Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study

Extraction of brain function pattern with visual-capture-task fMRI using dynamic time-window method in ADHD children

Attention deficit/Hyperactivity disorder (ADHD) has a great impact on children's development. This paper uses a novel adaptive brain state extraction algorithm to construct a dynamic time-window brain network, which captures the brain function pattern characteristics of ADHD children with higher temporal resolution. The test data were acquired by functional magnetic resonance imaging (fMRI) obtained from 23 children with ADHD during the visual-capture-task [age: (8.27 ± 2.77)]. A spatial standard deviation method is used after the initial data processing, to extract the brain activity pattern state; An improved clustering algorithm is constructed to verify the changes made to the dynamic time-window brain network model. There can be seen clear differences between each state within 0.05 s after the test. The results show that our improved new framework can effectively obtain the characteristics of dynamic brain functional connection strength changes during the task. In addition, the new algorithm is able to capture the dynamic changes of the brain network, with an 80 % improvement compared to traditional methods for the average modularity value Q. This work demonstrates a novel approach to find out the pattern changes between dynamic brain function connections, which can be of great significance for the adjuvant treatment of children with ADHD.

Dynamic functional connectivity changes of resting-state brain network in attention-deficit/hyperactivity disorder

Patients with attention-deficit/hyperactivity disorder (ADHD) have shown abnormal functional connectivity and network disruptions at the whole-brain static level. However, the changes in brain networks in ADHD patients from dynamic functional connectivity (DFC) perspective have not been fully understood. Accordingly, we executed DFC analysis on resting-state fMRI data of 25 ADHD patients and 27 typically developing (TD) children. A sliding window and Pearson correlation were used to construct the dynamic brain network of all subjects. The k-means+ + clustering method was used to recognize three recurring DFC states, and finally, the mean dwell time, the fraction of time spent for each state, and graph theory metrics were quantified for further analysis. Our results showed that ADHD patients had abnormally increased mean dwell time and the fraction of time spent in state 2, which reached a significant level (p < 0.05). In addition, a weak correlation between the default mode network was associated in three states, and the positive correlations between visual network and attention network were smaller than TD in three states. Finally, the integration of each network node of ADHD in state 2 is more potent than that of TD, and the degree of node segregation is smaller than that of TD. These findings provide new evidence for the DFC study of ADHD; dynamic changes may better explain the developmental delay of ADHD and have particular significance for studying neurological mechanisms and adjuvant therapy of ADHD.

Dynamic Properties of Human Default Mode Network in Eyes-Closed and Eyes-Open

The default mode network (DMN) reflects spontaneous activity in the resting human brain. Previous studies examined the difference in static functional connectivity (sFC) of the DMN between eyes-closed (EC) and eyes-open (EO) using the resting-state functional magnetic resonance imaging (rs-fMRI) data. However, it remains unclear about the difference in dynamic FC (dFC) of the DMN between EC and EO. To this end, we acquired rs-fMRI data from 19 subjects in two different statues (EC and EO) and selected a seed region-of-interest (ROI) at the posterior cingulate cortex (PCC) to generate the sFC map. We identified the DMN consisting of ten clusters that were significantly correlated with the PCC. By using a sliding-window approach, we analyzed the dFC of the DMN. Then, the Newman's modularity algorithm was applied to identify dFC states based on nodal total connectivity strength in each sliding-window. In addition, graph-theory based network analysis was applied to detect dynamic topological properties of the DMN. We identified three group-level dFC states (State1, 2 and 3) that reflects the strength of dFC within the DMN between EC and EO in different time. The following results were reached: (1) no significant difference in sFC between EC and EO, (2) dFC was lower in State2 but higher in State3 in EC than in EO, (3) lower clustering coefficient, local efficiency, and global efficiency, but higher characteristic path length in State2 in EC than in EO, and (4) lower nodal strength in the precuneus (PCUN), PCC, angular gyrus (ANG), middle temporal gyrus (MTG) and medial prefrontal cortex (MPFC) in State3 in EC. These results suggested different resting statuses, EC and EO, may induce different time-varying neural activity in the DMN.

Hub Patterns-Based Detection of Dynamic Functional Network Metastates in Resting State: A Test-Retest Analysis

The spontaneous dynamic characteristics of resting-state functional networks contain much internal brain physiological or pathological information. The metastate analysis of brain functional networks is an effective technique to quantify the essence of brain functional connectome dynamics. However, the widely used functional connectivity-based metastate analysis ignored the topological structure, which could be locally reflected by node centrality. In this study, 23 healthy young volunteers (21-26 years) were recruited and scanned twice with a 1-week interval. Based on the time sequences of node centrality, we promoted a node centrality-based clustering method to find metastates of functional connectome and conducted a test-retest experiment to assess the stability of those identified metastates using the described method. The hub regions of metastates were further compared with the structural networks' organization to depict its potential relationship with brain structure. Results of extracted metastates showed repeatable dynamic features between repeated scans and high overlapping rate of hub regions with brain intrinsic sub-networks. These identified hub patterns from metastates further highly overlapped with the structural hub regions. These findings indicated that the proposed node centrality-based metastates detection method could reveal reliable and meaningful metastates of spontaneous dynamics and indicate the underlying nature of brain dynamics as well as the potential relationship between these dynamics and the organization of the brain connectome.