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Neurocomputational mechanisms of reward-based online mood regulation in adolescents with bipolar disorder and major depressive disorder.

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  1. [1] § Materials and methods › fMRI data acquisition and preprocessing ↔ fMRI_Proc/03_funcpp02_23BPhappy.ipynb, lines 356–390 · score 0.86 · affine transform, dNwarpApply, align epi, motion corrected, AFNI, nonlinearly
  2. [2] § Materials and methods › fMRI general linear modeling ↔ fMRI_Proc/04_funcGLM1Loop_23BPhappy.ipynb, lines 12–64 · score 0.69 · dDeconvolve, parametrically modulated, GLM1, AFNI, regressors, GLMs
  3. [3] § Materials and methods › fMRI general linear modeling ↔ fMRI_Proc/04_funcGLM1_23BPhappy.ipynb, lines 35–54 · score 0.54 · dDeconvolve, AFNI, GLM1, regressors, GLMs

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The authors' code

Jupyter notebook · 537 lines · 23 KB · GPL-3.0 · 1 match

  1. # %% [markdown]
  2. # # 03_funcpp02
  4. # %% [markdown]
  5. # 功能像预处理的最核心部分。这一部分我们完成一下步骤
  6. # * Despike
  7. # * Slice-timing correction (时间层矫正)
  8. # * Distortion correction (图像畸变矫正)
  9. # * Motion correction (头动矫正)
  10. # * align anat 2 epi (结构像到功能像的配准)
  11. # * 把所有epi划到个体结构像空间
  12. # * 把所有epi的volume数据划到surface data
  13. # * 把所有epi的划到MNI标准空间
  14. # * Scale (标准化数据尺度，抓换成percent signal change)
  16. # %% [markdown]
  17. # ## Step 1. Despike
  19. # %%
  20. # ================================ despike =================================
  21. for run in runstr:
  22. ! 3dDespike -NEW -nomask -prefix pb01.{subj}.r{run}.despike pb00.{subj}.r{run}.tcat+orig
  24. ! rm pb00*
  26. # %% [markdown]
  27. # ## Step 2. Slice-timing correction
  29. # %%
  30. # ================================= tshift =================================
  31. for run in runstr:
  32. ! 3dTshift -tzero 0 -quintic -prefix pb02.{subj}.r{run}.tshift \
  33. -verbose -tpattern @{'../SliceTiming.txt'} pb01.{subj}.r{run}.despike+orig
  35. # 删掉上一步despike的data节省空间
  36. ! rm pb01*
  38. # %% [markdown]
  39. # ## Step 3. Distortion correction
  41. # %% [markdown]
  42. # 因为磁场不均匀的关系，图像会出现畸变。现在来做矫正。
  44. # %%
  45. # 首先找到
  46. for fwd,rev in zip(fwdfiles, revfiles):
  47. # create median datasets from forward and reverse time series
  48. ! 3dTstat -median -prefix rm.blip.med.fwd.r{fwd} pb02.{subj}.r{fwd}.tshift+orig
  49. ! 3dTstat -median -prefix rm.blip.med.rev.r{rev} pb02.{subj}.r{rev}.tshift+orig
  50. # automask the median datasets
  51. ! 3dAutomask -apply_prefix rm.blip.med.masked.fwd.r{fwd} rm.blip.med.fwd.r{fwd}+orig
  52. ! 3dAutomask -apply_prefix rm.blip.med.masked.rev.r{rev} rm.blip.med.rev.r{rev}+orig
  53. # compute the midpoint warp between the median datasets
  54. ! 3dQwarp -plusminus -pmNAMES Rev.r{rev} Fwd.r{fwd} \
  55. -pblur 0.05 0.05 -blur -1 -1 \
  56. -noweight -minpatch 9 \
  57. -noXdis -noZdis \
  58. -source rm.blip.med.masked.rev.r{rev}+orig \
  59. -base rm.blip.med.masked.fwd.r{fwd}+orig \
  60. -prefix blip_warp
  62. ! 3dNwarpApply -quintic -nwarp blip_warp_Fwd.r{fwd}_WARP+orig \
  63. -source rm.blip.med.masked.fwd.r{fwd}+orig \
  64. -prefix rm.blip.med.masked.fwd.post.r{fwd}
  66. ! 3dNwarpApply -quintic -nwarp blip_warp_Rev.r{rev}_WARP+orig \
  67. -source rm.blip.med.masked.rev.r{rev}+orig \
  68. -prefix rm.blip.med.masked.rev.post.r{rev}
  70. # 删掉多余文件
  71. ! rm blip_warp_Fwd.r{fwd}+orig* blip_warp_Rev.r{rev}+orig*
  73. # 修改个名字以便以后的操作
  74. ! 3dcopy blip_warp_Fwd.r{fwd}_WARP+orig blip_warp.r{fwd}_WARP+orig
  75. ! 3dcopy blip_warp_Rev.r{rev}_WARP+orig blip_warp.r{rev}_WARP+orig
  77. ! rm blip_warp_Fwd.r{fwd}+orig* blip_warp_Rev.r{rev}+orig* blip_warp_Fwd.r{fwd}_WARP+orig* blip_warp_Rev.r{rev}_WARP+orig*
  79. # for QC check，我们生成一个校正前和矫正后的平均EPI数据来作为distortion correction的效果证明
  80. ! 3dMean -prefix blip_pre_fwd.epi_mean.nii.gz rm.blip.med.masked.fwd.r*
