The temporal dedifferentiation of global brain signal fluctuations during human brain ageing

Abstract

The variation of brain functions as healthy ageing has been discussed widely using resting-state brain imaging. Previous conclusions may be misinterpreted without considering the effects of global signal (GS) on local brain activities. Up to now, the variation of GS with ageing has not been estimated. To fill this gap, we defined the GS as the mean signal of all voxels in the gray matter and systematically investigated correlations between age and indices of GS fluctuations. What’s more, these tests were replicated with data after hemodynamic response function (HRF) de-convolution and data without noise regression as well as head motion data to verify effects of non-neural information on age. The results indicated that GS fluctuations varied as ageing in three ways. First, GS fluctuations were reduced with age. Second, the GS power transferred from lower frequencies to higher frequencies with age. Third, the GS power was more evenly distributed across frequencies in ageing brain. These trends were partly influenced by HRF and physiological noise, indicating that the age effects of GS fluctuations are associated with a variety of physiological activities. These results may indicate the temporal dedifferentiation hypothesis of brain ageing from the global perspective.

Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging

“…Specific to the study of the effect of aging on resting-state connectivity, all of the denoising methods have found broad application, including GSR ( Betzel et al, 2014 ; Chan et al, 2014 ; Zhang et al, 2014 ; Geerligs et al, 2015 ; Siman-Tov et al, 2016 ; Stumme et al, 2020 ), WM-CSF ( Koch et al, 2010 ; Betzel et al, 2014 ; Chan et al, 2014 ; Song et al, 2014 ; Zhang et al, 2014 ; Grady et al, 2016 ; Siman-Tov et al, 2016 ; Farras-Permanyer et al, 2019 ; Varangis et al, 2019 ; Mancho-Fora et al, 2020 ; Xie et al, 2020 ; Zhong and Chen, 2022 ), CompCor ( Onoda and Yamaguchi, 2013 ; Hausman et al, 2020 ; Hamada et al, 2021 ; Patil et al, 2021 ; Podgórski et al, 2021 ), and ICA-AROMA ( Stumme et al, 2020 ). Moreover, some recent studies of aging used no physiological denoising ( Damoiseaux et al, 2008 ; Huang et al, 2015 ; Ao et al, 2022 ).…”

“…Second, we examine the effect of age on fcMRI measures as a variable of the denoising method. It is important to note that signal and noise structures alter in aging ( Van Dijk et al, 2012 ; Makedonov et al, 2013 ; Tsvetanov et al, 2015 ) including the frequency composition of the fMRI signal ( Yang et al, 2018 ; Ao et al, 2022 ), which may be related to physiological processes and to neurovascular coupling changes ( Yang et al, 2018 ). In fact, Geerligs et al (2017) suggested that head-motion effects can change with age, and that motion regression may erase some of the age-relevant functional differences, highlighting the importance of appropriate physiological denoising.…”

Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan

“…Analogous to our previous studies (Ao et al, 2022;Wang et al, 2014Wang et al, , 2015, the following steps were conducted. After noise regression, a point process analysis was performed to detect spontaneous neural events.…”

“…Drawing on our previous work (Ao et al, 2022 ), we aim to expand and bolster the notion of age‐related spatiotemporal dedifferentiation by investigating the variation of the spatiotemporal organization of GS topography across the adult lifespan using a large‐sample rs‐fMRI dataset. In addition to the dedifferentiation across distributed brain regions, we have observed a more homogenous distribution of GS power spectrum density (PSD) across frequency bands in the aging brain (Ao et al, 2022 ). We thus hypothesized that the GSCORR would become more homogenous with age in different brain regions and frequency bands.…”

Natural frequencies of neural activities and cognitions may serve as precise targets of rhythmic interventions to the aging brain

“…Neural activities at intrinsic frequencies form spectral fingerprints of brain functions ( Siegel et al, 2012 ). Throughout the human lifespan, frequencies of these neural activities, however, are constantly changing, as presented in the form of the shift of frequency between adjacent frequency bands and/or temporal dedifferentiation among multiple bands ( Alcauter et al, 2015 ; Yang et al, 2018 ; Ao et al, 2022 ). For instance, the individual alpha peak frequency slowed from 10 Hz at the age of 20 years (i.e., younger age) to 8.8 Hz at the age of 70 years (i.e., older age) and from 9.9 Hz in healthy adults aged 18–60 years old to 9.4 Hz in patients with schizophrenia of the same age range ( Scally et al, 2018 ; Ramsay et al, 2021 ).…”