This script checks if all the ROI atlases in functional space have all the ROIs



In [1]:

    
import numpy as np
import nibabel as nib



In [ ]:



In [2]:

    
atlas_path = '/home1/varunk/results_again_again/ABIDE1_Preprocess_Datasink/atlas_paths/atlas_file_list.npy'
atlas_files = np.load(atlas_path)



In [3]:

    
atlas_files[41]









    Out[3]:





'/home1/varunk/results_again_again/ABIDE1_Preprocess/motion_correction_bet/coreg_reg/atlas_resize_reg_directory/_subject_id_0050045/std2func_xform/fullbrain_atlas_thr0-2mm_resample_flirt.nii'



In [4]:

    
in_file = atlas_files[40]
atlas_values_list = nib.load(in_file).get_data().ravel()



In [5]:

    
universe = set(np.arange(274))



In [6]:

    
universe - set(atlas_values_list)









    Out[6]:





{255}



In [7]:

    
set(atlas_values_list);



In [8]:

    
# Now the real code:
import re
universe = set(np.arange(274))
missing_ROIs = []
for index,in_file in enumerate(atlas_files):
    sub_id_extracted = re.search('.+_subject_id_(\d+)', in_file).group(1)
    print(sub_id_extracted)
    atlas_values = nib.load(in_file).get_data()
    atlas_values_list = atlas_values.ravel()
    atlas_values_max = max(atlas_values_list)  
    missing_ROIs.append((index,sub_id_extracted,list(universe - set(atlas_values_list)),atlas_values_max))



In [9]:

    
missing_ROIs









    Out[9]:





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 (874, '0051196', [255], 274.0),
 (875, '0051197', [255], 274.0),
 (876, '0051198', [255], 274.0),
 (877, '0051199', [255], 274.0),
 (878, '0051201', [255], 274.0),
 (879, '0051202', [255], 274.0),
 (880, '0051203', [255], 274.0),
 (881, '0051204', [255], 274.0),
 (882, '0051205', [255], 274.0),
 (883, '0051206', [255], 274.0),
 (884, '0051207', [255], 274.0),
 (885, '0051208', [255], 274.0),
 (886, '0051209', [255], 274.0),
 (887, '0051210', [255], 274.0),
 (888, '0051211', [255], 274.0),
 (889, '0051212', [255], 274.0),
 (890, '0051213', [255], 274.0),
 (891, '0051214', [255], 274.0),
 (892, '0051215', [255], 274.0),
 (893, '0051216', [255], 274.0),
 (894, '0051217', [255], 274.0),
 (895, '0051218', [255], 274.0),
 (896, '0051219', [255], 274.0),
 (897, '0051220', [255], 274.0),
 (898, '0051221', [255], 274.0),
 (899, '0051222', [255], 274.0),
 (900, '0051223', [255], 274.0),
 (901, '0051224', [255], 274.0),
 (902, '0051225', [255], 274.0),
 (903, '0051226', [261, 255], 274.0),
 (904, '0051227', [255], 274.0),
 (905, '0051228', [255], 274.0),
 (906, '0051229', [255], 274.0),
 (907, '0051230', [255], 274.0),
 (908, '0051231', [255], 274.0),
 (909, '0051234', [255], 274.0),
 (910, '0051235', [255], 274.0),
 (911, '0051236', [255], 274.0),
 (912, '0051237', [255], 274.0),
 (913, '0051238', [255], 274.0),
 (914, '0051239', [255], 274.0),
 (915, '0051240', [261, 255], 274.0),
 (916, '0051241', [255], 274.0),
 (917, '0051248', [255], 274.0),
 (918, '0051249', [255], 274.0),
 (919, '0051250', [255], 274.0),
 (920, '0051251', [255], 274.0),
 (921, '0051252', [255], 274.0),
 (922, '0051253', [255], 274.0),
 (923, '0051254', [255], 274.0),
 (924, '0051255', [255], 274.0),
 (925, '0051256', [255], 274.0),
 (926, '0051257', [255], 274.0),
 (927, '0051258', [255], 274.0),
 (928, '0051259', [255], 274.0),
 (929, '0051260', [255], 274.0),
 (930, '0051261', [255], 274.0),
 (931, '0051262', [255], 274.0),
 (932, '0051263', [255], 274.0),
 (933, '0051264', [255], 274.0),
 (934, '0051265', [255], 274.0),
 (935, '0051266', [255], 274.0),
 (936, '0051267', [255], 274.0),
 (937, '0051268', [255], 274.0),
 (938, '0051269', [255], 274.0),
 (939, '0051271', [255], 274.0),
 (940, '0051272', [255], 274.0),
 (941, '0051273', [255], 274.0),
 (942, '0051274', [255], 274.0),
 (943, '0051275', [255], 274.0),
 (944,
  '0051276',
  [161,
   193,
   131,
   194,
   258,
   199,
   200,
   203,
   204,
   205,
   206,
   207,
   208,
   189,
   57,
   59,
   125,
   190,
   255],
  274.0),
 (945, '0051277', [255], 274.0),
 (946, '0051278', [255], 274.0),
 (947, '0051279', [255], 274.0),
 (948, '0051280', [255], 274.0),
 (949, '0051281', [255], 274.0),
 (950, '0051282', [255], 274.0),
 (951, '0051291', [255], 274.0),
 (952, '0051292', [255], 274.0),
 (953, '0051293', [255], 274.0),
 (954, '0051294', [255], 274.0),
 (955, '0051295', [255], 274.0),
 (956, '0051296', [255], 274.0),
 (957, '0051297', [255], 274.0),
 (958, '0051298', [255], 274.0),
 (959, '0051299', [255], 274.0),
 (960, '0051300', [255], 274.0),
 (961, '0051301', [255], 274.0),
 (962, '0051302', [255], 274.0),
 (963, '0051303', [255], 274.0),
 (964, '0051304', [255], 274.0),
 (965, '0051305', [255], 274.0),
 (966, '0051306', [255], 274.0),
 (967, '0051307', [255], 274.0),
 (968, '0051308', [255], 274.0),
 (969, '0051309', [255], 274.0),
 (970, '0051311', [255], 274.0),
 (971, '0051312', [255], 274.0),
 (972, '0051313', [255], 274.0),
 (973, '0051314', [255], 274.0),
 (974, '0051315', [255], 274.0),
 (975, '0051316', [255], 274.0),
 (976, '0051317', [255], 274.0),
 (977, '0051318', [255], 274.0),
 (978, '0051319', [255], 274.0),
 (979, '0051320', [255], 274.0),
 (980, '0051321', [255], 274.0),
 (981, '0051322', [255], 274.0),
 (982, '0051323', [255], 274.0),
 (983, '0051324', [255], 274.0),
 (984, '0051325', [255], 274.0),
 (985, '0051326', [255], 274.0),
 (986, '0051327', [255], 274.0),
 (987, '0051328', [255], 274.0),
 (988, '0051329', [255], 274.0),
 (989, '0051330', [255], 274.0),
 (990, '0051331', [255], 274.0),
 (991, '0051332', [255], 274.0),
 (992, '0051333', [255], 274.0),
 (993, '0051334', [255], 274.0),
 (994, '0051335', [255], 274.0),
 (995, '0051336', [255], 274.0),
 (996, '0051338', [255], 274.0),
 (997, '0051339', [255], 274.0),
 (998, '0051340', [255], 274.0),
 (999, '0051341', [255], 274.0),
 ...]



