Frequency‐specific coactivation patterns in resting‐state and their alterations in schizophrenia: An fMRI study

Abstract

The resting-state human brain is a dynamic system that shows frequency-dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub-bands from slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), to slow-2 (0.198-0.25 Hz), in addition to the typical lowfrequency range (0.01-0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow-5, 4, and 3, but not in slow-2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between-state transitions. Specifically, from slow-5 to slow-4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow-5. Using leave-one-pair-out, hold-out and resampling validations, the highest classification accuracy (84%) was achieved by slow-4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders.

Frequency dependent whole-brain coactivation patterns analysis in Alzheimer’s disease

“…At present, there are relatively few studies on the use of the frequency dependent whole-brain CAP method in the analysis of resting-state fMRI data for neurological diseases. Hang et al used the Frequency dependent CAP method to study schizophrenia and found that the spatial patterns of CAP remained consistent across different frequency bands, which is familiar with our findings ( Yang et al, 2022 ).…”

Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

“…The coactivation patterns can be estimated in a seed-based (Chen et al, 2015; Liu & Duyn, 2013) or seed-free manner (Liu et al, 2013). In accordance with our previous studies, we used a robust pipeline that performs the CAP analysis using a seed-free manner at the ROI-level (Yang et al, 2021; Yang et al, 2022). Besides the group-level analysis, we also constructed the individual CAP states at both subject-level and scanlevel (Figure 1A).…”

Connectivity of the Brain in the Light of Chemogenetic Modulation of Neuronal Activity

“…The activity of the functional connectivity networks assessed by rs-fMRI correlates well with cognitive abilities and behavior [ 49 ], as well as changes in the brain excitation level [ 10 , 50 ]. It is of practical importance that rs-fMRI signals in patients with mental [ 13 , 20 , 51 , 52 ] and neurodegenerative [ 53 ] diseases clearly differ in functional connectivity from those of a healthy human brain. Despite the fact that they make it possible to use rs-fMRI for disease diagnosis [ 54 ], the results related to these differences are usually difficult to interpret, since the BOLD signal is only weakly and indirectly related to the underlying neuronal activity.…”