[mcflirt related behavour] FMRIPREP: Strange motion estimations in confounds_timeseries.tsv (lots of zeros)

1

Hi, thank you for the fantastic work with FMRIPREP.
I just noticed something wrong in the output of FMRIPREP for a subject in a particular study: the motion parameters estimated by mcflirt are very strange, with a lot of zeros.

Here is the content of the .par file calculated by FMRIPREP v 20.2.1, launched through a singularity image on a cluster.

0  -0  0  0  0  0  
0  -0  0  0  0  0.00447239  
0  -0  0  0  0  0  
0  -0  0  0  0  0.0191925  
0  -0  0  0  0  0.0628555  
0  -0  0  0  0  0.017209  
0  -0  0  0  0  0.0281613  
0  -0  0  0  0  0.048671  
0  -0  0  0  0  0.033009  
0  -0  0  0  0  0.0355963  
0  -0  0  0  0  0.0576366  
0  -0  0  0  0  0.0620219  
0  -0  0  0  0  0.0925175  
0  -0  0  0  0  0.126866  
0  -0  0  0  0  0.0921991  
0  -0  0  0  0  0.109801  
-0.000191364  -0  0  0  0.00811606  0.122219  
0  -0  0  0  0  0.0582478  
0  -0  0  0  0  0.0827297  
0  -0  0  0  0  0.0980716  
0  -0  0  0  0  0.0827297  
0  -0  0  0  0  0.0944528  
0  -0  0  0  0  0.117998  
0  -0  0  0  0  0.0808601  
0  -0  0  0  0  0.102976  
0  -0  0  0  0  0.131909  
0  -0  0  0  0  0.092351  
0  -0  0  0  0  0.106535  
-0.000427752  -0  0  0  0.0143762  0.136727  
0  -0  0  0  0  0.0961919  
0  -0  0  0  0  0.102627  
0  -0  0  0  0  0.123394  
0  -0  0  0  0  0.107113  
0  -0  0  0  0  0.123394  
-0.00054936  -0  0  0  0.0193024  0.152163  
0  -0  0  0  0  0.106864  
0  -0  0  0  0  0.11994  
-0.000448399  -0  0  0  0.0154083  0.163825  
-0.000783789  -0  0  0  0.0288912  0.13257  
0  -0  0  0  0  0.113558  
0  -0  0  0  0  0.132912  
0  -0  0  0  0  0.156764  
-0.000372479  -0  0  0  0.0115785  0.138275  
0  -0  0  0  0  0.116287  
-0.000166065  -0  0  0  0.00540616  0.116486  
-0.000740251  -0  0  0  0.0235028  0.102367  
0  -0  0  0  0  0.114037  
0  -0  0  0  0  0.140291  
-0.00175279  -0  0  -6.52702e-09  0.0493044  0.1595  
-0.000197153  -0  0  0  5.22406e-05  0.12366  
0  -0  0  0  0  0.137041  
0  -0  0  0  0  0.133787  
0  -0  0  0  0  0.144343  
-0.000735956  -0  0  0  0.0217732  0.166572  
0  -0  0  0  0  0.114621  
0  -0  0  0  0  0.112106  
-0.000341602  -0  0  0  0.0120504  0.122635  
0  -0  0  0  0  0.108722  
-0.00021158  -0  0  0  0.00551495  0.121957  
-0.000818749  -0  0  0  0.0200347  0.149834  
-0.0014835  -0  0  0  0.0375855  0.147894  
-0.000943528  -0  0  -8.07734e-09  0.0176555  0.135368  
-0.000982362  -0  0  -1.62427e-08  0.0233289  0.140182  
-0.00133926  -0  0  1.75819e-08  0.0391506  0.147371  
-0.000887686  -0  0  0  0.0211863  0.122382  
-0.000887686  -0  0  0  0.0233287  0.136397  
-0.000887686  -0  0  0  0.029038  0.137936  
-0.00150882  -0  0  0  0.04782  0.197416  
-0.00177496  -0  0  0  0.0482741  0.160714  
-0.000887686  -0  0  0  0.0216596  0.0673434  
0.000291784  -0  0  0  -0.00859752  -0.0168126  
-0.000324092  -0  0  1.12491e-08  0.00980284  0.00975199  
-0.000400868  -0  0  1.90711e-08  0.0124655  0.0135178  
-0.000759071  -0  0  0  0.0289224  0.025009  
-0.000759071  -0  0  0  0.0189224  0.0222121  
-0.000759071  -0  0  0  0.0213984  0.0263032  
-0.000759071  -0  0  0  0.0262449  0.0279988  
-0.000870427  -0  0  -4.54908e-09  0.028963  0.0430893  
-0.000759071  -0  0  0  0.0289224  0.0376505  
-0.000759071  -0  0  0  0.0189224  0.0285597  
-0.000759071  -0  0  0  0.0219519  0.0307692  
-0.000759071  -0  0  0  0.0254049  0.0370649  
-0.000904236  -0  0  0  0.0289733  0.0431343  
-0.000759071  -0  0  0  0.0189224  0.0379633  
-0.000759071  -0  0  0  0.0264671  0.0377713  
-0.000810249  -0  0  7.04186e-09  0.0263422  0.0366939  
-0.000873452  -0  0  -1.30329e-08  0.0289636  0.0521208  
-0.000759071  -0  0  0  0.024868  0.0404205  
-0.000836378  -0  0  -4.70996e-09  0.0289508  0.0567661  
-0.000759071  -0  0  0  0.0225378  0.0429604  
-0.000759071  -0  0  0  0.0234125  0.0429604  
-0.000889932  -0  0  8.98288e-09  0.0289692  0.0572815  
-0.000759071  -0  0  0  0.0228801  0.0429604  
-0.000759071  -0  0  0  0.023136  0.0429604  
-0.000945386  -0  0  0  0.0330182  0.0575953  
-0.0008693  -0  0  4.34811e-09  0.028962  0.0487721  
-0.000834395  -0  0  -9.05915e-09  0.0225869  0.0494496  
-0.00175382  -0  0  0  0.054601  0.100815  
-0.00114931  -0  0  -1.29038e-08  0.0290579  0.0600871  
-0.000759071  -0  0  0  0.0139045  0.0429604  
