1. Human Biology and Medicine
  2. Neuroscience
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Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics

  1. Ming Song
  2. Yi Yang
  3. Jianghong He
  4. Zhengyi Yang
  5. Shan Yu
  6. Qiuyou Xie
  7. Xiaoyu Xia
  8. Yuanyuan Dang
  9. Qiang Zhang
  10. Xinhuai Wu
  11. Yue Cui
  12. Bing Hou
  13. Ronghao Yu
  14. Ruxiang Xu  Is a corresponding author
  15. Tianzi Jiang  Is a corresponding author
  1. Chinese Academy of Sciences, China
  2. PLA Army General Hospital, China
  3. Guangzhou General Hospital of Guangzhou Military Command, China
  4. University of Electronic Science and Technology of China, China
  5. University of Queensland, Australia
Research Article
Cite this article as: eLife 2018;7:e36173 doi: 10.7554/eLife.36173
33 figures, 9 tables and 3 additional files

Figures

Conceptual paradigm of the study.

CRS-R: Coma Recovery Scale Revised scale; GOS: Glasgow Outcome Scale.

https://doi.org/10.7554/eLife.36173.003
Data analysis pipeline.

All datasets involved in this study included resting state fMRI and clinical data. For the fMRI data in the training dataset, data analysis first encompassed preprocessing and imaging feature selection and extraction. Partial least square regression was then used to generate the regression model using the selected imaging features and clinical features in the training dataset. In this way, a prediction score that depicts the possibility of consciousness recovery was computed for each patient. The optimal cut-off value for classifying an individual patient as responsive or non-responsive was then calculated, and the prognostic classification model was obtained. The two testing datasets were only used to validate externally the regression and classification model.

https://doi.org/10.7554/eLife.36173.005
Imaging features involved in the prognostic regression model.

DMN.aMPFC, anterior medial prefrontal cortex in the default mode network; DMN.PCC, posterior cingulate cortex/precuneus in the default mode network; ExecuContr.DMPFC, dorsal medial prefrontal cortex in the executive control network; Auditory.MCC, middle cingulate cortex in the auditory network; Visual.R.V1, right lateral primary visual cortex in the visual network. DMN.aMPFC—ExecuContr.DMPFC: the functional connectivity between DMN.aMPFC and ExecuContr.DMPFC; Auditory.MCC—Visual.R.V1: the functional connectivity between Auditory.MCC and Visual.R.V1.

https://doi.org/10.7554/eLife.36173.006
Prognostic regression model.

In the three subplots, each color denotes a particular predictor. (A) Regression formula. (B) Predictor importance for each predictor in prognostic regression model. The vertical axis represents the sMC F-test value. The larger the sMC F-value, the more informative the predictor with respect to the regression model. (C) The imaging features in the model are rendered on a 3D surface plot template in medial view.

https://doi.org/10.7554/eLife.36173.007
The performance of the prediction model on the training dataset.

(A) Individual predicted scores for each DOC patient in the training dataset. The CRS-R score at the T0 time point is shown on the x axis and the predicted score on the y axis. The patients diagnosed as VS/UWS at the T0 time point are shown to the left of the vertical red solid line, whereas the patients diagnosed as MCS at this time point are shown to the right. The purplish red pentagram, imperial purple triangle and blank circle mark the patients with a GOS score ≥4,=3 and≤2, respectively, at the T1 time point. (B) Agreement between the CRS-R scores at the T1 time point and the predicted scores. The left panel shows the correlation between the CRS-R scores at the T1 time point and the predicted scores, and the right panel shows the differences between them using the Bland-Altman plot. (C) Bar chart showing the numbers or proportions of DOC patients in each band of predicted scores. In these two panels, the y axis shows the predicted score. (D) The area under the receiver-operating characteristic (ROC) curve. The star on the curve represents the point with the maximal sum of true positive and false negative rates on the ROC curve, which were chosen as the cut-off threshold for classification. Here, the corresponding predicted score = 13.9.

https://doi.org/10.7554/eLife.36173.008
The performance of the prediction model on the two testing datasets.

