12 月 - 医療情報システム研究室
Contents
1. fMRI functional Magnetic Resonance Imaging fMRI POMS Profile of Mood States 2 POMS POMS iX2. 4 RGB 62 c 2 d 2 e 3 f 3 Fig 2 63
3. MRI Magnetic Resonance Imaging INIRS functional Near Infrared Spectroscopy 3D 2 MRI MRI NMR Nuclear Magnetic Resonance MRI fix X CT Computed Tomography CT
4. functional Near Infrared Spectroscopy fNIRS BETUORUUAC UG 1000 Hz 2 Vlo V5 O V1 V5 Fig 1 a 2 1 V1 V1 Gf
5. 1 2 2 1 Je OD SEE URS m sm mp PH NE ss RIR s Table 1 I I Ia THc
6. ETE 230 DI 0o i S8 OO t E 2011 8 65 3 WR RRV Ef ZI Si cv
7. 2 1999 2000 E QOL 8
8. Tr 1 Working memory WM WM 7 WM 2 magnetic resonance imaging f MRI
9. 8 10 ms 12 ms 20 ms 4 m C HE D 39 DAS 15s 30s 255s Eig 3
10. 3 9 7
11. 0 t 4 1 21 2010 2 S Martin E Ann Christine P M Michael F J Andreas Functional near infrared spec troscopy A long term reliable tool for measuring brain activity during verbal fluency Neurolmage Vol 43 pp 147 155 2008 3 Fmri
12. 4 1 Fig 7 CHI CH2 GE ET me Voltage V Voltage V Bo 0 Time min Time min Time min Fig 7 5 5 1 Fig 8 Voltage V Voltage V da ema 00 0 0 02 0 4 0 6 0 8 LO 0 2 24 4 U 6 Time min Time min a b Fig 8 5 2
13. DM 3 1 1 9 9 7 PP j 0 45 90 135 4 Fig 2 a 4x4 A Fig Ab 3 1 2 M Gllow SRE short runs em
14. 1 E 3cm
15. 4 1 fiber A Vol 24 pp 1 11 2010 2 BEM Vol 55 No 5 pp 472 479 2007 70 3 2011 4 3 Vol 48 No 1 pp 36 38 2011 5
16. 3 2 1 MRI Mag netic resonance imagino fNIRS PET Positron emission AdveroHR ouk e gr25L cw 2 3 9 3 3 4 e WORT 100 7 100 7 93 7 86 7 e e
17. fMRI DTI 1 2 3 Di sphere GE comes e e e
18. D 2cm m UL ESD 2cm UL 3cm UL 2cm UL DA ESD 2 2 NBI BLI 53 Fig 1 Fig 1 a Fig 1 b NBI
19. Osaka 2004 BOLD E SALEMA ARES 0 bA 10 9 VRBE CU MEDIO LAIO ST 30 07 e 0 6 0 5 0 4 0 3 02 0 1 aPre 0 jm m Post 0 1 0 2 0 3 Signal change e S Qv v Q Co q e e v Q P 2 Region a Image 0 7 0 6 0 5 S 0 4 0 3 S c 02 o 01 s aliad o 0 m Post 0 1 0 2 0 3 S e e Qv gv cv cv e Q q w w ga og v Q JS as Region b Rehearsal 0 7 0 6 0 5 g 0 4 0 3 G c 02 o 0 1 mam AJ 0 m Post 0 1 0 2 0 3 S E g e Sr gv v Y o P X QV w C w RA vw e v Q Q Q Q Region c C
20. YES NO 1 HAE oH 36 2 52 12 3 dD Ha fNIRS Functional Near Infrared Spectroscopy HOT121B H DIR
21. 6 1 2 A 2 A B FA A B EA FA f 2 A B FA FA A B A B TR TE TR TE MRI 3 MRI PERIE b0 FA MRI TR TE MRI
22. RST MR RST fMRIT DTI 3 2 2 12 10 2 22 3 0 95 11 1 2 3 MR RST RST MRI fMRI 12 8 1 4
23. 2 SMA D 2 2 1 MACD SBD AA ENE LHS DEL UC G Matheron J serra
24. Fig 1 3 SACS ES Pei 3 Daneman amp Carpenter
25. lt D ROB
26. 2 60 30 X 4CH min max Lg znew T 7 maa min 2 4 2 Deep Learning 22 fNIRS 4 fold cross validation Et 1 random walk pre trainig Stacked Denoising Autoencoders
27. 1 5 Deep Learning k 81 67 NIRS fNIRS fNIRS TfNIRS 1 Vol 12 pp 72 78 2008 2
28. Fig 2 5 5 1 fNIRS PTG 7100 4x 4 24 CH x 5 22 CH
29. 1 Narrow Band Imaging NBD Blue Laser Imaging BLI Endscopic Mucosal Resection EMR Endscopic submucosal dis section ESD 0 NBI
30. online letter matching fMRI online letter matching POMS 1 2
31. HERPES SMA Superior mesenteric artery CT CT
32. 5 sec 5 sec Recognition X6 5 sec Fig 1 fMRI 1 1 RST 6 5 2 Recognition RST 3 XX 5 3 Control 2 5 4 Read 6 5 5 Control T 2 5
33. 3x9 0 22 55 157 5 8 O 8 3 3 Fig 2 3 1 2 3
34. RGB 1 drive db 1 2012 2 3
35. 4 2 fMRI RST RST al Fig 4
36. fNIRS Probe CH 2 PWM a MRI 3 iis fNIRS 3 fNIRS 3 MRI FNIRS 21 5 2 Fig 5 Fig 6
37. 3 3 1 3 780x480 3 2 1 RGB 2 3x9 8 8 RGB PE E DSR S RGB 2
38. 11 2011 37 30 2013 12 21 H fNIRS nk Kenichi TAKI fNIRS 1
39. DTI 2 DTI 2 4 DTI DTI 3 MRI 2 RFE p uc mac c 3
40. x y z Fig 7 Fig 8 fNIRS Probe CH fNIRS CH Table 1 Fig 5 Fig 6 Fig 8 NIRS Table 1
41. C a Deep Learning x Oi je Deep Learning 2 Deep Learning Deep Learning Deep Learning pre trainig fne training pre trainig
