From local properties to brain-wide organization: A review of intraregional temporal features in functional magnetic resonance imaging data

Abstract

Based on the fluctuations ensembled over neighbouring neurons, blood oxygen level-dependent (BOLD) signal is a mesoscale measurement of brain signals. Intraregional temporal features (IRTFs) of BOLD signal, extracted from regional neural activities, are utilized to investigate how the brain functions in local brain areas. This literature highlights four types of IRTFs and their representative calculations including variability in the temporal domain, variability in the frequency domain, entropy, and intrinsic neural timescales, which are tightly related to cognitions. In the brain-wide spatial organization, these brain features generally organized into two spatial hierarchies, reflecting structural constraints of regional dynamics and hierarchical functional processing workflow in brain. Meanwhile, the spatial organization gives rise to the link between neuronal properties and cognitive performance. Disrupted or unbalanced spatial conditions of IRTFs emerge with suboptimal cognitive states, which improved our understanding of the aging process and/or neuropathology of brain disease. This review concludes that IRTFs are important properties of the brain functional system and IRTFs should be considered in a brain-wide manner.

Keywords: BOLD signal; hierarchy; intraregional temporal features.

FIGURE 1

Illustration of intraregional temporal features (IRTFs). (a) Exemplary time‐series of blood oxygen level‐dependent (BOLD) signals. (b) Signal variability in temporal domain (i.e., variance here) represents the dynamic range of the time‐series. (c) Left panel: example functional magnetic resonance imaging (fMRI) power spectrum after denoising (Waschke et al., 2021); right panel: example neural power spectrum with peak oscillation component and 1/f‐like aperiodic component (Donoghue et al., 2020). (d) Illustration of the general rationale used to calculate entropy. Time‐series are divided into patterns of a certain length, which are compared throughout the data. A time‐series show pattern repetitions (blue segments and red segments). The more often the pattern repeats, the higher the entropy estimate (Waschke et al., 2021). (e) Exemplification of calculation of inter‐regional FC (left panel) and intraregional autocorrelation (right panel).

FIGURE 2

Common hierarchical organizations of brain attributes (Shafiei et al., 2020). (a) A total of 6441 intraregional temporal features (IRTFs) are extracted by the time series analysis tool (Fulcher et al., 2017), to generate a region × IRTFs matrix. Next, principal component analysis (PCA) performed on the matrix revealed organizational hierarchies by a combination of these IRTFs. The first two components were projected into two spatial maps: (b) a unimodal‐to‐transmodal hierarchy and (c) a ventromedial‐to‐dorsolateral gradient. (d) Similar hierarchical organizations of other brain attributes, including the first principal component of microarray gene expression data, functional connectivity, T1w/T2w ratio (which measures myelin thickness) and cortical thickness.