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1 Vol.4, No.5, 1 September 2014 Monthly Lecture Meeting Published by the Medical Information System Laboratory of Doshisha University, Kyotanabe, Japan
2
3 Medical Information System Laboratory Monthly Lecture Meeting Contents -fnirs fnirs SSVEP BMI MOCS... 41
4 fnirs Yui HASEGAWA fnirs n-back RST 5 n-back RST RST ) (Attention Deficit Hyperactivity Disorder: ADHD) 2) MRI fnirs(functional Near Infrared Spectroscopy) fnirs n-back (Reading Span Test: RST) ) 1
5 2 2) 2.2 4) ADHD 25 2) 2) fmri fnirs fnirs 1 5) fnirs 3 n-back RST 5 (21-25 ) 1 n-back RST n-back n-back 1958 N 6) three-back Fig Fig. 1 n-back 2
6 ) 8) Fig Rest: Task: 7 Rest Task Fig RST 2) RST Fig Rest: (5 ) Fig. 3 RST n-back n-back Fig. 4 3
7 Fig. 4 n-back 4.2 Fig. 5 Fig RST RST Fig. 6 C D 4
8 Fig. 6 RST 5 n-back n-back RST n-back 6 n-back RST fnirs n-back 7 n-back RST fnirs 5
9 1)., Vol. 18, pp. 1 5, ). :., Vol. 109, pp , ) Alan D Baddeley and Graham Hitch. Working memory. The psychology of learning and motivation, Vol. 8, pp , ) Pernille J Olesen, Helena Weserberg, and Torkel Klingberg. Increased prefrontal and parietal activity after training of working memory. nature neuroscience, Vol. 7, pp , ). nirs. MEDIX, Vol. 39, pp. 4 10, ) Wayne K. Kirchner. Age differences in short-term retention of rapidly changing information. Journal of Experimental Psychology, Vol. 55, pp , ),,. 2., Vol. 60, pp , ),.., Vol. 61, pp ,
10 fnirs Emiko SHIMOMURA fnirs LABNIRS ETG V1 V Hubel V1 1) V1 1) 2) V1 fnirs functional Near Infrared Spectroscopy fnirs V1 2 V1 V2 V5 3) 2.1 V1 V1 Fig. 1(b) m 2.2 V2 V2 V1 V2 2.3 V1 V5 V1 V2 V1 V3 V2 V1 7
11 (a) Fig. 1 (b) V LABNIRS ETG SAMSUNG SyncMaster E cm 4) ) Fig. 2 30s 10s 20s 5cm/s 2cm/s 1cm/s 3 (a) Fig. 2 (b) LABNIRS ETG Hz 8
12 5 5 LABNIRS 238 ETG t 4 LABNIRS ETG cm/s 2cm/s V1 1cm/s t- Fig. 3 Fig. 3 t- Fig. 3 (p 0.05) (Fig. 3) V2 V1 ETG-7100 LABNIRS Fig. 4 LABNIRS Fig. 5 ETG
13 5 LABNIRS t- V1 ETG-7100 ETG-7100 (Fig. 5) V1 LABNIRS LABNIRS V1 LABNIRS 6 LABNIRS 7 V1 LABNIRS ETG NIRS LABNIRS 1) D.H.Hubel and T.N.Wiesel. Receptive fields and functional architecture of monkey striate cortex. The Department of Physiology, Vol. 195, pp , ) K.Ikezoe, Y.Mori, K.Kitamura, H.Tamura, and I.Fujita. Relationship between the local structure of orientation map and the strength of orientation tuning of neurons in monkey v1: A 2-photon calcium imaging study. Neuroscience, Vol. 33, pp , ) D.H.Hubel and T.N.Wiesel. Receptive fields, binocular interaction and functional architecture in the cat s visual cortex. The Department of Physiology, Vol. 160, pp , ),,.,,., Vol. 40, pp , ) Y.Kamitani and F.Tong. Decoding the visual and subjective contents of the human brain. Neuroscience, Vol. 8, pp ,
14 Megumi MIYAJIMA fnirs(functional Near Infrared spectroscopy) 2 1) Fig. 1 レスト タスク レスト ゲーム課題 Time[s] Fig
![3 21 24 54 67 10[Hz] 116 fnirs(functional Near-Infrared](/docs-images/88/114888095/images/15-1.jpg "Spectroscopy:ETG- 7100, ) 10-20 (Oxy-Hb) 3.4 3.5 fnirs 2) Oxy-Hb 1.")