-0.00148559  -0  0  0  0.0408563  0.0779821  
-0.00111519  -0  0  -2.35861e-08  0.0290338  0.0615891  
-0.00102348  -0  0  0  0.0290035  0.0614001  
-0.00129422  -0  0  0  0.0390864  0.0755865  
-0.00109511  -0  0  -1.25358e-08  0.0290242  0.062465  
-0.00126711  -0  0  0  0.0318418  0.0697992  
-0.00211773  -0  0  0  0.0614238  0.111965  
-0.00242898  -0  0  2.30113e-08  0.0716032  0.117338  
-0.00219256  -0  0  1.09167e-08  0.0372083  0.088874  
-0.00191326  -0  0  5.30009e-09  0.0260243  0.0804232  
-0.00148432  -0  0  2.06295e-09  0.0226611  0.0785029  
-0.00194333  -0  0  9.01089e-09  0.0507733  0.093221  
-0.00167733  -0  0  -5.68775e-09  0.0321274  0.0753964  
-0.00156042  -0  0  1.06626e-08  0.0349065  0.0786504  
-0.00196579  -0  0  1.72504e-08  0.0528575  0.0938841  
-0.00171699  -0  0  6.4226e-09  0.0370894  0.0742428  
-0.00237496  -0  0  -2.65887e-08  0.0501817  0.126929  
-0.00220973  -0  0  -1.17329e-08  0.0312852  0.104245  
-0.00193187  -0  0.000123697  0.00361469  0.00542043  0.0811097  
-0.00212005  6.17674e-23  0.000142426  0.00473967  0.0335693  0.0887442  
-0.00212005  -0  7.98287e-05  9.31772e-05  0.0407738  0.0887442  
-0.00188865  1.53118e-23  0.000157377  0.0036788  0.014725  0.0884712  
-0.00212005  -0  0.000137038  0.00492261  0.0359246  0.10324  
-0.00249948  5.69776e-23  0.000131381  0.00590571  0.0459011  0.11671  
-0.00197571  -1.05077e-22  0.00024599  0.0046611  0.0161378  0.0922132  
-0.00212005  -0  0.000171642  0.00377332  0.0369635  0.104673  
-0.00298601  -2.73334e-23  0.000212627  0.00841265  0.0737839  0.165324  
-0.00233798  -0  0  -1.25866e-08  0.0551735  0.0856748  
-0.00162096  -0  0.000347363  0.00245417  0.0199704  0.0550364  
-0.00159322  -5.29396e-23  0.000180365  0.00255419  0.00875823  0.0618241  
-0.00217531  -0  0.00040153  0.00662344  0.035916  0.0953895  
-0.00210682  3.01271e-22  0.000591482  0.00684867  0.0310336  0.0891703  
-0.00178428  -0  0.000362458  0.00449139  0.0182784  0.0784714  
-0.00174397  -0  0.000309492  0.000366133  0.0258838  0.0795462  
-0.00236209  -0  0.000280881  0.000324503  0.0414708  0.119253  
-0.00216048  -3.9307e-23  0.000397645  0.000457944  0.0299363  0.0993626  
-0.00312048  -0  0.000420218  0.00319505  0.0382442  0.165198  
-0.00226226  -0  0.000467106  0.00566305  0.0517368  0.0936272  
-0.00178763  1.23577e-22  0.0005699  0.000599289  0.000462682  0.0803111  
-0.00187377  -0  0.000308173  0.000337163  0.0242024  0.0908283  
-0.00249684  -0  0.000348059  0.000385004  0.0463477  0.121349  
-0.0021104  -0  0.000371728  0.0026933  0.0219081  0.0932437  
-0.0021042  -0  0.000362401  0.000414193  0.0217697  0.103891  
-0.00252372  3.17808e-24  0.000495609  0.00432836  0.0327322  0.126481  
-0.0024841  -0  0.000606701  0.000690245  0.0283613  0.119402  
-0.00306788  2.76162e-22  0.000636786  0.000698688  0.0193677  0.148448  
-0.00221182  3.63253e-22  0.000662943  0.00655948  0.0437691  0.110735  
-0.00174729  -0  0.000613643  0.000632482  0.0113996  0.0686211  
-0.00168815  -0  0.000381354  0.000413208  0.0166618  0.064588  
-0.00209103  -2.11758e-22  0.000461875  0.000501978  0.042304  0.0866895  
-0.00186312  -0  0.000395001  0.000435191  0.0353774  0.06908  
-0.00177408  -0  0.000408415  0.000441992  0.0219358  0.066949  
-0.00208745  2.11758e-22  0.00065471  0.000699902  0.0303554  0.0866815  
-0.00239658  6.25865e-05  0.000755359  0.0008392  0.0374952  0.0941201  
-0.00195672  0.000145498  0.000917341  -0.00126485  0.0105429  0.0775215  
-0.00248624  -0  0.000743944  0.00685343  0.0428417  0.116399  
-0.002457  -7.92412e-23  0.000793845  0.000865065  0.0517397  0.087089  
-0.00186312  0.000114028  0.00104219  0.00368428  0.00640115  0.0675583  
-0.00165739  0.00011868  0.000730219  0.000829512  0.0123651  0.0636668  
-0.00186312  6.70427e-05  0.000609475  0.000693931  0.0327274  0.072101  
-0.00210206  -0  0.00070875  0.000793937  0.0447246  0.0831389  
-0.00186312  -0  0.00058588  0.000654468  0.0216723  0.0675932  
-0.00167662  5.60938e-05  0.000505083  0.00059466  0.0251936  0.067527  
-0.00193187  -0  0.000522683  0.000596229  0.0408612  0.0820155  
-0.00209944  -1.91224e-22  0.000440932  0.000508858  0.0470385  0.0767145  
-0.00164194  -0  0.000417095  0.000476385  0.0242349  0.0582118  
-0.00225924  1.92648e-23  0.000532703  0.000602445  0.0519083  0.100454  