(A) The individual predicted score (top panel) and agreement between the CRS-R scores at the T1 time point and the predicted scores (bottom panel) for the testing dataset ‘Beijing HDxt’. (B) The individual predicted score for each DOC patient in the testing dataset ‘Guangzhou HDxt’. The legend description is the same as for Figure 5.

https://doi.org/10.7554/eLife.36173.009
The sensitivity and specificity in the ‘subacute’ patients (i.e. duration of unconsciousness T0 ≤3 months) and those in the chronic phase (i.e. duration of unconsciousness T0 >3 months), respectively.
https://doi.org/10.7554/eLife.36173.010
Appendix 3—figure 1
The six brain functional network templates in this study.
https://doi.org/10.7554/eLife.36173.023
Appendix 4—figure 1
Cumulative distribution of head motion per volume (framewise displacement) for normal controls and DOC patients separately in the training dataset ‘Beijing 750’ (A1), the testing dataset ‘Beijing HDxt’ (A2), and the testing dataset ‘Guangzhou HDxt’ (A3).

The normal controls were shown in left column, whereas the DOC patients were shown in right column. No healthy control data were available for the Guangzhou centre. In both patients and controls, head position was stable to within 1.5 mm for the vast majority (>95%) of brain volumes.

https://doi.org/10.7554/eLife.36173.025
Appendix 4—figure 2
Correlations between motion artifact and neuroanatomical distance between the ROIs in this study.

Prior studies have shown that motion artifacts tend to vary with neuroanatomical distance between brain nodes. Here, we conducted quality control analyses as described in the previous study (Power et al., 2015). Specifically, we computed correlations between head motion (mean FD) and each resting state functional connectivity (RSFC) feature and plotted them as a function of neuroanatomical distance (mm) for subjects in the training dataset ‘Beijing 750’ (B1), the testing dataset ‘Beijing HDxt’ (B2), and the testing dataset ‘Guangzhou HDxt’ (B3). Smoothing curves (in red) were plotted using a moving average filter.

https://doi.org/10.7554/eLife.36173.026
Appendix 4—figure 3
Histogram of the remaining number of fMRI volumes after scrubbing for each population, specifically ‘Beijing 750’ datatset (C1), ‘Beijing HDxt’ dataset (C2), and ‘Guangzhou HDxt’ dataset (C3).
https://doi.org/10.7554/eLife.36173.027
Appendix 6—figure 1
The brain area connection features sorted by their Pearson's correlations to the CRS-R scores at the T1 time point in the training dataset ‘Beijing 750’.
https://doi.org/10.7554/eLife.36173.030
Appendix 6—figure 2
The functional connectivity features sorted by their Pearson's correlations to the CRS-R scores at the T1 time point across the DOC patients in the training dataset ‘Beijing 750’.
https://doi.org/10.7554/eLife.36173.031
Appendix 6—figure 3
The Circos map for the functional connectivity features that were significantly correlated to the CRS-R scores at the T1 time point across the DOC patients in the training dataset ‘Beijing 750’.
https://doi.org/10.7554/eLife.36173.032
Appendix 7—figure 1
Histogram depicting the imaging features included in CARS-PLSR models.
https://doi.org/10.7554/eLife.36173.036
Appendix 8—figure 1
The imaging subscores for all of the subjects in the three datasets.
https://doi.org/10.7554/eLife.36173.038
Appendix 9—figure 1
The distribution of the predicted imaging subscores of the healthy controls at different sites.
https://doi.org/10.7554/eLife.36173.040
Appendix 9—figure 2
The correlations between the fMRI signal-to-noise ratio (SNR) and the predicted imaging subscores in the healthy controls.
https://doi.org/10.7554/eLife.36173.041

