The Study Of Altered Dynamic And Static Functional Network Connectivity Related To Cognitive Decline In Individuals With White Matter Hyperintensities

Objective:White matter hyperintensities(WMH)are strongly associated with cognitive decline,but the neural mechanism of associated cognitive impairment in WMH patients is not well understood.We aimed to study static and dynamic functional network connectivity(sFNC and dFNC)in WMH patients to explore whether the alteration of connectivity is related to cognitive decline,and to investigate the pattern,variability and dynamic temporal attributes of dFNC in WMH patients.This study also explores whether dFNC can be used as a potential neuroimaging marker to provide new ideas on how to prevent WMH related cognitive decline in furture clinical studies.Methods: From January 2017 to March 2022,this study retrospectively included 178 elderly patients admitted to the Department of Neurology Outpatient and inpatient Department of Nanjing Drum Tower Hospital Affiliated to the School of Medicine of Nanjing University.All of these patients underwent multimodal magnetic resonance imaging(MRI)and cognitive assessments.After a series of rigorous screening,59 healthy control groups(HC),51 WMH with normal cognition(WMH-NC)and 68 WMH patients with mild cognitive impairment(WMH-MCI)were finally included.Preprocessing of resting functional MRI(rs-f MRI)data was based on statistical parameter mapping software package(SPM12)and brain imaging data processing and analysis software(DPABI V4.1).We used the spatial Group Independent Component Analysis(GICA)method in the GIFT package to extract resting state networks from all subjects,and the Mancovan toolbox was used to assess the strength of connections within and between sFNC networks.Based on sliding time window and k-means clustering,the network connection mode,variability and temporal characteristics of dFNC dynamic function were evaluated.Correlation analysis and mediation analysis were used to compare whether changes in sFNC and dFNC mediated the relationship between WMH and cognition.Finally,the Support Vector Machine(SVM)method was used to identify and classify WMH-MCI.Results:(1)An ANOVA analysis was used to compare the sFNC between three groups,and we found that the WMH-NC group had increased connectivity in the visual network(VN)and auditory network(AN)compared to the HC group.The correlation analysis showed a significant correlation between the sFNC of right lingual gyrus in VN and WMH volumes(r=-0.02,P=0.04).Further mediation analysis indicated that the within-network connectivity of the right lingual gyrus mediated the relationship between WMH and information processing speed function of TMT-A(indirect effect:0.24;95%CI: [0.03,0.88])and Stroop A(indirect effect: 0.05;95%CI: [0.001,0.14]).By constructing a predictive model based on SVM analysis,WMH-MCI subjects were differentiated from WMH-NC subjects with an accuracy of 79.83%,sensitivity of 74.51%,and specificity of89.71%.(2)The clustering analysis of dFNC states showed that the highest frequency of connections was observed in state 5(29%),followed by state 1(20%),state 2(21%),state 3(9%),state 4(11%),and state 6(10%).However,there was no significant difference in the fraction of time,mean dwell time,number of transitions,and transition matrices among the three groups.ANOVA analysis indicated significant differences in the variability of connectivity between left Frontoparietal Network(l FPN)-VN,LFPN-AN,and VN-Salience Network(SN)among the three groups.The variability of connectivity between LFPN-VN in the WMH-MCI group was closely related to the information processing function subdomain(r=0.277,p=0.022),and these significantly different variabilities,as SVM features,could classify WMH-MCI subjects from WMH subjects(accuracy=69.75%;sensitivity=54.90%;specificity=75.00%).Conclusion: WMH may modulate dynamic functional network connections between higher cognitive networks and other networks,enhancing the dynamic variability between left frontoparietal networks(l FPN)and visual networks to compensate for declines in higher cognitive function.Our findings reveal that the allocation of brain network resources needs to be dynamically regulated to maintain cognitive processing in WMH patients.Crucially,the dynamic reorganization of brain networks can be considered as a potential neuroimaging biomarker to identify WMH-related cognitive impairment.