Frequency-Specific Analysis of the Dynamic Reconfiguration of the Brain in Patients with Schizophrenia

Abstract

The analysis of resting-state fMRI signals usually focuses on the low-frequency range/band (0.01–0.1 Hz), which does not cover all aspects of brain activity. Studies have shown that distinct frequency bands can capture unique fluctuations in brain activity, with high-frequency signals (>0.1 Hz) providing valuable information for the diagnosis of schizophrenia. We hypothesized that it is meaningful to study the dynamic reconfiguration of schizophrenia through different frequencies. Therefore, this study used resting-state functional magnetic resonance (RS-fMRI) data from 42 schizophrenia and 40 normal controls to investigate dynamic network reconfiguration in multiple frequency bands (0.01–0.25 Hz, 0.01–0.027 Hz, 0.027–0.073 Hz, 0.073–0.198 Hz, 0.198–0.25 Hz). Based on the time-varying dynamic network constructed for each frequency band, we compared the dynamic reconfiguration of schizophrenia and normal controls by calculating the recruitment and integration. The experimental results showed that the differences between schizophrenia and normal controls are observed in the full frequency, which is more significant in slow3. In addition, as visual network, attention network, and default mode network differ a lot from each other, they can show a high degree of connectivity, which indicates that the functional network of schizophrenia is affected by the abnormal brain state in these areas. These shreds of evidence provide a new perspective and promote the current understanding of the characteristics of dynamic brain networks in schizophrenia.

Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer’s disease

“…When looking into the alterations of our dynamic functional measures along the AD continuum, we observed that, in general, the flexibility was decreased in all the RSNs, with the exception of the SM network. This result concurs with previous research emphasizing the critical role of network flexibility in cognitive processing and the loss of this function in a variety of neurological disorders (19,22,23). In addition, across the AD continuum, the dynamic integration coefficient was increased in all RSNs, and the dynamic recruitment coefficient was either increased or maintained in most RSNs, except for the VIS network, which showed a decrease in recruitment.…”

“…Novel methods in network neuroscience, such as multilayer network modeling and multilayer community detection (18), have been used to analyze brain activity over time measured with fMRI in healthy individuals (19)(20)(21) and in patients with schizophrenia (22,23). This approach normally separates fMRI data into a number of time windows or snapshots and a correlation measure is used to build a functional network for each time window (24).…”