Cordier Alix, Mary Alison, Vander Ghinst Marc, Goldman Serge, De Tiège Xavier, Wens Vincent
Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie translationnelles (LN T), Brussels, Belgium.
Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Neuropsychology and Functional Neuroimaging Research Unit (UR2NF) at Centre de Recherches Cognition et Neurosciences (CRCN), Brussels, Belgium.
Imaging Neurosci (Camb). 2024 Jul 17;2. doi: 10.1162/imag_a_00231. eCollection 2024.
The oscillatory nature of intrinsic brain networks is largely taken for granted in the systems neuroscience community. However, the hypothesis that brain rhythms-and by extension transient bursting oscillations-underlie functional networks has not been demonstratedElectrophysiological measures of functional connectivity are indeed affected by the power bias, which may lead to artefactual observations of spectrally specific network couplings not genuinely driven by neural oscillations, bursting or not. We investigate this crucial question by introducing a unique combination of a rigorous mathematical analysis of the power bias in frequency-dependent amplitude connectivity with a neurobiologically informed model of cerebral background noise based on hidden Markov modeling of resting-state magnetoencephalography (MEG). We demonstrate that the power bias may be corrected by a suitable renormalization depending nonlinearly on the signal-to-noise ratio, with noise identified as non-bursting oscillations. Applying this correction preserves the spectral content of amplitude connectivity, definitely proving the importance of brain rhythms in intrinsic functional networks. Our demonstration highlights a dichotomy between spontaneous oscillatory bursts underlying network couplings and non-bursting oscillations acting as background noise but whose function remains unsettled.
在系统神经科学界，大脑内在网络的振荡特性在很大程度上被视为理所当然。然而，大脑节律——进而扩展到瞬态爆发性振荡——构成功能网络的这一假设尚未得到证实。功能连接性的电生理测量确实会受到功率偏差的影响，这可能导致对并非真正由神经振荡（无论是否爆发）驱动的频谱特异性网络耦合进行人为观测。我们通过引入一种独特的组合来研究这个关键问题，即对频率依赖幅度连接性中的功率偏差进行严格数学分析，并结合基于静息态脑磁图（MEG）的隐马尔可夫模型的具有神经生物学依据的大脑背景噪声模型。我们证明，功率偏差可以通过一种合适的归一化来校正，这种归一化非线性地依赖于信噪比，其中噪声被识别为非爆发性振荡。应用这种校正可以保留幅度连接性的频谱内容，明确证明了大脑节律在内在功能网络中的重要性。我们的论证突出了网络耦合背后的自发振荡爆发与作为背景噪声但其功能仍未明确的非爆发性振荡之间的二分法。