  81. ! 3dMean -prefix blip_pre_rev.epi_mean.nii.gz rm.blip.med.masked.rev.r*
  82. ! 3dMean -prefix blip_post_fwd.epi_mean.nii.gz rm.blip.med.masked.fwd.post.r*
  83. ! 3dMean -prefix blip_post_rev.epi_mean.nii.gz rm.blip.med.masked.rev.post.r*
  85. # remove redundent file
  86. ! rm rm*
  88. # %% [markdown]
  89. # 这一步完成之后，可以手动打开
  90. # * blip_pre_fwd.epi_mean.nii.gz
  91. # * blip_pre_rev.epi_mean.nii.gz
  92. # * blip_post_fwd.epi_mean.nii.gz
  93. # * blip_post_rev.epi_mean.nii.gz
  94. #
  95. # 这四个文件也记录了对侧矫正的效果，只要有矫正效果，没有什么大问题即可。
  96. #
  97. # 然后把所有的run都做correction
  99. # %%
  100. for run in runstr:
  101. ! 3dNwarpApply -quintic -nwarp blip_warp.r{run}_WARP+orig \
  102. -source pb02.{subj}.r{run}.tshift+orig \
  103. -prefix pb03.{subj}.r{run}.blip
  105. # %% [markdown]
  106. # ## Step 4. Motion correction
  108. # %% [markdown]
  109. # 首先做motion correction。但是我们并不用其结果，主要是得到mc的线性转换矩阵
  111. # %%
  112. runstr.pop() #删去AP参照序列
  113. # ================================= volreg =================================
  114. base = 'vr_base_min_outlier+orig' #在前面生成
  115. # align each dset to base volume, then we separate whether we want to further align epi to anat
  116. for run in runstr:
  117. # register each volume to the base image
  118. ! 3dvolreg -verbose -zpad 1 -base {base} \
  119. -1Dfile dfile.r{run}.1D \
  120. -cubic \
  121. -1Dmatrix_save mat.r{run}.vr.aff12.1D \
  122. -prefix rm.epi.nomask.r{run} pb03.{subj}.r{run}.blip+orig
  124. # create an all-1 dataset to mask the extents of the warp
  125. ! 3dcalc -overwrite -a pb03.{subj}.r{run}.blip+orig -expr 1 -prefix rm.epi.all1
  127. # warp the all-1 dataset for extents masking
  128. ! 3dAllineate -base {base} \
  129. -input rm.epi.all1+orig \
  130. -1Dmatrix_apply mat.r{run}.vr.aff12.1D \
  131. -final NN -quiet \
  132. -prefix rm.epi.1.r{run}
  134. # make an extents intersection mask of this run across time domain
  135. # this makes a mask that all volumes in this run have valid numbers
  136. ! 3dTstat -min -prefix rm.epi.min.r{run} rm.epi.1.r{run}+orig
  138. # %% [markdown]
  139. # 计算更多的头动数值，以后可以用来GLM中的regressor
  141. # %%
  142. # 生成pb04.subj.r0*+volreg+orig
  143. # 生成epi_mean.nii.gz
  144. # motion打头的几个文件
  145. # 生成mc文件夹，里面放了矫正结果比较
  146. nRuns = nRuns-1
  147. # we take dmeaned motion (6), demean motion derivative (6) and their squares (12)
  148. # 合并 motion params
  149. ! cat dfile.r*.1D > dfile_rall.1D
  150. # 计算去均值的motion parameters (for use in regression)
  151. ! 1d_tool.py -infile dfile_rall.1D -set_nruns {nRuns} -demean -write motion_demean.1D
  152. # 计算一阶导数 (just to have)
  153. ! 1d_tool.py -infile dfile_rall.1D -set_nruns {nRuns} -derivative -demean -write motion_deriv.1D
  154. # calculate the square of demean and derivative of motion parameters (just to have)
  155. # for resting preproc, we usually have 24 motion regressor (6motion+6deriv+12 their square)
  156. np.savetxt('motion_demeansq.1D', np.loadtxt('motion_demean.1D')**2)
  157. np.savetxt('motion_derivsq.1D', np.loadtxt('motion_deriv.1D')**2)
  158. # create censor file motion_${subj}_censor.1D, for censoring motion
  159. ! 1d_tool.py -infile dfile_rall.1D -set_nruns {nRuns} \
  160. -show_censor_count -censor_prev_TR \
  161. -censor_motion {motion_censor} motion_{subj}
  163. # Estimate the motion parameter after motion correction, we can check the
  164. # effects of MC
  165. ! mkdir mc
  166. for run in runstr:
  167. ! 3dvolreg -verbose -zpad 1 -base {base} \
  168. -1Dfile dfile.r{run}_pos.1D \
  169. rm.epi.nomask.r{run}+orig
  170. ! rm volreg+orig*
  171. # make a single file of motion params
  172. ! cat dfile.r*_pos.1D > dfile_rall_pos.1D
  174. # make figure pre mc
  175. dfile_pre = np.loadtxt('dfile_rall.1D')
  176. plot(range(dfile_pre.shape[0]), dfile_pre[:,:3], color=['C0','C1','C2'], label=['roll(IS)','pitch(RL)','yaw(AP)'])