42. fNIRS FA St 42 LA esmo 1 Vol 76 p 7 1993 2 Rita Carter 2012 3 1 1 ml 1994 4 i 2002 5 Alison Abbott Neurobiology Music maestro please nature Vol 416 pp 12 14 2002 6 Jody C Culham Nancy G Kanwisher Neuroimaging of cognitive functio
43. o LV L VERH 1 3 3 1 2 4 20 1 eL 4 O O ea Hi s lS 20 5
44. s ODD 3 7 Attention Deficit Hyperactivity Disorder ADHD 8 END Daneman Carpenter 1980
45. FA Tracking TE TR b value NSA fm 3 3 e Tractgraphy MRIstudio Lt Fig 1200 2 Rwy MRIstudio Tractography a 22X crossing b kissing c fan ning Fig 12 8
46. fMRI fNIRS fanctional Near infrared spectroscopy 2 WM
47. LIFG DLPFC 7 RST RST MA Bamit DLPFC 1 5 2008 2 H Kondo N Osaka M Osaka and M Morishita The neural basis of executive function in working memory an fmri study based on individual differences NeuroImage Vol 21 pp 623 631 2004 3 7 2007 4
48. 1 FR 2002 SERE 2012 RST RST 1 1 RST
49. Fig 3 b 2 Tractography 6 7 Fig 3 c FACT Fiber Assignment by Continuous Tracking 2 Tractgraphy Table 1 a tractography b c FACT Fig 3 Tractography Table 1 tractgraphy
50. 38 FARE V 7 a b Fig 1 V4 2 5 V5 V5 MT Ef middle temporal MST medial superior temporal MT MST 2 6 2 Fig 1 b VI
51. Df Table 1 OI GRE Hora RERO D NISD Ba UT 969 HH 5 2 Z amp wv Kap 233360 o 43v 5 0 a T b NBI Fig 1 1 3 1
52. 3 2 8 8 22 lxx 15 1 A 3 3 MRI ECHELON Vega 1 5 T Fig 1 Table 1 Table 1 fi Axial TR ms 3000 TE ms 40 FOV mm 240 A 7 4 AF mn 6 18 Matrix Size 64 64 Fig 1 3 4 6 Fig 2 Rest Rest Rest Rest Rest s Fig 2 Rest Delay Multi 3 Rest 30 s C Delay Multi 84 s Delay Multi 2 Rest 1 2 Fig 3 Rest 30
53. Fig 5 b Fig 5 c LRE a 1 b 2 c 3 Fig 5 4 3 LRE Fig 6 Fig 6 a Fig 6 b Fig 6 c 56 4 4
54. 2 2 36 YES NO YES 0 9 0 8 97 c oe gH 0 5 NO UB 0 4 o3 E o2 0 1 e Yes No 0 0 0 5 1 0 929 _ 0 4 Fig 1 BTE 1 5 3 3 1 44 3 2 INIRS HOT121B 7 1 5 M 20 10
55. Reading Span Test RST O RST RST 2 RST ZU D 2008 X RST Anterior Cingulate Cortex ACC DorsoLateral PreFrontal Cortex DLPFC Superior Parietal Lobule SPL 9 Osaka 2004 functional Magnetic Resonance Imaging fMRI 24 Left Inferior Frontal Gyrus LIFG
56. DD UK 13 30 750 100 14 10096 ziX38 DANH 15 4 4
57. 59 TO
58. Minkowski set subtraction Minkowski set addition erosion dilation XeB x 3 bcB RAMIZ Xe B U Xp 4 bcB S X ze Xp L DEX 3 5 XoB lirz z beX becB 5 6 B b be B 6 A 5 A 6 5 B b z b EB XOB z b z X b B XoB iz B cx 7 BRXONHPES ERC LEDBORADIAMTCHAL E JX r b zeXbeB 8 be B XeB brz beB zeX E 9 rcx X B BB X LCC X X SB X B
59. 3 6 Eig a 1 10o0 KR OH PIA CAR gs Mor Peu MEM Rr O LI 777 IURIE y gonm 5155 qe NORA ODON CORE EN a b Fig 1 DTI 2 2 DTI DTI FA Fractional anisotropy MD Mean diffusivity 2 2 1 FA Fig 2 a KHE XE FA Fig 1 a Wo
60. Table 3 A B A TP TR FOV Thickness Interval Freq Phase Recon Matrix DTI 1 voxel size Fig 8 4 4 2 DTI Fig 4 d axial coronal sagital Fig 7 3 voxel volume pixel 10 Table 3 2 A DE B FOV mm 240 0 240 0 TR msec 2923 0 2317 0 TE msec 90 4 74 3 Thickness mm 3 0 3 0 Interval hmml 3 0 3 0 MPG Dir Tensor 21 2
61. MRIstudio FA FA Fig 6 a FA K 6 2 Tractography DTI 0 18 DEBE TC FiberTracking Fiber Voxel Fig 6 a Tracking MRIstudio FiberTrack
62. 15 coronal 1pixel x y 2 0 94 0 94 mm x 114pixel 0 94x114 107 16 mm y 12pixel 0 94x12 11 28 mm b axial 1pixel x y 2 3 00 0 94 mm 1pixel x y 2 0 94 3 00 mm x 37 pixel 3 00x37 111 00 mm X 14pixe 0 94x14 13 16 mm y 12pixel 0 94x12 11 28 mm y 4pixel 3 00x4 12 00 mm c sagital d coronal Fig 11 EA A B FA FA 6 1 3 FA FA Tracking FA Tracking FA Tracking