Tables

Table 1
Demographic and clinical characteristics of the patients in the three datasets.
https://doi.org/10.7554/eLife.36173.004
Beijing_750
(n = 63)
Beijing_HDxt
(n = 25)
Guangzhou_HDxt
(n = 24)
Gender, M/F36/2718/714/10
Etiology
 Trauma/Stroke/Anoxia17/21/2512/6/78/0/16
Age at the T0 (years)
 Mean (SD)42.8 (13.8)40.7 (15.2)39.3 (16.9)
 Range18.0 ~ 71.018.0 ~ 68.015.0 ~ 78.0
Time to MRI (months)
 Range1.0 ~ 77.01.0 ~ 44.01.0 ~ 10.0
 Mean (SD)7.4 (12.8)5.4 (8.4)2.3 (2.4)
 Median3.03.01.5
 Band
  [1,3]321320
  (3,6]1582
  (6,12]1132
  >12510
Follow-up time (months)
 Range12.0 ~ 51.014.0 ~ 53.027.0 ~ 78.0
 Mean (SD)21.0 (9.8)41.7 (8.4)52.2 (14.5)
 Median15.043.053.0
 Band
  [12,24]3820
  (24,48]24208
  >481316
Diagnosis at T0
MCS/VS17/465/208/16
CRS-R total score
 Mean (SD)7.3 (2.9)6.5 (2.3)7.1 (4.1)
 Range3.0 ~ 18.03.0 ~ 14.03.0 ~ 17.0
Outcome at T1
CRS-R total score
 Mean (SD)9.9 (5.1)12.7 (6.4)N/A
 Range3.0 ~ 22.05.0 ~ 23.0N/A
GOS score
 GOS = 5000
 GOS = 4551
 GOS = 3875
 GOS <= 2501318
  1. Abbreviations: CRS-R, Coma Recovery Scale–Revised; GOS, Glasgow Outcome Scale; MCS, minimally conscious state; N/A, not available; SD, standard deviation; VS, vegetative state/unresponsive wakefulness syndrome.