  177. plt.legend();plt.xlabel('time points');plt.ylabel('mm');
  178. plt.savefig('rots_pre.pdf');plt.close('all')
  179. plot(range(dfile_pre.shape[0]), dfile_pre[:,3:], color=['C3','C4','C5'], label=['dS','dL','dP'])
  180. plt.legend();plt.xlabel('time points (TR)');plt.ylabel('mm');
  181. plt.savefig('tran_pre.pdf');plt.close('all')
  182. # make figure pos mc
  183. dfile_pos = np.loadtxt('dfile_rall_pos.1D')
  184. plot(range(dfile_pos.shape[0]), dfile_pos[:,:3], color=['C0','C1','C2'], label=['roll(IS)','pitch(RL)','yaw(AP)'])
  185. plt.legend();plt.xlabel('time points');plt.ylabel('mm');
  186. plt.savefig('rots_pos.pdf');plt.close('all')
  187. plot(range(dfile_pos.shape[0]), dfile_pos[:,3:], color=['C3','C4','C5'], label=['dS','dL','dP'])
  188. plt.legend();plt.xlabel('time points (TR)');plt.ylabel('mm');
  189. plt.savefig('tran_pos.pdf');plt.close('all')
  190. # move file to directory
  191. ! mv rots*.pdf tran*.pdf dfile.r*_pos.1D dfile_r*_pos.1D mc/
  193. # note TRs that were not censored, note ktrs here is a str
  194. ktrs = unix_wrapper(f'1d_tool.py -infile motion_{subj}_censor.1D \
  195. -show_trs_uncensored encoded', wantreturn=True, verbose=0)
  197. # ----------------------------------------
  198. # create the extents mask: mask_epi_extents+orig and apply the task
  199. # (this is a mask of voxels that have valid data at every TR,
  200. # there might be some pixel out of extents during mc)
  201. ! 3dMean -datum short -prefix rm.epi.mean rm.epi.min.r*.HEAD
  202. ! 3dcalc -a rm.epi.mean+orig -expr "step(a-0.999)" -prefix mask_epi_extents
  203. # and apply the extents mask to the EPI data
  204. # (delete any time series with missing data)
  205. for run in runstr:
  206. ! 3dcalc -a rm.epi.nomask.r{run}+orig -b mask_epi_extents+orig \
  207. -expr "a*b" -prefix pb04.{subj}.r{run}.volreg
  208. ! rm -f rm.* # rm.epi.nomask are big files, remove them
  209. ! rm -f {base}*
  211. # calculate a mean epi volume for next step anat epi registration
  212. ! 3dMean -prefix rm.epi_mean.nii.gz pb04.{subj}.r*.volreg+orig.HEAD
  213. ! 3dTstat -prefix epi_mean.nii.gz rm.epi_mean.nii.gz
  214. ! rm rm* mask_epi_extents+orig*
  216. # 删掉上一步distortion correction的数据，节省空间
  217. ! rm pb03*
  219. # %% [markdown]
  220. # ## Step 5. anat2epi
  222. # %% [markdown]
  223. # 这一步非常重要，是前面处理好的功能像和结构像进行配准，配准的结果一定要手动检查
  225. # %%
  226. # 首先把用来配准的epi像make a copy
  227. ! 3dcopy epi_mean.nii.gz epi_mean_tmp.nii.gz
  229. # 关键步骤。我们是把结构像配准到功能像，但是我们得到的转换矩阵是从相反的，功能像到结构像，这个矩阵可以后面用来转化epi数据
  230. #
  231. ! align_epi_anat.py -anat2epi -anat {t1.str} \
  232. -save_skullstrip -suffix _al_junk \
  233. -epi epi_mean_tmp.nii.gz -epi_base 0 \
  234. -epi_strip 3dAutomask \
  235. -volreg off -tshift off -giant_move
  237. # 生成nifti文件，方便我们在fsleyes上面查看配准效果
  238. ! 3dcopy {subj}_SurfVol_al_junk+orig {subj}_SurfVol_al_junk.nii.gz
  240. # %% [markdown]
  241. # 完成上面一步之后，要打开fsleyes仔细检查配准的结果，其中underlay选择CN003_SurfVol_al_junk+orig, overlay选择epi_mean_tmp+orig。然后检查两者是否一致。如果不匹配，则需要重新进行上面的过程，在开始之前，需要先删除上面产生的文件。可以做
  242. # ! rm {subj}_SurfVol_al* {subj}_SurfVol_ns*
  243. # 确认配准没有问题之后，进行下一步操作
  245. # %%
  246. # note that we align anat 2 epi, however, this xfm is from epi space to anat space
  247. # note that this xfm is compatible with LPS+ space, which is the default space in AFNI
  248. ! mv {t1.strnosuffix[:-5]}_al_junk_mat.aff12.1D mat.anat2epi.aff12.1D
  249. # create an skull-striped anat_final dataset, aligned with stats
  250. ! 3dcopy {t1.strnosuffix[:-5]}_ns+orig anat_final.{subj}.nii.gz
  251. ! rm {t1.strnosuffix[:-5]}_ns+orig*
  252. # rewrite the name of aligned anat
  253. ! 3dcopy {t1.strnosuffix[:-5]}_al_junk+orig anat2epi.{subj}.nii.gz
  254. ! rm {t1.strnosuffix[:-5]}_al_junk+orig*
  256. # Invert xfm