-0.00226873  2.11758e-22  0.000638695  0.000731284  0.0518952  0.0769062  
-0.00160901  5.42082e-05  0.000733335  0.000842426  0.0150255  0.058705  
-0.00166049  5.22959e-05  0.000543249  0.000632545  0.0284925  0.0623157  
-0.00207338  2.11758e-22  0.000469575  0.00052634  0.0480234  0.0866774  
-0.00202316  5.69556e-05  0.000548451  0.000644874  0.0436308  0.0700594  
-0.00157162  7.46676e-05  0.000633614  0.000746881  0.0248613  0.0624953  
-0.00170685  6.78136e-05  0.000477594  0.00297749  0.0315956  0.065906  
-0.00215581  -8.37404e-23  0.000437233  0.000499892  0.0529549  0.0917877  
-0.0020249  5.06618e-05  0.000433098  0.000522413  0.0445136  0.0766395  
-0.00165456  -0  0.000450314  0.000512237  0.0266043  0.0650422  
-0.00248533  -2.09753e-22  0.000492904  0.000553564  0.0584653  0.11099  
-0.00207475  -0  0.000749901  0.000833652  0.0324502  0.0738095  
-0.0017088  0.000103594  0.000687524  0.00327385  0.0159978  0.0669556  
-0.00199691  -2.73574e-22  0.00063082  0.000679614  0.0372074  0.0865857  
-0.00203168  -2.11758e-22  0.000560156  0.000604453  0.0402411  0.0866235  
-0.00186312  -0  0.000533651  0.000576162  0.0278371  0.0689933  
-0.00186312  -0  0.000557852  0.000614765  0.0300963  0.070577  
-0.00201776  -2.11758e-22  0.000620933  0.00628183  0.045193  0.0866135  
-0.00242801  4.58215e-22  0.000568292  0.00775446  0.0551663  0.0994262  
-0.00197362  2.66245e-22  0.00061392  0.00646659  0.0306403  0.0745408  
-0.00277827  -6.36871e-05  0.000627618  0.0077002  0.0574682  0.127772  
-0.00169988  -7.12051e-05  0.000698946  0.00809897  0.0300904  0.0534305  
-0.00129524  -0.000171765  0.000654771  0.000623494  0.0197491  0.0204808  
-0.00163804  -7.70604e-05  0.000373697  0.000388176  0.0395724  0.0314316  
-0.00194343  -0  0.000274305  0.000310749  0.0496954  0.062844  
-0.00171057  -5.35587e-05  0.000261557  0.000279304  0.0391271  0.0583279  
-0.00113927  -0  0.000255988  0.000294222  0.0197614  0.0416231  
-0.00150966  -8.2542e-24  0.000206075  0.000237743  0.0369569  0.0513495  
-0.00143342  -0  0.000274072  0.000316559  0.0294314  0.041207  
-0.00155072  -0  0.000285388  0.000321945  0.0303735  0.0611338  
-0.00143342  -0.000115469  0.000610714  0.000633059  0.0314462  0.0326865  
-0.000912335  -0.000142938  0.00071257  0.000717736  0.00417532  0.0243102  
-0.00143342  -0.000160519  0.000519987  0.000513618  0.0368321  0.0456359  
-0.00143342  -0.000142118  0.000447602  0.000437399  0.0368521  0.0393196  
-0.00131745  -0.000104876  0.000465273  0.000465165  0.0296669  0.0349237  
-0.00133971  -0.000197709  0.000490176  0.000466555  0.0317223  0.0390774  
-0.00171346  -0.000331491  0.000680749  0.000619987  0.0477324  0.0636043  
-0.00150874  -0.000261111  0.000653127  0.000602309  0.0359634  0.0384044  
-0.00126512  -0.000161561  0.000612773  0.000597215  0.0252718  0.0310308  
-0.00152789  -0.000281506  0.00064509  0.000603129  0.0402621  0.0480516  
-0.00185867  -0.000225556  0.00119649  0.0153091  0.0445193  0.0484513  
-0.00106359  -0.000172752  0.00126418  0.0119853  0.00219164  0.0301908  
-0.0013656  -0.000146833  0.000806624  0.00401937  0.0290724  0.0379457  
-0.00181947  -0.000221938  0.00112133  0.0160831  0.0473352  0.0584088  
-0.00156068  -0.00019208  0.00100844  0.0133027  0.0324054  0.0379125  
-0.00116279  -0.00019219  0.00101058  0.00560925  0.0218292  0.0289786  
-0.00166806  -0.000191444  0.000948866  0.0130616  0.0429559  0.0523546  
-0.00164268  -0.000222012  0.000927831  0.00697104  0.040527  0.0456564  
-0.00117578  -0.000194706  0.000944782  0.00667368  0.0228279  0.0310701  
-0.00223423  -0.000280746  0.00116241  0.0166699  0.056788  0.0884536  
-0.0015509  -0.000299798  0.00117586  0.0112813  0.0290877  0.0428597  
-0.00143342  -0.000181405  0.000973034  0.00811763  0.0184894  0.0339206  
-0.00169622  -0.000205993  0.00093777  0.00728008  0.0379992  0.0502691  
-0.00179398  -0.000240611  0.00116361  0.0157588  0.045623  0.0583643  
-0.00137084  -0.000196183  0.000933915  0.00637363  0.0244393  0.032068  
-0.00158095  -0.000218578  0.00087677  0.00587136  0.034476  0.0430863  
-0.00205502  -0.000353739  0.0010466  0.00911535  0.0517591  0.0689762  
-0.00157841  -0.000259866  0.00111174  0.00829977  0.0208555  0.0381283  
-0.00116186  -0.000199239  0.00141987  0.0113942  0.00361141  0.0212998  
