MRI
Immaturities in Reward Processing and Its Influence on Inhibitory Control in Adolescence (Ring Reward)

OpenNeuro Accession Number: ds000121Files: 351Size: 3.49GB

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Immaturities in Reward Processing and Its Influence on Inhibitory Control in Adolescence (Ring Reward)
Immaturities in Reward Processing and Its Influence on Inhibitory Control in Adolescence (Ring Reward)
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  •   dataset_description.json
  •   participants.tsv
  •   README
  •   task-incentiveantisaccade_bold.json
  • derivatives
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README

Ring Rewards

Citation

Geier, C. F., R. Terwilliger, T. Teslovich, K. Velanova, and B. Luna. “Immaturities in Reward Processing and Its Influence on Inhibitory Control in Adolescence.” Cerebral Cortex 20, no. 7 (July 1, 2010): 1613–29. doi:10.1093/cercor/bhp225.

Behavdata (located in each subjects "beh" directories)

example:

1       182     1       400     7.5     reward108       108     5
2       185     -1      -1      19.5    reward532       532     11
3       181     1       400     25.5    reward007       7       13
4       161     1       467     31.5    neutral007      7       15

columns:

  1. trial num
  2. XDAT (catch, cue type and dot position)
  3. score (-1:drop, 0: incorrect, 1: correct, 2: error corrected)
  4. latency to first saccade in ms
  5. onset of cue (seconds)
  6. EPrime frame name (cue/condition and dot postion)
  7. dot position
  8. EPrime presentation number (includes fixations and catches)

Missing Data

subjmissing
sub007run 2
sub007run 3
sub007run 4
sub023run 4
sub023run 4
sub028run 3
sub028run 4

Task

coded in EPrime, recorded (XDATS) with ASL eye tracker

Order:   Cue (1.5s) | Red Centered Cross (1.5s) | Dot (1.5s)  |
XDATS:            start                      target          stop

Two condition types are distinguished by cue: * Neutral - circle of "#" * Reward - circle of "$"

Each with two catch trial formats: * Catch1: cue (1.5) | cross (1.5) * Catch2: cue (1.5)

Dot position is given in from left to right, 0 to 600.

Fixations are repeats of 1.5s white centered cross

Analysis

Briefly, our model consisted of 6 orthogonal regressors of interest (reward cue, neutral cue, reward preparation, neutral preparation, reward saccade response, neutral saccade response; “correct AS trials only”). We also included regressors for reward and neutral error trials (consisting of the entire trial), regressors for baseline, linear, and nonlinear trends, as well as 6 motion parameters included as “nuisance” regressors. A unique estimated impulse response function (IRF, i.e., hemodynamic response function) for each regressor of interest (reward and neutral cue, preparation, and saccade; “correct AS trials only”) was determined by a weighted linear sum of 5 sine basis functions multiplied by a data determined least squares–estimated beta weight. The estimated IRF reflects the estimated BOLD response to a type of stimulus (e.g., the reward cue) after controlling for variations in the BOLD signal due to other regressors. We specified the duration of the estimated response from stimulus onset (time = 0) to 18-s poststimulus onset (13 TR), a sufficient duration for the estimated BOLD response to return to baseline, for each separate epoch of the trial. We made no assumptions about its specific shape beyond using zero as the start point. Several goodness-of-fit statistics were calculated including partial F-statistics for each regressor and t-scores comparing each of the 5 estimated beta weights with zero.

Online info

event timing

v1v2v3v4
onseteventonseteventonseteventonsetevent
0neutral4260neutral5327.5reward1084.5neutral108
6reward0076reward00716.5neutralCatch213.5reward532
12neutralCatch116.5reward21419.5reward53231.5reward007
16.5neutral63322.5rewardCatch225.5reward00737.5neutral532
25.5neutralCatch225.5neutral63331.5neutral00743.5reward532
28.5reward63331.5neutral21442neutralCatch149.5reward007
42rewardCatch243.5neutral00746.5neutral42655.5rewardCatch2
45reward42649.5neutralCatch152.5reward63363neutralCatch1
51neutral10854rewardCatch266rewardCatch167.5neutral426
61.5reward63357neutralCatch170.5neutral53273.5neutral633
72neutral63366neutral10876.5neutral21479.5rewardCatch1
78neutral00772reward53288.5neutral42684neutralCatch2
84reward53278rewardCatch197.5reward42690neutral532
90reward00790reward426103.5reward21496reward108
96reward53296neutral007109.5reward214106.5reward426
114neutral214105reward633118.5reward108112.5reward007
123reward108114neutral108124.5neutralCatch2118.5neutralCatch1
129neutral007120neutral426127.5rewardCatch1123neutral108
135neutralCatch2126neutralCatch2132neutral007136.5neutral007
138neutral426141neutral532138neutralCatch2142.5neutralCatch2
151.5neutral214147reward633141neutral532148.5reward633
157.5rewardCatch2153reward007150neutral108154.5rewardCatch2
160.5rewardCatch1159neutral007156rewardCatch1157.5neutral633
169.5reward426165rewardCatch1174neutralCatch1163.5reward214
175.5neutral633172.5neutral633178.5neutral633175.5reward426
181.5reward108181.5neutral532184.5reward532186neutral214
187.5neutralCatch2187.5reward214190.5reward426205.5neutralCatch1
193.5neutral532193.5neutral426196.5reward633210rewardCatch2
199.5neutral007199.5neutralCatch2202.5neutral633213neutral007
205.5neutral532202.5neutral214211.5reward633219neutral633
211.5reward007208.5reward426220.5rewardCatch2225neutralCatch2
222reward633214.5rewardCatch1223.5rewardCatch2228reward633
228reward214222reward108231neutral214234rewardCatch1
234rewardCatch1241.5neutral633250.5reward007241.5reward633
238.5neutralCatch1252reward108256.5neutral007247.5neutral214
249reward214258neutralCatch1262.5reward007256.5neutral007
259.5neutral108262.5rewardCatch2268.5neutralCatch1265.5reward214
276rewardCatch2265.5reward633273neutral108271.5reward108
279neutralCatch1271.5reward007279neutral633277.5rewardCatch1
283.5rewardCatch1277.5neutralCatch2285rewardCatch2282neutral426

mriqc

mriqc was run on the dataset during preparation by OpenfMRI, and the outputs are located in the derivatives/ directory. (https://github.com/poldracklab/mriqc)

Defacing

Defacing was performed to preserve subject anonymity. The pydeface software was used to perform the defacing (https://github.com/poldracklab/pydeface)

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