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............................................................................................................................ 6
1.1 ............................................................................................... 6
1.2 − − ........................................ 6
1.3 DTI ............................................................ 7
1.4 – − .................................................... 7
1.5 TMS ................................................................. 8
1.6 DTI TMS .......................... 9
1.7 ............................................................................................................... 9
.............................................................................. 10
2.1 ..................................................................................................................... 10
2.2 ............................................................................................................................. 10
2.3 ............................................................................................................................. 11
2.3.1 .......................................................................................................... 11
2.3.2 .......................................................................................................... 11
2.3.3 DTI ...................................................................................................... 11
2.3.4 DTI .............................................................................................. 11
2.3.5 TBSS ............................................... 12
2.3.6 BRS ROI FA ........................................ 12
2.3.7 ...................................................................................................... 13
2.4 ............................................................................................................................. 14
2.4.1 .......................................................................................................... 14
2.4.2 TBSS ............................................... 15
2.4.3 BRS ................................................................................ 16
2.4.4 rFA ............. 17
2.5 ............................................................................................................................. 17
2.5.1 .................. 18
2.5.2 ROI . 18
2.5.3 .............................................. 20
2.5.4 .................................................................................. 20
2
−4 − .......................................................................... 21
3.1 ..................................................................................................................... 21
3.2 ............................................................................................................................. 21
3.3 ............................................................................................................................. 22
3.3.1 .......................................................................................................... 22
3.3.2 .............................................................................................. 22
3.3.3 DTI ...................................................................................................... 22
3.3.4 ROI .............................................................................. 22
3.3.5 .................................. 23
3.3.6 TMS MEP CMCT ................................. 23
3.4 ..................................................................................................................... 24
3.5 ............................................................................................................................. 26
