Brain Neural Circuits And Effector Brain Networks In Alzheimer's Disease

The brain is the command center for human advanced activities,and brain science occupies a core position in neuroscience research.The research of brain science mainly focuses on the modeling and analysis of brain neurons or brain circuits,using mathematical model to explore the neuronal correlates of brain oscillation activity.On the other hand,the study of brain network is mainly based on neuroimaging data,which describes the abnormal changes of brain network under different neuropsychiatric diseases.Motified by these facts,this thesis,combining the theory and technique of neurodynamics and statistical analysis,constructs neural circuit computational model and effective brain network associated with Alzheimer’s disease(AD)from neurocomputing perspective and date-driven perspective respectively.This thesis aims to detect the effect of synape loss on brain rhythm as well as reveal the characteristics and dynamic evolutionary complexity of brain effective connection in AD patients.The main contents and conclusions of this thesis are as follows:1.This work studies the thalamo-cortico-thalamic(TCT)dynamics modeling and alpha rhythm associated with Alzheimer’s disease.Firstly,a more biologically plausible modified computational model of TCT circuitry is constructed by incorporating the disinhibition property between different inhibitory interneurons as well as the full relay function of thalamic relay nucleus to the cortical module.Then,by decreasing synaptic connectivity parameters to mimic the neuropathological condition of synapse loss in AD,the correlation between synapse loss and alpha rhythm is simulated by means of power spectral analysis.Numerical simulation results indicate that decreases of excitatory synaptic connctions Cfte and Cpxe as well as inhibitory synaptic connctions Clfi and Ctii can lead to a decrease in alpha band power,that is,the alpha rhythmic content slowing.This phenomenon is consistent with the characteristics of EEG in AD patients observed in electrophysiological experiments.Finally,the underlying mechanism behind the alpha rhythmic changes is analyzed using nonlinear dynamical technique.The results reveal that decreases of synaptic connctions Cfte,Cpxe,Clfi and Ctii can make the thalamic module transfers from a limit cycle mode to a point attractor mode,which may lead to the alpha rhythm slowing in the modified TCT model.These results are helpful for early identification of biomarkers of EEG in AD and understanding the potential pathogenesis of AD.2.Based on functional magnetic resonance imaging(fMRI)data,the characteristics of effective brain network in AD patients is investigated.Firstly,brain regions with significant difference between normal subjects and AD patients are extracted by local consistent analysis.Statistical analysis results present that compared with normal subjects,the brain regions with decreased local consistency in AD patients mainly include right parahippocampal gyrus(rPHG),left cuneus(1CUN),bilateral middle temporal gyrus(TPOmid),right calcarine cortex(rCAL),right angular gyrus(rANG)and left posterior central gyrus(1PcoG).Then,these seven regions are defined as brain regions of interest,and the effective brain networks of normal subjects and AD patients in the resting state are constructed by means of Granger causality analysis model.Finally,the significant difference of effective brain networks between the two groups is discussed.The results of statistical analysis reveal that compared with normal subjects,the effective brain network of AD subjects has both weakened and strengthened brain regions.In particular,compared with normal subjects,the connection strengths from rPHG to left TPOmid as well as between 1CUN and 1PcoG of AD patients are significantly decreased.However,the effective connection from rCAL to left TPOmid of AD group is stronger than that of normal group.3.On the basis of fMRI data,this thesis explores the dynamic effective brain network and its dynamics complexity of normal elderly and AD patients in resting state.By using Granger causal analysis model based on sliding window,this thesis constructs the dynamic effective brain networks of normal subjects and AD patients,and analyzes the effective connection difference between the two groups.The statistical analysis results reveal that in most observation windows,the connection range of the effective brain network in AD subjects is slightly lower than that in normal elderly.As the sliding window moves backwards,the effective connection range of normal subjects increases slightly in the second and fourth observed windows;yet that of AD patients decreases significantly.Compared with normal elderly,the effective brain networks of AD subjects not only have region with decreased connection strength(that is right TPOmid)but also have region with enhanced connection strength(that is rCAL).Moreover,the complexity of the dynamic effective connection is quantitatively characterized by using sample entropy,and the difference of the sample entropy of different groups is analyzed.The statistical analysis resutls reveal that compared with normal subjects,the sample entropy of whole brain region and the sample entropies of the effect connections between rTPOmid,rPHG,1PcoG,rANG and 1CUN in AD patients significantly decrease.The reduction in sample entropy of the effective connection of AD patients means that the dynamic complexity of physiological process is relatively reduced,which is consistent with the decline of cognitive and memory function in AD patients.The results of this study provide useful neuroimaging theoretical basis for the early diagnosis and treatment of AD.