Keskin Kaan, Catal Yasir, Wolman Angelika, Cagdas Eker Mehmet, Saffet Gonul Ali, Northoff Georg
Department of Psychiatry, Ege University, Izmir, Turkey; SoCAT Lab, Ege University, Izmir, Turkey; Mind, Brain Imaging and Neuroethics Research Unit, University of Ottawa, Ontario, Canada.
Mind, Brain Imaging and Neuroethics Research Unit, University of Ottawa, Ontario, Canada.
Neuroimage. 2025 May 15;312:121221. doi: 10.1016/j.neuroimage.2025.121221. Epub 2025 Apr 15.
Understanding the brain's intrinsic architecture has long been a central focus of neuroscience, with recent advances shedding light on its topographic organization along uni and transmodal regions. How the brain's global uni-transmodal topography relates to psychological features like our sense of self remains yet unclear, though.
We here combine fMRI brain imaging with computational modeling (Wilson Cowan model) to better understand the temporal, spatial and physiological features underlying the distinction of self and non-self regions within the brain's global topography.
fMRI resting state shows lower myelin content, longer timescales (measured by the autocorrelation window/ACW), and lower global functional connectivity/synchronization (measured by global signal correlation/GSCORR) in self regions (based on the three-layer self topography; Qin et al. 2020) compared to non-self regions. Next, we fit the fMRI data with a neural mass model, the Wilson-Cowan model, which is enriched by structural and functional connectivity data from human MRI/fMRI. We first replicate the empirical data with longer ACW and lower GSCORR in self regions. Next, we demonstrate that self and non-self regions can, based on the same measures in the model, not only be distinguished within the brain's global topography but also within the unimodal and transmodal regions themselves, respectively. Finally, the neural mass model shows that such topographic differentiation relates to two physiological features: self regions exhibit higher intra-regional excitatory recurrent connection and higher levels in their basal neural excitation than non-self regions.
Our findings demonstrate the intrinsic nature of the distinction of self and non-self regions within the brain's global uni-transmodal topography as well as their underlying physiological differences with higher levels in both recurrent connections and neural excitation in self regions. The increased recurrent connections in self regions, together with their higher levels of neural excitation and the longer autocorrelation window, may be ideally suited to mediate their self-referential processing: this can thus be seen as a form of 'psychological recurrence' where one and the same input/stimulus is processed in a prolonged echo-chamber like way, that is, an internal echo within the self regions themselves.
长期以来,了解大脑的内在结构一直是神经科学的核心关注点,最近的进展揭示了其沿单模态和跨模态区域的拓扑组织。然而,大脑的全局单模态 - 跨模态拓扑结构与诸如自我意识等心理特征之间的关系仍不清楚。
我们在此将功能磁共振成像(fMRI)脑成像与计算建模(威尔逊 - 考恩模型)相结合,以更好地理解大脑全局拓扑结构中自我与非自我区域区分背后的时间、空间和生理特征。
功能磁共振成像静息状态显示,与非自我区域相比,自我区域(基于三层自我拓扑结构;Qin等人,2020)的髓磷脂含量更低、时间尺度更长(通过自相关窗口/ACW测量)以及全局功能连接性/同步性更低(通过全局信号相关性/GSCORR测量)。接下来,我们用神经团块模型(威尔逊 - 考恩模型)拟合功能磁共振成像数据,该模型通过来自人类磁共振成像/功能磁共振成像的结构和功能连接数据得到增强。我们首先在自我区域复制了具有更长ACW和更低GSCORR的实证数据。接下来,我们证明,基于模型中的相同测量方法,自我和非自我区域不仅可以在大脑的全局拓扑结构中区分,而且也可以分别在单模态和跨模态区域内区分。最后,神经团块模型表明,这种拓扑分化与两个生理特征有关:自我区域比非自我区域表现出更高的区域内兴奋性递归连接和更高水平的基础神经兴奋性。
我们的研究结果证明了大脑全局单模态 - 跨模态拓扑结构中自我与非自我区域区分及其潜在生理差异的内在本质,自我区域在递归连接和神经兴奋性方面水平更高。自我区域中增加的递归连接,连同其更高水平的神经兴奋性和更长的自相关窗口,可能非常适合介导其自我参照处理:因此,这可以被视为一种“心理递归”形式,其中同一输入/刺激以延长的回声室般的方式进行处理,即在自我区域本身内部产生回声。