Appendix 1—table 1
Demographic and clinical characteristics of patients in the ‘Beijing_750’ dataset.
https://doi.org/10.7554/eLife.36173.015
Patient aliasGenderAge (years)DiagnoseEtiologyStructural lesions on MRITime to MRI (months)Number of CRS-R assessmentsCRS-R score at T0CRS-R subscore at T0CRS-R score at T1CRS-R subscore at T1Follow-up(months)GOSPredicted score
001M36VS/UWSAnoxiaDiffuse pons damage1670221022244632315418.26
002M29MCSTraumaBilateral-temporo-parietal damage94183551132245622339415.31
003F33VS/UWSTraumaBilateral-frontal lobe damage, atrophy12571022022245532312422.88
004F28MCSTraumaL-frontal-temporal lobe damage14153351032245622319416.58
005M23MCSAnoxiaDiffuse cortical and subcortical atrophy34102321022145522313417.08
006M45MCSStrokeL-temporo-parietal damage9492221021733422312313.94
007M39MCSStrokeBrainstem damage14173451131944512312314.39
008F27MCSTraumaL-basal ganglia damage106123321031834512319316.09
009M23MCSTraumaDiffuse cortical and subcortical atrophy6491321021944422313310.94
010M42MCSStrokeL-basal ganglia damage3771031021941632312314.72
011M53MCSStrokeDiffuse cortical and basal ganglia (caudates) damage75113321021433212314311.55
012F40VS/UWSStrokeDiffuse cortical and basal ganglia damage5671121021433312212314.67
013M22VS/UWSTraumaL-frontal-temporo-parietal lobe damage3471121021533412227315.48
014F64VS/UWSStrokeL-thalamus, basal ganglia lesions147112102112331021728.28
015F42VS/UWSAnoxiaDiffuse anoxic cortical lesions14711210292221021429.02
016M45VS/UWSAnoxiaDiffuse anoxic cortical lesions95500210271121021528.65
017F60VS/UWSAnoxiaDiffuse anoxic cortical lesions44610210261021021327.71
018M42VS/UWSStrokeR-cerebral hemisphere lesions647112102711210214212.44
019M51VS/UWSAnoxiaDiffuse cortical and subcortical atrophy34711210271121022824.28
020F35VS/UWSAnoxiaBilateral-frontal lobe damage, atrophy24711210271121021325.87
021M71VS/UWSTraumaDiffuse cortical and subcortical atrophy66310110041011011324.46
022F30VS/UWSAnoxiaBilateral-basal ganglia damage24400200270221023826.92
023F58VS/UWSTraumaDiffuse cortical and subcortical atrophy24300210040021011425.09
024M23MCSTraumaR-basal ganglia (caudates) damage5571031021122320212214.57
025F66VS/UWSTraumaBilateral-temporo-parietal damage14610210281131023225.71
026F25VS/UWSAnoxiaDiffuse cortical and subcortical atrophy34510200261120023626.75
027M48VS/UWSAnoxiaDiffuse cortical and subcortical atrophy45711210281131022927.83
028F28MCSAnoxiaDiffuse cortical and subcortical atrophy5492221021123310232211.36
029M57VS/UWSAnoxiaDiffuse cortical and subcortical atrophy114610210261021023324.70
030M61MCSStrokeBilateral-temporo-parietal lobe damage24111341021122311212210.34
031M40VS/UWSAnoxiaDiffuse cortical and subcortical atrophy44400110250111022725.70
032M39VS/UWSStrokeR-basal ganglia damage, atrophy34711210271121021228.03
033M41VS/UWSAnoxiaDiffuse cortical and subcortical atrophy24500210250021021326.44
034M26VS/UWSStrokeDiffuse cortical and subcortical atrophy544711210271121023827.28
035F50VS/UWSAnoxiaDiffuse cortical and subcortical atrophy86610210291222021225.77
036F53VS/UWSStrokeBilateral brainstem, midbrain damage34511210071121022828.02
037M67VS/UWSStrokeR- brainstem, cerebellar damage14511210030020011222.04
038M45MCSStrokeDiffuse cortical and subcortical atrophy2591321021022211213210.91
039F35VS/UWSAnoxiaDiffuse cortical and subcortical atrophy346102102811220219210.24
040F46MCSTraumaDiffuse axonal injury777112222121333221251214.76
041M49VS/UWSStrokeBilateral-brainstem, cerebellar damage1047112102711210228210.87
042M45VS/UWSStrokeDiffuse cortical and basal ganglia damage34711210281221021927.59
043M18VS/UWSAnoxiaDiffuse cortical and subcortical atrophy856111102912310212210.85
044M53VS/UWSAnoxiaBilateral-occipital lobe damage, atrophy24300200171121023421.98
045M46VS/UWSTraumaR-temporo-parietal damage44610120261012021327.23
046F29VS/UWSAnoxiaDiffuse cortical and subcortical atrophy284711210291231021228.31
047F47MCSStrokeR-basal ganglia damage4758113102112222121229.66
