Functional Brain Connectivity Differences Between Different ADHD Presentations: Impaired Functional Segregation in ADHD-Combined Presentation but not in ADHD-Inattentive Presentation

Abstract

Introduction:Contrary to Diagnostic and Statistical Manual of Mental Disorders (DSM-5), fifth edition, some studies indicate that ADHD-inattentive presentation (ADHD-I) is a distinct diagnostic disorder and not an ADHD presentation.Methods:In this study, 12 ADHD-combined presentation (ADHD-C), 10 ADHD-I, and 13 controls were enrolled and their resting state EEG recorded. Following this, a graph theoretical analysis was performed and functional integration and segregation of brain network was calculated.Results:The results show that clustering coefficient of theta band was significantly different among three groups and significant differences were observed in theta global efficiency between controls and ADHD-C. Regarding the alpha band, a lower clustering coefficient was observed in control subjects. In the beta band, clustering coefficient was significantly different between the control and children with ADHD-C and also between ADHD-I and ADHD-C. The clustering coefficient, in the subjects with ADHD-C, demonstrated a rapid decline and was significantly lower than the subjects with ADHD-I and control.Conclusion:Decreased clustering, in high thresholds, may be associated with hyperactivity while increased segregation in low thresholds with inattentiveness. A different functional network occurs in the ADHD-C brain that is consistent with several studies that have reported ADHD-I as a distinct disorder.

Time estimation and beta segregation: An EEG study and graph theoretical approach

“…Increased prediction accuracy using clustering coefficient can suggest that time estimation at a long term duration may also correlate with the segregation at cortico-cortical pathways and non-localized functions. Consistent with this argument, abnormal beta segregation has been observed in disorders related to abnormal timing such as stuttering [ 58 ] and Parkinson’s disease [ 66 ]. This result is in agreement with the model which suggests that there is no linear metric of time and time may be perceived via patterns presented at the network level [ 13 ].…”

“…Functionally, the activity of sensory-motor beta is negatively associated with activity of the BG-thalamocortical circuits [ 53 , 54 ]. Therefore, abnormalities in the beta power are frequently reported in disorders associated with impairment of the BG-thalamocortical circuits; such as Parkinson’s disease [ 55 ], attention deficit and hyperactivity disorder [ 56 ] and also stuttering [ 57 , 58 ]. BG-thalamocortical circuits also play a critical role in the scalar timing models as an internal clock-accumulator structure.…”

“…In this approach, all of the matrix arrays with higher values than threshold were replaced by 1 and others arrays were changed to zero and weighed matrix was changed to binary matrix. Previous studies indicate that since differences may have occurred in a specific threshold, a wide range of thresholds should be tested [ 33 , 41 , 44 , 45 ]. We investigated the thresholds in three separate clusters; low (0.2 to 0.3), mid (0.3 to 0.4) and high (0.4 to 0.5) and graph indices were investigated in these thresholds.…”

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] . Although several criticisms (e.g., effect of volume conduction) have been raised against coherence 68 , still it is a useful and meaningful measure when coupling and synchronizing between neural units are important 64,[69][70][71] .…”

“…We used the function 'mscohere' in MATLAB to find magnitude-squared coherence in alpha band (8)(9)(10)(11)(12). Mathematically this function calculates 73 :…”