63. NOAA Hi Vol 30 No 1 pp 91 100 1989 3 G Castellano L Bonilha L Li and F Cendes Texture analysis of medical images Clinical Radiology Vol 59 No 12 pp 1061 1069 2004 4 M Galloway Texture analysis using gray level run lengths Computer Graphics and Image Processing Vol 4 No 2 pp 172 179 1975 5 J Ward Hierarchical Grouping to Optimize an Objective Function Journal of the Amer ican Statistical Association Vol 58 No 301 pp 236 244 1963 1 57 459 909 1359 a 1 459 8 1359 45 90 135 c 3 Fig 6 LRE 58 30 2013 12 21 RISE Kazuyuki SASAKI Em
64. 1 5 5 RST Fig 2 eeo ea Fig 2 1 60 2 RST RI DT 3 4 BAY 2 3 5 5 60 35 4 3 22 13 9 fNIRS fNIRS
65. 7 XoB z B CX 10 Xe X XDOBLKDA IZVI Xp 11 Xp Xo B eB 11 Fig 1 ZONT e XO Biz cr X 1 X B 2 IX o B BD X X X X ft
66. V1 V1 2 2 V2 V2 Ehrenstein 2 3 V3 V3 V3 V3 2 4 VA V4 V1
67. LRE Excel 55 Fig 3 4 4 1 Fig 4 NBI 3 Fig 4 a llc Fig 4 b Fig 4 a Fig 4 c Ia 50x50 16 a 1 b 2 c 3 Fig 4 4 2 Fig 5 Fig 5 a Fig 5 b Fig 5 c
68. RLN RPC 3 2 LRE 2 3 2 1 a Fig 3 uL Ve E Ward 9 4 1 1 3 2 2
69. So US TMD 6 POMS Intro 1 EE 4 pp 91 101 OCT 2007 2 POMS 1 2005 3 McNair DM Lorr M Droppleman LF Profile of Mood States Educational and Industrial Testing S
70. 13 Table 6 C D d Mr Max of FA 0 4251 Min of FA 0 1819 Mean of FA 0 2570 Number of Fibers 50 52 Maximum Length mm 12 5175 9 7819 Minimum Length mm 3 5837 3 9333 Average Length mm 6 5901 5 5004 Table 7 E F d d B Max of FA 0 5719 0 3604 Min of FA 0 1611 0 1449 Mean of FA 0 2833 0 2425 Number of Fibers 264 91 Maximum Length mm 23 0110 14 1926 Minimum Length mm 2 7118 3 1225 Average Length mm 8 3443 6 5335 Table 8 G BUER H 1 Eug C 40 0 41 7 Max of FA 0 5719 Min of FA 0 1611 Mean of FA 0 2833 Number of Fibers 1360 50 264 Maximum Lengthlmm 80 3137 12 5175 23 0110 Minimum Length mm 3 8353 3 5837 2 7118 Average Length mm 26 1006 6 5901 8 34493 14 MRIstudio Fiber Tracking Fig 10 1pixel x y 0 94 0 94 mm tpixel x y 3 00 0 94 mm x 100pixel 0 9
71. Eig 1 KEPOOBEPK MWR BO SH a 2 3 1 9
72. RST PD RST RST RST 4 4 1 RST RST 4 2 RST
73. Tractography 1 10 2 3 FA 2 DTI MRIstudio Tractography 4 2 Table 2 Table 2 ELE EE MRI ECELON Vega XE 1 5T Fig 4 a Fig 4 b RCX 10008 Fig 4 c PT 1 50 4 300 1 C Fig 4 d a MRI ECELON Vega b c d Fig 4 4 3 O e e 60 4096
74. 65 0 0 5 dB CH3 6 7 10 11 Ey 60 fNIRS Fig 2 N Fig 2 CH 4 Deep Learning 1 4 1 CH3 6 7 10
75. Diffusion Tensor Imaging DTI Voxel based analysis VBA Fractional Anisotopy FA 19 2 2 1 RST RST H
76. 1 MRI DTI DTI C DTI DTI DTI 3 1 2 EA FA FA FA
77. 1 5 4 1 MRI 2 2 5 MR WE Echelon Vega 1 5T fORP 932 Subject Response Package Cambrige Research Systems Presentation Neurobehavioral System Inc Gradient Echo Echo Planer Imaging GE EPI EPI RESpoiled Steady state Gradient echo RSSG T1 Diffusion Weighted Echo Planer Imaging DW EPI EPI MR Table 1 26 Table 1 MRI PSA Sequence GE EPI RSSG DW EPI TR ms 2500 9 4 2317 TE ms 50 4 0 14 3 Flip Angle 90 8 90 Field of View mm Matrix Size pixel 240 X 240 256 X 256 240 X 240 64x 64 256 X 256 128 X 128 Plane Axial Axial Axial Mode Interleaved Interleaved Slice Number 20 192 50 Thickness mm 6 0 1 0 3 0 Slice Interval mm 6 0 1 0 3 0 b Value s mm 1000 Motion Probing Gradient 21 2 6
78. Fig 5 CH CH lt 8 9
79. paired t test t 4 8 49 p lt 01 paired 7 test t 4 3 36 p lt 05 3 3 DTI FA t Fig 5 4 FA x y 2 46 42 16 paired t test t 4 21 10 p lt 001 uncorrected extent threshold voxels 10 4 4 1 Table 2 RST Osaka 2012
80. AF f xyz 5 e Transmitter e Receiver e Stylus Receiver Receiver Transmitter Stylus Receiver a b Fig 2 4 MRI Fig 3 a 2 20 3
81. NIRS 22 6 fNIRS Probe ED CH fNIRS 7 fNIRS Probe ED CH 2 fNIRS
82. Bx Fig 2 66 3 2 10 S 2
83. 5 RST RST O fMRI DTI RST FA 1 A Baddeley and G Hitch Working memory In The psychology of learni