In [10]:

    
miss_refined = []
for miss in missing_ROIs:
    if miss[3] == 274.0 and len(miss[2]) == 3:
        miss_refined.append((miss[1],miss))

miss_refined









    Out[10]:





[('0050011', (9, '0050011', [266, 268, 255], 274.0)),
 ('0050050', (46, '0050050', [266, 268, 255], 274.0)),
 ('0051571', (1085, '0051571', [266, 268, 255], 274.0))]



In [11]:

    
bad_subjects = []
for i in miss_refined:
    bad_subjects.append(i[0])



In [12]:

    
bad_subjects = list(map(int, bad_subjects))
bad_subjects









    Out[12]:





[50011, 50050, 51571]



In [13]:

    
bad_rois = []
for sub in missing_ROIs:
    bad_rois.extend(sub[2])



In [14]:

    
# (bad_rois)



In [15]:

    
%matplotlib inline
import matplotlib.pyplot as plt

bins = np.arange(275)
count_roi = plt.hist(bad_rois, bins = bins)
# plt.ylim([0, 10])

As seen from histogram most of the corrupted ROIs are in cerebellum.

Therefore I have chosen to consider only the ROIs that are in brainetome.
Moreover in some of the subjects, ROIs belonging to Brainnetome atlas are also missing.