  257. ! cat_matvec -ONELINE mat.anat2epi.aff12.1D -I > mat.epi2anat.aff12.1D
  259. # warp the volreg base EPI dataset back to anat to make a final version
  260. ! 3dAllineate -base anat_final.{subj}.nii.gz \
  261. -input epi_mean.nii.gz \
  262. -1Dmatrix_apply mat.epi2anat.aff12.1D \
  263. -prefix epi2anat.{subj}.nii.gz
  265. # Record final registration costs
  266. ! 3dAllineate -base epi2anat.{subj}.nii.gz -allcostX -input anat_final.{subj}.nii.gz > out.allcostX.txt
  268. # Take the snapshots to show the quality of alignment
  269. ! @snapshot_volreg epi2anat.{subj}.nii.gz anat_final.{subj}.nii.gz
  270. ! @snapshot_volreg anat_final.{subj}.nii.gz epi2anat.{subj}.nii.gz
  271. ! @snapshot_volreg anat2epi.{subj}.nii.gz epi_mean.nii.gz
  272. ! @snapshot_volreg epi_mean.nii.gz anat2epi.{subj}.nii.gz
  274. # %% [markdown]
  275. # ## Step 6. epi to individual anat
  277. # %% [markdown]
  278. # 现在我们有了epi到anat的配准，然后我们就可以把所有的epi的文件划到t1的空间。注意，这一步并不是从motion correction之后的文件，也就是pb03.CN003.r0X.volreg+orig。我们要从早distrotion correction之前的文件，把从DC-MC-epi2anat这三个转换一次性的做完
  280. # %%
  281. resolution=2.4
  282. # ======== Transform epi to match anat, add by RZ ===============
  283. # In this step we need to concatenate Distortion(opt)+motion+affine transformations
  284. # align each dset to base volume, then we separate whether we want to further align epi to anat
  285. for run in runstr:
  286. # concatenate MC+aff transforms
  287. ! cat_matvec -ONELINE mat.anat2epi.aff12.1D -I mat.r{run}.vr.aff12.1D > mat.r{run}.warp.aff12.1D
  289. # we apply distortion correction + motion correction + anat2epi transformation
  290. cmd = f'3dNwarpApply -quintic -nwarp "mat.r{run}.warp.aff12.1D blip_warp.r{run}_WARP+orig" \
  291. -master {t1} -dxyz {resolution} \
  292. -source pb02.{subj}.r{run}.tshift+orig \
  293. -prefix rm.epi.nomask.r{run}'
  294. unix_wrapper(cmd)
  296. # create an all-1 dataset to mask the extents of the warp
  297. ! 3dcalc -overwrite -a pb02.{subj}.r{run}.tshift+orig -expr 1 -prefix rm.epi.all1
  299. # we apply distortion correction + motion correction + anat2epi transformation
  300. cmd = f'3dNwarpApply -quintic -nwarp "mat.r{run}.warp.aff12.1D blip_warp.r{run}_WARP+orig" \
  301. -master {t1} -dxyz {resolution} \
  302. -ainterp NN -quiet \
  303. -source rm.epi.all1+orig \
  304. -prefix rm.epi.1.r{run}'
  305. unix_wrapper(cmd)
  307. # make an extents intersection mask of this run across time domain
  308. # this makes a mask that all volumes in this run have valid numbers
  309. ! 3dTstat -min -prefix rm.epi.min.r{run} rm.epi.1.r{run}+orig
  310. # below file is big, should be removed
  311. ! rm rm.epi.1.r{run}+orig*
  313. # %% [markdown]
  314. # 做了变化之后，也要处理一下mask的问题
  316. # %%
  317. # ----------------------------------------
  318. # create the extents mask: mask_epi_extents+orig
  319. # (this is a mask of voxels that have valid data at every TR,
  320. # there might be some pixel out of extents during mc)
  321. ! 3dMean -datum short -prefix rm.epi.mean rm.epi.min.r*.HEAD
  322. # and apply the extents mask to the EPI data
  323. ! 3dcalc -a rm.epi.mean+orig -expr "step(a-0.999)" -prefix mask_epi_extents
  325. # 去掉mask之外的voxel的数据
  326. for run in runstr:
  327. ! 3dcalc -a rm.epi.nomask.r{run}+orig -b mask_epi_extents+orig \
  328. -expr "a*b" -prefix pb05.{subj}.r{run}.al2anat
  329. # save to nifti format seems to reduce file size
  331. # rm.epi.nomask are big files, remove them
  332. ! rm rm.*
  334. # %% [markdown]
  335. # ## Step 7. epi volume 2 surface
  337. # %% [markdown]
  338. # 上一步我们已经把epi数据划到了和个体的结构像一个空间。很多时候我们需要做基于surface的数据分析。我们进一步把三维的epi数据插值划到surface上
  340. # %%
  341. surface_dir = FREESURFER_HOME+f'/subjects/{subj}/SUMA'
  342. # map volume data to the surface of each hemisphere
  343. for hemi in ['lh', 'rh']:
  344. for run in runstr:
  345. ! 3dVol2Surf -spec {surface_dir}/std.141.{subj}_{hemi}.spec \