84. 7 Jon Coll Angiol Vol 45 pp 61 67 2005 3 Dumitru Erhan Yoshua Bengio Aaron Courville Pierre Antoine Manzagol Pascal Vin cent and Samy Bengio Why does unsupervised pre training help deep learning The Journal of Machine Learning Research Vol 11 pp 625 660 2010 4 Salah Rifai Gregoire Mesnil Pascal Vincent Xavier Muller Yoshua Bengio Yann Dauphin and Xavier Glorot Higher order contractive auto encoder Machine Learning and Knowledge Discovery in Databases Lecture Notes in Computer Science Vol 6912 pp 645 660 2011 5 Pascal Vincent Hugo Larochelle Isabelle Lajoie Yoshua Bengio and Pierre Antoine Manzagol Stacked denoising autoencoders Learning useful representations in a deep network with a local denoising criterion The Journal of Machine Learning Research Vol 11 pp 3371 3408 2010 6 Pascal Vincent Hugo Larochelle Yoshua Bengio and Pierre Antoine Manzagol Ex tracting and composing robust features with denoising autoencoders CML 08 Proceed ings of the 25th international conference on Machine learning Vol 307 pp 1096 1103 2008 7 PUR KR RMS 2 Bex l ELEA Vol 45 pp 368 373 1989 8 PUK Vol J87 D I pp 420 423 2004 9
85. X 00000 00000 00000 XoB XoB oB 99000 ooooc 00007 00000 0000 00000 n 0000 00000 e 7 00600 00000 Em 00000 0000 Eig 1 2 4 2 ze Z f z 7 z 12 U f z z t Z oo lt t f x 12 2 3 61 glr g 2 foy int f 9 13 Eae sup b 90 14 3
86. CH CH CH 3 CH 3 CH pe SU SS S PV pes e CH 4 CH CH CH CH e CH CH CH 1 4 CH 3 5 6 7 CH 3 6 CH C 1 4 5 7 e CH CH
87. KA bh VCATEEOOBAT amp S 2 5 mA h oA EEOBRBIE DZ Fig 7 Middle frontal gyrus Insula Precentral sulcus Putamen Lingual grus GQ CO x i miia 1 Pol Tam a 1 ESTO Middle frontal gyrus OR gt Insula O Precentral sulcus Q GO o LL Putamen Lingual gyrus Fig 7 TMD WOR CREA OINEN p lt 001 cluster size gt 10 5 Fig 4 D Fig 5 Delay Multi Fig 7 5
88. b d W b b z 7650 A7J z b z xz Lyle z 9 Ly z z xlogz 1 z 1 log z 3 Stochastic Gradient Descent n N OL new old H o m H 4 We We vi ow a N new old T OLg Bw m ps 2 S 5 N mew _ told n OLg b b 7 2 T 6 m N 9 Fig 2 1 denoising Autoencoder Denoising Autoencoder Stacked Denoising Autoencoders input Corrupted input Representation Reconstru
89. Et Fig 4 Fig 4 4 fold cross validation 81 67 Fig 5 pr ii hi Fig 4 amp Fig 5 me Deep Learning Fig 4 3 A 1 min max Ga AC a HUMUM
90. MRI y Fig 9 MRI 4 5 DTI DW EPI e Scano 3planeA 15msec e Scano 3planeB 30msec e 5 map 34msec e DW EPI 4 6 FiberTracking FA Table 1 MRIstudio FiberTracking FA MRIstudio 1 DTI 1100 MPG21 1 Rec 7 MRIstudio 2 Automatic Image Registration 3 FA Tensor Color Map 4 FA Tract turnig angle Flip Eigen Vector L Table 4 Start Stop tracking FA 0 18 UE
91. 3 0 20 0 25 FA FA 0 2 FA FA 5 FA ROTH Hig 7 axial RL RO TE FiberTracking 6 Tracking FA Max of FA RO1 Region of Interest
92. LIFG bis PWR es LIFG DLPFC en se ee DLPFC RST
93. 3 4 1 30 DO POMS 1 T 50 10 1 T 2 3 3 1 online letter matching fMRI POMS
94. 4 C Fig 3 Fig 4 67 Fig 5 1ms Fig 6 Voltage V Time min Fig 6 Nb 2 ERV bue zs 53 58 BC Fig 4 CH1 CH2
95. FA EA X MRIstudio HiberTracking 17 1 NIR8 2010 2 Ray H Hashemi Jr William G Bradley and Christopher J Lisanti MRI TRA Po O Rs 2004 3 MRI 3 2013 4 Vol 11 No 2 pp 73 83 2010 18 30 2013 12 21 H MRTI FNIRS 3D
96. x ct MEDICAL IMAGING TECHNOLOGY 2013 3 2010 30 2013 12 21 ERHS Takuma SATO WR BA 1 2
97. 116 CH ATH ANO23 audio technica 22 25 15 22 C 25 C Z 5 2 3 5 Fig 3 dk y ANY E JON ca 7 5 Hz VI V2 V5
98. Deep Learning 6 5 1 en fNIRS functional Near Infrared Spectroscopy NIRS fMRI functional a Resonance Imaging PET Positron Emission Tomography D fNIRS iO x Zu e viti fNIRS 2 fNIRS fMRI EEG
99. 1 McNair 3 9 POMS Tension Anxiety Depression Dejection Anger Hostility EA Vigor DEJ Fatigue Confusion 6 EZ 2 65 0 1 2
100. Motion Probing Gradient MPG 1 KORTOM DTI
