PDF(Pubmed)
Abstract：
UNASSIGNED: An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods.
UNASSIGNED: Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC).
UNASSIGNED: EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups.
UNASSIGNED: The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ.
UNASSIGNED: FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes.
UNASSIGNED: Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.
UNASSIGNED: Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC).
UNASSIGNED: EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups.
UNASSIGNED: The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ.
UNASSIGNED: FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes.
UNASSIGNED: Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.
摘要：
■癫痫的一个重要挑战是定义治疗反应的生物标志物。许多脑电图（EEG）方法和指标已主要使用线性方法开发，例如，频谱功率和单个α频率峰值(IAF)。然而,大脑活动是复杂和非线性的，因此，有必要使用非线性方法探索EEG神经动力学。这里,我们使用分形维数(FD)，衡量整个大脑信号的复杂性，测量局灶性癫痫（FE）患者对抗癫痫治疗的反应，并将其与线性方法进行比较。
■在引入抗癫痫药物（ASM）之前（t1，命名为DR-t1）和之后（t2，命名为DR-t2）研究了25例局灶性癫痫患者。将DR-t1和DR-t2EEG结果与40个年龄匹配的健康对照（HC）进行比较。
■从两个不同的角度研究了EEG数据：δ中的频域-频谱特性，θ,α,β,以及γ波段和IAF峰值，和时域FD作为EEG信号的非线性复杂性的特征。比较三组的特征。
■ASM前后DR患者和HC的δ功率不同（DR-t1与HC,p<0.01和DR-t2vs.HC,p<0.01)。θ功率在DR-t1和DR-t2之间（p=0.015）以及DR-t1和HC之间（p=0.01）不同。α幂,类似于δ，ASM前后DR患者和HC之间存在差异(DR-t1与HC,p<0.01和DR-t2vs.HC,p<0.01)。DR-t1的IAF值低于DR-t2（p=0.048）和HC（p=0.042）。DR-t1的FD值低于DR-t2（p=0.015）和HC（p=0.011）。最后,贝叶斯因子分析显示，FD将DR-t1与DR-t2分离的可能性是IAF的195倍，是θ的231倍。
■在基线EEG信号中测量的FD是在检测对ASM的反应时比EEG功率或IAF更敏感的非线性大脑复杂性测量。这可能反映了神经活动的非振荡性质，FD更好地描述了。
■我们的工作表明，FD是监测FE中对ASM的响应的有希望的措施。
■在引入抗癫痫药物（ASM）之前（t1，命名为DR-t1）和之后（t2，命名为DR-t2）研究了25例局灶性癫痫患者。将DR-t1和DR-t2EEG结果与40个年龄匹配的健康对照（HC）进行比较。
■从两个不同的角度研究了EEG数据：δ中的频域-频谱特性，θ,α,β,以及γ波段和IAF峰值，和时域FD作为EEG信号的非线性复杂性的特征。比较三组的特征。
■ASM前后DR患者和HC的δ功率不同（DR-t1与HC,p<0.01和DR-t2vs.HC,p<0.01)。θ功率在DR-t1和DR-t2之间（p=0.015）以及DR-t1和HC之间（p=0.01）不同。α幂,类似于δ，ASM前后DR患者和HC之间存在差异(DR-t1与HC,p<0.01和DR-t2vs.HC,p<0.01)。DR-t1的IAF值低于DR-t2（p=0.048）和HC（p=0.042）。DR-t1的FD值低于DR-t2（p=0.015）和HC（p=0.011）。最后,贝叶斯因子分析显示，FD将DR-t1与DR-t2分离的可能性是IAF的195倍，是θ的231倍。
■在基线EEG信号中测量的FD是在检测对ASM的反应时比EEG功率或IAF更敏感的非线性大脑复杂性测量。这可能反映了神经活动的非振荡性质，FD更好地描述了。
■我们的工作表明，FD是监测FE中对ASM的响应的有希望的措施。