  346. -sv {subj}_SurfVol+orig \
  347. -surf_A smoothwm \
  348. -surf_B pial \
  349. -f_index nodes \
  350. -f_steps 10 \
  351. -map_func ave \
  352. -oob_value 0 \
  353. -grid_parent pb05.{subj}.r{run}.al2anat+orig \
  354. -out_niml pb05.{subj}.{hemi}.r{run}.surf.niml.dset
  356. # %% [markdown]
  357. # ## Step 8. epi to MNI anat
  359. # %%
  360. # ======== Transform epi to match anat, add by RZ ===============
  361. # In this step we need to concatenate Distortion(opt)+motion+affine transformations
  362. # align each dset to base volume, then we separate whether we want to further align epi to anat
  363. for run in runstr:
  365. # we apply distortion correction + motion correction + anat2epi + nonlinear warp to MNI transformation
  366. # 注意我们这里是相反的顺序来concatenate的
  367. cmd = f'3dNwarpApply -quintic -nwarp "anatQQ.{subj}_WARP.nii anatQQ.{subj}.aff12.1D mat.r{run}.warp.aff12.1D blip_warp.r{run}_WARP+orig" \
  368. -master MNI152_2009_template_SSW.nii.gz \
  369. -dxyz {resolution} \
  370. -source pb02.{subj}.r{run}.tshift+orig \
  371. -prefix rm.epi.nomask.r{run}'
  372. unix_wrapper(cmd)
  374. # create an all-1 dataset to mask the extents of the warp
  375. ! 3dcalc -overwrite -a pb02.{subj}.r{run}.tshift+orig -expr 1 -prefix rm.epi.all1
  377. # we apply distortion correction + motion correction + anat2epi transformation
  378. cmd = f'3dNwarpApply -quintic -nwarp "anatQQ.{subj}_WARP.nii anatQQ.{subj}.aff12.1D mat.r{run}.warp.aff12.1D blip_warp.r{run}_WARP+orig" \
  379. -master MNI152_2009_template_SSW.nii.gz \
  380. -dxyz {resolution} \
  381. -source rm.epi.all1+orig \
  382. -ainterp NN -quiet \
  383. -prefix rm.epi.1.r{run}'
  384. unix_wrapper(cmd)
  386. # make an extents intersection mask of this run across time domain
  387. # this makes a mask that all volumes in this run have valid numbers
  388. ! 3dTstat -min -prefix rm.epi.min.r{run} rm.epi.1.r{run}+tlrc
  389. # below file is big, should be removed
  390. ! rm rm.epi.1.r{run}+tlrc*
  392. # %% [markdown]
  393. # 做了变化之后，也要处理一下mask的问题
  395. # %%
  396. # ----------------------------------------
  397. # create the extents mask: mask_epi_extents+orig
  398. # (this is a mask of voxels that have valid data at every TR,
  399. # there might be some pixel out of extents during mc)
  400. ! 3dMean -datum short -prefix rm.epi.mean rm.epi.min.r*.HEAD
  401. # and apply the extents mask to the EPI data
  402. ! 3dcalc -a rm.epi.mean+tlrc -expr "step(a-0.999)" -prefix mask_epi_2mni_extents
  404. # 去掉mask之外的voxel的数据
  405. for run in runstr:
  406. ! 3dcalc -a rm.epi.nomask.r{run}+tlrc -b mask_epi_2mni_extents+tlrc \
  407. -expr "a*b" -prefix pb06.{subj}.r{run}.al2mni
  408. # save to nifti format seems to reduce file size
  410. # rm.epi.nomask are big files, remove them
  411. ! rm rm.*
  413. # %% [markdown]
  414. # ## Step 8.5 补充步骤，用于进行smooth操作
  416. # %%
  417. # 目的是对上一步step8所生成的pb06进行smooth 生成pb06s
  418. # 再对上上一步step7所生成的pb05***surf.niml文件进行smooth 生成pb05s*rh 和 pb05s*lh
  419. # 再对上上一步step7所生成的pb05.subj.r0*.al2anat+orig文件进行smooth 生成pb05s*.subj.r0*.blur+al2anat+orig
  420. for run in runstr:
  421. ! 3dmerge -1blur_fwhm 4.0 -doall -prefix pb06s.{subj}.r{run}.blur+al2mni+tlrc \
  422. pb06.{subj}.r{run}.al2mni+tlrc
  424. for run in runstr:
  425. ! 3dmerge -1blur_fwhm 4.0 -doall -prefix pb05s.{subj}.lh.r{run}.blur.surf.niml.dset \
  426. pb05.{subj}.lh.r{run}.surf.niml.dset
  428. for run in runstr:
  429. ! 3dmerge -1blur_fwhm 4.0 -doall -prefix pb05s.{subj}.rh.r{run}.blur.surf.niml.dset \
  430. pb05.{subj}.rh.r{run}.surf.niml.dset
  432. for run in runstr:
  433. ! 3dmerge -1blur_fwhm 4.0 -doall -prefix pb05s.{subj}.r{run}.blur+al2anat+orig \
  434. pb05.{subj}.r{run}.al2anat+orig
  436. # %% [markdown]
  437. # ## Step 9. 创造一个所有epi和mni模板共有的mask
  439. # %%