101. MNI e Smoothing fMRI Full Width at Half Maximum FWHM 8 mm SPM 8 e e Automated Anatomical Labeling AAL 1 x MarsBar 9 Region of Interest ROI BOLD Eus 2 2 0 8 DTI SPM 8 MATLAB e Normalization MINI e Coregistration T2 b0 T2
102. MBE ME Vol 133 pp 21 24 2003 4 K J Jeffries J B Fritz A R Braun Words in melody an h2 150 pet study of brain ac tivation during singing and speaking NEUROREPORT Vol 14 No 5 pp 749 154 2003 5 nirs NC Vol 110 pp 27 30 11 2010 6 REEF Nirs based bei 7sgz2072 2012 47 30 2013 12 21 H DeepLearning Hr RR Kenya HANAWA fNIRS
103. Fig 7 axial coronal sagital a b c Fig 5 1 anms 100 P mm Coronal a b Fig 6 5 s cos mm Coronal 0 2 20 u m 0 2mm 0 94 x 0 94 x 3 00 mm voxel voxel size voxel size 44 4 4 1 2 A A B
104. Multi 4 2 POMS POMS 5 LOS TMD Total Mood Disturbance TMD lt TMD rig 6 Multi TMD TMD Multi ms ms a Delay b Multi Fig 5 TMD Fig 6 TMD 2
105. VALLI Vol 105 pp 43 46 2005 10 2012 11 KAK Gender differences in influence of sound environments on performance of the memorizing numerical string task and cerebral blood flow changes In NeuroScience 2013 12 Adam Coates Honglak Lee and Andrew Y Ng An analysis of single layer networks in unsupervised feature learning The Journal of Machine Learning Research Vol 15 pp 215 223 2011 52 30 2013 12 21 H Katsutoshi HAYASHINUMA
106. CH 2 4 CH 3 5 6 7 CH 3 5 CH C 2 4 6 7 CH 41 3 Fig 4 Fig 5 6 CH CH Fig 4 CH CH Fig 5 CH CH CH CH 7 Fig 4 Fig 5 CH
107. IO M Pull 2000 2013 RST 1 12 e es e RST 2510 1 Osaka 2012 RST Gil 3 Takeuchi 2010
108. CT 3 MRI 9 lt 19 2 1 DTI Diffusion Tensor Image Fig 1 a Fig 1 b 2
109. ETG 7100 3 72 10 20 21 3 24 5 C 47 52 Fig 3 Fig 3 4 4 BTG 7100 1 0Hz Oxy Hb 10s JE a 10 30 10 Ratio lo d WI BE E Dorsolateral prefrontal cortex DLPFC Left Inferior frontal gyrus LIFG Fig 4 25 F
110. CH t 5 s 50 15 s 3 50 5 CH t CH t 50 50 CH CH 5 5 CH CH CH CH 1 CH CH CH
111. 2001 6 http www medicpro co jp posey01 htm1 2013 12 10 7 http www hotron co jp product02 3 php i 92 2013 12 10 8 Vol 40 pp 113 122 2008 9 A Vol 125 No 8 pp 683 691 2005 10 RRE Vol 16 No 5 pp 705 711 1998 11 C Vol 128 No 11 pp 1649 1656 2008 12 http www kinden co jp business keiso keiso10 htm1 2013 8 21
112. Cz Table 2 CB 48 5 45 7 45 7 42 8 6 Qum D 7 ALS
113. 100 1040 100 95 5 a5 95 SE S 5 A Sh 90 St 9 DI d Sir ou c DE 85 E ZE 55 F e 85 cem i mu m 2 EE S t se G an si 80 xX E m 75 dit E T5 s E 75 L 70 7 TO Prae Past Prae Fm Pre Post Image Rehearsal Control a Image b Rehearsal c Control Fig 2 RST Table 2 Strategy Pre 96 Post p value Effect size Image 82 81 7 09 90 63 4 96 0 02 1 26 08 2 95 77 08 4 50 0 50 0 00 82 29 4 77 82 81 4 75 0 46 0 11 Rehearsal None 3 2 fMRI 3 2 1 Read RST Table 3 paired t test p lt 001 uncorrected extent threshold voxels 10 7 7 A4 Hlki3 10 voxel p lt 001 MNI x 6 Sagittal Fig 3 paired t test p lt 001 uncorrected extent threshold voxels 10 Anterior A Posterior 3 2 2 BOLD ACC
114. 2 9 AEDES opening X B X Xo Boc xe 1 B IX B z B b z be B 2 IXOBiz amp 43 7 7v27 amp IX X X 2 3 1
115. MRI fNIRS 1 MEDICAMENT NEWS 1794 pp 1 3 2004 2 MRI http www kyoto u ac jp ja news data h h1 news6 2012 120904 1 htm 2012 2012 4 http www an shimadzu co jp bio nirs nirs2 htm 5 ETG 7100 4 2006 23 30 2013 12 21 ANM Shogo OBUCHI
116. VIDA CAS B Eis FAME AA 4 4 4 0 2 0 t 1 0 0 45 4 1 t 6 60 2 10 100 20 59 t 4 2 t Total Hb 7 5 Table 1