15 ( ) ( ) 21cm Fig. 2 3 ( : : ) 3 (a) (b) Fig [Hz] 116 fnirs(functional Near-Infrared Spectroscopy:ETG- 7100, ) (Oxy-Hb) fnirs 2) Oxy-Hb 1.0[Hz] Fig. 3 4 fnirs Fig. 3(a) 5 Fig. 3(b) A Oxy-Hb 120[s] Oxy-Hb 5 B C D E Oxy-Hb 12
![脳 流量濃度変化 [mmol*mm] 時間 被験者 A 被験者 B 被験者 C 被験者 D](/docs-images/88/114888095/images/16-0.jpg "被験者 E (a) (b) Fig.")
16 脳 流量濃度変化 [mmol*mm] 時間 被験者 A 被験者 B 被験者 C 被験者 D 被験者 E (a) (b) Fig. 3 5 A Oxy-Hb 5 Oxy-Hb 3) 4) 6 13
17 A Oxy-Hb 120[s] Oxy-Hb 1),.., Vol. 147, pp , ) John Jonides Tor D. Wager and Susan Reading. Neuroimaging studies of shifting attention: a meta-analysis. NeuroImage, Vol. 22, pp , ),,,,.., ),,,.., Vol. 15, pp ,
18 Akiho MURAKAMI fnirs 1 1) fnirs(functional near-infrared spectroscopy) 2 Inter Stimulus Interval ISI ISI (1) f(x) = 1 2πσ 2 e (x µ)2 /2σ 2 (1) k S(k) Tap(k) ITI(Inter Tap-onset Interval) SE(Synchronization Error) (2) (3) IT I = T ap(k + 1) T ap(k) (2) SE = T ap(k) S(k) (3) 3 fnirs
19 ) [ ] [ ] 3.3 fnirs ETG [s] 88 [s] 50 [s] (1) 0 [msec] 80 [msec] 2 30 [s] 88 [s] 50 [s] ISI 800 [ms] ISI 20ms 0 [ms] 80 [ms] ISI=800 [ms] ISI SE 2). presentation 500 [Hz] sin 100ms Fig SE SE Fig. 2 5 SE SE Fig. 2 SE SE Fig. 3 SE 4.2 T 30[s] 30 30[s] 30 T Fig. 4 Fig [msec] 16
20 Fig. 2 log SE Fig. 3 SE Fig. 4 17
21 5 SE Fig. 2 Fig. 4 =80 [msec] 3) 6 7 fnirs 1),, , pp , ) Nishimura T. Matumoto, M. A 623-dimensionally equidistributed uniform pseudorandom number generator. Mersenne Twister, Vol. 8-1,, ) Jiro Okuda. Thinking of the future and past - the roles of the frontal pole and the medial temporal obes. NeuroImage, Vol. 19,,
22 SSVEP BMI Takuya MORISHITA BMI BMI SSVEP LED 2 50Hz LED 4 26Hz 1 EEG Electroencephalograph fmri functional Magnetic Resonance Imaging MEG Magnetoencephalogram NIRS Near-Infrared Spectroscopy BMI Brain Machine Interface BMI QOL Quality Of Life 1) ALS Amyotrophic Lateral Sclerosis ALS 1) BMI SSVFEP Steady State Visual Evoked Potentials 2 BMI BMI EEG EEG BMI / SSVEP P300 ERP Event Related Potential 2) SSVEP P300 ERP BMI SSVEP Fig. 1 19
23 EEG PC Analysis Flicks RCcar Fig. 1 SSVEP BMI 2.1 SSVEP SSVEP SSVEP BMI 1) 2.2 P300 ERP P300 ERP 300ms P300 P300 1) SSVEP BMI LED Light Emitting Diode SSVEP % 1080lx 3 Table. 1 Table. 1 ( ) A 21 B 21 C Polymate AP1000 g.tec g.gammabox 1kHz ) A1 A2 AFz Oz O1 O2 POz PO3 PO4 PO6 PO7 P3 P4 P5 P6 P7 P8 14 Fig. 2 20