  440. # ====================== Mask =================================
  441. # Create 'full_mask' dataset (union mask)
  442. for run in runstr:
  443. ! 3dAutomask -prefix rm.mask_r{run}.nii.gz pb06.{subj}.r{run}.al2mni+tlrc
  445. # 创造一个所有功能像的run合起来的mask
  446. ! 3dmask_tool -inputs rm.mask_r*.nii.gz -union -prefix full_mask.{subj}.nii.gz
  447. ! rm rm.mask*
  449. # ---- create subject anatomy mask, mask_anat.$subj+orig ----
  450. # (resampled from aligned anat)
  451. # 把功能像resample到上面mask的分辨率
  452. ! 3dresample -master full_mask.{subj}.nii.gz -input \
  453. MNI152_2009_template_SSW.nii.gz'[0]' -prefix rm.resam.anat.nii.gz
  455. # convert to binary anat mask; fill gaps and holes
  456. ! 3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.anat.nii.gz \
  457. -prefix mask_anat.{subj}.nii.gz
  459. # 结合功能像和结构像的mask
  460. # compute tighter EPI mask by intersecting with anat mask
  461. ! 3dmask_tool -input full_mask.{subj}.nii.gz mask_anat.{subj}.nii.gz \
  462. -inter -prefix mask_epi_anat.{subj}.nii.gz
  463. # note Dice coefficient of masks, as well
  464. ! 3ddot -dodice full_mask.{subj}.nii.gz mask_anat.{subj}.nii.gz > out.mask_ae_dice.txt
  466. # 删掉多余文件
  467. ! rm rm*
  469. # %% [markdown]
  470. # ## Step 10. scale
  472. # %% [markdown]
  473. # 原始EPI图像的数据可能很大，从几百到4000不等。但是我们一般不考虑这个绝对强度，考虑的是信号增长的比例。所以把数据scale一下, 让所有的voxel的时间序列均值为100
  475. # %%
  476. # scale each voxel time series to have a mean of 100
  477. # (be sure no negatives creep in)
  478. # (subject to a range of [0,200])
  479. # scale volume data
  480. for run in runstr:
  481. ! 3dTstat -prefix rm.mean_r{run} pb06.{subj}.r{run}.al2mni+tlrc
  482. ! 3dcalc -a pb06.{subj}.r{run}.al2mni+tlrc -b rm.mean_r{run}+tlrc \
  483. -c mask_epi_2mni_extents+tlrc \
  484. -expr "c * min(200, a/b*100)*step(a)*step(b)" \
  485. -prefix pb07.{subj}.r{run}.scale
  487. # combine all datasets into one
  488. ! 3dTcat -prefix all_runs.{subj}.nii.gz pb07.{subj}.r*.scale+tlrc*
  489. # remove redundant file
  490. ! rm rm.mean*
  492. # 再scale surface data
  493. for hemi in ['lh', 'rh']:
  494. for run in runstr:
  495. ! 3dTstat -prefix rm.{hemi}.mean_r{run}.niml.dset \
  496. pb05.{subj}.{hemi}.r{run}.surf.niml.dset
  497. ! 3dcalc -a pb05.{subj}.{hemi}.r{run}.surf.niml.dset \
  498. -b rm.{hemi}.mean_r{run}.niml.dset \
  499. -expr 'min(200, a/b*100)*step(a)*step(b)' \
  500. -prefix pb07.{subj}.{hemi}.r{run}.scale.niml.dset
  502. # remove redundant file
  503. ! rm rm.*mean*
  505. # %%
  506. # 注意！！！这个cell的代码是对pb06s 以及pb05s 进行scale重置 也就是经过了smooth的数据
  507. # 生成pb07s*.scale
  508. # 这个文件被整合成all_runs_with_smooth
  509. # 生成pb07s*.scale.niml.dset
  511. # scale volume data
  512. for run in runstr:
  513. ! 3dTstat -prefix rm.mean_r{run} pb06s.{subj}.r{run}.blur+al2mni+tlrc
  514. ! 3dcalc -a pb06s.{subj}.r{run}.blur+al2mni+tlrc -b rm.mean_r{run}+tlrc \
  515. -c mask_epi_2mni_extents+tlrc \
  516. -expr "c * min(200, a/b*100)*step(a)*step(b)" \
  517. -prefix pb07s.{subj}.r{run}.scale
  519. # combine all datasets into one
  520. ! 3dTcat -prefix all_runs_with_smooth.{subj}.nii.gz pb07s.{subj}.r*.scale+tlrc*
  521. # remove redundant file
  522. ! rm rm.mean*
  524. # 再scale surface data
  525. for hemi in ['lh', 'rh']:
  526. for run in runstr:
  527. ! 3dTstat -prefix rm.{hemi}.mean_r{run}.niml.dset \
  528. pb05s.{subj}.{hemi}.r{run}.blur.surf.niml.dset
  529. ! 3dcalc -a pb05s.{subj}.{hemi}.r{run}.blur.surf.niml.dset \
  530. -b rm.{hemi}.mean_r{run}.niml.dset \
  531. -expr 'min(200, a/b*100)*step(a)*step(b)' \
  532. -prefix pb07s.{subj}.{hemi}.r{run}.scale.niml.dset
  533. # remove redundant file
  534. ! rm rm.*mean*
  536. # %% [markdown]
  537. # 到这一步，基本上最主要的预处理就完结了。下一步可以进一步针对surface数据跑GLM，因为afni的surface都是在freesurfer的模板上对其且标准化的，所以surface数据可以直接跑group analysis。也可以在已经划到MNI space上的volume数据跑GLM。以后在volume上跑GLM的结果可以直接用MNI152的T1像来可视化