117. DLPFC IFG 28 Table 3 Read RST RATRI DRICA EEE ASA LT BR p lt 001 uncorrected extent threshold voxels 10 Cortical region X y z Zzscore Cluster size Image Left Inferior Parietal 32 58 40 4 61 53 Right Frontal Middle 36 10 62 4 59 14 Left Precuneus 4 50 38 4 53 54 Left Inferior Frontal 48 30 22 4 43 40 Right Superior Occipital 28 82 24 4 41 23 Right Cuneus 18 74 32 4 40 24 Right Anterior Cingulate 6 48 16 4 23 13 Left Caudate 20 12 28 4 17 21 Right Middle Cingulate 0 18 26 3 99 22 Left Paracentral Lobule 14 26 80 3 91 11 Rehearsal Control Right Inferior Frontal 40 28 28 4 90 64 Right Middle Cingulate 4 12 36 4 25 12 Right Angular 54 54 34 4 19 26 Right Superior Temporal 48 36 8 4 09 31 Left Superior Parietal 18 60 60 4 03 19 Right Middle Occipita 48 74 30 4 00 18 Left Middle Frontal 44 28 46 3 99 21 Right Superior Frontal 20 48 18 3 96 16 Right SupraMarginal 60 38 36 3 87 28 Left Superior Temporal 66 32 18 3 78 18 a Image b Rehearsal c Control Fig 3 paired 7 test p lt 001 uncorrected extent threshold voxels 10 x 6 29 SPL Inferior Parietal Lobule IPL Precuneus RST BOLD Fig 4 Fig 4
118. MRIstudio FiberTracking FA 1 D 1 Diffusion Tensor Imaging DTT DTI Magnetic Resonance Imaging MRI
119. fine training back propagation pre training Denoising Autoencoder 2 1 Denoising Autoencoder Denoising Autoencoder DAE Cr z z gt fs y encode y fs Wz b 1 48 W Xd bll d y TOF RIZ decode z fs W y b 2 W W
120. A 2 A B 1 40 1 43 0 CT 2 A B 4 6 FA Table 6 2 40 0 41 7 CT 2 A B 4 6 FA Table 7 0 5 1 3 FA 40 0 A 4 6 FA Table 8 doo CURES 5 2 Tractography 5 1 3 FA FA FiberTracking
121. RGB 80 80 80 HU 12 1 06 m 1000 Hz 5 3 fNIRS 0 1 Hz LPF 10 s 20 s 0 1 5 2 3 4 5 4 CH
122. 3 RL Fig 3 b fNIRS Direct3D 3D 3D Fig 4 1 fNIRS CH 3 2 MRI 3 fNIRS MRI 4 3 a b Fig 3 EHE eie EI 2 Fig 4 5 5 1
123. C a b Fig 1 3 fNIRS NIRS fMRI functional Magnetic Res onance Imaging fNIRS 3 1 Fig 2 a Fig 2 b
124. FA 0 1 0 1 2 2 2 MD D Fig 2 b MD Fig 1 a a FA b MD Fig 2 3 Tractography Tractography lX Fig 3 a Tractography
125. KE Shunsuke OH TANI Probe figo CH 3D MRI fNIRS 3D Probe fg CH 1 4 5
126. Middle frontal gyrus Precentral sulcus Lingual gyrus Insula Putamen ERR ET CDE POMS 4 koc 6 fMRI POMS TMD Middle frontal gyrus Insula REDE O
127. SPM8 Statistical Parametric Mapping E Realign Coregister Normalise MNI Smoothing HWHM 8 mm 0 001 EL t 0 001 4 4 1 Delay Multi Fig 4 Fig 4 Delay Multi 1400 Delay 1200 g Multi 1000 Ems 600 400 1 2 3 4 Fig 4 Delay Multi Fig 5 Delay 1
128. fine training Table 1 22 pre training Stacked denoting Autoencoders fne training HWS IRIK 4 fold cross validation 1 50 4 3 HR EE ACH 2 min max Fig 4 3 120 3 50 30 60 1 ADE 1 2 3 4 5 e b e ive OWOH 120118 5018 30 50 1 Fig 3 100 100 90 90 80 80 70 70 SS 60 2 60 a 50 s 50 ai AQ ci 40 30 30 20 20 10 10 0 0 1 2 3 4 1 2 3 4 Fig 4 Fig 5
129. b0 e Smoothing FA FWHM 10mm FA 0 2 C FA 3 3 1 RST Fig 2 D RST Interquartile method LE 7 Table 2 RST paired 7 test t 4 4 39 p lt 05 0 5 TU zl Eus
130. 12 FA Min of FA FA E Mean of FA Traking Number of Fibers Tracking Maximum Length Minimum Length Average Length MRIstudio Table 4 FiberTracking Start tracking FA 0 18 Stop tracking FA 0 18 Trackt turning angle 45 Flip eigen vector Z component 5 5 1 TEA FA 5 1 1 FA A T 2 37 5 46 7 21 7 23 3 4 6 FA Table 5 GR Table 5 A B 937 52040 7 C 2579923 9 Max of FA 0 5856 0 5204 Min of FA 0 0984 0 1658 Mean of FA 0 2860 0 2552 Number of Fibers 1360 A88 Maximum Lengthlmml 80 3137 28 7950 Minimum Length mm 3 8353 3 8342 Average Length mm 26 1006 17 3278 5 1 2
131. 8 5 3 1 5 4 1 1 RST 5 5 25 bote 1 25 MRI 20 D 1 RST 2 4 fMRI Fig 1 El Control
132. EE MRIstudio Laboratory of Brain Anatomical MRI and Center Johns Hopkins University dTV Track Vis Ruopeng Wang Van J Wedeem Massachusetts General Hospital FiberTrack Philips Functool GE Healthcare CAMINO Microstructure Imaging Group University College London 4 4 1 FA DTI C Tractography Hi 2 1 DTI FA 2 1 FA DTI Table 1
133. 2 Fig 6 1 L 5 Ta Ilc LRE 1 2010 2