In [16]:

    
for i in zip(list(count_roi[0]),list(count_roi[1])):
    print(i)
# (Number of Subjects that doesnot have the ROI, ROI)









    



(0.0, 0)
(0.0, 1)
(0.0, 2)
(0.0, 3)
(0.0, 4)
(0.0, 5)
(0.0, 6)
(2.0, 7)
(1.0, 8)
(0.0, 9)
(0.0, 10)
(0.0, 11)
(0.0, 12)
(0.0, 13)
(0.0, 14)
(1.0, 15)
(0.0, 16)
(1.0, 17)
(0.0, 18)
(1.0, 19)
(0.0, 20)
(1.0, 21)
(0.0, 22)
(0.0, 23)
(0.0, 24)
(1.0, 25)
(1.0, 26)
(1.0, 27)
(0.0, 28)
(1.0, 29)
(0.0, 30)
(1.0, 31)
(0.0, 32)
(1.0, 33)
(0.0, 34)
(1.0, 35)
(0.0, 36)
(1.0, 37)
(0.0, 38)
(1.0, 39)
(0.0, 40)
(0.0, 41)
(0.0, 42)
(1.0, 43)
(0.0, 44)
(0.0, 45)
(0.0, 46)
(0.0, 47)
(0.0, 48)
(0.0, 49)
(0.0, 50)
(1.0, 51)
(0.0, 52)
(0.0, 53)
(0.0, 54)
(1.0, 55)
(0.0, 56)
(2.0, 57)
(0.0, 58)
(4.0, 59)
(3.0, 60)
(1.0, 61)
(0.0, 62)
(1.0, 63)
(0.0, 64)
(1.0, 65)
(0.0, 66)
(1.0, 67)
(1.0, 68)
(1.0, 69)
(1.0, 70)
(0.0, 71)
(0.0, 72)
(0.0, 73)
(0.0, 74)
(0.0, 75)
(0.0, 76)
(0.0, 77)
(0.0, 78)
(1.0, 79)
(0.0, 80)
(0.0, 81)
(0.0, 82)
(1.0, 83)
(0.0, 84)
(0.0, 85)
(0.0, 86)
(0.0, 87)
(0.0, 88)
(0.0, 89)
(0.0, 90)
(0.0, 91)
(0.0, 92)
(3.0, 93)
(12.0, 94)
(0.0, 95)
(0.0, 96)
(0.0, 97)
(0.0, 98)
(0.0, 99)
(0.0, 100)
(0.0, 101)
(0.0, 102)
(0.0, 103)
(0.0, 104)
(0.0, 105)
(0.0, 106)
(0.0, 107)
(0.0, 108)
(4.0, 109)
(5.0, 110)
(0.0, 111)
(1.0, 112)
(0.0, 113)
(0.0, 114)
(0.0, 115)
(0.0, 116)
(3.0, 117)
(7.0, 118)
(0.0, 119)
(0.0, 120)
(0.0, 121)
(0.0, 122)
(0.0, 123)
(0.0, 124)
(3.0, 125)
(5.0, 126)
(1.0, 127)
(1.0, 128)
(0.0, 129)
(0.0, 130)
(2.0, 131)
(1.0, 132)
(0.0, 133)
(0.0, 134)
(0.0, 135)
(0.0, 136)
(0.0, 137)
(0.0, 138)
(0.0, 139)
(0.0, 140)
(0.0, 141)
(0.0, 142)
(0.0, 143)
(0.0, 144)
(0.0, 145)
(0.0, 146)
(1.0, 147)
(1.0, 148)
(1.0, 149)
(1.0, 150)
(0.0, 151)
(0.0, 152)
(0.0, 153)
(0.0, 154)
(0.0, 155)
(0.0, 156)
(0.0, 157)
(0.0, 158)
(0.0, 159)
(0.0, 160)
(2.0, 161)
(1.0, 162)
(0.0, 163)
(0.0, 164)
(0.0, 165)
(0.0, 166)
(1.0, 167)
(0.0, 168)
(0.0, 169)
(0.0, 170)
(0.0, 171)
(0.0, 172)
(0.0, 173)
(0.0, 174)
(0.0, 175)
(0.0, 176)
(0.0, 177)
(0.0, 178)
(0.0, 179)
(0.0, 180)
(0.0, 181)
(0.0, 182)
(0.0, 183)
(0.0, 184)
(0.0, 185)
(0.0, 186)
(0.0, 187)
(0.0, 188)
(1.0, 189)
(1.0, 190)
(0.0, 191)
(0.0, 192)
(1.0, 193)
(1.0, 194)
(0.0, 195)
(0.0, 196)
(0.0, 197)
(0.0, 198)
(1.0, 199)
(1.0, 200)
(0.0, 201)
(0.0, 202)
(1.0, 203)
(1.0, 204)
(1.0, 205)
(1.0, 206)
(1.0, 207)
(1.0, 208)
(0.0, 209)
(0.0, 210)
(0.0, 211)
(0.0, 212)
(0.0, 213)
(0.0, 214)
(0.0, 215)
(0.0, 216)
(0.0, 217)
(0.0, 218)
(0.0, 219)
(0.0, 220)
(0.0, 221)
(0.0, 222)
(0.0, 223)
(0.0, 224)
(0.0, 225)
(0.0, 226)
(0.0, 227)
(0.0, 228)
(0.0, 229)
(0.0, 230)
(0.0, 231)
(0.0, 232)
(0.0, 233)
(0.0, 234)
(0.0, 235)
(0.0, 236)
(0.0, 237)
(0.0, 238)
(0.0, 239)
(0.0, 240)
(0.0, 241)
(0.0, 242)
(0.0, 243)
(0.0, 244)
(0.0, 245)
(0.0, 246)
(0.0, 247)
(0.0, 248)
(0.0, 249)
(0.0, 250)
(0.0, 251)
(0.0, 252)
(0.0, 253)
(3.0, 254)
(1102.0, 255)
(2.0, 256)
(4.0, 257)
(8.0, 258)
(4.0, 259)
(16.0, 260)
(50.0, 261)
(17.0, 262)
(16.0, 263)
(9.0, 264)
(13.0, 265)
(25.0, 266)
(13.0, 267)
(22.0, 268)
(8.0, 269)
(6.0, 270)
(9.0, 271)
(14.0, 272)
(8.0, 273)