-0.00209374  0.000169101  0.00120475  0.0280933  0.0523823  0.0463356  
-0.00164266  0.000119066  0.00115999  0.0252163  0.03206  0.0378954  
-0.00143342  0.000136013  0.000932957  0.0213893  0.00816111  0.0342551  
-0.00126354  0.00014587  0.000839561  0.0213027  0.0233979  0.0347683  
-0.0021194  0.000108994  0.00101263  0.02148  0.0470094  0.0789419  
-0.00151508  0.00011964  0.00101621  0.0214867  0.024678  0.0481503  
-0.00131905  0.000153056  0.000982945  0.0192374  0.0226218  0.0475874  
-0.00179489  0.000100464  0.00120723  0.0194818  0.0409888  0.0759098  
-0.00171168  9.67101e-05  0.00117083  0.0189228  0.0363418  0.0674417  
-0.00129911  0.000109395  0.00137108  0.0218668  0.0128559  0.0578999  
-0.00273176  0.000109374  0.00125352  0.0217262  0.064938  0.137685  
-0.00242815  0.000135587  0.00127758  0.021786  0.0495083  0.0811584  
-0.0016586  0.000195328  0.00139912  0.021913  0.000288638  0.0624559  
-0.00175819  0.00025  0.000863056  0.0183837  0.0272719  0.0737462  
-0.00239159  0.000159318  0.000900992  0.016287  0.0569399  0.100361  
-0.00196265  0.000195293  0.00104987  0.0182611  0.0361229  0.0779092  
-0.00186691  0.000193196  0.00112487  0.0187075  0.0247812  0.0856964  
-0.00260049  0.000150374  0.00117072  0.018745  0.0550493  0.117219  
-0.00237674  0.000131402  0.0012958  0.0189894  0.0375447  0.103009  
-0.00327125  0.00018542  0.00153272  0.0220833  0.0380444  0.15408  
-0.00223426  9.17359e-05  0.00137866  0.0218754  0.0491737  0.0908501  
-0.00175819  0.00025  0.00142085  0.0219429  0.0083872  0.0807658  
-0.00205244  0.000146008  0.00127118  0.0217526  0.0355416  0.100694  
-0.00248528  0.000124376  0.00143924  0.0219389  0.0489446  0.120763  
-0.00213515  0.000195886  0.00123729  0.0190266  0.0256332  0.0992352  
-0.00213173  0.000183864  0.00125375  0.0171355  0.0318543  0.102783  
-0.00226373  0.000146898  0.00129424  0.0217991  0.0314442  0.110121  
-0.00257968  0.000118504  0.00145987  0.0268971  0.0356252  0.127755  
-0.00366362  7.35514e-22  0.00169598  0.0221634  0.0558854  0.175889  
-0.00366811  -0  0.0015984  0.0220725  0.0455618  0.176893  
-0.00346558  -0.000119384  0.00180866  0.0222745  0.0413405  0.150391  
-0.00296728  -0  0.00166202  0.02213  0.0130133  0.130379  
-0.00296728  0.000129394  0.00142092  0.0219164  0.0366643  0.140874  
-0.00363527  0.000113813  0.00148948  0.0219986  0.0601626  0.170181  
-0.00313594  0.000157504  0.00145543  0.0219839  0.0305568  0.137048  
-0.00302718  0.000148155  0.00144325  0.0219876  0.0316415  0.140948  
-0.00362511  -0.000118768  0.00171464  0.0222032  0.0504949  0.1729  
-0.00376403  -0.000179615  0.0016631  0.0221609  0.0477203  0.167099  
-0.003204  -9.96579e-23  0.00172333  0.0223026  0.021122  0.144882  
-0.00365895  -0  0.0015776  0.0307799  0.0632946  0.179693  
-0.00291051  0.000119697  0.00135169  0.0218903  0.0550762  0.0998117  
-0.0023079  0.000153997  0.00133786  0.0195498  0.0245215  0.0813361  
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-0.00240717  0.000195514  0.000583485  0.0144769  0.0563147  0.0553158  
-0.00240802  0.000153529  0.00110071  0.0259687  0.0413735  0.0770382  
-0.00199739  0.000153894  0.000828533  0.0212853  0.0370673  0.0804207  
-0.00262268  0.000116235  0.000987979  0.021459  0.0631536  0.105727  
-0.00208373  0.000106894  0.00113925  0.0242726  0.0400342  0.0736955  
-0.00199739  0.000104847  0.0010486  0.0236373  0.0383607  0.0804053  
-0.00233876  8.26143e-05  0.000896232  0.0213366  0.0511565  0.100778  
-0.00252319  8.46646e-05  0.00109227  0.027596  0.0570275  0.116654  
-0.00276766  -0  0.00121533  0.0271553  0.0630792  0.126231  
-0.00216851  -5.58836e-22  0.00128859  0.0267309  0.0321997  0.0940536  
-0.00362479  7.07666e-05  0.00122089  0.0276327  0.0665066  0.183473  
-0.00287524  5.26087e-22  0.00121308  0.0296143  0.0610816  0.101681  
-0.00209227  -0.000197628  0.00195795  0.0418026  0.00840807  0.0843274  
0.000463747  -0.000221414  0.00194734  0.0421785  -0.00619677  -0.0208554  
-0.000710244  -0  0.000956709  0.0274488  0.0333565  0.00221396  
-0.0015191  -2.25565e-23  0.000872539  0.0212752  0.0469283  0.0367568  
-0.00154431  -0  0.00104622  0.0215044  0.0416437  0.0322072  
-0.00119948  -0  0.00113163  0.0215937  0.0173069  0.0364073  
-0.00208554  -0  0.00119505  0.0216594  0.0463426  0.0740364  
