataset, this was 257, which I have no clue where it came from... (note that there is also a difference in the values for Tr2 and Tr4.. this is also puzzling me)

Any intuitions about the whole issue would be really appreciated as I 've been puzzled and I cannot find a full explanation by my self. I am also attaching the events and timings file for the continuus AUX file so that you can see that I am not violating the trial borders by the trial start and end times that I have selected.

Thank you in advance and all the best,
Panagiotis



1 Label: 2 Time (sec): 122.2071 2 Label: 4 Time (sec): 128.2921 3 Label: 3 Time (sec): 134.2783 4 Label: 3 Time (sec): 140.3633 5 Label: 2 Time (sec): 146.4493 6 Label: 4 Time (sec): 152.2349 7 Label: 6 Time (sec): 158.2201 8 Label: 6 Time (sec): 164.1064 9 Label: 6 Time (sec): 169.9918 10 Label: 1 Time (sec): 175.8772 11 Label: 3 Time (sec): 182.063 12 Label: 3 Time (sec): 187.9484 13 Label: 1 Time (sec): 193.6342 14 Label: 5 Time (sec): 199.8199 15 Label: 5 Time (sec): 206.1056 16 Label: 4 Time (sec): 212.1915 17 Label: 3 Time (sec): 218.2766 18 Label: 4 Time (sec): 224.1629 19 Label: 4 Time (sec): 230.4485 20 Label: 3 Time (sec): 236.6343 21 Label: 3 Time (sec): 242.9199 22 Label: 3 Time (sec): 248.9052 23 Label: 4 Time (sec): 281.1098 24 Label: 3 Time (sec): 287.1958 25 Label: 1 Time (sec): 293.181 26 Label: 4 Time (sec): 299.0673 27 Label: 5 Time (sec): 305.3529 28 Label: 1 Time (sec): 311.5379 29 Label: 2 Time (sec): 317.2236 30 Label: 3 Time (sec): 323.1099 31 Label: 4 Time (sec): 329.0951 32 Label: 4 Time (sec): 334.8807 33 Label: 3 Time (sec): 340.9667 34 Label: 6 Time (sec): 346.9519 35 Label: 6 Time (sec): 353.2384 36 Label: 4 Time (sec): 359.2236 37 Label: 4 Time (sec): 365.3096 38 Label: 3 Time (sec): 371.2948 39 Label: 4 Time (sec): 377.4806 40 Label: 4 Time (sec): 407.505 41 Label: 3 Time (sec): 413.2915 42 Label: 3 Time (sec): 419.5771 43 Label: 2 Time (sec): 425.762 44 Label: 5 Time (sec): 431.9478 45 Label: 4 Time (sec): 438.1336 46 Label: 5 Time (sec): 444.2195 47 Label: 4 Time (sec): 450.4053 48 Label: 4 Time (sec): 456.2907 49 Label: 5 Time (sec): 462.0763 50 Label: 3 Time (sec): 467.9626 51 Label: 3 Time (sec): 473.9478 52 Label: 6 Time (sec): 480.1336 53 Label: 6 Time (sec): 485.9192 54 Label: 4 Time (sec): 492.2057 55 Label: 4 Time (sec): 498.3915 56 Label: 3 Time (sec): 504.6771 57 Label: 5 Time (sec): 510.6624 58 Label: 3 Time (sec): 516.3481 59 Label: 4 Time (sec): 522.1337 60 Label: 5 Time (sec): 528.1198 61 Label: 6 Time (sec): 533.8055 62 Label: 3 Time (sec): 539.7908 63 Label: 4 Time (sec): 545.8767 64 Label: 5 Time (sec): 551.6623 65 Label: 3 Time (sec): 557.5486 66 Label: 4 Time (sec): 563.8342 67 Label: 1 Time (sec): 570.0191 68 Label: 3 Time (sec): 576.2049 69 Label: 3 Time (sec): 581.9913 70 Label: 4 Time (sec): 587.6762 71 Label: 4 Time (sec): 593.5625 72 Label: 1 Time (sec): 599.2482 73 Label: 3 Time (sec): 605.2335 74 Label: 3 Time (sec): 611.5191 75 Label: 3 Time (sec): 617.3047 76 Label: 5 Time (sec): 623.4905 77 Label: 4 Time (sec): 629.7761 78 Label: 3 Time (sec): 636.0617 79 Label: 5 Time (sec): 641.7474 80 Label: 2 Time (sec): 647.7336 81 Label: 6 Time (sec): 653.4193 82 Label: 3 Time (sec): 659.105 83 Label: 5 Time (sec): 665.1901 84 Label: 6 Time (sec): 671.0764 85 Label: 2 Time (sec): 677.362 86 Label: 1 Time (sec): 683.6476 87 Label: 6 Time (sec): 689.7336 88 Label: 6 Time (sec): 695.9194 89 Label: 6 Time (sec): 701.705 90 Label: 3 Time (sec): 707.5904 91 Label: 2 Time (sec): 713.876 92 Label: 6 Time (sec): 719.9619

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