TY - JOUR
T1 - The brain's internal echo
T2 - Longer timescales, stronger recurrent connections and higher neural excitation in self regions
AU - Keskin, Kaan
AU - Catal, Yasir
AU - Wolman, Angelika
AU - Cagdas Eker, Mehmet
AU - Saffet Gonul, Ali
AU - Northoff, Georg
N1 - Publisher Copyright:
© 2025
PY - 2025/5/15
Y1 - 2025/5/15
N2 - Background: 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. Method: 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. Results: 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. Conclusion: 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.
AB - Background: 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. Method: 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. Results: 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. Conclusion: 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.
KW - Autocorrelation window
KW - Global signal correlation
KW - Intrinsic neural timescales
KW - Myelin
KW - Neural excitation
KW - Non-self
KW - Recurrent connections
KW - Resting state
KW - Self
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U2 - 10.1016/j.neuroimage.2025.121221
DO - 10.1016/j.neuroimage.2025.121221
M3 - Article
AN - SCOPUS:105002839042
SN - 1053-8119
VL - 312
JO - NeuroImage
JF - NeuroImage
M1 - 121221
ER -