Missing ROIs(Brainnetome)

ROI number 94 is missing in 12 subjects
- Check if these 12 subjects are included in analysis, if they are then ignore the ROI
Some others were also missing but in very few subjects -- Check
ROI number 255 (of Cerebellum) is missing in all 1102 subjects



In [17]:

    
#  Runall:
import pandas as pd

df = pd.read_csv('/home1/varunk/data/ABIDE1/RawDataBIDs/composite_phenotypic_file.csv') # , index_col='SUB_ID'

df = df.sort_values(['SUB_ID'])
# df = df.sort_values(['SUB+AF8-ID'])

bugs = ['51232','51233','51242','51243','51244','51245','51246','51247','51270','51310', '50045']

# sub 50045 has fewer ROIs coz of more shift of head



In [18]:

    
# '0051242' in bugs



In [19]:

    
# df



In [20]:

    
# selecting Autistic males(DSM IV) of age <= 18 years 
df_aut_lt18_m = df.loc[(df['SEX'] == 1) & (df['AGE_AT_SCAN'] <=18) & (df['DSM_IV_TR'] == 1)]



In [21]:

    
df_aut_lt18_m.shape









    Out[21]:





(214, 74)



In [22]:

    
df_aut_lt18_m_eyesopen = df.loc[(df['SEX'] == 1) & (df['AGE_AT_SCAN'] <=18) & (df['DSM_IV_TR'] == 1) & (df['EYE_STATUS_AT_SCAN'] == 1)]
df_aut_lt18_m_eyesopen;
df_td_lt18_m_eyesopen = df.loc[(df['SEX'] == 1) & (df['AGE_AT_SCAN'] <=18) & (df['DSM_IV_TR'] == 0) & (df['EYE_STATUS_AT_SCAN'] == 1)]
df_td_lt18_m_eyesopen;



In [23]:

    
df_aut_lt18_m_eyesopen_subid = df_aut_lt18_m_eyesopen.as_matrix(['SUB_ID']).squeeze()
df_td_lt18_m_eyesopen_subid = df_td_lt18_m_eyesopen.as_matrix(['SUB_ID']).squeeze()



In [24]:

    
# Sanity checks
set(df_aut_lt18_m_eyesopen_subid) - (set(df_aut_lt18_m_eyesopen_subid) - set(df_td_lt18_m_eyesopen_subid))









    Out[24]:





set()



In [25]:

    
df_aut_lt18_m_eyesclosed = df.loc[(df['SEX'] == 1) & (df['AGE_AT_SCAN'] <=18) & (df['DSM_IV_TR'] == 1) & (df['EYE_STATUS_AT_SCAN'] == 2)]
df_aut_lt18_m_eyesclosed;
df_td_lt18_m_eyesclosed = df.loc[(df['SEX'] == 1) & (df['AGE_AT_SCAN'] <=18) & (df['DSM_IV_TR'] == 0) & (df['EYE_STATUS_AT_SCAN'] == 2)]
df_td_lt18_m_eyesclosed;