-0.00239714  1.64439e-22  0.00135573  0.026337  0.0514465  0.0848406  
-0.00234587  7.43954e-05  0.00115743  0.0239413  0.0431786  0.105237  
-0.00161199  -2.00975e-22  0.000926832  0.0213423  0.0462628  0.0293084  
-0.000942668  3.59854e-24  0.00100975  0.0159598  0.00894639  0.0152767  
-0.00129165  -2.01949e-25  0.000977494  0.0213833  0.0228193  0.0296388  
-0.00140694  -0  0.00107577  0.0215013  0.0325326  0.0391341  
-0.00207117  -0  0.001162  0.0216078  0.053166  0.0625867  
-0.00153022  2.11758e-22  0.0012126  0.0216715  0.0344705  0.036775  
-0.000967447  -0  0.00151247  0.0289384  0.00433761  0.0329781  
-0.00165981  -0.000350137  0.00149965  0.0218661  0.0367221  0.046624  
-0.0017352  -0.000371495  0.00164874  0.0220424  0.0446216  0.0369395  
-0.00208988  -0.000543763  0.00187331  0.0269221  0.0344808  0.0498169  
-0.00182006  -0.000546808  0.00192121  0.0259372  0.0266497  0.0487964  
-0.00139096  -0.000528159  0.00187465  0.0255633  0.0124228  0.0404817  
-0.00233547  -0.000582968  0.00198944  0.0255432  0.0506834  0.0704333  
-0.00187502  -0.000451039  0.00175854  0.0286153  0.0344231  0.0429876  
-0.00161874  -0.000522911  0.00188479  0.0262542  0.017205  0.0454878  
-0.0016521  -0.000430757  0.00172985  0.0271675  0.0257549  0.0526435  
-0.00167458  -0.000446677  0.00171306  0.0273833  0.0298984  0.056445  
-0.00211068  -0.000476486  0.00178562  0.0247946  0.0415007  0.0692821  
-0.00279761  -0.000559359  0.00188061  0.0269115  0.0600304  0.0941836  
-0.0031  -0.00048071  0.00188094  0.026099  0.0535969  0.104977  
-0.00176099  -0.000408087  0.00200671  0.0223341  0.0110995  0.0464628  
-0.00176099  -0.000233766  0.001497  0.0218574  0.0208157  0.0465139  
-0.00237549  -0.000243112  0.00156484  0.0194481  0.0530849  0.0647906  
-0.00198178  5.92414e-05  0.00135153  0.0197372  0.037191  0.0464787  
-0.00161935  7.32859e-05  0.00138863  0.0162464  0.0224722  0.0464339  
-0.00185896  6.12044e-05  0.0012098  0.0160198  0.0370921  0.0566983  
-0.00204538  -0  0.0012537  0.0163136  0.0484635  0.0628806  
-0.0018571  6.36597e-05  0.00109359  0.0145329  0.0422335  0.0473723  
-0.00200621  8.482e-05  0.000982105  0.0129568  0.042111  0.0671385  
-0.00186356  2.53444e-22  0.0011688  0.0187736  0.0486691  0.0518841  
-0.00129197  7.57477e-05  0.0010325  0.0143065  0.0230243  0.0411625  
-0.0018401  7.43959e-05  0.0009736  0.0150833  0.0427482  0.06553  
-0.00247135  -2.62756e-23  0.000915975  0.0150315  0.0669685  0.0977473  
-0.00182523  0.000125008  0.00092434  0.0157031  0.0335457  0.0568127  
-0.00184759  0.000110416  0.00089987  0.0164312  0.0392089  0.0653806  
-0.0021671  7.56829e-05  0.000883086  0.0158646  0.0493819  0.0779392  
-0.00249379  5.14472e-05  0.00110352  0.0188367  0.0648584  0.0951101  
-0.00187459  9.57581e-05  0.00101022  0.0169934  0.0326913  0.0675443  
-0.00205888  4.23516e-22  0.00124411  0.0217129  0.0408117  0.0989783  
-0.00207084  0.000102522  0.00109926  0.0216083  0.0520405  0.0693207  
-0.00143472  7.91724e-05  0.000887651  0.0150339  0.024974  0.0367841  
-0.00161857  -0  0.00104158  0.0167472  0.0369123  0.0470857  
-0.00247799  4.43668e-22  0.00102303  0.0173726  0.0622205  0.0793144  
-0.00197524  5.25993e-05  0.00109121  0.0171757  0.0405723  0.0511712  
-0.00186972  5.78039e-05  0.00110775  0.0145657  0.0309589  0.057147  
-0.00182697  -2.58596e-22  0.00119256  0.0153578  0.0396684  0.0632505  
-0.00254373  -5.06971e-22  0.001169  0.0177274  0.0591367  0.0874726  
-0.00187474  -1.78803e-22  0.00112854  0.011721  0.0286008  0.0545854  
-0.00238412  -0  0.00128271  0.0197267  0.050085  0.0922547  
-0.00217426  5.76147e-22  0.00132851  0.0217857  0.0440571  0.055256  
-0.00161509  -0  0.000889158  0.00918389  0.0269351  0.0452825  
-0.00176099  2.11758e-22  0.000866455  0.0107476  0.0384067  0.0537285  
-0.00238729  -0  0.000882486  0.0128283  0.0609529  0.0798327  
-0.00224844  4.23516e-22  0.00106085  0.0175682  0.0495521  0.0629842  
-0.00189801  -2.29097e-22  0.000896602  0.0129652  0.0349104  0.0566572  
-0.00188442  7.54825e-23  0.00115061  0.016889  0.0392295  0.0633147  
-0.00237554  -0  0.000844919  0.00996129  0.0602675  0.054833  
-0.00142072  7.06438e-05  0.000809906  0.00932453  0.0270498  0.0332227  
-0.000526477  -0.000142589  0.000646753  0.00570889  0.0196806  0.0164719

