This seems like the culprit. Here's the output:
timing_tool.py -multi_timing stimuli/*tent.txt -tr 2.0 -warn_tr_stats
within-TR stimulus offset statistics (stimuli/sub104_imagine_amb_norming_tent.txt) :
per run
------------------------------
offset means 0.009 0.009 0.007
offset stdevs 0.002 0.003 0.003
overall: mean = 0.008 maxoff = 0.017 stdev = 0.0028
fractional: mean = 0.004 maxoff = 0.008 stdev = 0.0014
** WARNING: small maxoff suggests (almost) TR-locked stimuli
consider: timing_tool.py -round_times (if basis = TENT)
within-TR stimulus offset statistics (stimuli/sub104_imagine_att_norming_tent.txt) :
per run
------------------------------
offset means 0.008 0.011 0.008
offset stdevs 0.003 0.003 0.005
overall: mean = 0.009 maxoff = 0.018 stdev = 0.0038
fractional: mean = 0.005 maxoff = 0.009 stdev = 0.0019
** WARNING: small maxoff suggests (almost) TR-locked stimuli
consider: timing_tool.py -round_times (if basis = TENT)
within-TR stimulus offset statistics (stimuli/sub104_imagine_rel_norming_tent.txt) :
per run
------------------------------
offset means 0.011 0.011 0.007
offset stdevs 0.005 0.004 0.003
overall: mean = 0.010 maxoff = 0.021 stdev = 0.0044
fractional: mean = 0.005 maxoff = 0.011 stdev = 0.0022
** WARNING: small maxoff suggests (almost) TR-locked stimuli
consider: timing_tool.py -round_times (if basis = TENT)
I re-ran the analysis using the TENTzero function, as suggested by Gang. The output of that analysis produced much more reasonable values and the plot looked like an HRF. Do you recommend scrapping that and re-running the analysis with TENT and the rounded times or, if I am content with fewer modeling timepoints, does the TENTzero output still make sense?
Thanks!
Heather
