Objective White matter hyperintensity（WMH） is the core imaging marker for cerebral small vessel disease， and gait disturbance induced by WMH is a major cause of functional disability in middle-aged and elderly populations. Existing studies mostly focus on the static association between WMH and gait disturbance， while time-varying characteristics are observed in the functional connectivity of brain networks. The dynamic functional connectivity（dFC） technique can capture the real-time interaction characteristics of brain networks， providing a new perspective for analyzing the neural mechanism of WMH-related gait disturbance. This study aims to investigate the neuroimaging mechanism of patients with WMH and gait disturbance using the dFC technique and clarify the association of dynamic brain network imbalance with motor function and cognitive function. Methods Subjects were recruited in Nanjing Drum Tower Hospital from 2023 to 2025， and after screening based on inclusion and exclusion criteria， 93 subjects were enrolled in the group of WMH with gait disturbance （WMH-GD group）， 86 subjects were enrolled in the group of WMH without gait disturbance （WMH-nGD group）， and 92 subjects were enrolled in the normal control group （NC group）. Background data collection， neuropsychological assessment， gait testing， and cranial magnetic resonance imaging （MRI） scanning were performed for all subjects. The DynamicBC toolbox was used to perform the dFC analysis and extract the dynamic indicators including fractional windows（F）， mean dwell time （MDT）， number of transitions（NT）， and transition probability （TP）； network-based statistics（NBS） were used to identify differential connectivity between brain regions across groups； the correlation analysis was used to investigate the correlation between dynamic indicators and clinical parameters. Results The cluster analysis identified two brain functional connectivity states， i.e.， State Ⅰ （sparse and weak connectivity， accounting for 61.29%） and State Ⅱ （dense and strong connectivity， accounting for 38.71%）. Inter-group comparisons showed that compared with the WMH-nGD group， the WMH-GD group had significantly higher F value （72.48% vs 57.38%， P<0.05） and MDT （95.47 windows vs 54.46 windows， P<0.05） of State Ⅰ and a significantly lower value of NT （2.44 times vs 3.83 times）， as well as a significantly lower value of TP from State Ⅱ to State Ⅰ （TP Ⅱ→Ⅰ： 2.61% vs 5.84%， P<0.05） and a significantly higher value of TP from State Ⅱ to State Ⅱ （TP Ⅱ→Ⅱ：97.39% vs 94.16%， P<0.05）. The NBS analysis showed that compared with the WMH-nGD group， the WMH-GD group had a significant reduction in inter-regional connectivity between the occipital lobe， the parietal lobe， and the frontal lobe in State Ⅰ and a significant increase in connectivity within subcortical brain regions and between the limbic lobe and the subcortical region. The correlation analysis showed that in the WMH-GD group， Mini-Mental State Examination score was negatively correlated with MDT of State Ⅰ and TP Ⅱ→Ⅱ and was positively correlated with TP Ⅱ→Ⅰ and NT， and gait speed was positively correlated with NT. Conclusion Dynamic brain network imbalance is observed in patients with WMH and gait disturbance， which manifests as rigidity of the sparse and weak connectivity state， a reduction in transition flexibility， and reorganization of cortex-subcortex connectivity. These abnormalities are closely associated with cognitive and gait functions， suggesting that disruption of brain network metastability may be one of the core neural mechanisms underlying WMH-related gait disturbance.

Objective To explore the change of gait of middle-aged and elderly people with mild cognitive impairment in the community,the correlation between gait and cognitive domain,and the role of gait in early recognition of cognitive decline. Methods 140 people over 40 years old in Tongxing Village,Yancheng City,Jiangsu Province were enrolled.The subjects were divided into normal cognitive group (n=64) and mild cognitive impairment group(n=76)through the Montreal Cognitive Assessment and the Minimum Mental State Examination,and gait tests were conducted at the same time.The data were collected and statistically analyzed to explore the difference of gait indicators between the two groups,the relationship between gait indicators and cognitive domains,and the ability of gait indicators to recognize mild cognitive impairment. Results The gait of the mild cognitive impairment group was worse than that of the normal cognitive group in terms of space (stride length,step height,step width) and time (step speed,stride speed,swing speed).Partial correlation analysis showed that step width was negatively correlated with delayed recall;Step size,step width and delayed recall,step height and naming were positively correlated.The logistic regression model constructed by step speed,stride length,stride speed,swing speed,step height and step width can reliably identify the existence of MCI (AUC=0.761,95%CI 0.683-0.840,P<0.05). Conclusion In the middle-aged and elderly community,the spatial and temporal performance of gait of patients with mild cognitive impairment is worse than that of the normal cognitive population.There is a close relationship between spatial indicators and delayed recall and naming.The temporal and spatial characteristics of gait have the potential to identify cognitive decline at an early stage.
Mild cognitive impairment