3.5.1 MEP CMCT .......................................... 27
3.5.2 MEP CMCT ............................................... 27
3.5.3 .................................................................. 28
...................................................................................................................................... 29
4.1 ..................................................................................................................... 29
4.2 ............................................................................................................................. 29
4.3 ............................................................................................................................. 29
4.3.1 .......................................................................................................... 29
4.3.2 .............................................................................................. 30
4.3.3 DTI ...................................................................................................... 30
4.3.4 ROI .............................................................................. 31
4.3.5 MEP CMCT ........................................................... 31
4.3.6 ...................................................................................................... 31
4.4 ............................................................................................................................. 32
4.4.1 .......................................................................................................... 32
4.4.2 ROI rFA ............................. 33
4.4.3 MEP ............................................................ 34
4.4.4 rCMCT .................................................................... 35
4.5 ............................................................................................................................. 35
4.5.1 rFA MEP rCMCT ..... 36
3
4.5.2 rFA MEP rCMCT ..... 36
.......................................................................................... 38
.......................................................................................................................... 42
.................................................................................................................................. 43
4
1 n=23 ........................................................................................................14
2 BRS ROI rFA n=23 ............15
3 ....................................................................................................................24
4 ................................................................................................................30
5 n=17 ........................................................................................................32
1 TBSS n=10 n=13 .................................16
2 rFA ROC n=23 ...........................17
3 BRS rFA n=23 ....................................................19
4 DTT............................................................................................26
5 rFA .............................................................33
6 MEP ...................................................................34
7 rCMCT ...............................................................................35
8 DTI TMS ........................................38
n=16 ..........................39
10 DTI TMS ......................................40
11 n=17 ..........................41
5
ARAT action research arm test
BRS Brunnstrom recovery stage
CMCT central motor conduction time
DTI diffusion tensor imaging
DTT diffusion tensor tractography
FA fractional anisotropy
FMA Fugle-Meyer assessment
MEP motor evoked potential
MRC scale medical research council scale
MRI magnetic resonance imaging
rCMCT central motor conduction time ratio
