| The brain is a complex network,and the efficient transmission and processing of information depend on the information interaction between different brain regions.Complex brain network analysis is a method for quantitative evaluation of the interaction between brain regions,and has been widely used in the study of brain cognition,psychiatric or neurological diseases.However,defects in certain brain regions interfere with the brain’s processing of incoming information,leading to network dysfunction.Studies have shown that many brain disorders,including epilepsy and attention deficit hyperactivity disorder(ADHD),are known as brain network disorders and exhibit abnormal network patterns.Thus,this dissertation firstly focused on epilepsy,one of the most common and representative diseases of the nervous system,which is characterized by repeatability and unpredictability.Chronic recurrent seizures can cause severe damage to the patients’ behaviors and cognitive functions.Most existing studies focus on brain damage after long-term seizures,and few studies explore the physiological and pathological mechanisms leading to recurrent seizures.Therefore,it is of great significance to reveal the neural mechanism of long-term recurrent seizures for the development of clinical treatment and intervention means of this disease.Then we focused on attention deficit hyperactivity disorder(ADHD),one of the most common and representative psychiatric disorders,which is often found in children and is characterized by inattention,hyperactivity,and impulsivity.At present,the diagnosis of ADHD relies on scale evaluation and the description of the parents or school teachers of the children,which is highly subjective.Therefore,it is very necessary to study the potential neural mechanism of this disease for the development of objective and reliable biomarkers and treatment methods.Based on this,in this dissertation,we used high time resolution electroencephalogram(EEG)and complex brain network analysis to reveal the physiological and pathological mechanisms of epilepsy and ADHD,and further explore the potential application value of functional brain network in the clinical diagnosis and treatment of epilepsy and ADHD,respectively.The main works of this dissertation are as follows:1.The research on brain network mechanism of epilepsy.In collaboration with Sichuan Provincial People’s Hospital,we collected 24-hour EEG signals from 10 epileptic patients.We selected data from each patient 10 minutes before and 10 minutes after the seizure while awake to reveal changes in brain activity in both states.In this study,we first explored the brain differences before and after a seizure in terms of energy and brain complexity and flexibility.Specifically,we first used power spectral density(PSD)to calculate brain energy,while fuzzy entropy(FE)was used to measure brain complexity and flexibility.The findings revealed that in the δ band,six electrodes(i.e.,P3/4,F7/ 8,T5/6)showed increased PSD after seizure and the FE of pre-seizure in frontal,central and temporal regions were higher than that of post-seizure,indicating energy accumulation and reduced brain complexity and flexibility.Secondly,the network topologies of the two states were constructed respectively.The results showed that in the δ band,the network topology difference was characterized by the decrease of the long-range connection between the frontal,parietal,and occipital lobes,while the short-range connection between the frontal,temporal,central,and parietal lobes was enhanced after the seizure.Finally,in order to further explore the neural mechanism of recurrent seizures,we analyzed the correlation between PSD,FE,network properties and seizure number,respectively.The results showed that,in δ band,both PSD and FE had no correlation with seizure number.Even though,on the one hand,several electrodes showed energy accumulation(enhanced PSD)after epilepsy and the energy baseline elevated with seizure number increasing;on the other hand,several brain areas showed reduced brain complexity and flexibility(reduced FE)after epilepsy and the FE baseline also elevated with seizure number increasing.However,we found significant correlation between network properties and seizure number.In specific,the characteristic path length was positively correlated with seizure number,while the clustering coefficient,global efficiency,and local efficiency were all negatively correlated with seizure number,which demonstrated the unique advantage of brain network analysis in revealing the neural mechanism of recurrent seizures,and may be a potential effective biomarker for epilepsy research and clinical adjuvant therapy.In this study,we revealed the changes in brain activity after seizure through brain energy,entropy,and brain network,which may help to understand the underlying neural mechanisms especially the network mechanisms of recurrent seizures.2.The research on the brain network mechanism of ADHD.In cooperation with Shenzhen Luohu Maternal and Child Health Hospital,the P300 task EEG signals of 40 ADHD children and 31 healthy controls(HCs)were collected.In this study,we first found that there was no significant behavioral difference between ADHD children and HCs,which was specifically manifested as no significant difference in reaction accuracy and reaction time.Further,we expected to explore electrophysiological differences between these two groups.We first investigated the difference of event-related potential(ERP)between the two groups.The results showed that,on the one hand,compared with HCs,P200 and N200 amplitudes of ADHD children were significantly reduced,while P300 had no significant difference;on the other hand,when revealing cortical activity,HCs showed more brain activity than ADHD children mainly in the temporal and frontal lobes.Secondly,we constructed the network topologies of the two groups respectively,and the results showed that the long-range connectivity between the frontal and occipital lobes was enhanced and the connectivity between the frontal,parietal,and temporal lobes was weakened in the ADHD children.In this study,we revealed the atypical cognitive processing of ADHD children from the perspective of ERP and functional network,helping to understand its physiological and pathological mechanisms,and contributing to the development of objective and reliable biomarkers for the diagnosis of the disease.In conclusion,this study found abnormal changes in brain activity after epileptic seizure and abnormal cognitive processing in children with ADHD through the analysis of the EEG network,proving the unique advantage of EEG network in mining the dysfunctional information interaction pattern of patients’ brain,which is helpful for the development of the diagnosis and treatment of the two kinds of neurological diseases. |
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