In [26]:

    
df_aut_lt18_m_eyesclosed_subid = df_aut_lt18_m_eyesclosed.as_matrix(['SUB_ID']).squeeze()
df_td_lt18_m_eyesclosed_subid = df_td_lt18_m_eyesclosed.as_matrix(['SUB_ID']).squeeze()



In [27]:

    
# Sanity checks
set(df_aut_lt18_m_eyesclosed_subid) - (set(df_aut_lt18_m_eyesclosed_subid) - set(df_td_lt18_m_eyesclosed_subid))









    Out[27]:





set()



In [28]:

    
df_td_lt18_m_eyesclosed_subid









    Out[28]:





array([50031, 50033, 50034, 50035, 50043, 50044, 50045, 50047, 50048,
       50050, 50051, 50054, 50257, 50265, 50266, 50267, 50268, 50269,
       50724, 50725, 50726, 50727, 50728, 50731, 50732, 50733, 50734,
       50737, 50738, 50739, 50740, 50741, 50742, 51064, 51065, 51070,
       51071, 51072, 51073, 51074, 51075, 51076, 51077, 51133, 51134,
       51135, 51136, 51137, 51138, 51140, 51141, 51142, 51159, 51361, 51487])



In [29]:

    
# df_aut_lt18_m_eyesopen_subid



In [ ]:



In [ ]:



In [30]:

    
# set(bad_subjects)  - (set(bad_subjects) - set(df_aut_lt18_m_eyesopen_subid) )



In [31]:

    
# - set(bad_subjects);



In [32]:

    
sub_lt_246 = []
for sub in missing_ROIs:
#     missing_flag = False
#     for roi in sub[2]: # get the roi list for a subject
    if any(roi  < 246 for roi in sub[2]): # missing ROI is < 246 i.e belongs to brainettome
        sub_lt_246.append(sub)



In [33]:

    
len(sub_lt_246)









    Out[33]:





20



In [34]:

    
sub_lt_246









    Out[34]:





[(202, '0050279', [94, 255], 274.0),
 (209, '0050286', [94, 255], 274.0),
 (498,
  '0050643',
  [260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   270,
   271,
   272,
   273,
   69,
   70,
   94,
   109,
   110,
   117,
   118,
   255],
  259.0),
 (503,
  '0050648',
  [260, 262, 263, 265, 266, 267, 268, 272, 93, 94, 255],
  273.0),
 (506,
  '0050651',
  [257,
   258,
   259,
   260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   270,
   271,
   272,
   273,
   94,
   109,
   110,
   117,
   118,
   254,
   255],
  256.0),
 (507,
  '0050652',
  [258,
   260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   110,
   270,
   271,
   272,
   273,
   118,
   94,
   255],
  259.0),
 (508, '0050653', [272, 118, 93, 94, 255], 273.0),
 (510,
  '0050655',
  [256,
   257,
   258,
   259,
   260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   270,
   271,
   272,
   273,
   93,
   94,
   109,
   110,
   112,
   118,
   254,
   255],
  253.0),
 (513,
  '0050658',
  [257,
   258,
   259,
   260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   270,
   271,
   272,
   273,
   94,
   110,
   118,
   254,
   255],
  256.0),
 (516, '0050661', [94, 255], 273.0),
 (521,
  '0050667',
  [260, 261, 262, 263, 264, 265, 266, 267, 268, 271, 272, 94, 255],
  273.0),
 (558,
  '0050727',
  [128,
   131,
   132,
   255,
   7,
   8,
   147,
   148,
   149,
   150,
   25,
   26,
   161,
   162,
   55,
   57,
   59,
   60,
   65,
   67,
   68,
   125,
   126,
   127],
  274.0),
 (566, '0050736', [255, 59, 60, 7], 274.0),
 (576,
  '0050746',
  [33,
   35,
   255,
   37,
   39,
   167,
   43,
   15,
   79,
   17,
   63,
   19,
   51,
   21,
   61,
   83,
   27,
   29,
   31],
  274.0),
 (944,
  '0051276',
  [161,
   193,
   131,
   194,
   258,
   199,
   200,
   203,
   204,
   205,
   206,
   207,
   208,
   189,
   57,
   59,
   125,
   190,
   255],
  274.0),
 (1038, '0051462', [126, 255], 274.0),
 (1045, '0051469', [126, 255], 274.0),
 (1046, '0051470', [59, 60, 125, 126, 255], 274.0),
 (1048, '0051472', [126, 255], 274.0),
 (1072,
  '0051558',
  [260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   109,
   269,
   271,
   272,
   273,
   117,
   118,
   94,
   255],
  270.0)]



In [35]:

    
# Now to find the Subjects that have corrupted ROIs < 246 with modified list

corrupted_rois_lt_246 = []
subj = []
for sub in sub_lt_246:
    sub_2 = np.array(sub[2])
    modified_list = np.sort(sub_2[np.where(sub_2 < 246)[0]])
    corrupted_rois_lt_246.append((sub[1],modified_list))
    subj.append(int(sub[1]))



In [36]:

    
corrupted_rois_lt_246, subj









    Out[36]:





([('0050279', array([94])),
  ('0050286', array([94])),
  ('0050643', array([ 69,  70,  94, 109, 110, 117, 118])),
  ('0050648', array([93, 94])),
  ('0050651', array([ 94, 109, 110, 117, 118])),
  ('0050652', array([ 94, 110, 118])),
  ('0050653', array([ 93,  94, 118])),
  ('0050655', array([ 93,  94, 109, 110, 112, 118])),
  ('0050658', array([ 94, 110, 118])),
  ('0050661', array([94])),
  ('0050667', array([94])),
  ('0050727',
   array([  7,   8,  25,  26,  55,  57,  59,  60,  65,  67,  68, 125, 126,
          127, 128, 131, 132, 147, 148, 149, 150, 161, 162])),
  ('0050736', array([ 7, 59, 60])),
  ('0050746',
   array([ 15,  17,  19,  21,  27,  29,  31,  33,  35,  37,  39,  43,  51,
           61,  63,  79,  83, 167])),
  ('0051276',
   array([ 57,  59, 125, 131, 161, 189, 190, 193, 194, 199, 200, 203, 204,
          205, 206, 207, 208])),
  ('0051462', array([126])),
  ('0051469', array([126])),
  ('0051470', array([ 59,  60, 125, 126])),
  ('0051472', array([126])),
  ('0051558', array([ 94, 109, 117, 118]))],
 [50279,
  50286,
  50643,
  50648,
  50651,
  50652,
  50653,
  50655,
  50658,
  50661,
  50667,
  50727,
  50736,
  50746,
  51276,
  51462,
  51469,
  51470,
  51472,
  51558])



In [37]:

    
subj = set(subj)



In [38]:

    
# Subjects that are in the problematic set that are in the AUTISTIC SET
set(subj) - (set(subj) - set(df_aut_lt18_m_eyesopen_subid))









    Out[38]:





{50279}



In [39]:

    
# Subjects that are in the problematic set that are in the TD SET
set(subj) - (set(subj) - set(df_td_lt18_m_eyesopen_subid))









    Out[39]:





{51276}

Results: Remove & Ignore (Eyes Open):

Remove sub-51276 from the TD as it contains a lot of Corrupted ROIs
Ignore ROI 94 as sub-50279 belonging to Autistic group has corrupted ROI



In [40]:

    
# Subjects that are in the problematic set that are in the AUTISTIC SET
set(subj) - (set(subj) - set(df_aut_lt18_m_eyesclosed_subid))









    Out[40]:





{50746, 51472}



In [41]:

    
# Subjects that are in the problematic set that are in the TD SET
set(subj) - (set(subj) - set(df_td_lt18_m_eyesclosed_subid))









    Out[41]:





{50727}

Results: Remove & Ignore (Eyes Closed)

Remove sub - 50746 as it contains a lot of corrupted ROIs
Remove sub-50727 as it contains a lot of corrupted ROIs
Ignore ROI 126 as it is corrupted in sub-51472

Final Result:

ROIs to ignore in Brainnetome - 126, 94
Subjects to ignore 51276, 50746, 50727



In [ ]:



In [42]:

    
# SUB - ROI
# 0050279 - 94
# 0050286 - 94
# 0050643 - 69,70,94,109,110,117,118
# 0050648 - 93,94
# 0050651 - 94,109,110,117,118
# 0050652 - 110,118,94
# 0050653 - 93,94,118
# 0050655 - 93,94,109,110,112,118
# 0050658 - 94,110,118

Major realization:

As many of the cerebellum ROIs were missing how could I compute the brain maps of cerebellum ?
I think I sould look at the cerebellum ROIs once again to see which are the major brain areas that are missing in the cerebellum
Cerebellum regions reported in OHBM 2018:
- Right Crus I
- Left Crus I
- Right VI
- Left V
- Left VI
  
  Lets see which were wrong.