rFA fractional anisotropy ratio
ROI region of interest
ROA region of avoidance
TBSS tract-based spatial statistics
6
23 4
123 [1,2]
[3,4]
[4]
1.1
[5]
[6–10]
[11]
[7,8,12,13] [9,10]
1.2 − −
diffusion tensor imaging DTI DTI
magnetic resonance imaging MRI
[14,15]
fractional anisotropy FA 0 1
1 [16,17] FA
FA
[18] FA
[16]
FA
region of interest ROI
FA ROI ROI
7
FA [16,17]
diffusion tensor tractography DTT [19,20]
ROI DTI ROI
FA ROI
[16] ROI
[17]
Oxford FMRIB Software Library
http://fsl.fmrib.ox.ac.uk/fsl Tract-based spatial statistics TBSS
[21] DTT FA
ROI
ROI
[20]
1.3 DTI
Koyama [22–25] ROI
FA FA ratio rFA Brunnstrom recovery stage
BRS Medical research council MRC scale 1
5-7 ROI ROI
[22] [23] [23–25]
rFA BRS MRC scale Jang [26]
DTT DTT 6 modified brunnstrom
classification motricity index
DTT
DTI
DTI
1.4 – −
DTI
transcranial magnetic stimulation TMS TMS
motor evoked potential MEP central
8
motor conduction time CMCT
[27]
MEP
MEP → →
MEP
MEP [27]
CMCT
M F
MEP CMCT
[27]
1.5 TMS
TMS MEP CMCT
MEP MEP
[28–33] CMCT Heald [32]
CMCT CMCT
Escudero [33]
CMCT
Piron [34] MEP Hemiplegic stroke scale
MEP
Hendricks [35]
MEP Fugle-Meyer
assessment
Hendricks MEP
CMCT
MEP
CMCT
CMCT
9
1.6 DTI TMS
DTI TMS
[36–38] Stinear [37]
72
TMS MEP ROI FA asymmetry
FA- FA/ FA+ FA 3
Complete Notable Limited None
72 MRC scale 0-5
8 Complete
8 MEP MEP Notable
MEP FA asymmetry FA asymmmetry 0.15
Limited 0.15 None Stinaer
40 37
1.7
2 DTI ROI
3 DTI TMS
4 DTI TMS
5
10
2.1
diffusion tensor imaging DTI
[14,15] DTI
[28,39–41]
DTI ROI
FA
0.55 0.95
[22,25,37,42–47]
ROI
[25,42–44] [23,37]
[22,45] [46,47]
ROI ROI
[22,25,37] ROI [42–44] ROI
FA
[22,48,49]
FA [50]
DTI
2.2
ROI
DTI
ROI TBSS
FA
ROI FA
11
2.3
2.3.1
2013 10 2015 12
DTI
MRI
23 61.1±9.9
15 3
641
2.3.2
DTI
DSI studio http://dsi-studio.labsolver.org
DTI b0
Brunnstrom Recovery Stage BRS
[51]
2.3.3 DTI
1.5T-MRI TOSHIBA EXCELART Vantage, MRT-2003
DTI single shot spin echo EPI TR=10000ms TE=100ms
motion-probing gradient orientation=6 b=1000 field of view 260mm×260mm
1×1×1mm 128×128 3mm
2.3.4 DTI
DTI Oxford FMRIB Software Library [52] Eddy current correction
b0 brain
extraction tool b0
FMRIB’s diffusion toolbox FA
12
2.3.5 TBSS
TBSS
TBSS 2 FA
FA FA FA
[21]
[6–11]
BRS BRS
Ⅲ BRS Ⅳ10
66.3±10.1 6 4 13 57.2±7.9 9
4
TBSS FA
FA FA FMRIB58
FA FA FA
0.35 0.2 [21]
FA FA
0.35 FA
FA FA
n=5000 threshold-free cluster
enhancement [53] 0.01
2.3.6 BRS ROI FA
BRS ROI FA
FA FA FMRIB58
ROI ROI TBSS [23,25,42–
44,54,55] ROI Oxford FMRIB
Software Library JHU ICBM-DTI-81
white-matter labels atlas [56] ROI FA
FA FA FA ratio; rFA rFA=
FA / FA [25,44] rFA
ROI FA
13
2.3.7
t BRS DTI
Mann-Whitney U BRS
ROI rFA Spearman
BRS
receiver operating characteristic ROC
area under the curve AUC
R3.2.3 https://www.R-project.org/ 0.05
14
2.4
2.4.1
BRS 1
p<0.05 BRS p<0.01 BRS
p<0.01 BRS p<0.01 p<0.05
BRS BRS
BRS ⅢBRS Ⅲ p=0.98 p=0.37
p=0.73 DTI p=0.21 p=0.11
1 n=23
DTI diffusion tensor imaging
15
2.4.2 TBSS
TBSS
FA p<0.01 1
ROI
1 TBSS n=10 n=13
TBSS Montreal Neurological Institute 152 T1 FA
FA
Y=-13 Z=6
Z=13
Z=20
R L R L
16
2.4.3 BRS
TBSS
ROI rFA BRS
BRS rFA
2 rFA r=0.70 p<0.01 r=0.66 p<0.01
r=0.64 p<0.01 BRS
BRS
2 BRS ROI rFA n=23
rFA fractional anisotropy ratio
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2.4.4 rFA
BRS rFA ROC 2
rFA
0.906 0.692 1.000 AUC=0.846
BRS 0.863
0.846 0.700 AUC=0.808 0.906 0.562 1.000 AUC=0.839
3 BRS rFA
rFA
0.80 BRS
2 rFA ROC n=23
BRS rFA 0.906
0.692 1.000 AUC=0.846 0.863 0.846 0.700
AUC=0.808 0.906 0.562 1.000 AUC=0.839
2.5
TBSS ROI FA
ROI
BRS TBSS
FA
BRS ROI rFA
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
1−specificity
sensitivity
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
1−specificity
sensitivity
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
1−specificity
sensitivity
1–
18
BRS rFA
2.5.1
FA [22,48,49] Schaechter [50]
FA
FA [50]
Yin
[57] 3
Paralyzed Hand Function Assessment
TBSS
FA
FA FA
[57]
FA
[23]
FA
FA
DTI