134. 2 6 1 RST 7 paired 7 test p lt 05 RE Effect size Cohen s d 1 de 1 s2 52 2 Interquartile method Interquartile method Interquartile range IQR 2 6 2 fMRI Statistical Parametric Mapping 8 SPM 8 Wellcome Department of Cognitive Neurology MATLAB Mathworks Inc e Realignment e Coregistration e Normalization
135. HDV Fig 9 Fig 4 CHI CH CH2 CHI 15 LO L0 Time min Time min a EF I DI 0 b Fig 9 5 3 MAREK Fig 10 Fig 10 Fig 4 CHI CHA CHI CH4 0 CH1 CH4 CH1
136. 100 0 2mm EA P 078 2 HS B BH Fig 5 a Fig 5 b FF 5 c Table 2 30 2 3 4 Fig 6 a Fig 6 b 3
137. 121 No 1 pp 65 94 1997 1 2008 N Osaka M Osaka H Kondo M Morishita H Fukuyama and H Shibasaki The neu ral basis of executive function in working memory an fMRI study based on individual differences NeuroImage Vol 21 No 2 pp 623 631 2004 o E RST pp 203 223 1 2000 XB TRES Vol 82 No 6 pp 554 559 2012 M Osaka Y Otsuka and N Osaka Verbal to visual code switching improves working memory in older adults an fMRI study Frontiers in Human Neuroscience Vol 6 p 24 2012 H Takeuchi A Sekiguchi and Y Taki Training of working memory impacts structural connectivity The Journal of Neuroscience Vol 30 No 9 pp 3297 3303 2010 T N Mazoyer N Landeau D Papathanassiou F Crivello O Etard N Delcroix B Mazoyerand and M Joliot Automated Anatomical Labeling of Activations in SPM Using Macroscopic Anatomical Parcellation of the MNI MRI Single Subject Brain Neurolmage Vol 15 No 1 pp 273 289 2002 M Brett R
138. Valabregue J L Anton and J B Poline Region of interest analysis using an SPM toolbox In NeuroImage Vol 16 2002 1 hk 2012 E E Smith and J Jonides Storage and executive processes in the frontal lobes Science Vol 283 No 5408 pp 1657 1661 1999 T S Braver D M Barch J R Gray D L Molfese and A Snyder Anterior cingulate cortex and response conflict Effects of frequency inhibition and errors Cereb Cortez Vol 11 No 9 pp 825 836 2001 E K Miller L Li and R Desimone Activity of neurons in anterior inferior temporal cortex during a short term memory task The Journal of Neuroscience Vol 13 No 4 pp 1460 1478 1993 fia HR 1 2002 33 30 2013 12 21 H fNIRS ZTE Yukihiro SATO
139. 19 Ne2N EIAS o MD Nwe hitachi power solutions com products product03 p03 27 htm1 2013 8 21 14 IT Vol 2 No 2 pp 95 103 2008 15 BHMA 375 Vol 34 p 188 2007 71
140. CHA CH1 CH2 69 0 06 Time min Fig 10 MRE E 5 4 Fig 11 Fig 11 Fig 4 CH1 CHA CH 0 06 Time min Fig 11 6
141. 1 b factor 1000 1000 NSA 2 2 Freq 4 128 128 Phase 128 128 ReconMatrix 256 256 voxel size x y z mm 1 87 1 87 3 00 1 87 1 87 3 00 ReconMatrix voxel size x y z mm 0 94 0 94 3 00 0 94 0 94 3 00 E51 9 pow oo 9 p 256 240mm pixel 240mm pf i 256pixel 1voxel size Fig 8 1voxel size 11 4 4 3 Fig 4 c 30 DE 4 4 4 MRI MRI Fig 9 MRI
142. 4x100 94 00 mm x 4pixel 3 00x4 12 00 mm y 10pixel 0 94x10 9 40 mm y 10pixel 0 94x10 9 40 mm a axial b sagital 1pixel x y 2 0 94 3 00 mm X 100pixel 0 94x100 94 00 mm y 4pixel 3 00x4 12 00 mm c coronal Fig 10 MRIstudio Fiber Tracking Fig 11 a FA Tracking Start tracking FA Stop trackin FA 0 11 Fiber Tracking Fig 11 b Fig 11 c Fig 11 d 6 EE 6 1 TEA 6 1 1 FA A 10 B FA 0 06 FA 0 03 1000
143. Delay tablet 1 1 TABLET X X Multi Delay t XX 2 TABLET O Delay KBltealYa Multi KBltealia A hdd t t Eig 3 X X 0 5ls CD 2 5ls 28 X 84lsl 1 2 POMS 3 5
144. ER V3 VA B O ni aii de TE AU rie EA ne e V1 V2 V3 V5 3 Fig 2 4 5