I have - df_aut_lt18_m_eyesopen_subid that denotes the sub ids of the bin that I have considered.
Cerebellum ROIs are corrrupted from ROI number 254 onwards.
I have to find the set diff between



In [62]:

    
sub_gt_255 = []
for sub in missing_ROIs:
#     missing_flag = False
#     for roi in sub[2]: # get the roi list for a subject
    if any(roi  > 255 for roi in sub[2]): # missing ROI is < 246 i.e belongs to brainettome
        sub_gt_255.append(sub)



In [63]:

    
sub_gt_255









    Out[63]:





[(9, '0050011', [266, 268, 255], 274.0),
 (10, '0050012', [261, 255], 274.0),
 (15, '0050017', [266, 255], 274.0),
 (31, '0050035', [260, 262, 266, 268, 255], 274.0),
 (32, '0050036', [261, 255], 274.0),
 (34, '0050038', [261, 255], 274.0),
 (37, '0050041', [266, 255], 274.0),
 (41, '0050045', [260, 262, 263, 265, 266, 267, 268, 272, 255], 273.0),
 (46, '0050050', [266, 268, 255], 274.0),
 (51, '0050055', [261, 255], 274.0),
 (61, '0050106', [261, 255], 274.0),
 (66, '0050111', [261, 255], 274.0),
 (68, '0050113', [261, 255], 274.0),
 (71, '0050116', [261, 255], 274.0),
 (73, '0050118', [261, 255], 274.0),
 (79, '0050124', [261, 255], 274.0),
 (81, '0050126', [261, 255], 274.0),
 (83, '0050128', [261, 255], 274.0),
 (90, '0050135', [261, 255], 274.0),
 (92, '0050137', [261, 255], 274.0),
 (93, '0050142', [261, 255], 274.0),
 (99, '0050148', [261, 255], 274.0),
 (131, '0050192', [261, 255], 274.0),
 (135, '0050196', [261, 255], 274.0),
 (138, '0050199', [261, 255], 274.0),
 (232, '0050309', [261, 255], 274.0),
 (267, '0050344', [261, 255], 274.0),
 (300, '0050377', [261, 255], 274.0),
 (373, '0050465', [261, 255], 274.0),
 (411, '0050503', [261, 255], 274.0),
 (442, '0050552', [261, 255], 274.0),
 (467, '0050577', [261, 255], 274.0),
 (469, '0050601', [261, 255], 274.0),
 (498,
  '0050643',
  [260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   270,
   271,
   272,
   273,
   69,
   70,
   94,
   109,
   110,
   117,
   118,
   255],
  259.0),
 (499,
  '0050644',
  [258, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 271, 272, 273, 255],
  270.0),
 (500,
  '0050645',
  [256,
   257,
   258,
   259,
   260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   270,
   271,
   272,
   273,
   255],
  254.0),
 (503,
  '0050648',
  [260, 262, 263, 265, 266, 267, 268, 272, 93, 94, 255],
  273.0),
 (505, '0050650', [260, 262, 263, 266, 268, 255], 274.0),
 (506,
  '0050651',
  [257,
   258,
   259,
   260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   270,
   271,
   272,
   273,
   94,
   109,
   110,
   117,
   118,
   254,
   255],
  256.0),
 (507,
  '0050652',
  [258,
   260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   110,
   270,
   271,
   272,
   273,
   118,
   94,
   255],
  259.0),
 (508, '0050653', [272, 118, 93, 94, 255], 273.0),
 (510,
  '0050655',
  [256,
   257,
   258,
   259,
   260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   270,
   271,
   272,
   273,
   93,
   94,
   109,
   110,
   112,
   118,
   254,
   255],
  253.0),
 (512, '0050657', [272, 266, 268, 255], 273.0),
 (513,
  '0050658',
  [257,
   258,
   259,
   260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   269,
   270,
   271,
   272,
   273,
   94,
   110,
   118,
   254,
   255],
  256.0),
 (521,
  '0050667',
  [260, 261, 262, 263, 264, 265, 266, 267, 268, 271, 272, 94, 255],
  273.0),
 (523, '0050669', [260, 262, 263, 265, 266, 267, 268, 272, 255], 273.0),
 (537, '0050696', [261, 255], 274.0),
 (541, '0050700', [261, 255], 274.0),
 (556, '0050725', [261, 255], 274.0),
 (572, '0050742', [261, 255], 274.0),
 (582, '0050752', [261, 255], 274.0),
 (659, '0050969', [261, 255], 274.0),
 (700, '0051012', [261, 255], 274.0),
 (809, '0051126', [261, 255], 274.0),
 (903, '0051226', [261, 255], 274.0),
 (915, '0051240', [261, 255], 274.0),
 (944,
  '0051276',
  [161,
   193,
   131,
   194,
   258,
   199,
   200,
   203,
   204,
   205,
   206,
   207,
   208,
   189,
   57,
   59,
   125,
   190,
   255],
  274.0),
 (1021, '0051363', [261, 255], 274.0),
 (1044, '0051468', [261, 255], 274.0),
 (1059, '0051483', [261, 255], 274.0),
 (1072,
  '0051558',
  [260,
   261,
   262,
   263,
   264,
   265,
   266,
   267,
   268,
   109,
   269,
   271,
   272,
   273,
   117,
   118,
   94,
   255],
  270.0),
 (1080, '0051566', [260, 262, 263, 266, 268, 255], 274.0),
 (1085, '0051571', [266, 268, 255], 274.0),
 (1086, '0051572', [266, 263, 268, 255], 274.0),
 (1089, '0051575', [258, 260, 261, 262, 263, 265, 266, 267, 268, 255], 273.0),
 (1096, '0051582', [266, 255], 274.0),
 (1098, '0051584', [266, 268, 262, 255], 274.0)]