2 [57] [50]
1.5 3
FA [47,55] T2
3 [58]
Schaechter[50]
2.5.2 ROI
TBSS
ROI rFA BRS
19
rFA BRS ROI
FA
ROI [22,25,37,42–47]
ROI
ROI
rFA 0.906
AUC 0.8 rFA
0.80 0.85[42,54]
0.80 [46]
rFA BRS
rFA rFA 0.80
3 rFA=0.80
3 BRS rFA n=23
a BRS b BRS c BRS rFA
BRS rFA
rFA 0.80 BRS
rFA fractional anisotropy ratio
1 2 3 4 5 6
0.70
0.80
0.90
1.00
Hand_BRS and PLIC_rFA
Brunnstrom recovery stage (Hand)
rFA
1 2 3 4 5 6
0.70
0.80
0.90
1.00
UE_BRS and PLIC_rFA
Brunnstrom recovery stage (UE)
rFA
1 2 3 4 5 6
0.70
0.80
0.90
1.00
LE_BRS and PLIC_rFA
Brunnstrom recovery stage (LE)
rFA
r=0.70p<0.01
r=0.66p<0.01
r=0.64p<0.01
a b c
rFA= .80
20
2.5.3
BRS
BRS
[59–61]
DTI
[28,40] rFA
2.5.4
23 BRS
BRS
BRS
ROI rFA
ROI rFA
BRS 3
FA
BRS Fugl-Meyer
assessment[62]
21
−4 −
3.1
ROI
FA FA ratio rFA
rFA
rFA
DTI TMS
MEP CMCT
[27]
MEP
[28,32,33] MEP
[34,35] MEP
[35] MEP
CMCT
CMCT
[32,35]
CMCT [33]
CMCT
72
CMCT
TMS DTI
rFA TMS MEP CMCT
3.2
rFA 1 4 TMS MEP
CMCT
22
3.3
3.3.1
4
3 4
641
3.3.2
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BRS
[51] ARAT[63]
ARAT 6 4 6 3
4 4 0
1 2 3
57
[64,65]
3.3.3 DTI
1.5T-MRI TOSHIBA EXCELART Vantage MRT-2003
DTI single shot spin echo EPI TR=10000ms TE=100ms
motion-probing gradient orientation=6 b=1000 field of view 260mm×260mm
1×1×1mm 128×128 3mm
3.3.4 ROI
ROI Oxford FMRIB Software Library FSL, http://fsl.fmrib.ox.ac.uk/fsl
[52] ROI 1 eddy-current
correction brain extraction tool
b0
FMRIB’s diffusion toolbox FA FA
23
FMRIB58 FA
region of interest ROI [37]
ROI FSL JHU ICBM-DTI-81 white-matter labels
atlas [56] ROI FA FA
FA FA ratio rFA rFA= FA /
FA rFA 1 FA
3.3.5
diffusion tensor tractography DTT
ROI ROI
[19,20] ROI rFA DTT
DTT DSI studio
http://dsi-studio.labsolver.org DTT
multiple ROI approach[20] ROI
region of avoidance ROA
ROI FSL JHU ICBM-DTI-81
white-matter labels atlas Harvard-Oxford cortical structural atlases [56,66] FA
ROA
FA threshold > 0.2 maximum angle = 50°
3.3.6 TMS MEP CMCT
MEP M F
MEP
150%
MEP 5 MEP M F
M M F
16 CMCT MEP M F
CMCT=MEP
-(F +M -1)/2 [27] CMCT
CMCT CMCT ratio rCMCT
24
rCMCT= CMCT/ CMCT rCMCT 1
CMCT MEP MEP+ MEP-
rCMCT MEP rCMCT-
3.4
BRS ARAT MEP rCMCT DTT
3
3
BRS Brunnstrom recovery stage ARAT action arm research test rFA fractional
anisotoropy ratio DTT diffusion tensor tractography MEP motor evoked potential
rCMCT central motor conduction time ratio DTI diffusion tensor imagein TMS
transcranial magnetic stimulation
A
30
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4A MEP
rCMCT .00 0.88
BRS
ARAT 57 /57
B
50
ROI rFA 0.90 DTT
4B MEP
rCMCT 1.32
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MEP
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[30–33]
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MEP
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BRS
ARAT CMCT B
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Nine-hole peg test CMCT
CMCT
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MEP
MEP CMCT
[34,35] MEP BRS
MEP
MEP CMCT
MEP CMCT
29
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rCMCT
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rCMCT
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33 66
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FMRIB58 FA
region of interest ROI [37]
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FA rFA 1 FA
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rFA rFA
a b c
34
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MEP MEP MEP FMA
p<0.01 ARAT p<0.01 MEP 6
FMA p=0.43 MEP
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6 MEP
MEP FMA
n=17 b ARAT n=16 MEP n=6 MEP n=11
b FMA n=17
MEP n=5 MEP n=12
1 2
1020
3040
5060
70
FMA_UE vs MEP_UE
MEP_UE
FMA_UE
1 2
010
2030
4050
60
ARAT vs MEP_UE
MEP_UE
ARAT
1 2
1015
2025
3035
FMA_LE vs MEP_LE
MEP_LEFM
A_LE
** **
MEP
FMA
ARAT
FMA
MEP MEP
**p<0.01a b c
35
4.4.4 rCMCT
rCMCT ARAT r=-0.87 p<0.01
rCMCT FMA r=-0.57 p=0.07 FMA
r=-0.54 p=0.07 7
rCMCT 1.5 FMA ARAT