048M58VS/UWSStrokeBilateral-temporo-parietal lobe damage64711210281131022727.05
049M66VS/UWSAnoxiaL-frontal lobe damage44400200261021023824.79
050M34VS/UWSTraumaDiffuse axonal injury346112101812210214210.28
051F31MCSTraumaL-frontal-temporo-parietal lobe damage3511133202811220215215.56
052M33VS/UWSStrokeL-temporo-parietal lobe damage174610210281131021327.67
053F31VS/UWSAnoxiaDiffuse cortical and basal ganglia (caudates) damage14610210261021022728.36
054F28VS/UWSAnoxiaDiffuse cortical and subcortical atrophy34610210281122023229.23
055F26VS/UWSStrokeL-basal ganglia damage446102102610210212210.96
056M45VS/UWSTraumaDiffuse axonal injury14610210261021022929.05
057F69VS/UWSStrokeDiffuse cortical and subcortical atrophy446102102710220233212.43
058F68VS/UWSTraumaDiffuse axonal injury66711210291321021729.74
059M50VS/UWSStrokeL-frontal-temporo-parietal lobe damage34611110282220022727.01
060M60MCSTraumaBilateral brainstem, midbrain damage74111341021122311230211.69
061M44VS/UWSAnoxiaDiffuse cortical and subcortical atrophy246102102400210113210.48
062F35VS/UWSAnoxiaBilateral-basal ganglia damage35721110292311022729.07
063F43VS/UWSAnoxiaDiffuse cortical and subcortical atrophy247112102820211229210.09
Appendix 1—table 2
Demographic and clinical characteristics of patients in the ‘Beijing_HDxt’ dataset.
https://doi.org/10.7554/eLife.36173.016
Patient aliasGenderAge (years)DiagnoseEtiologyStructural lesions on MRITime to MRI (months)Number of CRS-R assessmentsCRS-R score at T0CRS-R subscore at T0CRS-R score at T1CRS-R subscore at T1follow-up(months)GOSPredicted score
001M19VS/UWSTraumaL-temporo-parietal lobe damage6471121022245622340420.37
002M26MCSTraumaR-thalamus, basal ganglia lesions36102321022345632347417.12
003F22VS/UWSTraumaL-temporal lobe damage4461021022245622347414.05
004M41VS/UWSStrokeBilateral brainstem, midbrain damage3461121012345632350420.23
005M36MCSStrokeBilateral brainstem damage446003102234563233947.75
006M34VS/UWSAnoxiaDiffuse cortical and subcortical atrophy1461111021432312331317.25
007F18VS/UWSTraumaDiffuse axonal injury3450120021433212341314.86
008M58MCSTraumaR-frontal lobe damage12481131021533312340315.62
009M41MCSTraumaR-frontal-temporo-parietal lobe damage15112330121834422342318.89
010M46VS/UWSStrokeL-brainstem, cerebellar damage7461021021433212353317.05
011M25VS/UWSAnoxiaDiffuse cortical and subcortical atrophy4661021021422412346318.07
012M58VS/UWSTraumaL-brainstem damage1771121021935512340310.75
013M36VS/UWSTraumaL-frontal-temporo-parietal lobe damage647112102102321024429.58
014M58VS/UWSTraumaR-frontal-temporo-parietal lobe damage44610210261021024526.69
015M65VS/UWSStrokeDiffuse cortical and subcortical atrophy34310000251011024324.01
016F24VS/UWSTraumaDiffuse axonal injury4466102102812210244214.03
017F46VS/UWSStrokeL-pons damage247112102711210240212.11
018M53VS/UWSAnoxiaDiffuse cortical and subcortical atrophy34410100261021024125.38
019F32VS/UWSTraumaL-temporo-parietal lobe damage346102102811220223213.76
020M41VS/UWSAnoxiaDiffuse cortical and subcortical atrophy244101002811220240212.06
021F33VS/UWSAnoxiaDiffuse cortical and subcortical atrophy756211002112322024724.55
022M49VS/UWSAnoxiaDiffuse cortical and subcortical atrophy24610210261021021428.97
023F25MCSAnoxiaBilateral thalamus, brainstem damage47144500231024002250212.42
024M63VS/UWSStrokeL-basal ganglia lesions54400110251011024828.22
025M68VS/UWSTraumaL-frontal-temporo-parietal lobe damage24500210260121024729.72
Appendix 1—table 3
Demographic and clinical characteristics of patients in the ‘Guangzhou_HDxt’ dataset.
https://doi.org/10.7554/eLife.36173.017
Patient aliasGenderAge (years)DiagnoseEtiologyTime to MRI (months)CRS-R score at T0CRS-R subscore at T0Follow-up (months)GOSPredicted score
001F15MCSAnoxia11313511259420.92
002M29MCSTrauma4911401261316.50
003F27MCSTrauma1910510229316.67
004F20MCSTrauma2811310241320.37
005M30MCSTrauma11511622363317.34
006M31MCSTrauma12044522351314.00
007M28VS/UWSAnoxia151020026929.71