In [ ]:



In [77]:

    
# Now to find the Subjects that have corrupted ROIs > 254 with modified list

corrupted_rois_gt_255 = []
subj = []
for sub in sub_gt_255:
    sub_2 = np.array(sub[2])
    modified_list = np.sort(sub_2[np.where(np.logical_and((sub_2 > 255),(sub_2 != 261)))[0]])
    if len(modified_list != 0):
        corrupted_rois_gt_255.append((sub[1],modified_list))
        subj.append(int(sub[1]))



In [78]:

    
subj, corrupted_rois_gt_255









    Out[78]:





([50011,
  50017,
  50035,
  50041,
  50045,
  50050,
  50643,
  50644,
  50645,
  50648,
  50650,
  50651,
  50652,
  50653,
  50655,
  50657,
  50658,
  50667,
  50669,
  51276,
  51558,
  51566,
  51571,
  51572,
  51575,
  51582,
  51584],
 [('0050011', array([266, 268])),
  ('0050017', array([266])),
  ('0050035', array([260, 262, 266, 268])),
  ('0050041', array([266])),
  ('0050045', array([260, 262, 263, 265, 266, 267, 268, 272])),
  ('0050050', array([266, 268])),
  ('0050643',
   array([260, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273])),
  ('0050644',
   array([258, 260, 262, 263, 264, 265, 266, 267, 268, 269, 271, 272, 273])),
  ('0050645',
   array([256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 269,
          270, 271, 272, 273])),
  ('0050648', array([260, 262, 263, 265, 266, 267, 268, 272])),
  ('0050650', array([260, 262, 263, 266, 268])),
  ('0050651',
   array([257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 269, 270,
          271, 272, 273])),
  ('0050652',
   array([258, 260, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273])),
  ('0050653', array([272])),
  ('0050655',
   array([256, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 269,
          270, 271, 272, 273])),
  ('0050657', array([266, 268, 272])),
  ('0050658',
   array([257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 269, 270,
          271, 272, 273])),
  ('0050667', array([260, 262, 263, 264, 265, 266, 267, 268, 271, 272])),
  ('0050669', array([260, 262, 263, 265, 266, 267, 268, 272])),
  ('0051276', array([258])),
  ('0051558',
   array([260, 262, 263, 264, 265, 266, 267, 268, 269, 271, 272, 273])),
  ('0051566', array([260, 262, 263, 266, 268])),
  ('0051571', array([266, 268])),
  ('0051572', array([263, 266, 268])),
  ('0051575', array([258, 260, 262, 263, 265, 266, 267, 268])),
  ('0051582', array([266])),
  ('0051584', array([262, 266, 268]))])



In [79]:

    
# Subjects that are in the problematic set that are in the AUTISTIC SET
set(subj) - (set(subj) - set(df_aut_lt18_m_eyesopen_subid))









    Out[79]:





set()



In [80]:

    
# Subjects that are in the problematic set that are in the TD SET
set(subj) - (set(subj) - set(df_td_lt18_m_eyesopen_subid))









    Out[80]:





{51276}

Results:

Due to conversion of 2mm to 3mm ROI 255 has disappeared
No subject in my bin has missing ROI of 254
There are many subjects that have ROI 261 Missing so we can drop ROI 261 from out analysis
Similarly there is just one subject in my bin with ROI 258 missing. But this ROI has not shown up anyways in my results. Ignoring this ROI for some time. Let's go ahead and drop ROI 258 as well from my analysis.
So finally I am dropping following ROIs from my analysis:
- 255 -
- 258 -
- 261 -