Objective:To investigate the heterogeneity of tumor density-enhancement complex (TDEC) based on MRI parameters in predicting the treatment response and prognosis for recurrent glioblastoma (rGBM) to guide the formulation of personalized clinical treatment strategies.Methods:A prospective cohort study was performed; 66 patients with postoperative rGBM were enrolled from Department of Neurosurgery, Affiliated Hospital of Jiangsu University. Multi-sequence MRI was performed, and diffused and enhanced data of the rGBM were utilized to construct TDEC as intratumoral sub-regions via pixel co-localization technique. Correlations among rGBM with different volume proportions of TDEC types and correlations of rGBM with different volume proportions of TDEC types with rGBM volume were analyzed in rGBM after bevacizumab (BEV) combined with radiotherapy. A pixel co-localization decoupling method was applied to assess the treatment response efficiency in individual TDEC subcomponents. The rGBM imaging phenotypes were identified through unsupervised clustering analysis, and progression-free survival (PFS) and overall survival (OS) between patients with different phenotypes were compared. The predictive value of TDEC heterogeneity in PFS and OS of rGBM patients under BEV plus radiotherapy was assessed. Results:Four distinct TDEC sub-regions (TDEC1-4) were identified; a significant negative correlation was observed between volume proportions of TDEC2 and TDEC3 ( r s=-0.558, P<0.001), as well as between volume proportions of TDEC3 and TDEC4 ( r s=-0.782, P<0.001), while TDEC composition (volume proportions of TDEC2-4) showed no significant correlation with tumor volume ( P>0.05). Following BEV combined with radiotherapy, significant sub-region-specific TDEC volume changes were observed (tumor volume minification rate of TDEC1[ΔV TDEC1]: 16.7% [13.8%, 20.1%]; ΔV TDEC2: 25.4% [21.9%, 29.0%]; ΔV TDEC3: 27.6% [23.5%, 31.2%]; ΔV TDEC4: 8.4% [6.1%, 10.7%], P<0.05); volume proportion of TDEC3 was positively correlated with tumor volume minification ( r s=0.702, P<0.001), whereas volume proportion of TDEC4 was negatively correlated tumor volume minification ( r s=-0.933, P<0.001). The volume reduction of TDEC1-3 was driven by combined effects of tumor cellular and enhancement components, while volume reduction of TDEC4 was primarily attributed to changes in tumor cellularity (ΔV ADC: 9.3%; ΔV T1C: 0.8%). Two distinct TDEC phenotypes with different survival outcomes were identified in rGBM patients (silhouette coefficient=0.584; TDEC type I: n=23; type II: n=43); significant difference in PFS and OS was noted between patients with TDEC type I and type II (PFS: χ2=11.191, P=0.001; OS: χ2=9.733, P=0.002). TDEC phenotype was an independent influencing factor for survival of rGBM patients under BEV combined with radiotherapy (PFS: HR=2.738, 95% CI: 1.815-3.938 , P=0.003; OS: HR=2.507, 95% CI: 1.851-3.660, P=0.007). Conclusion:TDEC sub-region helps efficiently characterize the rGBM heterogeneity; rGBM imaging phenotypes identified based on TDEC sub-region can independently predict the clinical outcomes: the prognosis of TDEC type I patients is better than that of TDEC type II patients.

Objective To investigate the predictive value of edema metabolic and hematoma dynamics changes on motor and cog-nitive recovery outcomes in patients with intracerebral hemorrhage(ICH).Methods The CT data of ICH patients were collected to evaluate hematoma volume changes from admission to day 3.On day 3,multivoxel magnetic resonance spectroscopy(MRS)was per-formed with region of interest located in the edema region and contralateral normal tissue.Motor and cognitive function recovery was assessed using the simplified F-M scale and the Montreal cognitive assessment(MoCA)on day 3 and at the 3-month follow-up,respec-tively.Overall clinical outcomes were assessed using the Glasgow outcome scale(GOS),and all patients were divided into good and poor outcome groups.Clinical data and metabolic differences in the edema region between the two groups were compared,respec-tively.Logistic regression analysis and receiver operating characteristic(ROC)curves were used to identify and evaluate independent prognostic factors.Subgroup analysis were performed via stratification of hematoma location.Results The logistic regression analy-sis indicated that intraventricular extension,hematoma changes,and the ratio of N-acetyl aspartate(NAA)around the hematoma to contralateral normal brain parenchyma NAA(rNAA)were inde-pendent prognostic factors for poor outcomes(P＜0.05).The area under the curve(AUC)for each factor and the combined model were 0.69,0.73,0.79,and 0.82,respectively.In patients with ICH in the basal ganglia region,△F-M was negatively correlated with hematoma changes and positively correlated with rNAA value(P＜0.001).In patients with ICH in the thalamic and lobar regions,△MoCA was not significantly correlated with hematoma changes(P＞0.05),but was positively correlated with rNAA value(P＜0.001).Conclusion The rNAA holds predictive value for motor and cognitive recovery outcomes following standard treatment.