03_funcpp02_23BPhappy.ipynb at commit d84fb3d, under GPL-3.0 · at the source

Overview

Authors: Yu-Feng Xia1, Yingyan Zhong2, Zi-Jian Cheng1, Enzhao Cong2,3, Yifeng Xu2, Ru-Yuan Zhang4,5,6
ORCID iDs: Yu-Feng Xia, Yingyan Zhong, Zi-Jian Cheng, Yifeng Xu, Ru-Yuan Zhang
  1. Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine and School of Psychology, 1954 Huashan Road, Xuhui District, Shanghai 200030, China
  2. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wanping South Road, Shanghai 200030, China
  3. Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, 301 Middle Yanchang Road, Jing’an District, Shanghai 200030, China
  4. School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, 5 Yiheyuan Road, Haidian Distrct, Beijing 100871, China
  5. IDG/McGovern Institute for Brain Research, Peking University, 5 Yiheyuan Road, Haidian Distrct, Beijing 100871, China
  6. Key Laboratory of Machine Perception (Ministry of Education), Peking University, 5 Yiheyuan Road, Haidian Distrct, Beijing 100871, China
Institutions: Shanghai Jiao Tong University (China); Shanghai Mental Health Center (China); Tongji University (China); Shanghai Tenth People's Hospital (China); Peking University (China)
Journal: Psychoradiology, volume 6, article kkag015
Dates: received 23 November 2025; accepted 6 April 2026; published online 10 April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/psyrad/kkag015 · PMID 42078071 · PMCID PMC13133896 · OpenAlex W4408886061
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/ahufm_v1
Status: code verified
Categories: fMRI (modality), human (organism), depression (population), bipolar (population)
Methods: Statistics, Connectivity, fMRI & imaging
Keywords: bipolar disorder, major depressive disorder, decision-making, functional magnetic resonance imaging, reward prediction error
Journal subjects: Naturalistic Neuroimaging: A New Perspective on Psychiatric Disorders
Topic: Bipolar Disorder and Treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (32441102); Shanghai Municipal Education Commission (2024AIZD014)
Citations: not cited yet (Europe PMC); 88 references in the paper