rCMCT p=0.05 rCMCT 1.3
7 rCMCT
rCMCT a FMA n=11 b ARAT n=10 c FMA
n=12 ARAT rCMCT
4.5
DTI TMS
ROI rFA FMA
ARAT FMA rFA
rFA 0.85
TMS MEP FMA
ARAT MEP FMA
rCMCT FMA FMA
0.0 0.5 1.0 1.5 2.0
1020
3040
5060
70
FMA_UE vs rCMCT_UE
rCMCT_UE
FMA
mot
or s
core
(UE)
0.0 0.5 1.0 1.5 2.0
010
2030
4050
60
ARAT vs rCMCT_UE
rCMCT_UE
ARAT
0.0 0.5 1.0 1.5 2.0
1015
2025
3035
FMA_LE vs rCMCT_LE
rCMCT_LE
FMA
mot
or s
core
(LE)
r=-0.57p=0.07
r=-0.54p=0.07
r=-0.87p<0.01
rCMCT
FMA
ARAT
FMA
rCMCT rCMCT
a b c
36
ARAT ARAT
4.5.1 rFA MEP rCMCT
1 BRS rFA
FMA ARAT rFA
[22–25,37,42–47]
rFA 0.85 rFA
ROI [42,46,54] 0.8 0.85
MEP
FMA ARAT
[28–33] MEP
rCMCT ARAT
CMCT
CMCT [31–33]
ARAT
CMCT FMA
ARAT
4.5.2 rFA MEP rCMCT
1 rFA BRS
FMA rFA
BRS stage 6
FMA
MEP FMA
rCMCT FMA
[34,35] TMS MEP
CMCT
central pattern generator CPG
37
CPG
[72]
Hendricks [35] MEP
CMCT
Maeshima [73] DTI
ROI FA
FA
FA 0.59 80%
ROI rFA
rFA 0.85
Maeshima
TMS MEP rCMCT
rCMCT 1.3
38
1 3 8
ARAT
DTI ROI rFA
rFA 0.85 rFA 0.85 TMS
MEP MEP
MEP rCMCT rCMCT >1.5
CMCT rCMCT 1
<1.5 CMCT 9
3
MEP
M MEP MEP
/M
8 DTI TMS
ARAT rFA
rFA<0.85
rFA>0.85 MEP MEP rCMCT
rCMCT>1.5 rCMCT<1.5
����rFA� ���
MEP���
<0.85�
���
rCMCT� �$%'*�
� �
>0.85�
)(�
'*�
"�.>1.5/�
��.<1.5/�
39
9 n=16
ARAT action research arm test
0 1 2 3 4
010
2030
4050
60
ARAT vs Group
Group
ARAT
� � �$%'*� ���
#�����
.�/�
40
10
FMA
rCMCT 1.3 3
11
FMA
2 MEP
M MEP MEP /M
MEP
10 DTI TMS
FMA
rFA rFA<0.85
rFA>0.85 MEP MEP
rCMCT rCMCT>1.3 rCMCT<1.3
����rFA� ��.��� ��!/�
MEP���
<0.85�
)(�
rCMCT�
'*�
"�.>1.3/� �$%'*.��� ��!/�
��.<1.3/�
� .��� ��!/�
>0.85�
��.��� ��!/�
41
11 n=17
○ × FMA
DTI TMS rFA
rCMCT
0 1 2 3 4
1520
2530
35
FMA_LE vs Group
Group
FMA_LE
� � ���
#�����
FMA��#-,+�
.�/�
�$%'*�
43
1. . 22
. , 2011;66(10):48–51.
2. , . . ,
2014;36(5):378–84.
3. . . :
, 1982;19(4):201–23.
4. Kwakkel G, Wagenaar RC, Kollen BJ, Lankhorst GJ. Predicting disability in stroke--a critical review of
the literature. Age Ageing, 1996;25(6):479–89.
5. Bear M, Connors B, Paradiso M, , , , . -
-. ; 2007. 352-353 p.
6. Hepp-Reymond MC, Wiesendanger M. Unilateral pyramidotomy in monkeys: Effect on force and speed
of a conditioned precision grip. Brain Res, 1972;36(1):117–31.
7. Murata Y, Higo N. Development and Characterization of a Macaque Model of Focal Internal Capsular
Infarcts. PLoS One, 2016;11(5):e0154752.
8. Murata Y, Higo N, Hayashi T, Nishimura Y, Sugiyama Y, Oishi T, et al. Temporal Plasticity Involved in
Recovery from Manual Dexterity Deficit after Motor Cortex Lesion in Macaque Monkeys. J Neurosci,
2015;35(1):84–95.
9. Schiemanck SK, Kwakkel G, Post MWM, Kappelle LJ, Prevo AJH. Impact of internal capsule lesions
on outcome of motor hand function at one year post-stroke. J Rehabil Med, 2008;40(2):96–101.
10. Wenzelburger R, Kopper F, Frenzel A, Stolze H, Klebe S, Brossmann A, et al. Hand coordination
following capsular stroke. Brain, 2005;128(1):64–74.
11. Lemon RN. Descending pathways in motor control. Annu Rev Neurosci, 2008;31(Cm):195–218.
12. Frost SB, Barbay S, Mumert ML, Stowe AM, Nudo RJ. An animal model of capsular infarct:
Endothelin-1 injections in the rat. Behav Brain Res, 2006;169(2):206–11.
13. Puentes S, Kaido T, Hanakawa T, Ichinohe N, Otsuki T, Seki K. Internal capsule stroke in the common
marmoset. Neuroscience, 2015;284:400–11.
14. Beaulieu C. The basis of anisotropic water diffusion in the nervous system - A technical review. NMR
Biomed, 2002;15(7–8):435–55.
15. Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci,
2003;4(6):469–80.
16. Abhinav K, Yeh FC, Pathak S, Suski V, Lacomis D, Friedlander RM, Fernandez-Miranda JC. Advanced
diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical
studies: A review. Biochim Biophys Acta - Mol Basis Dis, 2014;1842(11):2286–97.
17. Shimoji K, Tokumaru AM. White matter fiber tractography and quantitative analysis of diffusion tensor
44
imaging. Brain nerve = Shinkei kenkyū no shinpo, 2015;67(4):475–85.