Objective To investigate the predictive value of edema metabolic and hematoma dynamics changes on motor and cog-nitive recovery outcomes in patients with intracerebral hemorrhage(ICH).Methods The CT data of ICH patients were collected to evaluate hematoma volume changes from admission to day 3.On day 3,multivoxel magnetic resonance spectroscopy(MRS)was per-formed with region of interest located in the edema region and contralateral normal tissue.Motor and cognitive function recovery was assessed using the simplified F-M scale and the Montreal cognitive assessment(MoCA)on day 3 and at the 3-month follow-up,respec-tively.Overall clinical outcomes were assessed using the Glasgow outcome scale(GOS),and all patients were divided into good and poor outcome groups.Clinical data and metabolic differences in the edema region between the two groups were compared,respec-tively.Logistic regression analysis and receiver operating characteristic(ROC)curves were used to identify and evaluate independent prognostic factors.Subgroup analysis were performed via stratification of hematoma location.Results The logistic regression analy-sis indicated that intraventricular extension,hematoma changes,and the ratio of N-acetyl aspartate(NAA)around the hematoma to contralateral normal brain parenchyma NAA(rNAA)were inde-pendent prognostic factors for poor outcomes(P＜0.05).The area under the curve(AUC)for each factor and the combined model were 0.69,0.73,0.79,and 0.82,respectively.In patients with ICH in the basal ganglia region,△F-M was negatively correlated with hematoma changes and positively correlated with rNAA value(P＜0.001).In patients with ICH in the thalamic and lobar regions,△MoCA was not significantly correlated with hematoma changes(P＞0.05),but was positively correlated with rNAA value(P＜0.001).Conclusion The rNAA holds predictive value for motor and cognitive recovery outcomes following standard treatment.

Objective:To investigate the heterogeneity of tumor density-enhancement complex (TDEC) based on MRI parameters in predicting the treatment response and prognosis for recurrent glioblastoma (rGBM) to guide the formulation of personalized clinical treatment strategies.Methods:A prospective cohort study was performed; 66 patients with postoperative rGBM were enrolled from Department of Neurosurgery, Affiliated Hospital of Jiangsu University. Multi-sequence MRI was performed, and diffused and enhanced data of the rGBM were utilized to construct TDEC as intratumoral sub-regions via pixel co-localization technique. Correlations among rGBM with different volume proportions of TDEC types and correlations of rGBM with different volume proportions of TDEC types with rGBM volume were analyzed in rGBM after bevacizumab (BEV) combined with radiotherapy. A pixel co-localization decoupling method was applied to assess the treatment response efficiency in individual TDEC subcomponents. The rGBM imaging phenotypes were identified through unsupervised clustering analysis, and progression-free survival (PFS) and overall survival (OS) between patients with different phenotypes were compared. The predictive value of TDEC heterogeneity in PFS and OS of rGBM patients under BEV plus radiotherapy was assessed. Results:Four distinct TDEC sub-regions (TDEC1-4) were identified; a significant negative correlation was observed between volume proportions of TDEC2 and TDEC3 ( r s=-0.558, P<0.001), as well as between volume proportions of TDEC3 and TDEC4 ( r s=-0.782, P<0.001), while TDEC composition (volume proportions of TDEC2-4) showed no significant correlation with tumor volume ( P>0.05). Following BEV combined with radiotherapy, significant sub-region-specific TDEC volume changes were observed (tumor volume minification rate of TDEC1[ΔV TDEC1]: 16.7% [13.8%, 20.1%]; ΔV TDEC2: 25.4% [21.9%, 29.0%]; ΔV TDEC3: 27.6% [23.5%, 31.2%]; ΔV TDEC4: 8.4% [6.1%, 10.7%], P<0.05); volume proportion of TDEC3 was positively correlated with tumor volume minification ( r s=0.702, P<0.001), whereas volume proportion of TDEC4 was negatively correlated tumor volume minification ( r s=-0.933, P<0.001). The volume reduction of TDEC1-3 was driven by combined effects of tumor cellular and enhancement components, while volume reduction of TDEC4 was primarily attributed to changes in tumor cellularity (ΔV ADC: 9.3%; ΔV T1C: 0.8%). Two distinct TDEC phenotypes with different survival outcomes were identified in rGBM patients (silhouette coefficient=0.584; TDEC type I: n=23; type II: n=43); significant difference in PFS and OS was noted between patients with TDEC type I and type II (PFS: χ2=11.191, P=0.001; OS: χ2=9.733, P=0.002). TDEC phenotype was an independent influencing factor for survival of rGBM patients under BEV combined with radiotherapy (PFS: HR=2.738, 95% CI: 1.815-3.938 , P=0.003; OS: HR=2.507, 95% CI: 1.851-3.660, P=0.007). Conclusion:TDEC sub-region helps efficiently characterize the rGBM heterogeneity; rGBM imaging phenotypes identified based on TDEC sub-region can independently predict the clinical outcomes: the prognosis of TDEC type I patients is better than that of TDEC type II patients.

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.

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.