간질성 뇌파의 시공간 패턴 분석
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간질성 뇌파의 시공간 패턴 분석
김 정 애 , 한 승 기충북대학교 물리학과
임 태 규한국전자통신연구원 , 인체정보연구부
이 상 건 , 남 현 우서울대학교병원 신경과
.
간질 (Epilepsy) ?신경계의 변화에 의한 강한 발화 활동 - 신경세포의 과도한 발화 - 신경계의 변화에 의한 강한 동기화 : 흥분성 영향의 증가 ,
억제성 영향의 감소 - 간질성 발작 거동뇌파의 변화 , 뇌파 분석간질 뇌파의 특징 추출 , 간질 발현 시간 예측 , 간질 위치 추정
Invasive EEG(Epileptic seizure) 간질뇌파 ( 서울대병원 간질센터 )-Lateral Temporal Lobe Epilepsy(L-TLE) -Depth EEG-32 channels, 200sec recording (interictal, ictal, postictal)
-5subject
Time(sec)
channel
Interictal ictal
EEG Electrodes
54321
109876
1514131211
2019181716
21222324252627
28
29
30
Temporal changes, spatial dependence
Correlations between different measures
Robustness of pattern: inter-trials
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
Spatio-temporal Pattern
Power spectrum analysis(1):Frequency band: theta(3~7Hz), alpha(8~13Hz),
beta(20~30Hz)
spectrum sum: theta, alpha, beta is localization
power ratio :
-rhythm order : beta->alpha-> theta->total power
Total power 3~7hz 8~13hz 20~30hz
Spectrum sum
Power ratio
Power spectrum analysis: inter-trial variations
Time map of beta spectrum
Time
sgo1
sgo2
sgo3
sgo4
sgo5
Statistical analysis:
0])H[SLl
]H[SLl
(H[S])S,(SJS 22
11
212
pplogH[p],SSS,llL 22121
Change in statistical properties of amplitude distribution
- boundary between two distributions?
분석구간
Jensen-Shannon divergence)()1()( txtxtz
2S1SS
JS-E Kurt STD
Statistical measures at JS-E maximum
Temporal mapping of JS-E
Plot of JS-divergence:
Time of change in amplitude distribution
Position of most dominant changes
Statistical measures
Plot of JS-divergence: inter-trial variations
sgo1
sgo2
sgo3
sgo4
sgo5
Time map of JS-E
Time
Nonlinear analysisNonlinear dynamics underlying the bursting neural activities Nonlinear measures characterizing the temporal behaviors
Mutual information
ji
ij
ji,ij pp
)(p)ln(p)s(
Avera
ge m
utu
al in
form
ati
on
Time(sec)
channel
Corr
ela
tion
tim
e(m
sec)
Temporal mapping of correlation time
Plot of correlation time:
decrease in the correlation time during ital period no specific channel dependence
Variability of the spatial mapping
sgo1
sgo2
sgo3
sgo4
sgo5
Plot of correlation time:inter-trial variations
Temporal maping of correlation time
Time
sgo1 sgo2 sgo3 sgo4 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
Comparison of temporal maps of JS-E , Beta, Correlation time
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
Neural network model of Epileptic seizure generation: CA3 in Hippocampus [Tateno,1998]
synafleakKCaNa IIIIIIdt
dVC
- Pyramidal cell (Δ)
- Inhibitory inter-neuron ()
synleakKNa IIIdt
dVC
))(( VVtCI synsynsyn
- Synaptic current
Iaf : 해마 외부에서 가해지는 tonic input
k
kstimsynfield RIII /)(
- Field current
신경 모형계 (16x16) 의 시공간 발화 패턴
Cpp=0.001
Cpp=0.003
Cpp=0.005
Cpp=0.008
time
STDP (Spike-Timing Dependent Plasticity)
[G-q. Bi and M-m. Poo, 1998]
max)( CtFCC synsyn
0),/exp(
0),/exp()(
ttA
ttAtF
Normal hippocampus : A+ ~ A-Abnormal hippocampus : ?
Δt : tpost - tpre
A+ : maximal synaptic strengthening
A- : maximal synaptic weakening
gaf=0.005uS, CPI=0.02uS, CIP=0.02uS
A+ 와 A- 에 따른 신경모형계의 거동 변화 (2)
결론 및 논의Spatio-temporal pattern analysis• Power spectrum : - 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
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