18. Sampaio-Baptista C, Khrapitchev AA, Foxley S, Schlagheck T, Scholz J, Jbabdi S, et al. Motor skill
learning induces changes in white matter microstructure and myelination. J Neurosci,
2013;33(50):19499–503.
19. Mori S, Crain BJ, Chacko VP, Van Zijl P. Three-dimensional tracking of axonal projections in the brain
by magnetic resonance imaging. Ann Neurol, 1999;45(2):265–9.
20. Mori S, Van Zijl PCM. Fiber tracking: Principles and strategies - A technical review. NMR Biomed,
2002;15(7–8):468–80.
21. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based
spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage, 2006;31(4):1487–505.
22. Koyama T, Marumoto K, Miyake H, Domen K. Relationship between diffusion tensor fractional
anisotropy and long-term motor outcome in patients with hemiparesis after middle cerebral artery
infarction. J Stroke Cerebrovasc Dis, 2014;23(9):2397–404.
23. Koyama T, Tsuji M, Nishimura H, Miyake H, Ohmura T, Domen K. Diffusion tensor imaging for
intracerebral hemorrhage outcome prediction: Comparison using data from the corona radiata/internal
capsule and the cerebral peduncle. J Stroke Cerebrovasc Dis, 2013;22(1):72–9.
24. Koyama T, Tsuji M, Miyake H, Ohmura T, Domen K. Motor outcome for patients with acute
intracerebral hemorrhage predicted using diffusion tensor imaging: An application of ordinal logistic
modeling. J Stroke Cerebrovasc Dis, 2012;21(8):704–11.
25. Koyama T, Marumoto K, Miyake H, Domen K. Relationship between Diffusion Tensor Fractional
Anisotropy and Motor Outcome in Patients with Hemiparesis after Corona Radiata Infarct. J Stroke
Cerebrovasc Dis, 2013;22(8):1355–60.
26. Jang SH, Ahn SH, Sakong J, Byun WM, Choi BY, Chang CH, Bai D, Son SM. Comparison of TMS and
DTT for predicting motor outcome in intracerebral hemorrhage. J Neurol Sci, 2010;290(1–2):107–11.
27. Kobayashi M, Pascual-Leone A. Transcranial magnetic stimulation in neurology. Lancet Neurol,
2003;2(3):145–56.
28. Coupar F, Pollock A, Rowe P, Weir C, Langhorne P. Predictors of upper limb recovery after stroke: a
systematic review and meta-analysis. Clin Rehabil, 2012;26(4):291–313.
29. Brouwer BJ, Schryburt-Brown K. Hand Function and Motor Cortical Output Poststroke: Are They
Related? Arch Phys Med Rehabil, 2006;87(5):627–34.
30. Pizzi A, Carrai R, Falsini C, Martini M, Verdesca S, Grippo A. Prognostic value of motor evoked
potentials in motor function recovery of upper limb after stroke. J Rehabil Med, 2009;41(8):654–60.
31. Rapisarda G, Bastings E, de Noordhout AM, Pennisi G, Delwaide PJ. Can motor recovery in stroke
patients be predicted by early transcranial magnetic stimulation? Stroke, 1996;27(12):2191–6.
32. Heald A, Bates D, Cartlidge NE, French JM, Miller S. Longitudinal study of central motor conduction
45
time following stroke. 2. Central motor conduction measured within 72 h after stroke as a predictor of
functional outcome at 12 months. Brain, 1993;116(6):1371–85.
33. Escudero J V, Sancho J, Bautista D, Escudero M, López-Trigo J. Prognostic value of motor evoked
potential obtained by transcranial magnetic brain stimulation in motor function recovery in patients with
acute ischemic stroke. Stroke, 1998;29(9):1854–9.
34. Piron L, Piccione F, Tonin P, Dam M. Clinical correlation between motor evoked potentials and gait
recovery in poststroke patients. Arch Phys Med Rehabil, 2005;86(9):1874–8.
35. Hendricks HT, Pasman JW, Van Limbeek J, Zwarts MJ. Motor evoked potentials of the lower extremity
in predicting motor recovery and ambulation after stroke: A cohort study. Arch Phys Med Rehabil,
2003;84(9):1373–9.
