1. 간질성 뇌파의 시공간 패턴 분석 김 정 애 , 한 승 기 임 태 규 이 상 건 , 남 현 우 충북대학교 물리학과
김 정 애 , 한 승 기
충북대학교 물리학과
임 태 규
한국전자통신연구원, 인체정보연구부
이 상 건 , 남 현 우
서울대학교병원 신경과 .

2. 간질(Epilepsy) ? 신경계의 변화에 의한 강한 발화 활동 -신경세포의 과도한 발화
-신경계의 변화에 의한 강한 동기화: 흥분성 영향의 증가,
억제성 영향의 감소
-간질성 발작 거동
뇌파의 변화, 뇌파 분석
간질 뇌파의 특징 추출, 간질 발현 시간 예측, 간질 위치 추정

3. Invasive EEG(Epileptic seizure)
간질뇌파(서울대병원 간질센터)
Lateral Temporal Lobe Epilepsy(L-TLE)
Depth EEG
32 channels, 200sec recording (interictal, ictal, postictal)
-5subject
EEG Electrodes
Time(sec)
channel
Interictal ictal 5 4 3 2 1 10 9 8 7 6 15 14 13 12 11 20 19 18 17 16 21 22 23 24 25 26 27 28 29 30

4. Spatio-temporal Pattern
Linear analysis: power spectrum
-theta(3-7Hz), alpha(8-13Hz), beta(20-30Hz)
Statistical analysis: Jensen-Shannon divergence
- amplitude, peak time, variance, kurtosis
Nonlinear analysis: mutual information
-correlation time, correlation dimension
Temporal changes, spatial dependence
Correlations between different measures
Robustness of pattern: inter-trials

5. Power spectrum analysis(1):
Frequency band: theta(3~7Hz), alpha(8~13Hz), beta(20~30Hz)
Total power ~7hz ~13hz ~30hz
Spectrum sum
Power ratio
spectrum sum: theta, alpha, beta is localization
power ratio :
-rhythm order : beta->alpha-> theta->total power

6. Power spectrum analysis: inter-trial variations
sgo1
sgo2
sgo3
sgo4
sgo5
Time map of beta spectrum
Time

7. Statistical analysis:
Change in statistical properties of amplitude distribution
boundary between two distributions?
Jensen-Shannon divergence
분석구간

8. Statistical measures at JS-E maximum Temporal mapping of JS-E
Plot of JS-divergence:
JS-E Kurt STD
Statistical measures at JS-E maximum
Temporal mapping of JS-E
Time of change in amplitude distribution
Position of most dominant changes
Statistical measures

9. Plot of JS-divergence: inter-trial variations
sgo1
sgo2
sgo3
sgo4
sgo5
Time map of JS-E
Time

10. Nonlinear analysis
Nonlinear dynamics underlying the bursting neural activities
Nonlinear measures characterizing the temporal behaviors
Mutual information
Average mutual information

11. Plot of correlation time:
Time(sec)
channel
Correlation time(m sec)
Temporal mapping of correlation time
decrease in the correlation time during ital period
no specific channel dependence
Variability of the spatial mapping

12. Temporal maping of correlation time
Plot of correlation time:inter-trial variations
Temporal maping of correlation time
Time
sgo1
sgo2
sgo3
sgo4
sgo5

13. Comparison of temporal maps of JS-E , Beta, Correlation time
sgo sgo sgo sgo sgo5
JS-E
Beta
Correl.
time
Partial overlapping between the temporal maps of JS-E and beta
No similarity with the temporal map of correlation time

14. Correlation between inter-trials and different maps of JS-E, beta spectrum , and correlation time
High inter-trial correlations for JS-E and beta spectrum
Low inter-trial correlation for correlation time
Correlations between different maps are weak

15. Neural network model of Epileptic seizure generation: CA3 in Hippocampus [Tateno,1998]
Pyramidal cell (Δ)
- Inhibitory inter-neuron ()
- Synaptic current
Field current
 Iaf : 해마 외부에서 가해지는 tonic input

16. 신경 모형계(16x16)의 시공간 발화 패턴
Cpp=0.001
Cpp=0.003
Cpp=0.005
Cpp=0.008
time

17. STDP (Spike-Timing Dependent Plasticity)
[G-q. Bi and M-m. Poo, 1998]
Δt : tpost - tpre
A+ : maximal synaptic strengthening
A- : maximal synaptic weakening
Normal hippocampus : A+ ~ A-
Abnormal hippocampus : ?

18. A+와 A-에 따른 신경모형계의 거동 변화 (2)
gaf=0.005uS, CPI=0.02uS, CIP=0.02uS

19. 결론 및 논의 Spatio-temporal pattern analysis Power spectrum : JS-entropy :
- spatio-temporal pattern of beta rhythm is more informative
- lateral temporal lobe epilepsy is close to the hippocampus
- beta rhythm is generated in the hippocampus
JS-entropy :
- earlier rise of JS entropy in several channels
- the position of rises are consistent with diagnostic of the medical doctors
- the shape of distribution function, es. kurtosis : seizure generation
Mutual information : correlation time
- short correlation time for ictal rhythm
- non-specific map: no information on the localization
Neural network model of seizure generation:
- CA3 model + Spike-Timing Dependent Plasticity
- unbalance between synaptic strengthening and weakining