And in the confounds.timeseries.tsv output, I get translational and rotational columns (and their derivatives) with almost only zeros, which is not normal. With SPM on the same bold images with reference to the first scan of the run, I get quite different motion parameters estimates and no zeros.

FMRIPREP report no error whatsoever and If one looks at the HTML report, all look clean, the FD curve looks as expected and the carpet plot is quite clean for that subject who didn’t move. There is obviously something wrong. It doesn’t seem to affect the preprocessed images but it is an issue when you want to use the motion parameters as a nuisance regressors, adding wrong parameters and especially a column with a lot of zeros is a problem for the GLM.

Here is the summary of the command launch by fmriprep:

* fMRIPrep version: 20.2.1
* fMRIPrep command: `/usr/local/miniconda/bin/fmriprep --fs-license-file /work/freesurfer/license.txt /work/PhantomPain /work/PhantomPain/derivatives participant --participant-label testpilote2 testpilote3 -w /work/temp_data_PhantomPain --mem-mb 50000 --omp-nthreads 10 --nthreads 12 --ignore slicetiming --fd-spike-threshold 0.5 --output-spaces MNI152NLin2009cAsym MNI152NLin6Asym --return-all-components --cifti-output 91k`
* Date preprocessed: 2021-01-22 20:05:24 +0000