36. Stinear CM, Barber PA, Smale PR, Coxon JP, Fleming MK, Byblow WD. Functional potential in
chronic stroke patients depends on corticospinal tract integrity. Brain, 2007;130(1):170–80.
37. Stinear CM, Barber PA, Petoe M, Anwar S, Byblow WD. The PREP algorithm predicts potential for
upper limb recovery after stroke. Brain, 2012;135(8):2527–35.
38. Kwon YH, Son SM, Lee J, Bai DS, Jang SH. Combined study of transcranial magnetic stimulation and
diffusion tensor tractography for prediction of motor outcome in patients with corona radiata infarct. J
Rehabil Med, 2011;43(5):430–4.
39. Stinear CM, Ward NS. How useful is imaging in predicting outcomes in stroke rehabilitation? Int J
Stroke, 2013;8(1):33–7.
40. Jang SH. Prediction of motor outcome for hemiparetic stroke patients using diffusion tensor imaging: A
review. NeuroRehabilitation, 2010;27(4):367–72.
41. Kumar P, Kathuria P, Nair P, Prasad K. Prediction of Upper Limb Motor Recovery after Subacute
Ischemic Stroke Using Diffusion Tensor Imaging: A Systematic Review and Meta-Analysis. J stroke,
2016;18(1):50–9.
42. Kusano Y, Seguchi T, Horiuchi T, Kakizawa Y, Kobayashi T, Tanaka Y, Seguchi K, Hongo K.
Prediction of functional outcome in acute cerebral hemorrhage using diffusion tensor imaging at 3T: a
prospective study. Ajnr, 2009;30(8):1561–5.
43. Liu X, Tian W, Qiu X, Li J, Thomson S, Li L, Wang HZ. Correlation analysis of quantitative diffusion
parameters in ipsilateral cerebral peduncle during wallerian degeneration with motor function outcome
after cerebral ischemic stroke. J Neuroimaging, 2012;22(3):255–60.
44. Thomalla G, Glauche V, Koch MA, Beaulieu C, Weiller C, Röther J. Diffusion tensor imaging detects
early Wallerian degeneration of the pyramidal tract after ischemic stroke. Neuroimage,
2004;22(4):1767–74.
45. Groisser BN, Copen WA, Singhal AB, Hirai KK, Schaechter JD. Corticospinal tract diffusion
abnormalities early after stroke predict motor outcome. Neurorehabil Neural Repair, 2014;28(8):751–60.
46
46. Yoshioka H, Horikoshi T, Aoki S, Hori M, Ishigame K, Uchida M, et al. Diffusion tensor tractography
predicts motor functional outcome in patients with spontaneous intracerebral hemorrhage. Neurosurgery,
2008;62(1):97–103.
47. Yu C, Zhu C, Zhang Y, Chen H, Qin W, Wang M, Li K. A longitudinal diffusion tensor imaging study
on Wallerian degeneration of corticospinal tract after motor pathway stroke. Neuroimage,
2009;47(2):451–8.
48. Li Y, Wu P, Liang F, Huang W. The microstructural status of the corpus callosum is associated with the
degree of motor function and neurological deficit in stroke patients. PLoS One, 2015;10(4):1–17.
49. Liu G, Dang C, Chen X, Xing S, Dani K, Xie C, et al. Structural remodeling of white matter in the
contralesional hemisphere is correlated with early motor recovery in patients with subcortical infarction.
Restor Neurol Neurosci, 2015;33(3):309–19.
50. Schaechter JD, Fricker ZP, Perdue KL, Helmer KG, Vangel MG, Greve DN, Makris N. Microstructural
status of ipsilesional and contralesional corticospinal tract correlates with motor skill in chronic stroke
patients. Hum Brain Mapp, 2009;30(11):3461–74.
51. Brunnstrom S. Motor testing procedures in hemiplegia: based on sequential recovery stages. Phys Ther,
1966;46(4):357–75.
52. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, et al. Advances
in functional and structural MR image analysis and implementation as FSL. Neuroimage,
2004;23(SUPPL. 1).
53. Smith SM, Nichols TE. Threshold-free cluster enhancement: Addressing problems of smoothing,
threshold dependence and localisation in cluster inference. Neuroimage, 2009;44(1):83–98.
54. Maeda T, Ishizaki K, Yura S. Can diffusion tensor imaging predict the functional outcome of
supra-tentorial stroke? No To Shinkei, 2005;57(1):27–32.
