Objective:To explore the structural brain network changes in healthy first-degree relatives of depressed patients and their relationship with depressive episodes.Methods:Prospectively, 200 healthy first-degree relatives of depressed patients admitted to Jiangsu University Hospital from May 2017 to June 2018 were collected. Meanwhile, 50 matched healthy controls without family history of depression (HC/FH-) were collected by questionnaire in the nearby community as study subjects. All study subjects underwent systemic magnetic resonance imaging scans and assessment of relevant scales after enrollment, followed by longitudinal follow-up (every 3 months) for up to 3 years. The diagnostic and statistical manual of mental disorders, 4th edition, structured interview was used to assess whether the subjects became depressed during the follow-up period. First-degree relatives who experienced depression during follow-up were included in the group of first-degree relatives who experienced depression (DD/FH+), whereas first-degree relatives who did not experience depression were included in the group of first-degree relatives who did not experience depression (HC/FH+). Subjects′ depression severity and whether they experienced major stressful life events were assessed by the 24-item Hamilton Depression Rating Scale (HDRS) and the Holmes and Rahe Social Readjustment Rating Scale, respectively. Correlations between subjects′ brain structural networks and HDRS scores were explored based on Pearson correlation analysis. Logistic regression models were constructed to investigate the predictive efficacy of brain structural network attributes on depression.Results:Significant group differences existed in the HC/FH- group (50 cases), HC/FH+ group (115 cases), and DD/FH+ group (21 cases) in feeder connectivity (17.62±1.34, 17.03±1.39, 15.82±1.12, F=13.63, P<0.001), global efficiency (0.24±0.03, 0.23±0.03, 0.22±0.03, F=4.73, P=0.010), right insula node efficiency (0.20±0.02, 0.21±0.01, 0.20±0.01, F=4.62, P=0.011), left hippocampal node efficiency (0.27±0.01, 0.27±0.01, 0.24±0.02, F=18.56, P<0.001), and left amygdala node efficiency (0.24±0.02, 0.24±0.02, 0.23±0.01, F=3.40, P=0.036). Logistic regression models showed feeder connectivity ( OR=0.55, 95% CI 0.38-0.78, P=0.001) and left hippocampal nodal efficiency ( OR=0.58, 95% CI 0.40-0.81, P<0.001) predicted the occurrence of final depression and had good predictive efficacy with an area under the curve of 0.75, 0.78, respectively. Correlation analysis showed that feeder connectivity ( r=-0.58, P=0.006) and left hippocampal node efficiency ( r=-0.60, P=0.004) at baseline in the DD/FH+ group correlated with their HDRS scores at the first follow-up. Conclusion:Among healthy first-degree relatives of depressed patients, those who exhibit decreased feeder connectivity and left hippocampal nodal efficiency are susceptible to developing this disease.

Objective:To examine the structural network changes in participants with amnestic mild cognitive impairment(aMCI)and investigate the correlation between these changes and the onset of Alzheimer's disease(AD).Methods:In this prospective study, a total of 100 individuals with amnestic mild cognitive impairment(aMCI)were enrolled as the research group.Additionally, 25 healthy individuals who were matched in terms of age and sex were enrolled as healthy controls.Upon enrollment, all participants underwent MRI scans, neuropsychological assessments, and clinical evaluations.The participants were then followed every 6 months for a period of 36 months or until they withdrew from the study.Based on the outcome of the follow-up(whether Alzheimer's disease occurred), the aMCI participants were divided into two groups: stable aMCI group and progressive aMCI group.The Chinese version of the Brief Mental State Examination(MMSE), the Montreal Cognitive Assessment(MoCA), the Clinical Dementia Rating Scale(CDR), and the Auditory Word Learning Test(AVLT)were utilized to evaluate the overall mental and cognitive status of the subjects.Pearson correlation analysis was employed to investigate the relationship between structural network changes and cognitive decline.Logistic regression was performed to analyze the predictive ability of structural network changes in determining the onset of AD.Results:Compared to the stable aMCI group, the progressive aMCI group exhibited lower levels of global efficiency( P=0.002), local efficiency( P=0.007), feeder connections( P=0.003), local connections( P=0.008), and right precuneus nodal efficiency( P=0.010).Correlation analysis revealed that global efficiency( r=0.604, P=0.002), feeder connections( r=0.513, P=0.012), and right precuneus nodal efficiency( r=0.504, P=0.014)were correlated with AVLT-delay scores(baseline)in the progressive aMCI group.A logistic regression model demonstrated that global efficiency, feeder connections, and right precuneus nodal efficiency could significantly predict the onset of AD(all P<0.05, AUCunited=0.797, 95% CI: 0.684-0.884, sensitivity=73.91, 95% CI: 51.6-89.8, specificity=76.60, 95% CI: 62.0-87.7). Conclusions:Among participants with aMCI, individuals who exhibit lower global efficiency, feeder connections, or right precuneus nodal efficiency are at a higher risk of developing AD.These indicators are anticipated to serve as new targets for clinical intervention.