Abstract

Background: The overlapping symptoms between bipolar disorder (BD) and major depressive disorder (MDD) pose a challenge in diagnosis and treatment. A prevailing hypothesis suggests that mood dysregulation may be linked to impairments in the reward system, but the neurocomputational differences between BD and MDD remain elusive. This study investigates whether atypical reward processing affects subjective mood in adolescents with BD and MDD. Our research aims to elucidate the behavioral and neural differences between the two groups, facilitating more accurate and timely diagnosis and intervention.

Methods: Forty-five adolescents (aged ≤ 19 years) diagnosed with BD-II in depressed mood states (N = 25) or MDD (N = 20) completed a risky gambling task while their brain responses were recorded using functional magnetic resonance imaging (fMRI). Several computational models were constructed to uncover the associations between various reward components (e.g. reward prediction errors, RPE) and trial-wise fluctuations in subjective mood during the task.

Results: Adolescents with BD exhibited a lower best choice rate and a higher uncertain choice rate compared to those with MDD. Computational modeling and mediation analysis suggested a tripartite mediating relationship between RPE-mood association, decision rationality, and symptom severity. Using fMRI, we observed significant RPE-related activation in the ventral striatum, which showed a slight positive correlation with the RPE-mood association. We also noted subtle differences in several brain regions (i.e. medial orbitofrontal cortex) between the BD and MDD groups. These differences were further associated with manic symptoms.

Conclusion: Decision rationality mediated the association between RPE-mood association and symptom severity. Relative to adolescents with MDD, those with BD showed decreased decision rationality, along with modest but distinct reward-related neural patterns on fMRI. These findings highlight the crucial role of reward processing in mood regulation and provide preliminary neurocomputational evidence that may inform future diagnostic biomarker development.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

GITSyfX/CCNN-23BPhappy

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: d84fb3d15c2f5b52575d6eb4d0cbd23a69188870, 20 May 2025
Languages: MATLAB (53), Jupyter (12)
Size: 120 files, 65 scripts
Software Heritage: not archived
Found in: the text, “Computational modeling of mood regulation”
Holds: license file, 12 notebooks
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (24 files), Psychtoolbox (14 files), AFNI (10 files), Optimization Toolbox (9 files), Parallel Computing Toolbox (9 files), FSL (2 files), SciPy (2 files), FreeSurfer (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), SPM (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
66 files
At the source: github.com/GITSyfX/CCNN-23BPhappy

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 2 funders, 84 references.

Cite

This paper

Xia, Y.-F., Zhong, Y., Cheng, Z.-J., Cong, E., Xu, Y., & Zhang, R.-Y. (2026). Neurocomputational mechanisms of reward-based online mood regulation in adolescents with bipolar disorder and major depressive disorder. Psychoradiology, 6, kkag015. https://doi.org/10.1093/psyrad/kkag015

BibTeX

@article{xia2026neurocomputational,
author = {Xia, Yu-Feng and Zhong, Yingyan and Cheng, Zi-Jian and Cong, Enzhao and Xu, Yifeng and Zhang, Ru-Yuan},
title = {{Neurocomputational mechanisms of reward-based online mood regulation in adolescents with bipolar disorder and major depressive disorder}},
journal = {Psychoradiology},
year = {2026},
month = apr,
volume = {6},
pages = {kkag015},
publisher = {Oxford University Press},
issn = {2634-4416},
doi = {10.1093/psyrad/kkag015},
url = {https://doi.org/10.1093/psyrad/kkag015},
pmid = {42078071},
pmcid = {PMC13133896}
}

RIS

TY - JOUR
AU - Xia, Yu-Feng
AU - Zhong, Yingyan
AU - Cheng, Zi-Jian
AU - Cong, Enzhao
AU - Xu, Yifeng
AU - Zhang, Ru-Yuan
TI - Neurocomputational mechanisms of reward-based online mood regulation in adolescents with bipolar disorder and major depressive disorder
T2 - Psychoradiology
J2 - Psychoradiology
PY - 2026
DA - 2026/04/10
VL - 6
SP - kkag015
SN - 2634-4416
PB - Oxford University Press
DO - 10.1093/psyrad/kkag015
UR - https://doi.org/10.1093/psyrad/kkag015
LA - en
ER -

CSL-JSON

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