55. Kuzu Y, Inoue T, Kanbara Y, Nishimoto H, Fujiwara S, Ogasawara K, Ogawa A. Prediction of motor
function outcome after intracerebral hemorrhage using fractional anisotropy calculated from diffusion
tensor imaging. Cerebrovasc Dis, 2012;33(6):566–73.
56. Mori S, Oishi K, Jiang H, Jiang L, Li X, Akhter K, et al. Stereotaxic white matter atlas based on
diffusion tensor imaging in an ICBM template. Neuroimage, 2008;40(2):570–82.
57. Yin D, Yan X, Fan M, Hu Y, Men W, Sun L, Song F. Secondary degeneration detected by combining
voxel-based morphometry and tract-based spatial statistics in subcortical strokes with different outcomes
in hand function. Am J Neuroradiol, 2013;34(7):1341–7.
58. Orita T, Tsurutani T, Izumihara a., Kajiwara K, Matsunaga T. Pyramidal tract Wallerian degeneration
and correlated symptoms in stroke. Eur J Radiol, 1994;18(1):26–9.
59. Baird AE, Dambrosia J, Janket SJ, Eichbaum Q, Chaves C, Silver B, et al. A three-item scale for the
early prediction of stroke recovery. Lancet, 2001;357(9274):2095–9.
47
60. Schiemanck SK, Kwakkel G, Post MWM, Kappelle LJ, Prevo AJH. Predicting Long-Term
Independency in Activities of Daily Living After Middle Cerebral Artery Stroke: Does Information
From MRI Have Added Predictive Value Compared With Clinical Information? Stroke,
2006;37(4):1050–4.
61. Saver JL, Johnston KC, Homer D, Wityk R, Koroshetz W, Truskowski LL, Haley EC. Infarct volume as
a surrogate or auxiliary outcome measure in ischemic stroke clinical trials. The RANTTAS Investigators.
Stroke, 1999;30(2):293–8.
62. Fugl-Meyer AR, Jääskö L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. a
method for evaluation of physical performance. Scand J Rehabil Med, 1975;7(1):13–31.
63. Lyle RC. A performance test for assessment of upper limb function in physical rehabilitation treatment
and research. Int J Rehabil Res Int Zeitschrift für Rehabil Rev Int Rech réadaptation, 1981;4(4):483–92.
64. Hsieh CL, Hsueh IP, Chiang FM, Lin PH. Inter-rater reliability and validity of the Action Research arm
test in stroke patients. Age Ageing, 1998;27(2):107–14.
65. Platz T, Pinkowski C, van Wijck F, Kim I-H, di Bella P, Johnson G. Reliability and validity of arm
function assessment with standardized guidelines for the Fugl-Meyer Test, Action Research Arm Test
and Box and Block Test: a multicentre study. Clin Rehabil, 2005;19(4):404–11.
66. Desikan RS, S??gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling
system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.
Neuroimage, 2006;31(3):968–80.
67. Wade DT, Langton-Hewer R, Wood VA, Skilbeck CE, Ismail HM. The hemiplegic arm after stroke:
measurement and recovery. J Neurol Neurosurg Psychiatry, 1983;46(6):521–4.
68. Olsen TS. Arm and leg paresis as outcome predictors in stroke rehabilitation. Stroke, 1990;21(2):247–
51.
69. Nakayama H, Jørgensen HS, Raaschou HO, Olsen TS. Recovery of upper extremity function in stroke
patients: the Copenhagen Stroke Study. Arch Phys Med Rehabil, 1994;75(4):394–8.
70. Sanford J, Moreland J, Swanson LR, Stratford PW, Gowland C. Reliability of the Fugl-Meyer
assessment for testing motor performance in patients following stroke. Phys Ther, 1993;73(7):447–54.
71. Sullivan KJ, Tilson JK, Cen SY, Rose DK, Hershberg J, Correa A, et al. Fugl-meyer assessment of
sensorimotor function after stroke: Standardized training procedure for clinical practice and clinical trials.
Stroke, 2011;42(2):427–32.
72. , . .
; 2016. 2-11 p.
73. Maeshima S, Osawa A, Nishio D, Hirano Y, Kigawa H, Takeda H. Diffusion tensor MR imaging of the
pyramidal tract can predict the need for orthosis in hemiplegic patients with hemorrhagic stroke. Neurol
Sci, 2013;34(10):1765–70.