Evidence for a Resting State Network Abnormality in Adults Who Stutter

Abstract

Neural network-based investigations of stuttering have begun to provide a possible integrative account for the large number of brain-based anomalies associated with stuttering. Here we used resting-state EEG to investigate functional brain networks in adults who stutter (AWS). Participants were 19 AWS and 52 age-, and gender-matched normally fluent speakers. EEGs were recorded and connectivity matrices were generated by LORETA in the theta (4–8 Hz), alpha (8–12 Hz), beta1 (12–20 Hz), and beta2 (20–30 Hz) bands. Small-world propensity (SWP), shortest path, and clustering coefficients were computed for weighted graphs. Minimum spanning tree analysis was also performed and measures were compared by non-parametric permutation test. The results show that small-world topology was evident in the functional networks of all participants. Three graph indices (diameter, clustering coefficient, and shortest path) exhibited significant differences between groups in the theta band and one [maximum betweenness centrality (BC)] measure was significantly different between groups in the beta2 band. AWS show higher BC than control in right temporal and inferior frontal areas and lower BC in the right primary motor cortex. Abnormal functional networks during rest state suggest an anomaly of DMN activity in AWS. Furthermore, functional segregation/integration deficits in the theta network are evident in AWS. These deficits reinforce the hypothesis that there is a neural basis for abnormal executive function in AWS. Increased beta2 BC in the right speech–motor related areas confirms previous evidence that right audio–speech areas are over-activated in AWS. Decreased beta2 BC in the right primary motor cortex is discussed in relation to abnormal neural mechanisms associated with time perception in AWS.

Abnormalities in hubs location and nodes centrality predict cognitive slowing and increased performance variability in first-episode schizophrenia patients

“…1). To date, many studies have been carried out using MST to reconstruct the global neural networks in such clinical conditions as, for example, epilepsy 29,30 , stuttering 31 , Alzheimer’s disease 32 and in brain maturation research 33 . These studies confirmed the usefulness of the MST algorithms to identify network irregularities in the mentioned populations.…”

Baseline Difference in Quantitative Electroencephalography Variables Between Responders and Non-Responders to Low-Frequency Repetitive Transcranial Magnetic Stimulation in Depression

“…Another limitation of the EEG part of the study is the use of a relatively small number of electrodes, however, EEG assessments in a real clinical setting are usually conducted with 19 or 21 scalp electrodes. Although the number of EEG electrodes is related to the precision of source estimation, several studies indicate that a reliable LORETA/eLORETA estimation can be achieved with just 19 channels (91)(92)(93)(94)(95)(96). Nevertheless, eLORETA accuracy still depends to an extent on the EEG montage density and the relatively small number of electrodes that we were limited to suggest some caution in interpreting our findings.…”

Synchrony and complexity in state-related EEG networks: an application of spectral graph theory

“…Although high-density EEG studies with more than 64 recording channels can display a highly spatial resolution of brain function and the number of electrodes or nodes can improve the GTA analysis, several recent studies with limited recording channels indicate that reliable results can also be obtained using standard 10/20 system with 19 EEG channels 12,13,[60][61][62][63] . In order to compute a functional brain network, we used coherence as a simple and well-studied EEG connectivity measure 4,12,14,[64][65][66][67] .…”

“…Mathematically, the leaf fraction is equal to the number of nodes with a degree of value equal to '1' (N (k=1) ) divided by N-1, where N represents the total number of nodes in a tree 51 . When the tree has a central node connected to all other nodes, the value of the leaf fraction is maximal; on the other hand, the leaf fraction is at a minimum for a tree with a line shape where one node is connected uniquely to another node in the network 13 . In terms of integration of a network, trees with a network in the shape of a line that have low leaf fraction value are deemed to be less integrated than graphs with a flower shape and high leaf fraction value 56 .…”

“…We performed FDR separately for weighted graph measures (i.e., clustering coefficient, characteristic shortest path), MST measures (i.e., betweenness centrality, leaf fraction, eccentricity, and diameter) and dynamical measures (i.e., largest eigenvalue, energy and entropy). The permutation test was carried out using a previously created MATLAB function 13,14 . After that, an individual comparison was performed to identify significant measures between the two conditions.…”