I also tried with fmriprep v 20.2.0 and got the same results with wrong motion parameters estimates from mcflirt and in the confounds_timeseries.tsv output.

Here is a screenshot of the HTML report of the same run for which the .par file gets many zeros:

Capture d’écran 2021-01-23 Ă  00.03.02
Capture d’écran 2021-01-23 Ă  00.03.021380×570 248 KB

Has anyone already encounter this behavior of mcflirt and can it be trusted? Thank you!

2 Likes
2

Hi again,

I went on trying to understand what is going on and I believe that there is a weird interaction between the SBref volume taken as the reference for the realignment for the bold run and the cost function used in mcflirt.
For information, the TR we used is 1.1s and the contrast in the SBref image is much stronger that for the volumes in the bold run. This is expected and that is why the SBref volume is used to realign the bold space to the anatomical space usually with some boundary based registration.
But for mcflirt, there may be an issue and the difference of contrast between the SBref volume and a bold volume is not the same as the difference of contrast between two bold volumes from the same run.
Here is the SBref volume:

Capture d’écran 2021-01-24 à 23 48 03
Capture d’écran 2021-01-24 à 23 48 031434×371 92.6 KB

and the first volume of the corresponding run:
Capture d’écran 2021-01-24 à 23 48 52
Capture d’écran 2021-01-24 à 23 48 521437×370 68.8 KB

To illustrate my point, here is an observation: the plot of the second column of the .par file, output by mcflirt, with three different execution related to the same bold run:

  • the reference is the 1st volume of the run (blue curve)
  • the reference is SBref and the cost function is the default, i.e. normcorr (green curve)
  • the reference is SBref and the cost function is mutual info (red curve)

=> there is something fishy with mcflirt, SBref and the cost function normcorr ! I never noticed that before even though we used fmriprep for many projects and subjects, maybe it is especially problematic with subjects with extremely low movements? I will also ask the FSL forum about this point as it is an issue not specific to fmriprep but rather to FSL-mcflirt.
Also since this approach SBref - mcflirt is also used by the HCP, I will dig in that discussion to see if that was observed already.

par_results_mcflirt
par_results_mcflirt1332×486 127 KB

2 Likes
3

To summarize: the mcflirt behavior (giving a lot of 0 in the motion parameters) seems to be coming from three factors:

  • subject with actually very low motions: looping through the movie with FSLeyes confirms that: no visible motion on the naked eye, just the pulsatile fluctuation of blood flow visible on the neck.
  • cost function (normcorr, default from mcflirt) which seems to be putting a bias toward 0 for very small motions (this is a hint that I got after discussion with the HCP folks about this issue).
  • interaction with the SBref image with a different contrast (2mm iso acquisition, MB=5 and TR=1.12s) as a target for the realignment.