145. Vol 3 No 9 21 December 2013 The Monthly Lecture Meeting zs 30 BI3AIS ri EAR PEA PERES Published by the Medical Information System Laboratory of Doshisha University Kyotanabe J apan Medical Information System Laboratory The Monthly Lecture Meeting Contents tMRI HL E B B D D D D HE UL D D D DE CE OE OE UE D DO D UE U o peee K E e DN EE E EN E EN E E EEEE EE EEN EEE KERR TINE Es 1 6 MRIHHHHHHHHHHHHHHHINIRSHHHHHHHHHHHHHHHI 3DHHHHHHLH LU LI HE SEDI es o E E E E e E e E A E E EEE s e S E E E E IIHHHHHHHHHHHHHHHHHH INIHRSHHHHHHHHHHHLH INIRSIHHIHHHHHHHHHHHHHHHHHHHHHHHH ESB DE DE EE DICERE DI BEES B EVE EAE ERE EAE ET EE E DeepLearning LB B D HL BL UO DL UL D DE OL OD DE B D DE BE D EE CEU eee E e E EAE EAEE EE E EE EE EEEE n Ee EE E AE E Ee a e E e e EE EE EEEE E E E e E p eE EE EE E N AE e E Ae E ENE EE Es OO LEELAEHET ses E res ER a 19 24 34 38 44 48 03 59 65 30 2013 12 21 H fMRI Tatsuya OKAMURA fMRI
146. ction i mil Reconstruction error Fig 1 denoising Autoencoder 3 fINIRS 7 8 9 10 HC BEC 1 Lol 3 1 fNIRS fNIRS ETG 7100 11 22 5 15 MSF 1 11 22 5 1 5 fNIRS 49 22 4 25 1 C 40 61 11 00 17 00 fNIRS 10 20
147. ervice 1992 4 McNair DM Heuchert JWP Profile of Mood States Technical Update 2003 Multi Health Systems Inc 2003 5 4 2002 6 Etienne K Gianpaolo B Pietro P Seth P Jordan G The role of the anterior prefrontal cortex in human cognition letters to nature Vol 399 pp 148 158 1999 7 Vinod M Lucina QU Saliency switching attention and control a network model of insula function Springer Vol 214 pp 655 667 2010 30 2013 12 21 H RO Miku MORIGUCHI DTI DTI DTI
148. ig 4 5 Table 1 13 9 Table 1 RST us ale cpe e i y k y v 44 45 52 52 52 52 28 32 32 aa 40 64 do RST5 5 Fig 5 LIFG DLPFC Oxy Hb 5 36 du z E 3c S E a O T E T ps e S Su N B m ES t 300 N LIFG Timels els DLPFC Fic 5 6 EE LIFG DLPFC LIFG DLPFC
149. ing Fig 11 a 0 18 Tracking Tracking D 5 Tracking 1 1 BL 16 7 2 e FA
150. ng and moti vation Academic Press 8 edition 1974 2 A Baddeley The episodic buffer A new component of working memory Trends in cognitive Sciences Vol 4 No 11 pp 417 423 2000 3 M A Just and P A Carpenter A capacity theory of comprehension Individual differences in working memory Psychological Review Vol 99 No 1 pp 122 149 1992 4 M Daneman and P A Carpenter Individual differences in working memory and read ing Journal of Verbal Learning and Verbal Behavior Vol 19 No 4 pp 450 4606 1980 5 P C Kyllonen and R E Christal Reasoning ability is little more than working memory capacity Intelligence Vol 14 No 4 pp 389 433 1990 6 P J Olesen H Weserberg and T Klingberg Increased prefrontal and parietal activity after training of working memory nature neuroscience Vol 7 No 1 pp 75 79 2004 32 7 10 Nl 11 12 13 14 15 16 17 18 19 20 21 T Klingberg E Fernell P J Olesen M Johnson P Gustafsson K Dahlstrom C G Gillberg H Forssberg and H Westerberg Computerized training of working memory in children with ADHD a randomized controlled trial Journal of American Academy of Child and Adolescent Psychiatry Vol 44 No 2 pp 177 186 2005 R A Barkley Behavioral inhibition sustained attention and executive functions Constructing a unifying theory of ADHD Psychological Bulletin Vol
151. ns in human parietal cortex Neuroimaging Vol 11 pp 157 163 2001 7 Neurolmage Vol 25 pp 708 717 2005 43 30 2013 12 21 fNIRS Tetsuhiro TAKAMOTO ALS PAY 7 4 5 1 ALS Amyotrophic Lateral Sclerosis 7000 30 10 Do
152. ontrol Fig 4 BOLD p lt 01 p lt 05 31 Right ITG a Image b Rehearsal c Control Fig 5 FA paired 7 test p lt 001 uncorrected extent threshold voxels 10 x 42 4 3 Fig 5 FA FA aj 20 21
153. phasis LRE long runs empha sis GLN gray level nonuniformity RLN run length nonuniformity RPC run percent age 5 9 Nx 2 9 77 9 1 N Ta y Pugo 1 i 0 j 1 54 1 2 3 4 I0 4 o o o 4000 11010 4000 2 3000 0010 3310 0 3100 0 0 0 459 0123 1100 4000 012 313 4000 4000 3000 3000 2 111 3 1 0 0 3 0 0 0 3030 0 909 0 135 a b Fig 2 5 pda SRE gt 3 E 2 207323 g 1 N LRE M 5 jP ij 0 3 0 j 1 2 1 g 1 N GLN Epson 4 0 9 1 1 N g 1 2 RIN Ys yore 5 j 1 L 0O 9 N RPC js 2 2 P 3 0 6 SRE LRE GLN
Download Pdf Manuals
Related Search