24 Fig cm LED 2 50Hz 2Hz 25 Fig. 3 Fig. 4 60Hz Unity 523lx 7.4cm LED 5mm LED36 Arduino UNO 530Lx 3.5cm 50cm 10s 15s レスト 10s 閉眼 タスク 15s 開眼注視 レスト 10s 閉眼 Fig. 3 (a) (b) LED Fig. 4 21
25 3.5 MATLAB FFT Fast Fourier Transform LED 4 26Hz 12Hz LED A Oz Fig. 5 Fig. 5 A Oz 12Hz LED A B C Fig. 5 LED Fig. 6 A Fig. 6(a) Oz O1 O2 B Fig. 6(b) Oz O1 O2 PO7 PO8 C Fig. 6(c) Oz O1 O2 POz PO3 PO4 Oz O1 O2 (a) A (b) B (c) C Fig Hz Oz Table. 2 - B C 10Hz 10Hz 12 18Hz Fig. 7 A Fig. 7(a)., B Fig. 7(b) 10Hz, A. C Fig. 7(c) 12Hz.,,.,BMI,. 22
26 Table. 2 Oz [Hz] A[uV 2 ] B [uv 2 ] C[uV 2 ] (a) A (b) B Fig. 7 (c) C 12 18Hz 23
27 4 SSVEP 5 BMI QOL BMI SSVEP LED LED 4 26Hz BMI 1),. BCI., Vol. 19, No. 3, pp , ),,. BCI (BCI/BMI ).. NC,, Vol. 109, No. 280, pp , )., pp , 2,
28 Izumi ISHIDA tracking tracking tractography tracking tracking FA D FA FA FA 1 (Diffusion Tensor Image: DTI) DTI fiber tracking DTI 1) fiber tracking 1) DTI fiber tracking DTI (Magnetic Resonance Imaging: MRI) DTI (Diffusion Weighted Image: DWI) fiber tracking 2, 3 mm fiber tracking fiber tracking fiber tracking 2 (Diffusion Weighted Image: DWI) MRI (Motion Probing Gradient: MPG) Einstein-Smoluchowski (1) x 2 D t DWI x 2 = 2Dt (1) 3 (Diffusion Tensor Image: DTI) DTI 6 MPG DWI Fig. 1 x 2 y z D 25
29 D xx D xy D xz D = D yx D yy D yz (2) D zx D zy D zz DWI D xx 1 MPG x MPG D xx D yy D zz D xy D xz D yz D 2) S = S 0 exp( bg T Dg) (3) Fig. 1 (2) λ 1 λ 2 λ 3 e 1 e 2 e 3 λ 1 λ 2 λ 3 e 1 e 2 e 3 (Apparent Diffusion Coefficient: ADC) (Fractional Anisotropy: FA) FA FA FA DTI FA ADC FA FA FA F A = ADC = λ 1 + λ 2 + λ (λ1 ADC) 2 + (λ 2 ADC) 2 + (λ 3 ADC) 2 2 λ λ λ2 3 (4) (5) (2) (4) (5) FA fiber tracking 4 Fiber tracking (5) FA (2) 26
30 fiber tracking tractography (2) X s X(0) F dx(s) ds = F (X(s)) (6) F (2) 1 tracking (crossing) (kissing) (fanning) Fig. 2 DTI 2) tractography DWI tractography 交叉 crossing) 接吻 kissing) 扇状 (fanning) Fig tractography MRI tractography tractography 1 tractography tractography 30 MPG 2) 5 tractography tracking Matlab D (5) FA 3 MPG B0 MPG 6 6 DWI
31 1 1mm 1mm 1mm x : y : z : MPG (3) D (4) (5) FA FA Fig. 3 3 FA Fig. 3 FA (z = 14) 7 FA tractography tracking tractography i X i 1 Runge-Kutta tracking 2) X i+1 = X i + ϵ F (X i ) (7) tractography tracking tractography 8 fiber tracking tractography tracktigraphy tractography FA FA FA tractography tracking tractography tractography 1).., Vol. 58, pp , ),,,. MRI[ 3 ]., 3,