Anyhow, even though the motion corrected runs seem fine, I think it is more problematic to have a lot go zeros, than to have none. In some runs, one or two columns of the motion parameters are only zeros, and therefore so are the derivative and squared ones, which is problematic if one uses those columns full of zeros in the GLM as nuisance regressors. This affects also the Framewise displacement estimation, one can see the difference between the FD curve calculate by MRIQC (with AFNI 3dvolreg) and the one calculated with with FMRIPREP with FSL- mcflirt:

FMRIPREP (mcflirt)

Capture d’écran 2021-01-25 à 19 19 07
Capture d’écran 2021-01-25 à 19 19 071345×542 248 KB

MRIQC(3dvolreg)(same run as above)

Capture d’écran 2021-01-25 à 19 21 55
Capture d’écran 2021-01-25 à 19 21 551347×583 283 KB

MRIQC (mcflirt) (same run)

Capture d’écran 2021-01-25 à 23 35 15
Capture d’écran 2021-01-25 à 23 35 151329×434 205 KB

FD_mean calculated by FMRIPREP: 0.035mm
FD_mean calculated by MRIQC (AFNI 3dvolreg): 0.112 mm
FD_mean calculated by MRIQC (FSL mcflirt): 0.104 mm

bids_name fd_mean_mriqc_3dvolreg fd_mean_mriqc_mcflirt fd_mean_fmriprep
sub-testpilote2_ses-01_task-VibStim_run-01_bold 0,194680121 0,178982256 0,136
sub-testpilote2_ses-01_task-VibStim_run-02_bold 0,186500352 0,175467697 0,136
sub-testpilote2_ses-01_task-VibStim_run-03_bold 0,196197217 0,180603697 0,146
sub-testpilote2_ses-01_task-VibStim_run-04_bold 0,18933964 0,174164599 0,143
sub-testpilote3_ses-01_task-VibStim_run-01_bold 0,116184151 0,100634748 0,06
sub-testpilote3_ses-01_task-VibStim_run-02_bold 0,103993465 0,090084196 0,064
sub-testpilote3_ses-01_task-VibStim_run-03_bold 0,097314613 0,092405695 0,038
sub-testpilote3_ses-01_task-VibStim_run-04_bold 0,105072874 0,111065972 0,013
sub-testpilote3_ses-02_task-VibStim_run-01_bold 0,112095188 0,104452559 0,035
sub-testpilote3_ses-02_task-VibStim_run-02_bold 0,112124946 0,11514835 0,025
sub-testpilote3_ses-02_task-VibStim_run-03_bold 0,115953908 0,114734764 0,021
sub-testpilote3_ses-02_task-VibStim_run-04_bold 0,114908759 0,112333997 0,015
sub-testpilote3_ses-02_task-VibStim_run-05_bold 0,114918923 0,102249177 0,018

image
image732×384 24.9 KB

2 Likes
4

Just want to say thank you for this breakdown. I ran into exactly this issue in a pipeline of mine, and was able to solve it quickly based on this (using mutual information was indeed the fix). Probably saved me a lot of time debugging! I guess my post also confirms that this is a replicable problem with MCFLIRT and SBrefs.

Cheers!

5

Interesting,
the dependence of coregistration on the cost function is important, and I do not see why mutual information is not the default (may be it is less efficient than normalize CC when both image have the same contrast 
 ?)
but for sure it need to be used when the contrast change are important

An alternative, would have been to realign to the first EPI volume, and then realign the mean EPI to SBref

May be adding the motion as extra regressor is not a good idea, if you have so few motion 
 (but I agree it is more simple to add it always, than to try to find the good thershold 
) but this is an other debate
++

1 Like
6

Here is a new case that I came across, where the difference in motion estimation is rather quite different (especially at the beginning of the run) if I compare MRIQC (using 3dvolreg), FMRIPREP v21.0.2 (with SBref used as reference) or FMRIPREP v21.0.2 (with --ignore sbref):

MRIQC:

https://user-images.githubusercontent.com/32452352/192492088-84ec0da4-a2bd-44a8-b6e5-e106f589fb34.png(image larger than 4 MB)

FMRIPREP v21.0.2 with SBref used as reference:

Capture d’écran 2022-09-27 Ă  11 33 11
Capture d’écran 2022-09-27 Ă  11 33 112824×1364 628 KB

FMRIPREP v21.0.2 with --ignore sbref:

Capture d’écran 2022-09-27 Ă  11 33 02
Capture d’écran 2022-09-27 Ă  11 33 022832×1416 644 KB

I would suggest to move toward adding an option in FMRIPREP to automatically change the cost fonction of mcflirt when SBref is used, which is a relative quick fix before moving to 3dvolreg.