Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
Young Epilepsy, Lingfield, UK.
Hum Brain Mapp. 2024 May;45(7):e26700. doi: 10.1002/hbm.26700.
The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R = .89) and beta (R = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.
运动后β反弹已经被广泛地使用脑磁图(MEG)进行研究,并且可以被各种任务参数以及疾病可靠地调节。我们最近的研究表明,反弹,我们通常将其概括为“任务后反应”(PTRs),是大脑中普遍存在的现象,在θ、α和β频段的整个皮层中都有发生。目前,尚不清楚在工作记忆之后的 PTRs 是否是由短暂爆发驱动的,这些爆发是短暂的高振幅活动时刻,类似于驱动运动后β反弹的那些爆发。在这里,我们使用三状态单变量隐马尔可夫模型(HMM),它可以在没有先验频率内容或响应时间知识的情况下识别爆发,来比较在工作记忆和运动视觉 MEG 数据中驱动 PTRs 的爆发。我们的结果表明,在工作记忆和运动视觉任务中,PTRs 是由全谱瞬态爆发驱动的。这些爆发在皮层上具有非常相似的频谱内容变化,在两个任务的α(R=0.89)和β(R=0.53)频段之间相关性很强。爆发在皮层上的持续时间也具有相似的变化(例如,两个任务的运动皮层都有长持续时间的爆发),在两个任务之间的皮层区域之间相关性很强(R=0.56),在两个数据集的所有区域的平均值约为 300ms。最后,我们证明了 HMM 能够分离 MEG 数据中的感兴趣信号,使得 HMM 概率时间历程与反应时间的相关性比来自相同脑区的频率滤波功率包络更强。总体而言,我们表明,不同任务之间的诱导 PTRs 是由具有相似特征的爆发驱动的,可以使用 HMM 来识别。鉴于任务之间的爆发相似,我们认为皮层上的 PTRs 可能是由共同的潜在神经现象驱动的。