32 Chiyuki ITO DTI DTI FA DTI DTIStudio FiberTracking FA 1 (Diffusion Tensor Imaging:DTI) DTI (Magnetic Resonance Imaging:MRI) (Motion Probing Gradient:MPG) DTI DTI DTI 2 (Diffusion Tensor Imaging:DTI) MPG diffusion weighted imaging DWI diffusion anisotrophy DTI DTI 1) 2.1 FA FA Fig. 1 2) 2.2 Tractography (fiber tracking) tractography 3) 29
33 拡散異 性が強い ( 線維質 ) 拡散異 性が弱い ( 非線維質 ) Fig. 1 FA DTI FA ( m) 3.2 Table. 1 2) Table. 1 MRI ECELON Vega 1.5T Fig. 2(a) RCX-1000S EYELA Fig. 2(b) (a) MRI (b) Fig ( 0.2 ) ( 6mm 8mm) Fig. 3 30
![0 3.0 MPG Dir ( )[Tensor] 21 21 b-factor( ) 1000 1000 NSA( ) 2 2 Freq ( ) 128 64 Phase ( ) 128 64 ReconMatrix( ) 256](/docs-images/88/114888095/images/34-1.jpg "256 voxel size x,y,z[mm] 1.88 1.88 3 3 3 3 3.4.2 MRI MRI Fig. 4 MRI 4 4.1 40 60 80 Fig. 5 Fig. 6 Fig.")
34 mm 2 4 m FA Fig A B Table. 2 Table. 2 2 A B FOV( ) [mm] TR( )[msec] TE( )[msec] Thickness( )[mm] Interval( )[mm] MPG Dir ( )[Tensor] b-factor( ) NSA( ) 2 2 Freq ( ) Phase ( ) ReconMatrix( ) voxel size x,y,z[mm] MRI MRI Fig. 4 MRI Fig. 5 Fig. 6 Fig. 7 tractography FA A B FA (Max) Table. 3 Table. 4 31
35 Z MRI Y 線維束 X Fig. 4 MRI Tractography FA Tractography FA (a) A Fig (b) B Tractography FA Tractography FA (a) A (b) B Fig Tractography FA Tractography FA (a) A (b) B Fig Table. 3 A [ ] FA Max Table. 4 B [ ] FA (Max) Fig. 5 Fig. 6 Fig
36 80 FA 60 Table. 3 Table. 4 FA A B A B 2 6 A B FA 7 DTI DTI FA FA FA FA 1). new imaging. No.19, pp ). DTI. 32, pp ). MRI.,,
37 Makoto TAKENAKA DPC 1 DPC Diagnosis Procedure Combination 2 DPC(Diagnosis Procedure Combination: ) 2.1 DPC (DPC ) DPC DPC DPC (DPC ) DPC 2.2 DPC ( ) DPC DPC (MDC:Major Diagnostic Category) MDC ICD-10( ) 1 2 Fig. 1 1) 2.3 DPC International Statistical Classification of Diseases and Related Health Problems: 10 (2003 ) 34
38 xx 99 x0xx 1MDC 1 層目 2 層目 3 層目 9 重症度等 2MDC における分類 3 入院目的 ( 今回は使用しない ) 4 年齢 出生時体重等 5 手術 8 副傷病名 7 手術 処置等 2 6 手術 処置等 1 Fig. 1 DPC DPC 3 DPC DPC DPC MDC 5 MDC 5 MDC 2 35
39 4 Web Django MySQL web HTML 2 Flow1-4 Fig. 2 Flow1. Flow2. ( ) Flow3. Flow2 Flow4. (a) Flow1 (b) Flow2 (c) Flow3 (d) Flow4 Fig. 2 5 GoogleMap DPC DPC web JavaScript 3 CSS 4 Linked Open Data 1) Dpc ( ) /dpc all.pdf. 2 Hyper Text Markup Language Web 3 web 4 Cascading Style Sheets web 36