008M48MCSTrauma1122341025529.11
009M46VS/UWSTrauma2410200165212.58
010M78VS/UWSAnoxia141001024929.20
011M39VS/UWSAnoxia150021025124.20
012F46VS/UWSAnoxia2400110246214.49
013M39VS/UWSAnoxia251020024327.30
014M16VS/UWSAnoxia2300100271214.17
015M25MCSAnoxia1121351027828.56
016F76VS/UWSAnoxia541001025923.57
017M36VS/UWSTrauma2500120265217.28
018M32VS/UWSAnoxia1061021025625.66
019F49VS/UWSAnoxia161021024924.55
020F52VS/UWSAnoxia130001022725.08
021M62VS/UWSAnoxia2300100228210.26
022F33VS/UWSAnoxia261021022929.09
023F28VS/UWSAnoxia9610210267212.62
024F57VS/UWSAnoxia150021024224.72
Appendix 1—table 4
Demographic of healthy controls in the ‘Beijing_750’ dataset.
https://doi.org/10.7554/eLife.36173.018
AliasGenderAgeHandedness
NC001F40Right
NC002M50Right
NC003F34Right
NC004M25Right
NC005M28Right
NC007F24Right
NC008F47Right
NC009F22Right
NC010F60Right
NC012F26Right
NC013M21Right
NC014F27Right
NC015M40Right
NC016M44Right
NC017F22Right
NC018M50Right
NC019M27Right
NC020F43Right
NC021F25Right
NC022M54Right
NC023F52Right
NC026M46Right
NC027F52Right
NC028M29Right
NC029F46Right
NC030M44Right
NC031M30Right
NC032M31Right
NC033M32Right
NC034M30Right
Appendix 1—table 5
Demographic of healthy controls in the ‘Beijing_HDxt’ dataset.
https://doi.org/10.7554/eLife.36173.019
AliasGenderAgeHandedness
NC001_HDxtM44Right
NC002_HDxtM42Right
NC003_HDxtM30Right
NC004_HDxtM40Right
NC005_HDxtM30Right
NC006_HDxtM30Right
NC007_HDxtF58Right
NC008_HDxtF54Right
NC009_HDxtF41Right
NC010_HDxtF41Right
Appendix 2—table 1
Brain networks and ROIs in this study.
https://doi.org/10.7554/eLife.36173.021
Brain networkROI nameROI
abbreviation
Peak MNI coordinatesReferences
Default mode(Raichle, 2011; Demertzi et al., 2015)
Anterior medial prefrontal cortexaMPFC−1 54 27
Posterior cingulate cortex/precuneusPCC0 –52 27
Left lateral parietal cortexL.LatP−46 –66 30
Right lateral parietal cortexR.LatP49 –63 33
Executive control(Seeley et al., 2007; Raichle, 2011)
Dorsal medial PFCDMPFC0 27 46
Left anterior prefrontal cortexL.PFC−44 45 0
Right anterior prefrontal cortexR.PFC44 45 0
Left superior parietal cortexL. Parietal−50 –51 45
Right superior parietal cortexR. Parietal50 –51 45
Salience(Seeley et al., 2007; Raichle, 2011; Demertzi et al., 2015)
Left orbital frontoinsulaL.AIns−40 18 –12
Right orbital frontoinsulaR.AIns42 10 –12
Dorsal anterior cingulatedACC0 18 30
Sensorimotor(Raichle, 2011; Demertzi et al., 2015)
Left primary motor cortexL.M1−39 –26 51
Right primary motor cortexR.M138 –26 51
Supplementary motor areaSMA0 –21 51
Auditory(Raichle, 2011; Demertzi et al., 2015)
Left primary auditory cortexL.A1−62 –30 12
Right primary auditory cortexR.A159 –27 15
Middle cingulate cortexMCC0 –7 43
Visual(Demertzi et al., 2015)
Left primary visual cortexL.V1−13 –85 6
Right primary visual cortexR.V18 –82 6
Left associative visual cortexL.V4−30 –89 20
Right associative visual cortexR.V430 –89 20
Appendix 6—table 1
The brain area connection features and their Pearson's correlations to the CRS-R scores at the T1 time point across the DOC patients in the training dataset ‘Beijing 750’.
https://doi.org/10.7554/eLife.36173.033
ROI namePearson's correlation coefficient and p value
**DMN.aMPFCr = 0.514, p=0.000
**ExecuContr.L.Parietalr = 0.429, p=0.000
**DMN.PCCr = 0.420, p=0.001
**DMN.R.LatPr = 0.407, p=0.001
**ExecuContr.DMPFCr = 0.405, p=0.001
*ExecuContr.R.Parietalr = 0.363, p=0.003
*Sensorimotor.SMAr = −0.332, p=0.008
*ExecuContr.R.PFCr = 0.320, p=0.011
*Auditory.R.A1r = 0.315, p=0.012
*DMN.L.LatPr = 0.298, p=0.018
*ExecuContr.L.PFCr = 0.291, p=0.021
*Sensorimotor.L.M1r = 0.267, p=0.035
Auditory.L.A1r = 0.206, p=0.105
Salience.R.AInsr = −0.187, p=0.142
Sensorimotor.R.M1r = 0.167, p=0.191
Visual.L.V4r = −0.151, p=0.236
Salience.dACCr = −0.104, p=0.418
Salience.L.AInsr = 0.075, p=0.560
Visual.R.V1r = 0.065, p=0.611
Auditory.MCCr = 0.053, p=0.682
Visual.R.V4r = −0.031, p=0.809
Visual.L.V1r = −0.028, p=0.830
  1. **: p<0.05, FDR corrected; *: p<0.05, uncorrected.

Appendix 6—table 2
Functional connectivity features and their Pearson's correlations to the CRS-R scores at the T1 time point across the DOC patients in the training dataset ‘Beijing 750’.
https://doi.org/10.7554/eLife.36173.034
Functional connectivityPearson's correlation coefficient and p-value
DMN.aMPFC - ExecuContr.DMPFCr = −0.489, p=0.000
*DMN.L.LatP - Visual.L.V4r = −0.421, p=0.001
*Auditory.MCC - Visual.R.V1r = 0.375, p=0.002
*ExecuContr.R.PFC - ExecuContr.R.Parietalr = 0.361, p=0.004
*ExecuContr.DMPFC - Auditory.MCCr = −0.351, p=0.005
*ExecuContr.L.PFC - Salience.dACCr = −0.335, p=0.007
*Sensorimotor.R.M1 - Sensorimotor.SMAr = −0.330, p=0.008
*Sensorimotor.R.M1 - Auditory.L.A1r = 0.319, p=0.011
*Salience.dACC - Visual.R.V1r = 0.319, p=0.011
*ExecuContr.DMPFC - Sensorimotor.L.M1r = −0.310, p=0.013
*DMN.R.LatP - Visual.R.V4r = −0.306, p=0.015
*ExecuContr.L.Parietal - Sensorimotor.L.M1r = −0.302, p=0.016
*DMN.aMPFC - Salience.dACCr = −0.292, p=0.020
*DMN.aMPFC - Sensorimotor.L.M1r = −0.286, p=0.023
*DMN.aMPFC - DMN.PCCr = 0.275, p=0.029
*ExecuContr.R.Parietal - Visual.R.V4r = −0.270, p=0.033
*DMN.aMPFC - Sensorimotor.R.M1r = −0.268, p=0.034
*ExecuContr.R.Parietal - Sensorimotor.R.M1r = −0.263, p=0.037
*Sensorimotor.L.M1 - Sensorimotor.SMAr = −0.261, p=0.039
*DMN.R.LatP - Sensorimotor.R.M1r = −0.261, p=0.039
*ExecuContr.R.Parietal - Visual.L.V4r = −0.257, p=0.042
*Salience.dACC - Visual.L.V4r = 0.256, p=0.043
*ExecuContr.DMPFC - Sensorimotor.R.M1r = −0.255, p=0.043
*DMN.aMPFC - Visual.L.V1r = 0.251, p=0.047
*Salience.R.AIns - Sensorimotor.L.M1r = 0.250, p=0.049
*DMN.L.LatP - Sensorimotor.SMAr = 0.248, p=0.050
  1. Specifically, the functional connectivity features were the functional connectivity between any pair of ROIs. As there were more than 200 functional connectivity features, because of space limitations, only the functional connectivity features that were significantly correlated to the CRS-R scores at the T1 time point are shown. **: p<0.05, FDR corrected; *: p<0.05, uncorrected.

Data availability

We have provided anonymised demographic and clinical characteristics of the DOC patients in Appendix 1. We have made the analysis pipeline, including fMRI preprocessing, feature calculation and extraction, regression and classification, and the results visualization publicly available. Also, we have uploaded the fMRI signals in each of region of interest for every DOC patient and healthy control involved in this study. Anyone is welcome to download them from GitHub (https://github.com/realmsong504/pDOC; copy archived at https://github.com/elifesciences-publications/pDOC).

Additional files

Supplementary file 1

Some examples of the warped ROIs in the default mode network for one healthy control and three DOC patients with a GOS score of 2,3 and 4, respectively.

https://doi.org/10.7554/eLife.36173.011
Supplementary file 2

Details about single-domain prognostic models and comparisons of the single-domain and combination models.

https://doi.org/10.7554/eLife.36173.012
Transparent reporting form
https://doi.org/10.7554/eLife.36173.013

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