40 Nachi TANAKA 1 ( ) 3cm ( ) Fig. 1 Target vessel Superficial vessel Fat 2 1. RGB RGB 8. RGB HSV [pixel] H [pixel] 500[pixel] H
41 Fat Target vessel Superficial vessel Fig. 1 Fig Fig f(i, j) g(i, j) (1) g(x, y) = w w n= w n= w w f(i + m, j + n) exp( m2 + n 2 ) exp( w n= w n= w 2σ 2 1 exp( m2 + n 2 2σ1 2 ) exp( (f(i, j) f(i + m, j + n))2 2σ2 2 ) (f(i, j) f(i + m, j + n))2 2σ2 2 ) (1) w σ 1 σ 2 σ 1 = 50 σ 2 = Fig. 1 Fig [pixel] ) 8 38
42 Image 480pixels Opening pixels Structuring element 3pixels 9pixels Rotation from 0 to every 22.5 Fig. 3 (a) (b) Fig HSV RGB HSV RGB HSV 2) 500[pixel] H(Hue ) H H 500[pixel] H S(Saturation ) V(Value ) 3 3 Fig. 4 Fig. 4(b) Fig. 5 Fig. 4 Fig. 4 39
43 Fig. 5 4 Windows 5 windows 1) A. BUYANDALAI,,,,,.., Vol. 110, No. 364, pp , ),,. hsv., Vol. 49, No. 6, pp ,
44 MOCS Yudai GOTO Yui HASEGAWA Akiho MURAKAMI Megumi MIYAGIMA Chiyuki ITO Mayumi HORI Emiko SIMOMURA Abstract 12 MOCS MOCS (Intelligent Management System:ITS) ITS 1) 2) ITS Mobile Operate Control System(MOCS) 2 ITS ITS ITS ETC ETC ITS 3) ITS ITS ITS (Universal Traffic Transport System :UTMS) 1) 3 UTMS CO 2 NOx 41
45 UTMS UTMS21(Next Generation Universal Traffic Management: ) 1) UTMS21 (ITCS) Advanced Mobile Information Systems :AMIS (Public Transportation Priority System :PTPS) (Dynamic Route Guidance Systems :DRGS) (MOCS) (Help system for Emergency Life saving and Public safety :HELP) (Environmental Protection Management Systems :EPMS) (Driving Safety Support Systems :DSSS) (Intelligent Integrated ITV Systems :IIIS) 4) Table.1 PTPS AMIS IIIS DSSS EPMS MOCS DRGD HELP Table. 1 UTMS21. IC. MOCS 4 MOCS Mobile Operation Control System MOCS ( ) ID ITCS 1) 4.1 5)
46 ., 0 120km 4) (Down-Link:DL) (Up-Link:UL) UL 64Kbps 276B DL 1024KB 10KB ID20byte 4byte 4byte 4byte 5) ) 双 向通信 光ビーコン Fig. 1 5 MOCS 5.1 MOCS 14 3 ID ITCS (PTPS) MOCS ( 7) 5.2 MOCS FAST M-MOCS(Medical Mobile Operation Control System) ID 8) 43
47 6 MOCS 9) 7 ITS UTMS21 ITCS 8 MOCS ITCS UTMS21 1)., pp , ITS, ),., 2014/4/10. 3). ict /4/10. 4) UTMS. Utms society of japan /4/10. 5),,,., 2014/4/10. 6) KDDI. Designing the future kddi. $ E3%82%B9/%E5%85%89%E3%83%93%E3%83%BC%E3%82%B3%E3%83%B3.html$, 2014/4/10. 7). keihan /4/10. 8). m-mocs /4/10. 9) /4/10. 44
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